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• Published: 09 February 2022

Perspectives in machine learning for wildlife conservation

• Devis Tuia   ORCID: orcid.org/0000-0003-0374-2459 1   na1 ,
• Benjamin Kellenberger 1   na1 ,
• Sara Beery 2   na1 ,
• Blair R. Costelloe   ORCID: orcid.org/0000-0001-5291-788X 3 , 4 , 5   na1 ,
• Silvia Zuffi   ORCID: orcid.org/0000-0003-1358-0828 6 ,
• Benjamin Risse   ORCID: orcid.org/0000-0001-5691-4029 7 ,
• Alexander Mathis   ORCID: orcid.org/0000-0002-3777-2202 8 ,
• Mackenzie W. Mathis   ORCID: orcid.org/0000-0001-7368-4456 8 ,
• Frank van Langevelde   ORCID: orcid.org/0000-0001-8870-0797 9 ,
• Tilo Burghardt 10 ,
• Roland Kays   ORCID: orcid.org/0000-0002-2947-6665 11 , 12 ,
• Holger Klinck 13 ,
• Martin Wikelski   ORCID: orcid.org/0000-0002-9790-7025 3 , 4 ,
• Iain D. Couzin   ORCID: orcid.org/0000-0001-8556-4558 3 , 4 , 5 ,
• Grant van Horn 13 ,
• Margaret C. Crofoot 3 , 4 , 5 ,
• Charles V. Stewart 14 &
• Tanya Berger-Wolf   ORCID: orcid.org/0000-0001-7610-1412 15 , 16  

Nature Communications volume  13 , Article number:  792 ( 2022 ) Cite this article

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• Computer science
• Conservation biology

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.

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Animal diversity is declining at an unprecedented rate 1 . This loss comprises not only genetic, but also ecological and behavioral diversity, and is currently not well understood: out of more than 120,000 species monitored by the IUCN Red List of Threatened Species, up to 17,000 have a ‘Data deficient’ status 2 . We urgently need tools for rapid assessment of wildlife diversity and population dynamics at large scale and high spatiotemporal resolution, from individual animals to global densities. In this Perspective, we aim to build bridges across ecology and machine learning to highlight how relevant advances in technology can be leveraged to rise to this urgent challenge in animal conservation.

How are animals currently monitored? Conventionally, management and conservation of animal species are based on data collection carried out by human field workers who count animals, observe their behavior, and/or patrol natural reserves. Such efforts are time-consuming, labor-intensive, and expensive 3 . They can also result in biased datasets due to challenges in controlling for observer subjectivity and assuring high inter-observer reliability, and often unavoidable responses of animals to observer presence 4 , 5 . Human presence in the field also poses risks to wildlife 6 , 7 , their habitats 8 , and humans themselves: as an example, many wildlife and conservation operations are performed from aircraft and plane crashes are the primary cause of mortality for wildlife biologists 9 . Finally, the physical and cognitive limitations of humans unavoidably constrain the number of individual animals that can be observed simultaneously, the temporal resolution and complexity of data that can be collected, and the extent of physical area that can be effectively monitored 10 , 11 .

These limitations considerably hamper our understanding of geographic ranges, population densities, and community diversity globally, as well as our ability to assess the consequences of their decline. For example, humans conducting counts of seabird colonies 12 and bats emerging from cave roosts 13 tend to significantly underestimate the number of individuals present. Furthermore, population estimates based on extrapolation from a small number of point counts have large uncertainties and can fail to capture the spatiotemporal variation in ecological relationships, resulting in erroneous predictions or extrapolations 14 . Failure to monitor animal populations impedes rapid and effective management actions 3 . For example, insufficient monitoring, due in part to the difficulty and cost of collecting the necessary data, has been identified as a major challenge in evaluating the impact of primate conservation actions 15 and can lead to the continuation of practices that are harmful to endangered species 16 . Similarly, poaching prevention requires intensive monitoring of vast protected areas, a major challenge with existing technology. Protected area managers invest heavily in illegal intrusion prevention and the detection of poachers. Despite this, rangers often arrive too late to prevent wildlife crime from occurring 17 . In short, while a rich tradition of human-based data collection provides the basis for much of our understanding of where species are found, how they live, and why they interact, modern challenges in wildlife ecology and conservation are highlighting the limitations of these methods.

Recent advances in sensor technologies are drastically increasing data collection capacity by reducing costs and expanding coverage relative to conventional methods (see the section “New sensors expand available data types for animal ecology”, below), thereby opening new avenues for ecological studies at scale (Fig.  1 ) 18 . Many previously inaccessible areas of conservation interest can now be studied through the use of high-resolution remote sensing 19 , and large amounts of data are being collected non-invasively by digital devices such as camera traps 20 , consumer cameras 21 , and acoustic sensors 22 . New on-animal bio-loggers, including miniaturized tracking tags 23 , 24 and sensor arrays featuring accelerometers, audiologgers, cameras, and other monitoring devices document the movement and behavior of animals in unprecedented detail 25 , enabling researchers to track individuals across hemispheres and over their entire lifetimes at high temporal resolution and thereby revolutionizing the study of animal movement (Fig.  1 c) and migrations.

a The BirdNET algorithm 61 was used to detect Carolina wren vocalizations in more than 35,000 h of passive acoustic monitoring data from Ithaca, New York, allowing researchers to document the gradual recovery of the population following a harsh winter season in 2015. b Machine-learning algorithms were used to analyze movement of savannah herbivores fitted with bio-logging devices in order to identify human threats. The method can localize human intruders to within 500 m, suggesting `sentinel animals' may be a useful tool in the fight against wildlife poaching 148 . c TRex, a new image-based tracking software, can track the movement and posture of hundreds of individually-recognized animals in real-time. Here the software has been used to visualize the formation of trails in a termite colony 149 . d , e Pose estimation software, such as DeepPoseKit ( d ) 75 and DeepLabCut ( e ) 74 , 142 allows researchers to track the body position of individual animals from video imagery, including drone footage, and estimate 3D postures in the wild. Panels b , c , and d are reproduced under CC BY 4.0 licenses. Panels b and d are cropped versions of the originals; the legend for panel b has been rewritten and reorganized. Panel e is reproduced with permission from Joska et al. 142 .

There is a mismatch between the ever-growing volume of raw measures (videos, images, audio recordings) acquired for ecological studies and our ability to process and analyze this multi-source data to derive conclusive ecological insights rapidly and at scale. Effectively, ecology has entered the age of big data and is increasingly reliant on sensors, advanced methodologies, and computational resources 26 . Central challenges to efficient data analysis are the sheer volume of data generated by modern collection methods and the heterogeneous nature of many ecological datasets, which preclude the use of simple automated analysis techniques 26 . Crowdsourcing platforms like eMammal ( emammal.si.edu ), Agouti ( agouti.eu ), and Zooniverse ( www.zooniverse.org ) function as collaborative portals to collect data from different projects and provide tools to volunteers to annotate images, e.g., with species labels of the individuals therein. Such platforms drastically reduce the cost of data processing (e.g., ref. 27 reports a reduction of seventy thousand dollars), but the rapid increase in the volume and velocity of data collection is making such approaches unsustainable. For example, in August 2021 the platform Agouti hosted 31 million images, of which only 1.5 million were annotated. This is mostly due to the manual nature of the current annotation tool, which requires human review of every image. In other words, methods for automatic cataloging, searching, and converting data into relevant information are urgently needed and have the potential to broaden and enhance animal ecology and wildlife conservation in scale and accuracy, address prevalent challenges, and pave the way forward towards new, integrated research directives.

Machine learning (ML, see glossary in Supplementary Table  1 ) deals with learning patterns from data 28 . Presented with large quantities of inputs (e.g., images) and corresponding expected outcomes, or labels (e.g., the species depicted in each image), a supervised ML algorithm learns a mathematical function leading to the correct outcome prediction when confronted with new, unseen inputs. When the expected outcomes are absent, the (this time unsupervised) ML algorithm will use solely the inputs to extract groups of data points corresponding to typical patterns in the data. ML has emerged as a promising means of connecting the dots between big data and actionable ecological insights 29 and is an increasingly popular approach in ecology 30 , 31 . A significant share of this success can be attributed to deep learning (DL 32 ), a family of highly versatile ML models based on artificial neural networks that have shown superior performance across the majority of ML use cases (see Table  1 and Supplementary Table  2 ). Significant error reduction of ML and DL with respect to traditional generalized regression models has been reported routinely for species richness and diversity estimation 33 , 34 . Likewise, detection and counting pipelines moved from rough rule of thumb extrapolations from visual counts in national parks to ML-based methods with high detection rates. Initially, these methods proposed many false positives which required further human review 35 , but recent methods have been shown to maintain high detection rates with significantly fewer false positives 36 . As an example, large mammal detection in the Kuzikus reserve in 2014 was improved significantly by improving the detection methodologies, from a recall rate of 20% 35 to 80% 37 (for a common 75% precision rate). Finally, studies involving human operators demonstrated that ML enabled massive speedups in complex tasks such as individual and species recognition 38 , 39 and large-scale tasks such as animal detection in drone surveys 40 . Recent advances in ML methodology could accelerate and enhance various stages of the traditional ecological research pipeline (see Fig.  2 ), from targeted data acquisition to image retrieval and semi-automated population surveys. As an example, the initiative Wildlife Insights 41 is now processing millions of camera trap images automatically (17 million in August 2021), providing wildlife conservation scientists and practitioners with the data necessary to study animal abundances, diversity, and behavior. Besides pure acceleration, use of ML also massively reduces analysis costs, with reduction factors estimated between 2 and 10 42 .

Traditional ecological research pipeline (colored text and boxes) and contributions of ML to the different stages discussed in this paper (black text).

A growing body of literature promotes the use of ML in various ecological subfields by educating domain experts about ML approaches 29 , 43 , 44 , their utility in capitalizing on big data 26 , 45 , and, more recently, their potential for ecological inference (e.g., understanding the processes underlying ecological patterns, rather than only predicting the patterns themselves) 46 , 47 . Clearly, there is a growing interest in applying ML approaches to problems in animal ecology and conservation. We believe that the challenging nature of ecological data, compounded by the size of the datasets generated by novel sensors and the ever-increasing complexity of state-of-the-art ML methods, favor a collaborative approach that harnesses the expertise of both the ML and animal ecology communities, rather than an application of off-the-shelf ML methodologies to ecological challenges. Hence, the relation between ecology and ML should not be unidirectional: integrating ecological domain knowledge into ML methods is essential to designing models that are accurate in the way they describe animal life. As demonstrated by the rising field of hybrid environmental algorithms (leveraging both DL and bio-physical models 48 , 49 ) and, more broadly, by theory-guided data science 50 , such hybrid models tend to be less data-intensive, avoid incoherent predictions, and are generally more interpretable than purely data-driven models. To reach this goal of an integrated science of ecology and ML, both communities need to work together to develop specialized datasets, tools, and knowledge. With this objective in mind, we review recent efforts at the interface of the two disciplines, present success stories of such symbiosis in animal ecology and wildlife conservation, and sketch an agenda for the future of the field.

New sensors expand available data types for animal ecology

Sensor data provide a variety of perspectives to observe wildlife, monitor populations, and understand behavior. They allow the field to scale studies in space, time, and across the taxonomic tree and, thanks to open science projects (Table  2 ), to share data across parks, geographies, and the globe 51 . Sensors generate diverse data types, including imagery, soundscapes, and positional data (Fig.  3 ). They can be mobile or static, and can be deployed to collect information on individuals or species of interest (e.g., bio-loggers, drones), monitor activity in a particular location (e.g., camera traps and acoustic sensors), or document changes in habitats or landscapes over time (satellites, drones). Finally, they can also be opportunistic, as in the case of community science. Below, we discuss the different categories of sensors and the opportunities they open for ML-based wildlife research.

Studies frequently combine data from multiple sensors at the same geographic location, or data from multiple locations to achieve deeper ecological insights. Sentinel-2 (ESA) satellite image courtesy of the U.S. Geological Survey.

Stationary sensors

Stationary sensors provide close-range continuous monitoring over long time scales. Sensors can be image-based (e.g., camera traps) or signal-based (e.g., sound recorders). Their high level of temporal resolution allows for detailed analysis, including presence/absence, individual identification, behavior analysis, and predator-prey interaction. However, because of their stationary nature, their data is highly spatiotemporally correlated. Based on where and when in the world the sensor is placed, there is a limited number of species that can be captured. Furthermore, many animals are highly habitual and territorial, leading to very strong correlations between data taken days or even weeks apart from a single sensor 52 .

Camera traps are among the most used sensors in recent ML-based animal ecology papers, with more than a million cameras already used to monitor biodiversity worldwide 20 . Camera traps are inexpensive, easy to install, and provide high-resolution image sequences of the animals that trigger them, sufficient to specify the species, sex, age, health, behavior, and predator-prey interactions. Coupled with population models, camera-trap data has also been used to estimate species occurrence, richness, distribution, and density 20 . But the popularity of camera traps also creates challenges relative to the quantity of images and the need for manual annotation of the collections: software tools easing the annotation process are appearing (see, e.g., AIDE in Table  1 ) and many ecologists have already incorporated open-source ML approaches for filtering out blank images (such as the Microsoft AI4Earth MegaDetector 36 , see Table  1 ) into their camera trap workflows 52 , 53 , 54 . However, problems related to lack of generality across geographies, day/night acquisition, or sensors are still major obstacles to production-ready accurate systems 55 . The increased scale of available data due to de-siloing efforts from organizations like Wildlife Insights ( www.wildlifeinsights.org ) and LILA.science ( www.lila.science ) will help increase ML accuracy and robustness across regions and taxa.

Bioacoustic sensors are an alternative to image-based systems, using microphones and hydrophones to study vocal animals and their habitats 56 . Networks of static bioacoustic sensors, used for passive acoustic monitoring (PAM), are increasingly applied to address conservation issues in terrestrial 57 , aquatic 58 , and marine 59 ecosystems. Compared to camera traps, PAM is mostly unaffected by light and weather conditions (some factors like wind still play a role), senses the environment omnidirectionally, and tends to be cost-effective when data needs to be collected at large spatiotemporal scales with high resolution 60 . While ML has been extensively applied to camera trap images, its application to long-term PAM datasets is still in its infancy and the first DL-based studies are only starting to appear (see Fig.  1 a, ref. 61 ). Significant challenges remain when utilizing PAM. First and foremost among these challenges is the size of data acquired. Given the often continuous and high-frequency acquisition rates, datasets often exceed the terabyte scale. Handling and analyzing these datasets efficiently requires access to advanced computing infrastructure and solutions. Second, the inherent complexity of soundscapes requires noise-robust algorithms that generalize well and can separate and identify many animal sounds of interest from confounding natural and anthropogenic signals in a wide variety of acoustic environments 62 . The third challenge is the lack of large and diverse labeled datasets. As for camera trap images, species- or region-specific characteristics (e.g., regional dialects 63 ) affect algorithm performance. Robust, large-scale datasets have begun to be curated for some animal groups (e.g., www.macaulaylibrary.org and www.xeno-canto.org for birds), but for many animal groups as well as relevant biological and non-biological confounding signals, such data is still nonexistent.

Remote sensing

Collecting data on free-ranging wildlife has been restricted traditionally by the limits of manual data collection (e.g., extrapolating transect counts), but have increased greatly through the automation of remote sensing 35 . Using remote sensing, i.e., sensors mounted on moving platforms such as drones, aircraft, or satellites—or attached to the animals themselves—allows us to monitor large areas and track animal movement over time.

On-animal sensors are the most common remote sensing devices deployed in animal ecology 10 . They are primarily used to acquire movement trajectories (i.e., GPS data) of animals, which can then be classified into activity types that relate to the behavior of individuals or social groups 10 , 64 . Secondary sensors, such as microphones, video cameras, heart rate monitors, and accelerometers, allow researchers to capture environmental, physiological, and behavioral data concurrently with movement data 65 . However, power supply and data storage and transmission limitations of bio-logging devices are driving efforts to optimize sampling protocols or pre-process data in order to conserve these resources and prolong the life of the devices. For example, on-board processing solutions can use data from low-cost sensors to identify behaviors of interest and engage resource-intensive sensors only when these behaviors are being performed 66 . Other on-board algorithms classify raw data into behavioral states to reduce the volume of data to be transmitted 67 . Various supervised ML methods have shown their potential in automating behavior analysis from accelerometer data 68 , 69 , identifying behavioral state from trajectories 70 , and predicting animal movement 71 .

Unmanned aerial vehicles (UAVs) or drones for low-altitude image-based approaches, have been highlighted as a promising technology for animal conservation 72 , 73 . Recent studies have shown the promise of UAVs and deep learning for posture tracking 74 , 75 , 76 , semi-automatic detection of large mammals 42 , 77 , birds 78 , and, in low-altitude flight, even identification of individuals 79 . Drones are agile platforms that can be deployed rapidly—theoretically on demand—and with limited cost. Thus, they are ideal for local population monitoring. Lower altitude flights in particular can provide oblique view points that partially mitigate occlusion by vegetation. The reduced costs and operation risks of UAVs further make them an increasingly viable alternative to low-flying manned aircraft.

Common multi-rotor UAV models are built using inexpensive hardware and consumer-level cameras, and only require a trained pilot with flight permissions to perform the survey. To remove the need for a trained pilot, fully autonomous UAV platforms are also being investigated 79 . However, multi-rotor drone-based surveys remain limited in the spatial footprint that can be covered, mostly because of battery limitations (which become even more stringent in cold climates like Antarctica) and local legislation. Combustion-driven fixed wing UAVs flying at high altitudes and airplane-based acquisitions can overcome some of these limitations, but are significantly more costly and preclude close approaches for visual measurements of animals. Finally, using drones also has a risk of modifying the behavior of the animals. A recent study 80 showed that flying at lower altitudes (e.g., lower than 150 m) can have a significant impact on group and individual behavior of mammals, although the severity of wildlife disturbance from drone deployments will depend heavily on the focal species, the equipment used, and characteristics of the drone flight (such as approach speed and altitude) 81 —this is a rapidly changing field and advances that will limit noise are likely to come. More research to quantify and qualify such impacts in different ecosystems is timely and urgent, to avoid both biased conclusions and increased levels of animal stress.

Satellite data is used to widen the spatial footprint and reduce invasive impact on behavior. Public programs such as Landsat and Sentinel provide free and open imagery at medium resolution (between 10 and 30 m per pixel), which, though usually not sufficient for direct wildlife observations, can be useful for studying their habitats 34 , 82 . Meanwhile, commercial very high resolution (less than one meter per pixel) imagery is narrowing the gap between UAV acquisitions and large-scale footprinting with satellites. Remote sensing has a long tradition of application of ML algorithms. Thanks to the raster nature of the data, remote sensing has fully adopted the new DL methods 83 , which are nowadays entering most fields of application that exploit satellite data 49 . In animal ecology, studies focused on large animals such as whales 84 or elephants 85 attempt direct detection of the animals on very high-resolution images, increasingly with DL. When focusing on smaller-bodied species, studies resort to aerial surveys to increase resolution in order to directly visualize the animals or focus on the detection of proxies instead of the detection of the animal itself (e.g., the detection of penguin droppings to locate colonies 86 ). More research is currently required to really harness the power of remote sensing data, which lies, besides the large footprint and image resolution, in the availability of image bands beyond the visible spectrum. These extra bands are highly appreciated in plant ecology 87 and multi- and hyperspectral DL approaches 88 are yet to be deployed in animal ecology, where they could help advancing the characterization of habitats.

Community science for crowd-sourcing data

An alternative to traditional sensor networks (static or remote) is to engage community members as wildlife data collectors and processors 89 , 90 . In this case, community participants (often volunteers) work to collect the data and/or create the labels necessary to train ML models. Models trained this way can then be used to bring image recognition tasks to larger scale and complexity, from filtering out images without animals in camera trap sequences to identifying species or even individuals. Several annotation projects based on community science have appeared recently (Table  2 ). For simple tasks like animal detection, community science effort can be open to the public, while for more complex ones such as identifying bird species with subtle appearance differences (“fine-grained classification”, also see the glossary), communities of experts are needed to provide accurate labels. A particularly interesting case is Wildbook (see Box  1 and Table  1 ), which routinely screens videos from social media platforms with computer vision models to identify individuals; community members (in this case video posters) are then queried in case of missing or uncertain information. Recent research shows that ML models trained on community data can perform as well as annotators 91 . However, it is prudent to note that the viability of community science services may be limited depending on the task and that oftentimes substantial efforts are required to verify volunteer-annotated data. This is due to annotator errors, including misdetected or mislabeled animals due to annotator fatigue or insufficient knowledge about the annotation task, as well as systematic errors from adversarial annotators. Another form of community science is the use of images acquired by volunteers: in this case, volunteers replace camera traps or UAVs and provide the raw data used to train the ML model. Although this approach sacrifices control over image acquisitions and is likewise prone to inducing significant noise to datasets, for example through low-quality imagery, it provides a substantial increase in the number of images and the chances of photographing species or single individuals in different regions, poses, and viewing angles. Community science efforts also increase public engagement in science and conservation. The Great Grevy’s Rally, a community science-based wildlife census effort occurring every 2 years in Kenya 92 , is a successful demonstration of the power of community science-based wildlife monitoring.

Box 1 Wildbook: successes at the interface between community science and deep learning

Wildbook, a project of the non-profit Wild Me, is an open-source software platform that blends structured wildlife research with artificial intelligence, community science, and computer vision to speed population analysis and develop new insights to help conservation (Fig.  4 ). Wildbook supports collaborative mark-recapture, molecular ecology, and social ecology studies, especially where community science and artificial intelligence can help scale-up projects. The image analysis of Wildbook can start with images from any source—scientists, camera traps, drones, community scientists, or social media—and use ML and computer vision to detect multiple animals in the images 100 to not only classify their species, but identify individual animals applying a suite of different algorithms 101 , 147 . Wildbook provides a technical solution for wildlife research and management projects for non-invasive individual animal tracking, population censusing, behavioral and social population studies, community engagement in science, and building a collaborative research network for global species. There are currently Wildbooks for over 50 species, from sea dragons to zebras, spanning the entire planet. More than 80 scientific publications have been enabled by Wildbook. Wildbook data has become the basis for the IUCN Red List global population numbers for several species, and supported the change in conservation status for whale sharks from “vulnerable” to “endangered”. Wildbook’s technology also enabled the Great Grevy’s Rally, the first-ever full species census for the endangered Grevy’s zebra in Kenya, using photographs captured by the public. Hosted for the first time in January 2016, it has become a regular event, held every other year. Hundreds of people, from school children and park rangers, to Nairobi families and international tourists, embark on a mission to photograph Grevy’s zebras across its range in Kenya, capturing ~50,000 images over the 2-day event. With the ability to identify individual animals in those images, Wildbook can enable an accurate population census and track population trends over time. The Great Grevy’s Rally has become the foundation of the Kenya Wildlife Service’s Grevy’s zebra endangered species management policy and generates the official IUCN Red List population numbers for the species. Wildbook’s AI enables science, conservation, and global public engagement by bringing communities together and working in partnership to provide solutions that people trust.

Wildbook allows scientists and wildlife managers to leverage the power of communities and ML to monitor wildlife populations. Images of target species are collected via research projects, community science events (e.g., the Great Grevy’s Rally; see text), or by scraping social media platforms using Wildbook AI tools. Wildbook software uses computer vision technology to process the images, yielding species and individual identities for the photographed animals. This information is stored in databases on Wildbook data management servers. The data and biological insights generated by Wildbook facilitates exchange of expertise between biologists, data scientists, and stakeholder communities around the world.

Machine learning to scale-up and automate animal ecology and conservation research

The sensor data described in the previous section has the potential to unlock ecological understanding on a scale difficult to imagine in the recent past. But to do so, it must be interpreted and converted to usable information for ecological research. For example, such conversion can take the form of abundance mapping, individual animal re-identification, herd tracking, or digital reconstruction (three-dimensional, phenotypical) of the environment the animals live in. The measures yielded by this conversion, reviewed in this section, are also sometimes referred to as animal biometrics 93 . Interestingly, the tasks involved in the different approaches show similarities with traditional tasks in ML and computer vision (e.g., detection, localization, identification, pose estimation), for which we provide a matching example in animal ecology in Fig.  5 .

Imagery can be used to capture a range of behavioral and ecological data, which can be processed into usable information with ML tools. Aerial imagery (from drones, or satellites for large species) can be used to localize animals and track their movements over time and model the 3D structure of landscapes using photogrammetry. Posture estimation tools allow researchers to estimate animal postures, which can then be used to infer behaviors using clustering algorithms. Finally, computer vision techniques allow for the identification and re-identification of known individuals across encounters.

Wildlife detection and species-level classification

Conservation efforts of endangered species require knowledge on how many individuals of the species in question are present in a study area. Such estimations are conventionally realized with statistical occurrence models that are informed by sample-based species observations. It is these observations where imaging sensors (camera traps, UAVs, etc.), paired with ML models that detect and count individuals in the imagery, can provide the most significant input. Early works used classical supervised ML algorithms (algorithms needing a set of human-labeled annotations to learn, see Supplementary Table  2 ): these algorithms were used to make the connection between a set of characteristics of interest extracted from the image (visual descriptors, e.g., color histograms, spectral indices, etc., also see the glossary) and the annotation itself (presence of an animal, species, etc.) 35 , 94 . Particularly in camera trap imagery, foreground (animal) segmentation is occasionally performed as a pre-processing step to discard image parts that are potentially confusing for a classifier. These approaches, albeit good in performance, suffer from two limitations: first, the visual descriptors need to be specifically tailored to the problem and dataset at hand. This not only requires significant engineering efforts, but also bears the risk of the model becoming too specific to the particular dataset and external conditions (e.g., camera type, background foliage amount, and movement type) at hand. Second, computational restrictions in these models limit the number of training examples, which is likely to have detrimental effects on variations in data (temporal, seasonal, etc.), thus reducing the generalization capabilities to new sensor deployments or regions. For these reasons, detecting and classifying animal species with DL for the purpose of population estimates is becoming increasingly popular for images 52 , 53 , acoustic spectrograms 95 , and videos 96 . Models performing accurately and robustly on specific classes (e.g., the MegaDetector - see Box  2  - or AIDE; see Table  1 ) are now being used routinely and integrated within open systems supporting ecologists performing both labeling and detection, respectively counting of their image databases. Issues related to dependence of the models performance to specific training locations are still at the core of current developments 52 , an issue known in ML as “domain adaptation” or “generalization”.

Box 2 AI for Wildlife Conservation in Practice: the MegaDetector

One highly-successful example of open source AI for wildlife conservation is the Microsoft AI for Earth MegaDetector 36 (Fig.  6 ). This generic, global-scale human, animal, and vehicle detection model works off-the-shelf for most camera trap data, and the publicly-hosted MegaDetector API has been integrated into the wildlife monitoring workflows of over 30 organizations worldwide, including the Wildlife Conservation Society , San Diego Zoo Global , and Island Conservation . We would like to highlight two MegaDetector use cases, via Wildlife Protection Solutions (WPS) and the Idaho Department of Fish and Game (IDFG). WPS use the MegaDetector API in real-time to detect threats to wildlife in the form of unauthorized humans or vehicles in protected areas. WPS connect camera traps to the cloud via cellular networks, upload photos, run them through the MegaDetector via the public API, and return real-time alerts to protected area managers. They have over 400 connected cameras deployed in 18 different countries, and that number is growing rapidly. WPS used the MegaDetector to analyze over 900K images last year alone, which comes out to 2.5K images per day. They help protected areas detect and respond to threats as they occur, and detect at least one real threat per week across their camera network.

Idaho is required to maintain a stable population of protected wolves. IDFG relies heavily on camera traps to estimate and monitor this wolf population, and needs to process the data collected each year before the start of the next season in order to make informed policy changes or conservation interventions. They collected 11 million camera trap images from their wolf cameras last year, and with the MegaDetector integrated into their data processing and analysis pipeline, they were able to fully automate the analysis of 9.5 million of those images, using model confidence to help direct human labeling effort to images containing animals of interest. Using the Megadetector halved their labeling costs, and allowed IDFG to label all data before the start of the next monitoring season, whereas manual labeling previously resulted in a lag of ~5 years from image collection to completion of labeling. The scale and speed of analysis required in both cases would not be possible without such an AI-based solution.

The near-universal need of all camera trap projects to efficiently filter empty images and localize humans, animals, and vehicles in camera trap data, combined with the robustness to geographic, hardware, and species variability the MegaDetector provides due to its large, diverse training set makes it a useful, practical tool for many conservation applications out of the box. The work done by the Microsoft AI for Earth team to provide assistance running the model via hands-on engineering assistance, open-source tools, and a public API have made the MegaDetector accessible to ecologists and a part of the ecological research workflow for over 30 organizations worldwide.

Individual re-identification

Another important biometric is animal identity. The standard for identification of animal species and identity is DNA profiling 97 , which can be difficult to scale to large, distributed populations 54 , 93 . As an alternative to gene-based identification, manual tagging can be used to keep track of individual animals 10 , 93 . Similar to counting and reconstruction (see next section), computer vision recently emerged as a powerful alternative for automatic individual identification 54 , 98 , 99 , 100 . The aim is to learn identity-bearing features from the appearance of animals. Identifying individuals from images is even more challenging than species recognition, since the distinctive body patterns of individuals might be subtle or not be sufficiently visible due to occlusion, motion blur, or overhead viewpoint in the case of aerial imagery. Yet, conventional 101 and more recently DL-based 38 , 54 , 102 methods have reached strong performance for specific taxa, especially across small populations. Some species have individually-unique coat or skin markings that assist with re-identification: for example, accuracy exceeded 90% in a study of 92 tigers across 8000 video clips 103 . However, effective re-identification is also possible in the absence of patterned markings: a study of a small group of 23 chimpanzees in Guinea applied facial recognition techniques to a multi-year dataset comprising 1 million images and achieved >90% accuracy 38 . This study compared the DL model to manual re-identification by humans: where humans achieved identification accuracy between 20% (novices) and 42% (experts) with an annotation time between 1 and 2 h, the DL model achieved an identification accuracy of 84% in a matter of seconds. The particular challenges for animal (re-)identification in wild populations are related to the difficulty of manual curation, larger populations, changes in appearance (e.g., due to scars, growth), few sightings per individual, and the frequent addition of new individuals that may enter the system due to birth or immigration, therefore creating an “open-set” problem 104 wherein the model must deal with “classes” (individuals) unseen during training. The methods must have the ability to identify not only animals that have been seen just once or twice but also recognize new, previously unseen animals, as well as adjust decisions that have been made in the past, reconciling different views and biological stages of an animal.

Animal synthesis and reconstruction

3D shape recovery and pose estimation of animals can provide valuable, non-invasive insights on wild species in their natural environment. The 3D shape of an individual can be related to its health, age, or reproductive status; the 3D pose of the body can provide finer information with respect to posture attributes and allows, for instance, kinematic as well as behavioral analyses. For pose estimation, marker-less methods based on DL have tremendously improved over the last years and already impacted biology 105 . Various user-friendly toolboxes are available to extract the 2D posture of animals from videos (Fig.  1 d, e), while the user can define which body parts should be estimated (reviewed in ref. 76 ). Extracting a dense set of body surface points is also possible, as elegantly shown in ref. 106 , where the DensePose technique originally developed for humans was extended to chimpanzees. The reconstruction of the 3D shape and pose of animals from images often follows a model-based paradigm, where a 3D model of the animal is fit to visual data. Recent work defines the SMAL (Skinned Multi-Animal Linear) model, a 3D articulated shape model for a set of quadruped families 107 . Biggs et al. built on this work for 3D shape and motion of dogs from video 108 and for recovery of dog shape and pose across many different breeds 109 . In ref. 110 , the SMAL model has been used in a DL approach to predict 3D shape and pose of the Grevy’s zebra from images. 3D shape models have been recently defined also for birds 111 . Image-based 3D pose and shape estimation methods provide rich information about individuals but require, in addition to accurate shape models, prior knowledge about the animal’s 3D motion.

Reconstructing the environment

Wildlife behavior and conservation cannot be dissociated from the environment animals evolve and live in. Studies have shown that animal observations like trajectories highly benefit from additional cues included in the environmental context 112 . Satellite remote sensing has become an integral part to study animal habitats, biological diversity, and spatiotemporal changes of abiotic conditions 113 , since it allows to map quantities like land cover, soil moisture, or temperature at scale. Reconstructing the 3D shape of the environment has also become central in behavior studies: for example, 3D reconstructions of kill sites for lions in South Africa revealed novel insights into the predator-prey relationships and their connection to ecosystem stability and functioning 114 , while 3D spatial reconstructions shed light on the impact of forest structures on bat behavior 115 . Such spatial reconstructions of the environment can either be extracted by using dedicated sensors such as LiDAR 116 or can be reconstructed from multiple images, either by stitching the images into a unified two-dimensional panorama (e.g., mosaicking 117 ) or by computing the three-dimensional environment from partially overlapping images (e.g., structure from motion 118 or simultaneous localization and mapping 119 ). All these approaches have strongly benefited from recent ML advancements 120 , but have seldom been applied for wildlife conservation purposes, where they could greatly help when dealing with images acquired by moving or swarms of sensors 121 . However, applying these techniques to natural wildlife imagery is not trivial. For example, unconstrained continuous video recordings at potentially high frame-rates will result in large image sets which require efficient image processing 117 . Moreover, ambiguous environmental appearances and structural errors such as drift accumulate over time and therefore decrease the reconstruction quality 118 . Last but not least, a variety of inappropriate camera motions or environmental geometries can result in so-called critical configurations which cannot be resolved by the existing optimization schemes 122 . As a consequence, cues from additional external sensors are usually integrated to achieve satisfactory environmental reconstructions from video data 123 .

Modeling species diversity, richness, and interactions

Analyses of biodiversity, represented by such measures as species abundance and richness, are foundational to much ecological research and many conservation initiatives. Spatially explicit linear regression models have been conventionally used to predict species and community distribution based on explanatory variables such as climate and topography 124 , 125 . Non-parametric ML techniques like Random Forest 126 have been successfully used to predict species richness and have shown significant error reduction with respect to the traditional counterparts used in ecology, for example in the estimation of richness distributions of fishes 127 , 128 , spiders 129 , and small mammals 130 . Tree-based techniques have also been used to predict species interactions: for example, regression trees significantly outperformed classical generalized linear models in predicting plant-pollinator interactions 33 . Tree-based methods are well-suited to these tasks because they perform explicit feature ranking (and thus feature selection) and are able to model nonlinear relationships between covariates and species distribution. More recently, graph regression techniques were deployed to reconstruct species interaction networks in a community of European birds with promising results, including better causality estimates of the relations in the graph 131 .

Attention points and opportunities

Machine and deep learning are becoming necessary accelerators for wildlife research and conservation actions in natural reserves. We have discussed success stories of the application of approaches from ML into ecology and highlighted the major technical challenges ahead. In this section, we want to present a series of “attention points" that highlight new opportunities between the two disciplines.

What can we focus on now?

State-of-the-art ML models are now being applied to many tasks in animal ecology and wildlife conservation. However, while an out-of-the-box application of existing open tools is tempting, there are a number of points and potential pitfalls that must be carefully considered to ensure responsible use of these approaches.

Inherent model biases and generalization . Most ecological datasets suffer from some degree of geographic bias. For example, many open imagery repositories such as Artportalen.se , Naturgucker.de , and Waarneming.nl collect images from specific regions, and most contributions on iNaturalist 132 (see Table  2 ) come from the Northern hemisphere. Such biases need to be understood, acknowledged, and communicated to avoid incorrect usage of methods or models that by design may only be accurate in a specific geographic region. Biases are not limited to the geographical provenance of images: the type of sensors used (RGB vs . infrared or thermal), the species they depict, and the imbalance in the number of individuals observed per species 55 , 132 must also be considered when training or using models to avoid potentially catastrophic drop-offs in accuracy, and transparency around the training data and the intended model usage is a necessity 133 .

Curating and publishing well-annotated benchmark datasets without doing harm . The long-term advancement of the field will ultimately require the curation of large, diverse, accurately labeled, and publicly available datasets for ecological tasks with defined evaluation metrics and maintained code repositories. However, opening up existing datasets (and especially when using private-owned images acquired by non-professionals as in ref. 92 ) is both a necessary and difficult challenge for the near future. Fostering a culture of individual and cross-institutional data sharing in ecology will allow ML approaches to improve in robustness and accuracy. Furthermore, proper credit has to be given to the data collectors, for example through appropriate data attribution and digital object identifiers (DOIs) for datasets 133 .

Understanding the ethical risks involved . Computer scientists must also be aware of the ethical and environmental risks of publishing certain types of datasets. It is important to understand the limits of open data sharing in animal conservation in nature parks. In some cases, it is imperative that the privacy of the data be preserved, for instance to avoid giving poachers access to locations of animals in near-real-time 134 . Security of rangers themselves is also at stake; for example, the flight path of drones might be backtracked to reveal their location.

Standards of quality control are urgently needed . Accountability for open models needs to be better understood. The estimations of models remain approximations and need to be treated as such: population counts without uncertainty estimation can lead to erroneous and potentially devastating conclusions. Increased quality control on the adequacy of a model to a new scientific question or study area is important and can be achieved by close cooperation between model developers (who have the ability to design, calibrate, and run the models at their best) and practitioners (who have the domain and local knowledge). Without such quality control measures, relying on model-based results is risky and could have difficult-to-evaluate impacts on research in animal ecology, as incorrect results hidden in a suboptimally trained model will become more and more difficult to detect. Computer scientists must be aware that errors by their models can lead to erroneous decisions on site that can be catastrophic for the population they are trying to preserve or for the populations that live at the border of human/wildlife conflicts.

Environmental and financial costs of machine learning . ML is not free. Training and running models with millions of parameters on large volumes of data requires powerful, somewhat specialized hardware. Purchasing prices of such machines alone are often prohibitively high especially for budget-constrained conservation organizations; programming, running, and maintenance costs further add to the bill. Although cloud computing services exist that forgo the need of hardware management, they likewise pose per-resource costs that quickly scale to several thousands of dollars per month for a single virtual machine. Besides monetary costs, ML also uses significant amounts of energy: recently, it has been estimated that large, state-of-the-art models for understanding natural language emit as much carbon as several cars in their entire lifetime 135 . Even though the models currently used in animal ecology are far from such a carbon footprint, environmental costs of AI are often disregarded, as energy consumption of large calculations is still considered an endless resource (assuming that the money to pay for it is available). We believe this is a mistake, since disregarding environmental costs of ML models equals exchanging one source environmental harm (loss and biodiversity) for another (increase of emissions and energy consumption). Particular care needs to be paid to designing models that are not oversized and that can be trained efficiently. Smaller models are not only less expensive to train and use, their lighter computational costs allow them to be run on smaller devices, opening opportunities for real-time ML “on the edge”—i.e., within the sensors themselves.

What’s new: vast scientific opportunities lie ahead

In the previous sections, we describe the advances in research at the interface of ML, animal ecology, and wildlife conservation. The maturity of the various detection, identification, and recognition tools opens a series of interesting perspectives for genuinely novel approaches that could push the boundaries towards true integration of the disciplines involved.

Involving domain knowledge from the start . The ML and DL fields have focused mainly on black box models that learn correlations from data directly, and domain knowledge has been repeatedly ignored in favor of generic approaches that could fit to any kind of dataset. Such universality of ML is now strongly questioned and the inductive bias of traditional DL models is challenged by new approaches that bridge domain knowledge, fundamental laws, and data science. This “hybrid models” paradigm 48 , 50 is one of the most exciting avenues in modern ML and promises real collaboration between domains of application and ML, especially when coupled with algorithmic designs that allow interpretation and understanding of the visual cues that are being used 136 . This line of interdisciplinary research is small but growing, with several studies published in recent years. A representative one is Context R-CNN 52 for animal detection and species classification, which leverages the prior knowledge that backgrounds in camera trap imagery exhibit little variation over time and that camera traps acquire data with low sampling frequency and occasional dropouts. By integrating image features over long time spans (up to a month), the model is able to increase mean species identification precision in the Snapshot Serengeti dataset 137 by 17.9%. In another example 138 , the hierarchical structure of taxonomies, as well as locational priors, are leveraged to constrain plant species classification from iNaturalist in Switzerland, leading to improvements of state-of-the-art models of about 5%. Similarly ref.  139 , incorporate knowledge about the distribution of species as well as photographer biases into a DL model for species classification in images and report accuracy improvements of up to 12% in iNaturalist over a baseline without such priors. Finally ref.  140 , used expert knowledge of park rangers to augment sparse and noisy records of poaching activity, thereby improving predictions of poaching occurrence and enabling more efficient use of limited patrol resources in a Chinese nature reserve. These approaches challenge the dogma of ML models learning exclusively from data and achieve more efficient model learning (since base knowledge is available from the start and does not have to be re-learnt) and enhanced plausibility of the solutions (because the solution space can be constrained to a range of ecologically plausible outcomes).

Laboratories as development spaces . In recent years, modern ML has rapidly changed laboratory-based non-invasive observation of animals 76 , 105 . Neuroscience studies in particular have embraced novel tools for motion tracking, pose estimation (Fig.  1 d, e), and behavioral classification (e.g., ref. 141 ). The high level of control (e.g., of lighting conditions, sensor calibration, and environment) afforded by laboratory settings facilitated the rapid development of such tools, many of which are now being adopted for use in field studies of free-moving animals in complex natural environments 75 , 142 . In addition, algorithmic insights gained in the lab can be transferred back into the wild—studies on short videos or camera traps can leverage lab-generated data that is arguably less diverse, but easier to control. This opens interesting research opportunities for the adaptation of lab-generated simulation to real-world conditions, similar to what has been observed in the field of image synthesis for self driving 143 and robotics 144 in the last decade. Thus, laboratories rightly serve as the ultimate development space for such in-the-wild applications.

Towards a new generation of biodiversity models . Statistical models for species richness and diversity are routinely used to estimate abundances and study species co-occurrence and interactions. Recently, DL methods have also started to be employed to model species’ ecological niches 82 , 145 , facilitated by the development of machine-learning-ready datasets such as GeoLifeCLEF. GeoLifeCLEF curated a dataset of 1.9 million iNaturalist observations from North America and France depicting over 31,000 species, together with environmental predictors (land cover, altitude, climatic data, etc.), and asked users to predict a ranked list of likely species per geospatial grid cell. The task is complex: only positive counts are provided, no absence data are available, and predictions are counted as correct if the ground truth species is among the 30 predicted with highest confidence. This challenging task remains an open challenge—the winners of the 2021 edition achieved only an approximate 26% top-30 accuracy.

A recent review of species distribution modeling aimed at ML practitioners 146 provides an accessible entry point for those interested in tackling the challenges in this complex, exciting field. Open challenges include increasing the scale of joint models geospatially, temporally, and taxonomically, building methods that can leverage multiple data types despite bias from non-uniform sampling strategies, incorporating ecological knowledge such as species dispersal and community composition, and expanding methods for the evaluation of these models.

Finally, we wish to re-emphasize that the vision described here cannot be achieved without interdisciplinary thinking: for all these exciting opportunities, processing big ecological data is necessitating analytical techniques of such complexity that no single ecologist can be expected to have all the technical expertise (plus domain knowledge) required to carry out groundbreaking studies 65 . Cross-disciplinary collaborations are undeniably a critical component of ecological and conservation research in the modern era. Mutual understanding of the field-specific vocabularies, of the fields’ expectations, and of the implications and consequences of research ethics are within reach, but require open dialogs between communities, as well as cross-domain training of new generations.

Conclusions

Animal ecology and wildlife conservation need to make sense of large and ever-increasing streams of data to provide accurate estimations of populations, understand animal behavior and fight against poaching and loss of biodiversity. Machine and deep learning (ML; DL) bring the promise of being the right tools to scale local studies to a global understanding of the animal world.

In this Perspective , we presented a series of success stories at the interface of ML and animal ecology. We highlighted a number of performance improvements that were observed when adopting solutions based on ML and new generation sensors. Although often spectacular, such improvements require ever-closer cooperation between ecologists and ML specialists, since recent approaches are more complex than ever and require strict quality control and detailed design knowledge. We observe that skillful applications of state-of-the-art ML concepts for animal ecology now exist, thanks to corporate (e.g., Wildlife Insights) and research (AIDE, MegaDetector, DeepLabCut) efforts, but that there is still much room (and need) for genuinely new concepts pushed by interdisciplinary research, in particular towards hybrid models and new habitat distribution models at scale.

Inspired by these observations, we provided our perspective on the missing links between animal ecology and ML via a series of attention points, recommendations, and vision on future exciting research avenues. We strongly incite the two communities to work hand-in-hand to find digital, scalable solutions that will elucidate the loss of biodiversity and its drivers and lead to global actions to preserve nature. Computer scientists have yet to integrate ecological knowledge such as underlying biological processes into ML models, and the lack of transparency of current DL models has so far been a major obstacle to incorporating ML into ecological research. However, an interdisciplinary community of computer scientists and ecologists is emerging, which we hope will tackle this technological and societal challenge together.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

We thank Mike Costelloe for assistance with figure design and execution. S.B. would like to thank the Microsoft AI for Earth initiative, the Idaho Department of Fish and Game, and Wildlife Protection Solutions for insightful discussions and providing data for figures. M.C.C. and T.B.W. were supported by the National Science Foundation (IIS 1514174 & IOS 1250895). M.C.C. received additional support from a Packard Foundation Fellowship (2016-65130), and the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research. C.V.S. and T.B.W. were supported by the US National Science Foundation (Awards 1453555 and 1550853). S.B. was supported by the National Science Foundation Grant No. 1745301 and the Caltech Resnick Sustainability Institute. I.D.C. acknowledges support from the ONR (N00014-19-1-2556), and I.D.C., B.R.C., M.W., and M.C.C. from, the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy-EXC 2117-422037984. M.W.M. is the Bertarelli Foundation Chair of Integrative Neuroscience. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

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These authors contributed equally: Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe.

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School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Devis Tuia & Benjamin Kellenberger

Department of Computing and Mathematical Sciences, California Institute of Technology (Caltech), Pasadena, CA, USA

Max Planck Institute of Animal Behavior, Radolfzell, Germany

Blair R. Costelloe, Martin Wikelski, Iain D. Couzin & Margaret C. Crofoot

Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany

Department of Biology, University of Konstanz, Konstanz, Germany

Blair R. Costelloe, Iain D. Couzin & Margaret C. Crofoot

Institute for Applied Mathematics and Information Technologies, IMATI-CNR, Pavia, Italy

Silvia Zuffi

Computer Science Department, University of Münster, Münster, Germany

Benjamin Risse

School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Alexander Mathis & Mackenzie W. Mathis

Environmental Sciences Group, Wageningen University, Wageningen, Netherlands

Frank van Langevelde

Computer Science Department, University of Bristol, Bristol, UK

Tilo Burghardt

Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA

Roland Kays

North Carolina Museum of Natural Sciences, Raleigh, NC, USA

Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA

Holger Klinck & Grant van Horn

Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA

Charles V. Stewart

Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA

Tanya Berger-Wolf

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D.T. coordinated the writing team; D.T., B.K., S.B., and B.C. structured and organized the paper with equal contributions; all authors wrote the text; B.C. created the figures.

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Conservation, Management and Monitoring of Forest Resources in India

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• Mehebub Sahana 0 ,
• Gopala Areendran 1 ,
• Krishna Raj 2

Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester, UK

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National Forest Inventory in India: Developments Toward a New Design to Meet Emerging Challenges

Understanding the Drivers of Forest Degradation

Advanced scientific methods and tools in sustainable forest management: a synergetic perspective.

• Forest resources
• Deforestation
• Conservation ecology
• Forest management
• Habitat fragmentation
• Biodiversity
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Table of contents (20 chapters)

Front matter, forest conservation ecology, introduction to forest resources in india: conservation, management and monitoring perspectives.

• Mehebub Sahana, G. Areendran, Krishna Raj, Akhil Sivadas, C. S. Abhijitha, Kumar Ranjan

Assessment of Carbon Sequestration Using InVEST Model in Delhi, India

• Supreet Kaur, Deepakshi Babbar, Omar Sarif, Aparajita Ghatak, Abolfazl Jaafari

Assessments of Bio-physical Characteristics of Vegetation Cover in Western Part of Purulia District in West Bengal

• Shyamal Dutta, Soumen Chatterjee

Delineating the Mangrove Patches Along Coastal Kerala Using Geographical Information System, Satellite Data and Field Validation

• P. Pranav, N. Nandini Menon, U. Shameem, K. G. Mini, Grinson George

Forest Conservation and Society

Significance of social systems in forest and biodiversity conservation: experiences from jangal mahals of west bengal, india.

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‘Green Placemaking’ in Kolkata: Role of Urban Greens and Urban Forestry

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Forest Resource Scenario in Industrial Town: A Study of Asansol-Durgapur Region

• Jaidul Islam, Partha Pratim Sarkar, Abdur Rahman, Ram Avtar

Floristic Composition and Inventorization of Forest Resources in Some Selected Forest Areas of Paschim Bardhaman District, West Bengal, India

• Saikat Mondal, Pinaki Chattopadhyay, Arnab Banerjee, Debnath Palit

Forest Management

Assessment of forest cover change, community responses, and conservation strategy: evidence from north sikkim district, india.

• Sushmita Chakraborty, Arunima Chanda

Carbon Stock Assessment in Sub-humid Tropical Forest Stands of the Eastern Himalayan Foothills

• Prakash Rai, Gopal Shukla, Vineeta, Jahangeer A. Bhat, Sumit Chakravarty

A People’s Biodiversity Register of Henry’s Island, Indian Sundarban

• Riya Chakraborty, Nabendu Sekhar Kar, Raja Ghosh

Applications of Geospatial Technology on the Forest Management in Three Districts of North Bengal, India

• Swarnali Mukhopadhyay, Suman Sinha

Forest Monitoring Using GIS and Remote Sensing

An assessment of the temporal changes in land cover and forest fragmentation using geospatial techniques: a case study from the central indian highlands.

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Analyzing the Trend, Pattern, and Hotspots of Forest Fires Using Geospatial Techniques: A Case Study of Almora District, India

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Approaches and Methodologies on Mapping Vegetation Cover and Biodiversity Status Using Remote Sensing and Spatial Analysis: A Systematic Review

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Mehebub Sahana

Gopala Areendran, Krishna Raj

About the editors

Dr Mehebub Sahana is a cultural and environmental geographer with an interest in analysing land-use changes with special respect to spaces, politics, and the governance of the living and materialistic world. His present research interests include social-environmental interface, socio-ecological resilience and systems thinking; geohazards; landscape ecology; multi hazard risk assessment; land-use change; rural-urban conversion and the socio-political implications of land-use dynamics. He is currently working as a Research Associate at the School of Environment, Education & Development, The University of Manchester, UK. Previously, he was employed as a Lecturer/research consultant (2018-2019) at Indira Gandhi Conservation Monitoring Centre (IGCMC), WWF-India, New Delhi. He received his Ph.D. in Environmental Geography (2018) from the Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India. He has contributed more than 50 scientific researchpapers in international journals on issues pertaining to land-use changes and environmental degradation and their links with climate change induced vulnerabilities.

Dr Gopala Areendran is the Director of Indira Gandhi Conservation Monitoring Centre (IGCMC) at WWF – India. He has more than 25 years of experience as a professional and leader in geospatial technology and has been the driving force of location-based data monitoring and analytics since 2001 at WWF-India. His work at WWF ranges from addressing conservation issues occurring in various landscapes, with a high focus on the tiger, elephant, and rhinoceros to overseeing several institutional GIS based projects. Dr Areendran has an MS degree in Ecology from Pondicherry University and PhD from Wildlife Institute of India.  He has also worked with leading research centres like Salim Ali Centre for Ornithology and Natural History (SACON), Wildlife Institute of India (WII) and Madras Environmental Society (MES), Chennai and Institute of Remote Sensing, Anna University, Chennai. He has published several research papers and reports in peer reviewed journals, and were involved in publication of 4 books in the capacity of editor, and co- author.  As a pioneer of geospatial education, has also supervised over a hundred masters and research students.

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Book Title : Conservation, Management and Monitoring of Forest Resources in India

Editors : Mehebub Sahana, Gopala Areendran, Krishna Raj

DOI : https://doi.org/10.1007/978-3-030-98233-1

Publisher : Springer Cham

eBook Packages : Earth and Environmental Science , Earth and Environmental Science (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Hardcover ISBN : 978-3-030-98232-4 Published: 05 August 2022

Softcover ISBN : 978-3-030-98235-5 Published: 05 August 2023

eBook ISBN : 978-3-030-98233-1 Published: 04 August 2022

Edition Number : 1

Number of Pages : XVI, 571

Number of Illustrations : 5 b/w illustrations, 199 illustrations in colour

Topics : Environmental Management , Forestry , Monitoring/Environmental Analysis , Analytical Chemistry , Sustainable Development

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Key message

Adaptation of forest management to climate change requires an understanding of the effects of climate on forests, industries and communities; prediction of how these effects might change over time; and incorporation of this knowledge into management decisions. This requires multiple forms of knowledge and new approaches to forest management decisions. Partnerships that integrate researchers from multiple disciplines with forest managers and local actors can build a shared understanding of future challenges and facilitate improved decision making in the face of climate change.

Climate change presents significant potential risks to forests and challenges for forest managers. Adaptation to climate change involves monitoring and anticipating change and undertaking actions to avoid the negative consequences and to take advantage of potential benefits of those changes.

This paper aimed to review recent research on climate change impacts and management options for adaptation to climate change and to identify key themes for researchers and for forest managers.

The study is based on a review of literature on climate change impacts on forests and adaptation options for forest management identified in the Web of Science database, focusing on papers and reports published between 1945 and 2013.

One thousand one hundred seventy-two papers were identified in the search, with the vast majority of papers published from 1986 to 2013. Seventy-six percent of papers involved assessment of climate change impacts or the sensitivity or vulnerability of forests to climate change and 11 % (130) considered adaptation. Important themes from the analysis included (i) predicting species and ecosystem responses to future climate, (ii) adaptation actions in forest management, (iii) new approaches and tools for decision making under uncertainty and stronger partnerships between researchers and practitioners and (iv) policy arrangements for adaptation in forest management.

Conclusions

Research to support adaptation to climate change is still heavily focused on assessing impacts and vulnerability. However, more refined impact assessments are not necessarily leading to better management decisions. Multi-disciplinary research approaches are emerging that integrate traditional forest ecosystem sciences with social, economic and behavioural sciences to improve decision making. Implementing adaptation options is best achieved by building a shared understanding of future challenges among different institutions, agencies, forest owners and stakeholders. Research-policy-practice partnerships that recognise local management needs and indigenous knowledge and integrate these with climate and ecosystem science can facilitate improved decision making.

1 Introduction

Anthropogenic climate change presents potential risks to forests and future challenges for forest managers. Responding to climate change, through both mitigation and adaptation, may represent a paradigm shift for forest managers and researchers (Schoene and Bernier 2012 ). Climate change is resulting in increasing air temperature and changing precipitation regimes, including changes to snowfall and to the timing, amount and inter-annual variability of rainfall (IPCC 2013 ). Forests are widespread, long-lived ecosystems that are both intensively and extensively managed. They are potentially sensitive to these longer term climatic changes, as are the societies and economies that depend on them (Bernier and Schöne 2009 ). Climate change increases the potential consequences of many existing challenges associated with environmental, social or economic change.

Whilst forest ecosystems are resilient and many species and ecosystems have adapted historically to changing conditions, future changes are potentially of such magnitudes or will occur at rates that are beyond the natural adaptive capacity of forest species or ecosystems, leading to local extinctions and the loss of important functions and services, including reduced forest carbon stocks and sequestration capacity (Seppälä et al. 2009 ).

Recent global warming has already caused many changes in forests (Lucier et al. 2009 ). Aspects of climate change may be positive for some tree species in some locations. Tree growth is observed to be increasing in some locations under longer growing seasons, warmer temperatures and increased levels of CO 2 . However, many projected future changes in climate and their indirect effects are likely to have negative consequences for forests. Observed shifts in vegetation distribution (Kelly and Goulden 2008 ; Lenoir et al. 2010 ) or increased tree mortality due to drought and heat in forests worldwide (Allen et al. 2010 ) may not be due to human-induced climate change but demonstrate the potential impacts of rapid climate change. These impacts may be aggravated by other human-induced environmental changes such as increases in low elevation ozone concentrations, nitrogenous pollutant deposition, the introduction of exotic insect pests and pathogens, habitat fragmentation and increased disturbances such as fire (Bernier and Schöne 2009 ). Other effects of climate change may also be important for forests. Sea level rise is already impacting on tidal freshwater forests (Doyle et al. 2010 ) and tidal saltwater forests (mangroves) are expanding landward in sub-tropical coastal reaches taking over freshwater marsh and forest zones (Di Nitto et al. 2014 ).

With projected future change, species ranges will expand or contract, the geographic location of ecological zones will shift, forest ecosystem productivity will change and ecosystems could reorganise following disturbances into ecological systems with no current analogue (Campbell et al. 2009 ; Fischlin et al. 2009 ). Forests types differ in their sensitivity to climatic change. Bernier and Schöne ( 2009 ) considered boreal, mountain, Mediterranean, mangrove and tropical moist forests most vulnerable to climate change. However, there has been recent debate about the vulnerability of tropical moist forests (Corlett 2011 ; Huntingford et al. 2013 ; Feeley et al. 2012 ), and temperate forests in areas subject to drier climates may be more at risk (Choat et al. 2012 ).

Adapting to these changing and uncertain future conditions can be considered from a number of perspectives (McEvoy et al. 2013 ). Policy and management might be directed at avoiding or reducing the impact of climate-related events, reducing vulnerability to future climatic conditions, managing a broader suite of climate ‘risks’ or increasing resilience and capacity in forest ecological and production systems to recover from climate ‘shocks’.

Adapting forest management to climate change involves monitoring and anticipating change and undertaking actions to avoid the negative consequences or take advantage of potential benefits of those changes (Levina and Tirpak 2006 ). Adopting the principles and practices of sustainable forest management (SFM) can provide a sound basis for addressing the challenges of climate change. However, Innes et al. ( 2009 ) pointed out that our failure to implement the multi-faceted components of sustainable forest management in many forests around the world is likely to limit capacity to adapt to climate change. Forest managers will need to plan at multiple spatial and temporal scales and adopt more adaptive and collaborative management approaches to meet future challenges.

Whilst forest managers are accustomed to thinking in long time scales—considering the long-term implications of their decisions and factoring in uncertainty and unknowns into management—many are now responding to much shorter term social or economic imperatives. Local forestry practices are often based on an implicit assumption that local climate conditions will remain constant (Guariguata et al. 2008 ). Other social and economic changes will also continue to drive changes in forest management (Ince et al. 2011 ). For example, a growing global population, rapid economic development and increased wealth are driving demand for food and fibre crops and forest conversion to agriculture in many developing countries (Gibbs et al. 2010 ). Climate change mitigation objectives are increasing demands for wood-based bioenergy and the use of wood in construction and industrial systems. Increasing urbanisation is changing the nature of social demands on forests, and decreasing rural populations is limiting the availability of labour and capacity for intensive forest management interventions.

Ecosystem-based adaptation is being promoted as having the potential to incorporate sustainable management, conservation and restoration of ecosystems into adaptation to climate change (IUCN 2008 ). This can be achieved more effectively by integrating ecosystem management and adaptation into national development policies through education and outreach to raise societal awareness about the value of ecosystem services (Vignola et al. 2009 ).

Kimmins ( 2002 ) invoked the term ‘future shock’, first coined by Toffler ( 1970 ) to describe the situation where societal expectations from forests were changing faster than the institutional capacity for change in forest management organisations. The pace of climate change is likely to intensify this phenomenon. Empirically based management based on traditional ‘evidence-based’ approaches therefore will potentially not develop quickly enough for development of effective future management options. How can managers consider rapid change and incorporate the prospect of very different, but uncertain, future climatic conditions into their management decisions? What types of tools are needed to improve decision making capacity?

This study aimed to review the literature on studies to support forest management in a changing climate. It builds on the major review of Seppala ( 2009 ), in particular Chapter 6 of that report by Innes et al. ( 2009 ).

The study involved a systematic assessment of the literature based on the database Web of Science (Thomson-Reuters 2014 ), an online scientific citation indexing service that provides the capacity to search multiple databases, allowing in-depth exploration of the literature within an academic or scientific discipline.

The following search terms were used in the titles of publications:

(forest* or tree* or (terrestrial and ecosystem)) and climat* and (adapt* or impact* or effect* or respons*) and

(forest* or tree*) and climat* and vulnerabilit* or sensitivit*)

The search was restricted to publications between 1945 and 2013. References related solely to climate change mitigation were excluded, as were references where the word ‘climate’ simply referred to a study in a particular climatic zone. This left a database of 1172 publications for analyses (a spreadsheet of the papers revealed in the search can be obtained from the author). References were classified into various types of studies and different regions, again based on the titles. Not all papers identified in the search are referenced. The selection of themes for discussion and papers for citation was a subjective one, based on scanning abstracts and results from relevant individual papers. The focus was important themes from key papers and literature from the last 5 years. The review includes additional papers not revealed in the search relating to these themes including selected papers from the literature in the year 2014.

Of the published papers relating to climate impacts or adaptation selected for analysis, the vast majority of papers were published from 1986 onwards. The earliest paper dated from 1949 (Gentilli 1949 ) analysing the effects of trees on climate, water and soil. Most studies prior to 1986 (and even some published later) focused on the effects of trees on local or wider regional climate (Lal and Cummings 1979 ; Otterman et al. 1984 ; Bonan et al. 1992 ), the implications of climate variability (Hansenbristow et al. 1988 ; Ettl and Peterson 1995 ; Chen et al. 1999 ), studies of tree and forest responses across climatic gradients (Grubb and Whitmore 1966 ; Bongers et al. 1999 ; Davidar et al. 2007 ) or responses to historical climate (Macdonald et al. 1993 ; Huntley 1990 ; Graumlich 1993 ).

One thousand twenty-six papers specifically addressed future climate change (rather than historical climate or gradient analysis). Of these, 88 % studied impacts, effects, vulnerability or responses to climate change in tree species, forests, forest ecosystems or the forest sector (Fig.  1 ). The first study analysing the potential impacts of future climate change on terrestrial ecosystems was published in 1985 (Emanuel et al. 1985 ) with other highly cited papers soon after (Pastor and Post 1988 ; Cannell et al. 1989 ).

Publication numbers by publication year for publications relating to climate change and forests from a search of the Web of Science database to the end of 2013 (1025 in total, 896 publications studied climate change impacts, responses or vulnerability, 129 studied adaptation)

Twelve percent of papers (129) considered adaptation options, including 10 papers on adaptation in the forest sector. The first papers to focus on adaptation in the context of climate change were in 1996 with a number of papers published in that year (Kienast et al. 1996 ; Kobak et al. 1996 ; Dixon et al. 1996 ). Publications were then relatively few each year until the late 2000s with numbers increasing to 11 in 2009, 22 in 2010 and 27 in 2011. Publications on adaptation dropped to 14 papers in 2013. The ratio of adaptation-related papers has increased more recently, with 19 % of total publications on adaptation in the last 5 years. Most papers considering adaptation since the early 2000s have related to the integration of adaptation and forest management (e.g. Lindner 2000 ; Spittlehouse 2005 ; Kellomaki et al. 2008 ; Guariguata 2009 ; Bolte et al. 2009 ; Keskitalo 2011 ; Keenan 2012 ; Temperli et al. 2012 ).

Analyses of the implications of climate change for the forest sector have focused heavily on North America: Canada (Ohlson et al. 2005 ; Van Damme 2008 ; Rayner et al. 2013 ; Johnston et al. 2012 ) and the USA (Joyce et al. 1995 ; Blate et al. 2009 ; Kerhoulas et al. 2013 ); and Europe (Karjalainen et al. 2003 ; von Detten and Faber 2013 ). There has been a stronger consideration in recent years of social, institutional and policy issues (Ogden and Innes 2007b ; Kalame et al. 2011 ; Nkem et al. 2010 ; Spies et al. 2010 ; Somorin et al. 2012 ) and the assessment of adaptive capacity in forest management organisations and in society more generally (Keskitalo 2008 ; Lindner et al. 2010 ; Bele et al. 2013a ).

Regionally, there have been relatively few published journal articles on impacts or adaptation in forests in the Southern Hemisphere (Hughes et al. 1996 ; Williams 2000 ; Pinkard et al. 2010 ; Gonzalez et al. 2011 ; Mok et al. 2012 ; Breed et al. 2013 ), although there have been more studies in the grey literature for Australian forests (Battaglia et al. 2009 ; Cockfield et al. 2011 ; Medlyn et al. 2011 ; Stephens et al. 2012 ). There have been some valuable analyses for the tropics (Guariguata et al. 2008 , 2012 ; Somorin et al. 2012 ; Feeley et al. 2012 ).

Analysis of the publications identified the following key themes: (i) predicting species and ecosystem responses to future climate, (ii) adaptation actions in forest management, (iii) new approaches and tools for decision making under uncertainty and stronger partnerships between researchers and practitioners and (iv) policy arrangements for adaptation in forest management. These are discussed in more detail below.

3.1 Predicting species and ecosystem responses to future climate

Forest managers have long used climatic information in a range of ways in planning and decision making. Climate information has been used extensively to define and map vegetation types and ecological zones and for modelling habitat distributions of vertebrates and invertebrates (Daubenmire 1978 ; Pojar et al. 1987 ; Thackway and Cresswell 1992 ), for species and provenance selection (Booth et al. 1988 ; Booth 1990 ) and seed zone identification (Johnson et al. 2004 ), for forest fire weather risk assessment and fire behaviour modelling (Carvalho et al. 2008 ), for modelling forest productivity (Battaglia et al. 2004 ) and analysing the dynamics of a range of ecological processes (Anderson 1991 ; Breymeyer and Melillo 1991 ). Predicting species responses to future climate change presents a different set of challenges, involving consideration of predictions of future climate that are often outside the historical range of variability of many species. These challenges are discussed in the next section.

3.1.1 Species responses to climate

Aitken et al. ( 2008 ) argued that there were three possible fates for forest tree populations in rapidly changing climatic conditions: persistence through spatial migration to track their ecological niches, persistence through adaptation to new conditions in current locations or the extirpation of the species. Predicting the potential fate of populations in these conditions requires the integration of knowledge across biological scales from individual genes to ecosystems, across spatial scales (for example, seed and pollen dispersal distances or breadth of species ranges) and across temporal scales from the phenology of annual developmental cycle traits to glacial and interglacial cycles.

Whilst there has been widespread use of climatic information to predict future distributions in species distribution models (SDMs, Pearson and Dawson 2003 ; Attorre et al. 2008 ; Wang et al. 2012 ; Ruiz-Labourdette et al. 2013 ), understanding of the range of climatic and non-climatic factors that will determine the future range of a particular species remains limited. Many now feel that SDMs are of limited value in adaptation decision making or species conservation strategies. Some of these limitations are summarised in Table  1 .

For example, models indicate significant shifts in patterns of tree species distribution over the next century but usually without any intrinsic consideration of the biological capacity of populations to move, internal population dynamics, the extent and role of local adaptation or the effects of climate and land use (Aitken et al. 2008 ; Thuiller et al. 2008 ). In a recent study, Dobrowski et al. ( 2013 ) found that the predicted speed of movement of species to match the predicted rate of climate change appears to be well beyond the historical rates of migration. Whilst modelled outputs suggest that migration rates of 1000 m per year or higher will be necessary to track changing habitat conditions (Malcolm et al. 2002 ), actual migration rates in response to past change are generally considered to have been less than 100 m per year. This was reinforced by model predictions that incorporate species dispersal characteristics for five tree species in the eastern USA indicated very low probabilities of dispersal beyond 10–20 km from current species boundaries by 2100 (Iverson et al. 2004 ). Corlett and Westcott ( 2013 ) also argued that plant movements are not realistically represented in models used to predict future vegetation or carbon-cycle feedbacks and that fragmentation of natural systems is likely to slow migration rates.

However, these estimates do not account for the role of humans in influencing tree species distributions, which they have done for thousands of years (Clark 2007 ), and managed translocation may be an option for conserving many tree species, but there are significant unresolved technical and social questions about implementing translocation at a larger scale (Corlett and Westcott 2013 ).

Most early SDMs relied primarily on temperature envelopes to model future distribution, but factors such as precipitation and soil moisture are potentially more limiting and more important in determining distribution patterns (Dobrowski et al. 2013 ). Aitken et al. ( 2008 ) found that the degree to which variation in precipitation explains phenotypic variation among populations is greater in general for populations from continental than from maritime climates and greater for lower latitude than higher latitude populations. However, precipitation alone is often not a good predictor of variation and there is often a strong interaction with temperature (Andalo et al. 2005 ). Heat to moisture index or aridity is probably more important in determining future distribution or productivity than precipitation alone (Aitken et al. 2008 ; Harper et al. 2009 ; Wang et al. 2012 ). Soil properties (depth, texture and organic matter content) have a major influence on plant-available water, but few SDMs incorporate these.

Future precipitation is proving more difficult to model than temperature, due to the complex effects of topography, and there are more widely varying estimates between global circulation models (GCMs) of future change in precipitation (IPCC 2013 ). As such, there is more uncertainty around the extent to which moisture stress will change with warming and the extent to which natural selection pressures will change as a result. Even without changes in precipitation, increased temperatures will increase the length of growing season and potential evapotranspiration (PET) resulting in more water use over the year and greater risk plant water shortage and drought death.

Changes in the intervals of extreme events (extreme heat, cold, precipitation, humidity, wind) may also matter more than changes in the mean. Current forecasting approaches that produce future climate averages may make it difficult to detect non-linear ecosystem dynamics, or threshold effects, that could trigger abrupt ecosystem change (Campbell et al. 2009 ). Zimmermann et al. ( 2009 ) found that predictions of spatial patterns of tree species in Switzerland were improved by incorporating measures of extremes in addition to means in SDMs.

The risks of future climate will also depend on the management goal. If the aim is simply to conserve genetic diversity, risks of extinction or reduction in genetic diversity may be overstated by SDMs because much of the genetic variation within tree species is found within rather than among their populations, and the extinction of a relatively large proportion of a population is generally likely to result in relatively little overall loss of genetic diversity (Hamrick 2004 ). Local habitat heterogeneity (elevation, slope aspect, moisture, etc.) can preserve adaptive genetic variation that, when recombined and exposed to selection in newly colonised habitats, can provide for local adaptation. The longevity of individual trees can also retard population extinction and allow individuals and populations to survive until habitat recovery or because animal and wind pollination can provide levels of pollen flow that are sufficient to counteract the effects of genetic drift in fragmented populations. Consequently, widespread species with large populations, high fecundity and higher levels of phenotypic plasticity are likely to persist and adapt and have an overall greater tolerance to changing climates than predicted by SDMs (Alberto et al. 2013 ).

Tree species distributions have always been dynamic, responding to changing environmental conditions, and populations are likely to be sub-optimal for their current environments (Namkoong 2001 ; Wu and Ying 2004 ). These lag effects are important in predicting species responses to climate change. In a modelling study of Scots pine and silver birch, Kuparinen et al. ( 2010 ) predicted that after 100 years of climate change, the genotypic growth period length of both species will lag more than 50 % behind the climatically determined optimum. This lag is reduced by increased mortality of established trees, whereas earlier maturation and higher dispersal ability had comparatively minor effects. Thuiller et al. ( 2008 ) suggest that mechanisms for incorporating these ‘trailing edge’ effects into SDMs are a major area of research potential.

Trees are also capable of long-distance gene flow, which can have both adaptive evolution benefits and disadvantages. Kremer et al. ( 2012 ) found that there may be greater positive effects of gene flow for adaptation but that the balance of positive to negative consequences of gene flow differs for leading edge, core and rear sections of forest distributions.

Epigenetics—heritable changes that are not caused by changes in genetic sequences but by differences in the way DNA methylation controls the degree of gene expression—is another complicating factor in determining evolutionary response to climate change (Brautigam et al. 2013 ). For example, a recent study in Norway spruce ( Picea abies ) showed that the temperature during embryo development can dramatically affect cold hardiness and bud phenology in the offspring. In some cases, the offspring’s phenotype varied by the equivalent of 6° of latitude from what was expected given the geographic origin of the parents. It remains uncertain whether these traits are persistent, both within an individual’s lifetime and in its offspring and subsequent generations (Aitken et al. 2008 ). It is suggested that analysis of the epigenetic processes in an ecological context, or ‘ecological epigenetics’, is set to transform our understanding of the way in which organisms function in the landscape. Increased understanding of these processes can inform efforts to manage and breed tree species to help them cope with environmental stresses (Brautigam et al. 2013 ). Others argue that whilst investigating this evolutionary capacity to adapt is important, understanding responses of species to their changing biotic community is imperative (Anderson et al. 2012 ) and ‘landscape genomics’ may offer a better approach for informing management of tree populations under climate change (Sork et al. 2013 ).

These recent results indicate the importance of accounting for evolutionary processes in forecasts of the future dynamics and productivity of forests. Species experiencing high mortality rates or populations that are subject to regular disturbances such as storms or fires might actually be the quickest to adapt to a warming climate.

Species life history characteristics are also not usually well represented in most climate-based distribution models. Important factors include age to sexual maturity, fecundity, seed dispersal, competition or chilling or dormancy requirements (Nitschke and Innes 2008b ).

Competitive relationships within and between species are likely to be altered by climate change. Most models also assume open site growth conditions, rather than those within a forest, where the growth environment will be quite different. However, increased disturbance associated with climate change may create stand reinitiation conditions more often than has occurred in the past, altering competitive interactions.

Process-based models of species range shifts and ecosystem change may capture more of the life history variables and competition effects that will be important in determining responses to climate change (Kimmins 2008 ; Nitschke and Innes 2008a , b ). These can provide the basis for a more robust assessment framework that integrates biological characteristics (e.g. shade tolerance and seedling establishment) and disturbance characteristics (e.g. insect pests, drought and fire topkill). Matthews et al. ( 2011 ) integrated these factors into a decision support system that communicates uncertainty inherent in GCM outputs, emissions scenarios and species responses. This demonstrated a greater diversity among species to adapt to climate change and provides a more practical assessment of future species projections.

In summary, whilst SDMs and other climate-based modelling approaches can provide a guide to potential species responses, the extent to which future climate conditions will result in major range shifts or extinction of species is unclear and the value of this approach in adaptation and decision making is limited. The evidence from genetic studies seems to suggest that many species are reasonably robust to potential future climate change. Those with a wide geographic range, large populations and high fecundity may suffer local population extinction but are likely to persist and adapt whilst suffering adaptational lag for a few generations. For example, Booth ( 2013 ) considered that many eucalyptus species, some of which are widely planted around the world, had a high adaptive capacity even though their natural ranges are quite small.

However, large contractions or shifts in distribution could have significant consequences for different forest values and species with small populations, fragmented ranges, low fecundity or suffering declines due to introduced insects or diseases may have a higher sensitivity and are at greater risk in a changing climate (Aitken et al. 2008 ).

3.1.2 Ecosystem responses to climate

Projecting the fate of forest ecosystems under a changing climate is more challenging than for species. It has been well understood for some time that species will respond individualistically to climate change, rather than moving in concert, and that this is likely to result in ‘novel’ ecosystems, or groups of species, that are not represented in current classifications (Davis 1986 ). Forecasts need to consider the importance of these new species interactions and the confounding effects of future human activities.

Climate change affects a wide range of ecosystem functions and processes (Table  2 ). These include direct effects of temperature and precipitation on physiological and reproductive processes such as photosynthesis, water use, flowering, fruiting and regeneration, growth and mortality and litter decomposition. Changes in these processes will have effects on species attributes such as wood density or foliar nutrient status. Indirect effects will be exhibited through changing fire and other climate-driven disturbances. These will ultimately have impacts on stand composition, habitat structure, timber supply capacity, soil erosion and water yield.

Most early studies of forest ecosystem responses to climate change were built around ecosystem process models at various scales (Graham et al. 1990 ; Running and Nemani 1991 ; Rastetter et al. 1991 ). A number of recent studies have investigated the effects of past and current climate change on forest processes, often with surprising effects (Groffman et al. 2012 ).

Observed forest growth has increased recently in a number of regions, for example over the last 100 years in Europe (Pretzsch et al. 2014 ; Kint et al. 2012 ), and for more recent observations in Amazon forests (Phillips et al. 2008 ). In a major review, Boisvenue and Running ( 2006 ) found that at finer spatial scales, a trend is difficult to decipher, but globally, based on both satellite and ground-based data, climatic changes seemed to have a generally positive impact on forest productivity when water was not limiting. However, there can be a strong difference between species, complicating ecosystem level assessments (Michelot et al. 2012 ), and there are areas with little observed change (Schwartz et al. 2013 ). Generally, there are significant challenges in detecting the response of forests to climate change. For example, in the tropics, the lack of historical context, long-term growth records and access to data are real barriers (Clark 2007 ) and temperate regions also have challenges, even with well-designed, long-term experiments (Leites et al. 2012 ).

Projections of net primary productivity (NPP) under climate change indicate that there is likely to be a high level of regional variation (Zhao et al. 2013 ). Using a process model and climate scenario projections, Peters et al. ( 2013 ) predicted that average regional productivity in forests in the Great Lakes region of North America could increase from 67 to 142 %, runoff could potentially increase from 2 to 22 % and net N mineralization from 10 to 12 %. Increased productivity was almost entirely driven by potential CO 2 fertilization effects, rather than by increased temperature or changing precipitation. Productivity in these forests could shift from temperature limited to water limited by the end of the century. Reyer et al. ( 2014 ) also found strong regional differences in future NPP in European forests, with potential growth increases in the north but reduced growth in southern Europe, where forests are likely to be more water limited in the future. Again, assumptions about the impact of increasing CO 2 were a significant factor in this study.

In a different type of study using analysis of over 2400 long-term measurement plots, Bowman et al. ( 2014 ) found that there was a peaked response to temperature in temperate and sub-tropical eucalypt forests, with maximum growth occurring at a mean annual temperature of 11 °C and maximum temperature of the warmest month of 25–27 °C. Lower temperatures directly constrain growth, whilst high temperatures primarily reduced growth by reducing water availability but they also appeared to exert a direct negative effect. Overall, the productivity of Australia’s temperate eucalypt forests could decline substantially as the climate warms, given that 87 % of these forests currently experience a mean annual temperature above the ‘optimal’ temperature.

Incorporating the effects of rising CO 2 in models of future tree growth continues to be a major challenge. The sensitivity of projected productivity to assumptions regarding increased CO 2 was high in modelling studies of climate change impacts in commercial timber plantations in the Southern Hemisphere (Kirschbaum et al. 2012 ; Battaglia et al. 2009 ), and a recent analysis indicated a general convergence of different model predictions for future tree species distribution in Europe, with most of the difference between models due to the way in which this effect is incorporated (Cheaib et al. 2012 ). Increased CO 2 has been shown to increase the water-use efficiency of trees, but this is unlikely to entirely offset the effects of increased water stress on tree growth in drying climates (Leuzinger et al. 2011 ; Booth 2013 ). In general, despite studies extending over decades and improved understanding of biochemical processes (Franks et al. 2013 ), the impacts of increased CO 2 on tree and stand growth are still unresolved (Kallarackal and Roby 2012 ).

Integrating process model outputs with spatially explicit landscape models can improve understanding and projection of responses and landscape planning and this could provide for simulations of changes in ecological processes (e.g. tree growth, succession, disturbance cycles, dispersal) with other human-induced changes to landscapes (Campbell et al. 2009 ).

Investigation of current species responses to changing climate conditions may also guide improved prediction of patterns of future change in ecosystem distribution. For example, Allen et al. ( 2010 ) suggest that spatially explicit documentation of environmental conditions in areas of forest die-off is necessary to link mortality to causal climate drivers, including precipitation, temperature and vapour pressure deficit. Better prediction of climate responses will also require improved knowledge of belowground processes and soil moisture conditions. Assessments of future productivity will depend on accurate measurements of rates (net ecosystem exchange and NPP), changes in ecosystem level storage (net ecosystem production) and quantification of disturbances effects to determine net biome production (Boisvenue and Running 2006 ).

Hydrological conditions, runoff and stream flow are of critical importance for humans and aquatic organisms, and many studies have focused on the implications of climate change for these ecosystem processes. However, most of these have been undertaken at small catchment scale (Mahat and Anderson 2013 ; Neukum and Azzam 2012 ; Zhou et al. 2011 ) with few basin-scale assessments (van Dijk and Keenan 2007 ). However, the effects of climate and forest cover change on hydrology are complicated. Loss of tree cover may increase stream flow but can also increase evaporation and water loss (Guardiola-Claramonte et al. 2011 ). The extent of increasing wildfire will also be a major factor determining hydrological responses to climate change (Versini et al. 2013 ; Feikema et al. 2013 ).

Changing forest composition will also affect the habitat of vertebrate and invertebrate species. The implications of climate change for biodiversity conservation have been subject to extensive analysis (Garcia et al. 2014 ; Vihervaara et al. 2013 ; Schaich and Milad 2013 ; Clark et al. 2011 ; Heller and Zavaleta 2009 ; Miles et al. 2004 ). An integrated analytical approach, considering both impacts on species and habitat is important. For example, in a study of climate change impacts on bird habitat in the north-eastern USA, the combination of changes in tree distribution and habitat for birds resulted in significant impacts for 60 % of the species. However, the strong association of birds with certain vegetation tempers their response to climate change because localised areas of suitable habitat may persist even after the redistribution of tree species (Matthews et al. 2011 ).

Understanding thresholds in changing climate conditions that are likely to result in a switch to a different ecosystem state, and the mechanisms that underlie ecosystem responses, will be critical for forest managers (Campbell et al. 2009 ). Identifying these thresholds of change is challenging. Detailed process-based ecosystem research that identifies and studies critical species interactions and feedback loops, coupled with scenario modelling of future conditions, could provide valuable insights (Kimmins et al. 1999 , 2008 ; Walker and Meyers 2004 ). Also, rather than pushing systems across thresholds into alternative states, climate change may create a stepwise progression to unknown transitional states that track changing climate conditions, requiring a more graduated approach in management decisions (Lin and Petersen 2013 ).

Ultimately, management decisions may not be driven by whether we can determine future thresholds of change, but by observing the stressors that determine physiological limits of species distributions. These thresholds will depend on species physiology and local site conditions, with recent research demonstrating already observed ecosystem responses to climate change, including die-back of some species (Allen et al. 2010 ; Rigling et al. 2013 ).

3.1.3 Fire, pests, invasive species and disturbance risks

Many of the impacts of a changing future climate are likely to be felt through changing disturbance regimes, in particular fire. Forest fire weather risk and fire behaviour prediction have been two areas where there has been strong historical interaction between climate science and forest management and where we may see major tipping points driving change in ecosystem composition (Adams 2013 ). Fire weather is fundamentally under the control of large-scale climate conditions with antecedent moisture anomalies and large-scale atmospheric circulation patterns, further exacerbated by configuration of local winds, driving fire weather (Brotak and Reifsnyder 1977 ; Westerling et al. 2002 , 2006 ). It is therefore important to improve understanding of both short- and long-term atmospheric conditions in determining meteorological fire risk (Amraoui et al. 2013 ).

Increased fuel loads and changes to forest structure due to long periods of fire exclusion and suppression are increasing fire intensity and limiting capacity to control fires under severe conditions (Williams 2004 , 2013 ). Increasing urbanisation is increasing the interface between urban populations and forests in high fire risk regions, resulting in greater impacts of wildfire on human populations, infrastructure and assets (Williams 2004 ). Deforestation and burning of debris and other types of human activities are also introducing fire in areas where it was historically relatively rare (Tacconi et al. 2007 ).

In a recent study, Chuvieco et al. ( 2014 ) assessed ecosystem vulnerability to fire using an index based on ecological richness and fragility, provision of ecosystem services and value of houses in the wildland–urban interface. The most vulnerable areas were found to be the rainforests of the Amazon Basin, Central Africa and Southeast Asia; the temperate forest of Europe, South America and north-east America; and the ecological corridors of Central America and Southeast Asia.

In general, fire management policies in many parts of the world will need to cope with longer and more severe fire seasons, increasing fire frequency, and larger areas exposed to fire risk. This will especially be the case in the Mediterranean region of Europe (Kolström et al. 2011 ) and other fire-prone parts of the world such as South Eastern Australia (Hennessy et al. 2005 ). This will require improved approaches to fire weather modelling and behaviour prediction that integrate a more sophisticated understanding of the climate system with local knowledge of topography, vegetation and wind patterns. It will also require the development of fire management capacity where it had previously not been necessary. Increased fire weather severity could push current suppression capacity beyond a tipping point, resulting in a substantial increase in large fires (de Groot et al. 2013 ; Liu et al. 2010 ) and increased investment in resources and management efforts for disaster prevention and recovery.

Biotic factors may be more important than direct climate effects on tree populations in a changing climate. For example, insects and diseases have much shorter generation length and are able to adapt to new climatic conditions more rapidly than trees. However, if insects move more rapidly to a new environment whilst tree species lag, some parts of the tree population may be impacted less in the future (Regniere 2009 ).

The interaction of pests, diseases and fire will also be important. For example, this interaction will potentially determine the vulnerability of western white pine ( Pinus monticola ) ecosystems in Montana in the USA. Loehman et al. ( 2011 ) found that warmer temperatures will favour western white pine over existing climax and shade tolerant species, mainly because warmer conditions will lead to increased frequency and extent of wildfires that facilitates regeneration of this species.

3.2 Adaptation actions in forest management

The large majority of published studies relating to forests and climate change have been on vulnerability and impacts. These have increased understanding of the various relationships between forest ecosystems and climate and improved capacity to predict and assess ecosystem responses. However, managers need greater guidance in anticipating and responding to potential impacts of climate change and methods to determine the efficiency and efficacy of different management responses because they are generally not responding sufficiently to potential climate risks.

3.2.1 Adaptation actions at different management levels

A number of recent reviews have described adaptation actions and their potential application in different forest ecosystems being managed for different types of goods or services (Bernier and Schöne 2009 ; Innes et al. 2009 ; Lindner et al. 2010 ; Kolström et al. 2011 ), and adaptation guides and manuals have been developed (Peterson et al. 2011 ; Stephens et al. 2012 ) for different types of forest and jurisdictions. Adaptation actions can be primarily aimed at reducing vulnerability to increasing threats or shocks from natural disasters or extreme events, or increasing resilience and capacity to respond to progressive change or climate extremes. Adaptation actions can be reactive to changing conditions or planned interventions that anticipate future change. They may involve incremental changes to existing management systems or longer term transformational changes (Stafford Smith et al. 2011 ). Adaptation actions can also be applied at the stand level or at ownership, estate or national scales (Keskitalo 2011 ).

Recent research at the stand level in forests in the SE USA showed that forest thinning, often recommended in systems that are likely to experience increased temperature and decreased precipitation as a result of climate change, will need to be more aggressive than traditionally practised to stimulate growth of large residual trees, improve drought resistance and provide greater resilience to future climate-related stress (Kerhoulas et al. 2013 ).

An analysis of three multi-aged stand-level options in Nova Scotia, Canada, Steenberg et al. ( 2011 ) found that leaving sexually immature trees to build stand complexity had the most benefit for timber supply but was least effective in promoting resistance to climate change at the prescribed harvest intensity. Varying the species composition of harvested trees proved the most effective treatment for maximising forest age and old-growth area and for promoting stands composed of climatically suited target species. The combination of all three treatments resulted in an adequate representation of target species and old forest without overly diminishing the timber supply and was considered most effective in minimising the trade-offs between management values and objectives.

An estate level analysis of Austrian Federal Forests indicated that management to promote mixed stands of species that are likely to be well adapted to emerging environmental conditions, silvicultural techniques fostering complexity and increased management intensity might successfully reduce vulnerability, with the timing of adaptation measures important to sustain supply of forest goods and services (Seidl et al. 2011 ).

Whilst researchers are analysing different management options, the extent to which they are being implemented in practice is generally limited. For example, in four regions in Germany, strategies for adapting forest management to climate change are in the early stages of development or simply supplement existing strategies relating to general risk reduction or to introduce more ‘nature-orientated’ forest management (Milad et al. 2013 ). Guariguata et al. ( 2012 ) found that forest managers across the tropics perceived that natural and planted forests are at risk from climate change but were ambivalent about the value of investing in adaptation measures, with climate-related threats to forests ranked below others such as clearing for commercial agriculture and unplanned logging.

Community-based management approaches are often argued to be the most successful approach for adaptation. An analysis of 38 community forestry organisations in British Columbia found that 45 % were researching adaptation and 32 % were integrating adaptation techniques into their work (Furness and Nelson 2012 ). Whilst these community forest managers appreciated support and advice from government for adaptation, balancing this advice with autonomy for communities to make their own decisions was considered challenging.

In a study of communities impacted by drought in the forest zone of Cameroon, Bele et al. ( 2013b ) identified adaptive strategies such as community-created firebreaks to protect their forests and farms from forest fires, the culture of maize and other vegetables in dried swamps, diversifying income activities or changing food regimes. However, these coping strategies were considered to be incommensurate with the rate and magnitude of change being experienced and therefore no longer seen as useful. Some adaptive actions, whilst effective, were resource inefficient and potentially translate pressure from one sector to another or generated other secondary effects that made them undesirable.

3.2.2 Integrating adaptation and mitigation

In considering responses to climate change, forest managers will generally be looking for solutions that address both mitigation objectives and adaptation. To maintain or increase forest carbon stocks over the long term, the two are obviously inextricably linked (Innes et al. 2009 ). Whilst there are potentially strong synergies, Locatelli et al. ( 2011 ) identified potential trade-offs between actions to address mitigation and the provision of local ecosystem services and those for adaptation. They argued that mitigation projects can facilitate or hinder the adaptation of local people to climate change, whereas adaptation projects can affect ecosystems and their potential to sequester carbon.

Broadly, there has been little integration to date of mitigation and adaptation objectives in climate policy. For example, there is little connection between policies supporting the reducing emissions from deforestation and forest degradation plus (REDD+) initiatives and adaptation. Integrating adaptation into REDD+ can advance climate change mitigation goals and objectives for sustainable forest management (Long 2013 ). Kant and Wu ( 2012 ) considered that adaptation actions in tropical forests (protection against fire and disease, ensuring adequate regeneration and protecting against coastal impacts and desertification) will improve future forest resilience and have significant climate change mitigation value.

3.2.3 Sector-level adaptation

Analyses of climate change impacts and vulnerability at the sector level have been undertaken for some time (Lindner et al. 2002 ; Johnston and Williamson 2007 ; Joyce 2007 ). However, it has recently been argued (Wellstead et al. 2014 ) that these assessments, which focus on macro system-level variables and relationships, fail to account for the multi-level or polycentric nature of governance and the possibility that policy processes may result in the non-performance of critical tasks required for adaptation.

Joyce et al. ( 2009 ) considered that a toolbox of management options for the US National Forests would include the following: practices focused on reducing future climate change effects by building resistance and resilience into current ecosystems and on managing for change by enabling plants, animals and ecosystems to adapt to climate change. Sample et al. ( 2014 ) demonstrated the utility of this approach in a coniferous forest management unit in northwestern USA. It provided an effective means for guiding management decisions and an empirical basis for setting budgetary and management priorities. In general, more widespread implementation of already known practices that reduce the impact of existing stressors represents an important ‘no regrets’ strategy.

Johnston and Hesseln ( 2012 ) found that barriers to implementing adaptation across forest sector managers in Canada included inflexible tenure arrangements and regulatory environments which do not support innovation. Echoing calls for wider implementation of SFM as a key adaptation strategy (Innes et al. 2009 ), they argued that forest certification systems, participating in the Canadian model forest programme, and adopting criteria and indicators of SFM can support sectoral level adaptation.

Decentralised management approaches are considered to be a more appropriate governance arrangement for forest management, but Rayner et al. ( 2013 ) argued that a decentralised forest policy sector in Canada has resulted in limitations where policy, such as adaptation, requires a coherent national response. Climate change adaptation has led to an expansion of departmental mandates that is not being addressed by better coordination of the available policy capacity. Relevant federal agencies are not well represented in information networks, and forest policy workers report lower levels of internal and external networking than workers in related policy subsectors.

Economic diversification can be a valuable strategy to improve resilience to climate-related shocks. This can take a range of forms: developing new industries or different types of forest-based industries based on different goods or services. For the timber sector, the value of diversification as a risk management strategy for communities is open to question. Ince et al. ( 2011 ) pointed out that the forest sector operates in an international market and is susceptible to changes in the structure of this market. In the US forest sector, globalization has accelerated structural change, favouring larger and more capital-intensive enterprises and altering historical patterns of resource use. They suggest that future markets for timber will be driven by developments in these larger scale enterprises and may not lead to expansion of opportunities for smaller scale forest enterprises because development of niche markets or customised products is likely to be pursued aggressively by larger globally oriented enterprises to develop branding, product identity and product value. How to best diversify for adaptation therefore remains an open question.

Consequently, whilst policies that support economic diversification will be important, this may involve diversification well beyond traditional sectors. For example, in areas where there is a high probability that forests will be lost in favour of other ecosystems, such as grasslands, managers should recognise early on that their efforts and resources may best be focused outside forests (Innes et al. 2009 ). These adjustments will involve taking into account the perceptions of climate risk by various stakeholders, including individuals, communities, governments, private institutions and organisations (Adger et al. 2007 ). Vulnerability assessments and adaptation measures also need to be developed in a framework that takes into account the vulnerabilities and actions in other sectors that are linked to the forest sector, such as food, energy, health and water (Sonwa et al. 2012 ).

3.3 New approaches to decision making

Climate change presents new challenges for forest managers. Change is likely to happen faster than traditional, empirical approaches can provide evidence to support changes in management. Uncertainties in a range of aspects of future climate may also not be reduced through investment in research. Given that management for activities such as timber production can no longer be based solely on empirically derived growth and yield trajectories and management plans must incorporate uncertainty and the increased probability of extreme events, what types of tools are available to support these approaches? This section presents key points from the literature on decision making under uncertainty, adaptive management and resilience as a guide to future decision making in forest management.

3.3.1 Decision making under uncertainty

The future conditions for forest managers are subject to a high degree of uncertainty, and the future prospects for reducing these large uncertainties are limited. There is uncertainty regarding the trajectory of future increases in atmospheric greenhouse gases, what kind of effects these might have on the climate system and the effects of climatic changes on ecological and social systems and their capacity to adapt (see Fig.  2 ) (Wilby and Dessai 2010 ).

The cascade of uncertainty (Wilby and Dessai 2010 )

Consequently, many forest managers consider that the future situation is too uncertain to support long-term and potentially costly decisions that may be difficult to reverse. Dessai and Hulme ( 2004 ) argued that uncertainty per se should not be a reason for inaction. However, the critical issue for managers is deciding the types of actions to take and the timing and conditions under which they should be taken (Ogden and Innes 2007a ). A more reactive ‘wait and see’ approach (or ‘purposeful procrastination’) might be justified if uncertainty or costs are high relative to the expected impacts and risks, or if it is cheaper to implement interventions by waiting until after a significant disturbance (e.g. replanting an area with more fire- or drought-resistant tree species after a wildfire or drought-induced insect outbreak).

Effective adaptation requires setting clear objectives. Managers and policy makers need to decide whether they are trying to facilitate ecosystem adaptation through changing species composition or forest structure or trying to engineer resistance to change through proactive management strategies (Joyce et al. 2008 ). Establishing objectives often depends on the integration of the preferences of different stakeholders (Prato 2008 ), but changing social preferences presents another source of potential uncertainty.

Risk assessment and management provide a foundation for decision making in considering climate change in natural resource management. This approach provides both a qualitative and quantitative framework for evaluating climate change effects and adaptation options. Incorporating risk management approaches into forest management plans can provide a basis for managers to continue to provide forest conditions that meet a range of important values (Day and Perez 2013 ).

However, risk approaches generally requiring assigning probabilities to future events. In a comprehensive review, Yousefpour et al. ( 2011 ) identified a growing body of research literature on decision making under uncertainty, much of which has focused on price uncertainty and variation in timber production but is extending to multiple forest management objectives and other types of risk. They argue that we are actually in a stochastic transition from one known stable (but variable) climate state to a new but largely unknown and likely more rapidly changing set of future conditions.

Decision makers themselves may also not be the rational actors assumed by these models, with their decisions taken according to quite different assumptions, preferences and beliefs (Ananda and Herath 2009 ; Couture and Reynaud 2008 ). Therefore, the communication approach will be important in determining whether the information is acted on. In a recent study, Yousefpour et al. ( 2014 ) considered that the speed with which decision makers will form firm beliefs about future climate depends on the divergence among climate trajectories, the speed of change and short-term climate variability. Using a Bayesian modelling approach, they found that if a large change in climate occurs, the value of investing in knowledge and taking an adaptive approach would be positive and higher than a non-adaptive approach. In communicating about uncertainty, it may be better to focus discussion on the varying time in the future when things will happen, rather than on whether they will happen at all (Lindner et al. 2014 ).

Increased investment in climate science and projections or species distribution modelling may not necessarily decrease uncertainty in climate projections or impacts. Climate models are best viewed as heuristic tools rather than as accurate forecasts of the future (Innes et al. 2009 ). Trajectories of change in many other drivers of forest management (social, political or economic) are also highly uncertain (Keskitalo 2008 ) and the effects of these on the projected performance of management can be the same order of magnitude, requiring an integrated social-ecological perspective to adaptation (Seidl and Lexer 2013 ).

In a more ‘decision-centred’ approach, plausible scenarios of the potential range of future conditions are required. These can be derived from climate models but do not need to be accurate and precise ‘predictions’ of future climate states (Wilby and Dessai 2010 ). To support this type of approach, research needs to focus on improved understanding of tree and ecosystem responses and identifying those aspects of climate to which different forest types are most sensitive.

Devising strategies that are able to meet management objectives under a range of future scenarios is likely to be the most robust approach, recognising that these strategies are unlikely to be optimal under all future conditions. In some cases, the effect of different scenarios on forest growth may not be that great and differences in the present value of different management options are relatively small. For example, Eriksson et al. ( 2011 ) found that there was limited benefit in attempting to optimise management plans in accordance with future temperature scenarios.

Integration of climate change science and adaptation in forest management planning is considered important for building resilience in forest social and ecological systems (Keskitalo 2011 ; D’Amato et al. 2011 ; Chmura et al. 2011 ; Parks and Bernier 2010 ; Lindner et al. 2014 ). Forest restoration is becoming a more prominent aspect of forest management in many parts of the world and restoration approaches will also need to integrate understanding of future climate change to be successful (Stanturf et al. 2014 ).

3.3.2 Adaptive management, resilience and decisions

Adaptive management provides a mechanism to move forward when faced with future uncertainty (Innes et al. 2009 ). It can be viewed as a systematic process for continually improving management policies and practices by monitoring and then learning from the outcomes of operational programmes as a basis for incorporating adaptation actions into forest management. Whilst many management initiatives purport to implement these principles, they often lack essential characteristics of the approach (Innes et al. 2009 ).

However, effective adaptation to changing climate cannot simply involve adaptive management as it is currently understood. The pace of climate change is not likely to allow for the use of management as a tool to learn about the system by implementing methodologies to test hypotheses concerning known uncertainties (Holling 1978 ). Future climatic conditions may result in system states and dynamics that have never previously existed (Stainforth et al. 2007 ), so observation of past experience may be a poor guide for future action. Management will need to be more ‘forward-looking’, considering the range of possible future conditions and planning actions that consider that full range.

How does this translate into the practical guidance forest managers are seeking on how to adapt their current practices and, if necessary, their goals (Blate et al. 2009 )? Managers will need to consider trade-offs between different objectives under different conditions. For example, Seidl et al. ( 2011 ) showed that, to keep climate vulnerability in an Austrian forest low, Norway spruce will have to be replaced almost entirely by better adapted species. However, indicator weights that favoured timber production over C storage or biodiversity exerted a strong influence on the results. Wider social implications of imposing such drastic changes in forest landscapes will also deserve stronger consideration in decision making.

Ecosystem management will need to be reframed to accommodate the risks of a changing climate. Adaptive strategies, even without specific information on the future climate conditions of a target ecosystem, would enhance social and ecological resilience to address the uncertainties due to changing climate (Mori et al. 2013 ). These are likely to be more subject to change over the short to medium term, in response to more rapidly changing conditions.

Analysis of ecosystem resilience can provide a framework for these assessments. Resilience can be defined as ‘the capacity of ecosystems to absorb disturbance and reorganise so as to retain essentially the same function, structure and feedbacks – to have the same identity’ (Walker and Salt 2012 ). It is a function of the capacity of an ecosystem to resist change, the extent and pace of change and the ability of an ecosystem to reorganise following disturbance. The concept of resilience holds promise for informing future forest management, but Rist and Moen ( 2013 ) argue that its contributions are, so far, largely conceptual and offer more in terms of being a problem-framing approach than analytical or practical tools. There may also be trade-offs involved with focusing on resilience through retention of current species composition or using a more adaptation-oriented management approach after disturbances (Buma and Wessman 2013 ). Complexity theory and concepts can provide an appropriate framework for managing resilience (Messier et al. 2013 ).

Management decisions will ultimately depend on the costs and benefits of different options, but there are few examples of decision making frameworks that compare the costs of future impacts with the costs of different actions and the efficacy of those actions in reducing impacts. Ogden and Innes ( 2009 ) used a structured decision making process to identify and assess 24 adaptation options that managers considered important to achieve their regional goals and objectives of sustainable forest management in light of climate change. In the analysis of options for biodiversity conservation, Wintle et al. ( 2011 ) found that the amount of funding available for adaptation was a critical factor in deciding options aimed at minimising species extinctions in the mega-diverse fynbos biome of South Africa. When the available budget is small, fire management was the best strategy. If the budget is increased to an intermediate level, the marginal returns from more fire management were limited and the best strategy was added habitat protection. Above another budget threshold, increased investment should go into more fire management. By integrating ecological predictions in an economic decision framework, they found that making the choice of how much to invest is as important as determining what actions to take.

3.3.3 Adaptation as a social learning process

Whilst adaptation has been defined as ‘adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects’ (Levina and Tirpak 2006 ), adaptation is essentially about meeting future human needs (Spittlehouse and Stewart 2003 ; Hahn and Knoke 2010 ). Consequently, it is inherently a social process. Forest landscapes are social-ecological systems that involve both nature and society (Innes et al. 2009 ), and resolving trade-offs between different management objectives to meet the different needs in society is an important element of sustainable forest management. As Kolström et al. ( 2011 ) pointed out, some proposed adaptation measures may change the balance between current objectives and stakeholder interests, and it will be important to consider the relative balance of different measures at the stand, management unit and landscape scales.

Those investigating adaptive management also recognise that it goes beyond the focus on scientific methods, statistical designs or analytical rigour favoured by its early proponents and that there is now an expectation of much greater stakeholder involvement, with the concept being renamed by some as adaptive, collaborative management (Innes et al. 2009 ). SFM and adaptation are as much about those who inhabit, work in or utilise forests as it is about managing the forest ecosystems themselves (White et al. 2010 ; Pramova et al. 2012 ; Fischer et al. 2013 ).

The choice of adaptation options will thus likely be relatively complex, even in cases where information and policy have been developed, and communication measures for forest management have been well formulated. Making such choices may require considerable knowledge, competence and commitment for implementation at the local level (Keskitalo 2011 ). Effective adaptation will require much greater cooperation between stakeholders, more flexibility for management actions and commitment of time to develop the social license for action in the absence of conclusive evidence or understanding. This will require venues for sharing perspectives on the nature of the problem (Fig.  3 ).

Adaptation as a social learning process. There is a need to provide situations to share different viewpoints on the nature of the problem as a basis for developing shared solutions (image source: John Rowley, http://ch301.cm.utexas.edu/learn/ )

3.3.4 Local and indigenous knowledge

The promotion of community-based forest management may increase local adaptive capacity by putting decisions in the hands of those people who first feel the effects of climate change (Gyampoh et al. 2009 ). In this context, local knowledge systems based on long-term observation and experience are likely to be of increasing importance in decision making. Adaptation strategies can benefit from combining scientific and indigenous knowledge, especially in developing countries (Gyampoh et al. 2009 ), with the translation of local forest knowledge into the language of formal forest science being considered an important step towards adaptation (Roberts et al. 2009 ). However, conservation and natural resource managers in government agencies have often discounted traditional local management systems (Scott 2005 ), although Spathelf et al. ( 2014 ) provided a useful approach for capturing local expert knowledge. Linking this type of knowledge with broader scientific understanding of ecosystem functioning and the global climate system will be a major challenge, requiring consideration of both technical and cultural issues (Caverley 2013 ), including intellectual property concerns of indigenous people (Lynch et al. 2010 ).

3.4 Policy arrangements for adaptation

Increasingly, many are arguing that effectively responding to climate change will require polycentric and multi-level governance arrangements (Peel et al. 2012 ). However, Nilsson et al. ( 2012 ) found that institutionalising of knowledge and knowledge exchange regarding climate change adaptation in Sweden was weak and that improved mechanisms are required for feedback from the local to the national level. Recent studies have described stronger relationships between scientific research and forest management to assess trade-offs and synergies, for participatory decision making and for shared learning (Blate et al. 2009 ; Littell et al. 2012 ; Klenk et al. 2011 ).

Many papers emphasised the need for greater flexibility in the policies, cultures and structures of forest management organisations (Brown 2009 ; von Detten and Faber 2013 ; Rayner et al. 2013 ). Because no single community or agency can prepare on their own for future impacts, inter-sectoral policy coordination will be required to ensure that policy developments in related policy sectors are not contradictory or counterproductive. Greater integration of information, knowledge and experience and collaborative projects involving scientists, practitioners and policy makers from multiple policy communities could increase focus on resilience, identify regions of large-scale vulnerability and provide a more rigorous framework for the analysis of vulnerability and adaptation actions (Thomalla et al. 2006 ).

There is also likely to be a greater need for cross-border implementation of different forest management options, requiring greater coordination between nation states and sub-national governments (Keenan 2012 ). Policy is the product of both ‘top-down’ and ‘bottom-up’ processes and these might sometimes be in conflict. Simply having ‘good policy’ in place is unlikely to be sufficient, as a great deal of what takes place at ‘street level’ is not determined by formal aims of central policy (Urwin and Jordan 2008 ). Having the right policies can send a strong political signal that adaptation needs to be considered seriously but flexibility in policy systems will be required to facilitate adaptive planning.

4 Discussion and conclusions

This broad survey of the literature indicated that, whilst there has been considerable development in research and thinking about adaptation in forest management over the last 10 years, research is still strongly focused on assessment of future impacts, responses and vulnerability of species and ecosystems (and in some cases communities and forest industries) to climate change. There has been some movement from a static view of climate based on long-term averages to a more detailed understanding of the drivers of different climate systems and how these affect the factors of greatest influence on different forest ecosystems processes, such as variability and extremes in temperature or precipitation or fire disturbance. For example, Guan et al. ( 2012 ) demonstrated that quasi-periodic climate variation on an inter-annual (ENSO) to inter-decadal (PDO) time scale can significantly influence tree growth and should be taken into account when assessing the impact of climate changes on forest productivity.

Adaptation is, in essence, about making good decisions for the future, taking into account the implications of climate change. It involves recognising and understanding potential future climate impacts and planning and managing for their consequences, whilst also considering the broader social, economic or other environmental changes that may impact on us, individually or collectively. To effectively provide a role in mitigation, delivering associated ecosystem services and benefits in poverty reduction (Eliasch 2008 ) forest management will have to adapt to a changing and highly variable climate. In achieving this, the roles and responsibilities of different levels of government, the private sector and different parts of the community are still being defined.

The broader literature emphasises that adaptation is a continuous process, involving a process of ‘adapting well’ to continuously changing conditions (Tompkins et al. 2010 ). This requires organisational learning based on past experience, new knowledge and a comprehensive analysis of future options. This can take place through ‘learning by doing’ or through a process of search and planned modification of routines (Berkhout et al. 2006 ). However, interpreting climate signals is not easy for organisations, the evidence of change is ambiguous and the stimuli are not often experienced directly within the organisation. For example, many forest managers in Australia currently feel little need to change practices to adapt to climate change, given both weak policy signals and limited perceived immediate evidence of increasing climate impacts (Cockfield et al. 2011 ). To explain and predict adaptation to climate change, the combination of personal experience and beliefs must be considered (Blennow et al. 2012 ). ‘Climate smart’ forest management frameworks can provide an improved basis for managing forested landscapes and maintaining ecosystem health and vitality based on an understanding of landscape vulnerability to future climatic change (Fig. 4 ) (Nitschke and Innes 2008a ).

Components of climate smart forest management (after Nitschke and Innes 2008a , b )

Many are now asking, do we really need more research to start adapting forest management to climate change? Whilst adaptation is often considered ‘knowledge deficit’ problem—where scientists provide more information and forest managers will automatically make better decisions—the reality is that the way in which this information is presented and how it is interpreted and received, will play major roles in determining potential responses. Successful adaptation will require dissemination of knowledge of potential climate impacts and suitable adaptation measures to decision makers at both practice and policy levels (Kolström et al. 2011 ) but it needs to go well beyond that.

Adaptation is, above all, a social learning process. It requires an understanding of sense of place, a capacity for individuals and society to consider potential future changes and what they mean for their circumstances. Leaders in forest management organisations will need to support a greater diversity of inputs into decision making, avoid creating rigid organisational hierarchies that deter innovation, and be inclusive, open and questioning (Konkin and Hopkins 2009 ). They will need to create more opportunities for interaction between researchers, managers and the community and space for reflection on the implications and the outcomes of management actions and unplanned events. Researchers will need to develop new modes of communication, providing knowledge in forms that are appropriate to the management decision and suitable for digestion by a range of different audiences.

From this analysis, key gaps in knowledge for adaptation may not be improved climate scenarios or better understanding of the biophysical responses of individual tree species or forest ecosystems to future climate. Knowledge gaps lie more in understanding the social and community attitudes and values that drive forest management and the decision making processes of forest managers, in order to work out how ‘climate intelligence’ can be built in to these processes.

The impacts of changing climate will vary locally. Consequently, managers must be given the flexibility to respond in ways that meet their particular needs and capacity to choose management options that are applicable to the local situation (Innes et al. 2009 ). This may not be consistent with rigid indicator-driven management assessment processes like forest certification. Whilst policy to support climate change mitigation is primarily a task for national governments and international agreements and processes, responsibility for supporting adaptation will fall more to sub-national and local governments, communities and the private sector. More active management will be required if specific values are to be maintained, particularly for forests in conservation reserves. This will require additional investment, but there has been little analysis to support the business case for investment in adaptation or to determine who should pay, particularly in developing countries.

We need to strengthen the relationship between climate science, forest research, forest managers and the community. Key challenges will include the setting of objectives for desired future conditions and accepting that we may not be able to maintain everything that forests have traditionally provided. It is important to discuss and agree on common goals in order to cope with, or benefit from, the challenges of future climates. Actively managing our forest ecosystems effectively and intelligently, using the best available knowledge and foresight capacity, can make those goals a reality.

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Acknowledgments

Thanks to Linda Joyce for her comments on an earlier draft of this paper, to a number of anonymous reviewers for their thoughtful suggestions and to many colleagues that I have discussed these ideas with over the past five years.

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Keenan, R.J. Climate change impacts and adaptation in forest management: a review. Annals of Forest Science 72 , 145–167 (2015). https://doi.org/10.1007/s13595-014-0446-5

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Fire Management

TNC has been working with fire in forests since 1962, when we conducted our first controlled burn. Our approach has evolved from primarily focusing on managing our preserves to a holistic model that includes equitable policy and funding, supporting Indigenous fire practitioners as they revitalize traditional fire cultures and elevate Indigenous leadership on fire, growing and diversifying the ranks of those who work with fire, and helping communities develop ways to live more safely with wildfire in forested areas and other landscapes.

In the United States, TNC helps lead the  Fire Learning Network ,  Fire Adapted Communities Learning Network ,  Indigenous Peoples Burning Network , maps conditions through  LANDFIRE , helps train new fire workers in  TREX  programs, and performs controlled burns across tens of thousands of acres each year.

In some forests we are combining ecological thinning with a safe reintroduction of fire to  improve forest health and habitat .

Our Work With Fire

Urban Forestry

Trees in urban neighborhoods improve air quality and mental health, lower air temperatures, decrease flooding, and provide habitat for wildlife. The Nature Conservancy is working around the United States to increase and improve tree cover and health for people and urban nature.

The Nature Conservancy is working with urban foresters, local organizations, and residents of many neighborhoods around the US  to improve outcomes for residents and students in tree-related education and workforce development programs through tree planting and care. We know that mature shade trees provide the most benefits for our communities—so the Healthy Trees, Healthy Cities Initiative helps protect these most important urban trees from damage, disease, pests, and other threats. We partner with the USDA Forest Service Northern Research Station and the University of Georgia, along with civic ecologists and conservation professionals, to monitor and maintain mature urban trees.

Our Cities Work

Indigenous-led Stewardship

Many Indigenous communities around the world already manage forests in ways that draw from their traditions and values. In many cases, these practices sequester more carbon and result in improved economic outcomes for communites compared to other forestry practices. 

For example, in Canada many communities are already taking different values into account, ranging from biodiversity to water resilience, and food security to local economies. Not only does this approach benefit local communities and provinces, but it benefits the whole of Canada and the wider world, contributing to the global fight against climate change. 

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Reducing Impacts of Pests & Pathogens

Every year, insects and diseases damage an average of 50+ million forest acres in the USA—and another 40 million acres in Canada—critically harming up to 15% of forest cover. While some of these forest insects and diseases are native to their ecosystems and their actions are part of the natural cycles of forested areas, others are non-native invasive species that damage and kill trees at uncontrolled and accelerated rates. 

Implementing improved forest management practices—such as ecological thinning, prescribed fire, and the use of biological controls for the reduction of forest pest populations—can increase the resilience of forests to pests and pathogens. Other actions taken at a global scale, such as strengthening the international trade requirements for the prevention of invasive species hiding in cargo and packaging, serve to prevent new damaging pests from entering new forests.

Slowing Forest Pests

Liana Cutting

Climate change and other human disturbances are causing woody vine infestations to intensify, especially in forests subject to selective logging. While lianas are fundamental components of most tropical and some temperate forest ecosystems, they decrease tree survival and growth rates, thereby decreasing timber yields in managed forests and reducing carbon storage wherever they are abundant. Dozens of previous experimental studies document that in response to liana removal, tree growth rates often double.

Research shows that strategic liana cutting, primarily in selectively logged forests, can substantially increase timber production and provide forest managers with access to voluntary carbon markets. By using science to guide the targeted reduction of lianas in selected areas, both biodiversity and carbon sequestration goals can be better met.

Read a Study

Planting trees is a tried-and-true way to fight climate change.

Reforestation—or the practice of restoring tree cover to an area that was once forested, either by planting trees or allowing trees to regrow—is a tried-and-true natural climate solution.

Research led by The Nature Conservancy has shown that  in the United States, planting trees  on frequently flooded lands, open urban spaces, degraded pastures and other formerly forested, under-utilized areas has the potential to capture up to 535 million metric tonnes of carbon dioxide each year.

Quote : Susan Cook-Patton

Planting a tree, or simply letting seedlings grow in our own backyards, represents something we can do now to reignite our hope for a better future.

Reforestation Hub

Reforestation Hub is a web-based tool produced by TNC and American Forests.

But there is more to reforestation than planting millions of trees. We need the  right  trees and the  right  places. The Reforestation Hub, a free, online tool developed by TNC and American Forests, is a starting point for understanding this opportunity. Tools like this will help ensure reforestation is as effective as possible.

The Science of Restoring Forests

Tree planting is a promising natural solution to climate change and comes with enormous benefits beyond climate mitigation, such as biodiversity, habitat connectivity, improved community livelihoods, and improved freshwater and air quality. TNC and partners advance important science to help ensure efforts to restore forests are effective and equitable.

Urban Tree Cover and Health

npj Urban Sustainability |

An ambitious, nationwide program of urban tree-planting could reduce health imbalances between neighborhoods and help communities adapt to a changing climate. See how trees can reduce heat-related health risks in cities.

Accounting for albedo

Nature Communications |

Provides a global analysis of where restoration of tree cover is most effective at cooling the global climate system—considering not just the cooling from carbon storage but also the warming from decreased albedo. See how albedo impacts tree planting.

Tree Diversity

Restoration Ecology |

Planting forests with diverse species can help ensure their success. Learn why diversity matters in forests.

Natural Forest Regrowth

Letting forests regrow naturally has the potential to absorb up to 8.9 billion metric tons of carbon dioxide from the atmosphere each year through 2050, while still maintaining native grasslands and current levels of food production. Learn how forest regrowth can contribute to climate goals.

TNC works with governments, corporations, Indigenous Peoples and thousands of partners around the world to protect, restore and sustainably manage forests.

Examples of our work in forests around the world

You play an important role in improving the health of forests, in your own neighborhood and across the globe. Here's how you can help.

Plant a Billion Trees

Donate to help us plant and care for trees in critical forests around the world in Brazil, China, Colombia, Kenya, Tanzania, Mexico and the United States. Plant your tree now.

Family Forest Carbon Program

Owners of small forests in the U.S. can leverage the carbon-storing power of their trees in the fight against climate change and earn revenue by enrolling in this program. Learn how to get your family forest involved.

Don't Move Firewood

Moving firewood across long distances can potentially transport invasive species that cause damage to forests. You can make a difference by using local or heat-treated firewood whenever you need wood for your campsite, cabin or home heating. Learn how you can stop the spread of forest pests and pathogens.

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More Forest Stories

6 Ways Trees Benefit All of Us

From a city park to a vast forest, trees deliver for us when we help them thrive. Here are 6 ways.

Living Carbon: Stories of Nature’s Climate Solutions

In this series, we showcase innovative carbon projects in Africa, the Pocono Mountains of northeastern Pennsylvania, Chile's Valdivian Forest, and the Emerald Edge of North America's Pacific Coast.

Reforesting the U.S.

The Reforestation Hub identifies up to 148 million acres of total opportunity for reforestation, which could capture up to 535 million metric tonnes of carbon dioxide a year.

By Susan Cook-Patton

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JSmol Viewer

Drones for conservation in protected areas: present and future.

1. Introduction

3. results and discussion, 3.1. state of the art: drones in protected areas, 3.1.1. wildlife research and management, 3.1.2. ecosystem monitoring, 3.1.3. law enforcement, 3.1.4. ecotourism, 3.1.5. environmental management and disaster response, 3.2. current challenges on the integration of drones in protected areas, 3.2.1. legal barriers and ethical constraints, 3.2.2. impact of drones on wildlife and ecosystems, 3.2.3. costs of drone operation, 3.2.4. technological challenges, 3.3. linking drone platforms and sensors with conservation, 3.4. knowledge gaps and recommendations for future research, supplementary materials, author contributions, acknowledgments, conflicts of interest.

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Jiménez López, J.; Mulero-Pázmány, M. Drones for Conservation in Protected Areas: Present and Future. Drones 2019 , 3 , 10. https://doi.org/10.3390/drones3010010

Jiménez López J, Mulero-Pázmány M. Drones for Conservation in Protected Areas: Present and Future. Drones . 2019; 3(1):10. https://doi.org/10.3390/drones3010010

Jiménez López, Jesús, and Margarita Mulero-Pázmány. 2019. "Drones for Conservation in Protected Areas: Present and Future" Drones 3, no. 1: 10. https://doi.org/10.3390/drones3010010

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• Open access
• Published: 28 August 2024

Conservation of wild food plants from wood uses: evidence supporting the protection hypothesis in Northeastern Brazil

• Roberta de Almeida Caetano 1 , 3 ,
• Emilly Luize Gomes da Silva 3 ,
• Luis Fernando Colin-Nolasco 2 , 3 ,
• Rafael Ricardo Vasconcelos da Silva 1 , 3 ,
• Adriana Rosa Carvalho 4 &
• Patrícia Muniz de Medeiros 1 , 3  

Journal of Ethnobiology and Ethnomedicine volume  20 , Article number:  81 ( 2024 ) Cite this article

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The interplay between different uses of woody plants remains underexplored, obscuring our understanding of how a plant’s value for one purpose might shield it from other, more harmful uses. This study examines the protection hypothesis by determining if food uses can protect woody plants (trees and shrubs) from wood uses. We approached the hypothesis from two distinct possibilities: (1) the protective effect is proportional to the intensity of a species’ use for food purposes, and (2) the protective effect only targets key species for food purposes.

The research was conducted in a rural community within “Restinga” vegetation in Northeast Brazil. To identify important food species for both consumption and income (key species) and the collection areas where they naturally occur, we conducted participatory workshops. We then carried out a floristic survey in these areas to identify woody species that coexist with the key species. Voucher specimens were used to create a field herbarium, which, along with photographs served as visual stimuli during the checklist interviews. The interviewees used a five-point Likert scale to evaluate the species in terms of perceived wood quality, perceived availability, and use for food and wood purposes. To test our hypothesis, we used Cumulative Link Mixed Models (CLMMs), with the wood use as the response variable, food use, perceived availability and perceived quality as the explanatory variables and the interviewee as a random effect. We performed the same model replacing food use for key species food use (a binary variable that had value 1 when the information concerned a key species with actual food use, and value 0 when the information did not concern a key species or concerned a key species that was not used for food purposes).

Consistent with our hypothesis, we identified a protective effect of food use on wood use. However, this effect is not directly proportional to the species’ food use, but is confined to plants with considerable domestic food importance. Perceived availability and quality emerged as notable predictors for wood uses.

We advocate for biocultural conservation strategies that enhance the food value of plants for their safeguarding, coupled with measures for non-edible woody species under higher use-pressure.

A growing body of research points to the potential effects of chronic anthropogenic disturbances leading to the gradual extinction of local species and alterations in vegetation structure [ 1 , 2 ]. Among these disturbances, the impact of forest product utilization has been highlighted, demonstrating that while wood use is crucial for local communities, especially in developing countries, it often results in more pronounced impacts on plant populations [ 3 , 4 ].

While wood uses exerts considerable pressure on plant resources, in some socio-ecological contexts, the extraction of non-timber forest products (NTFP) can also be harmful to plant populations [ 5 , 6 ]. For example, the intensive harvesting of foliage and bark from tree species [ 6 ]. Nevertheless, the extraction of NTFPs, particularly the harvesting of wild fruits, generally has a lesser impact on forest structure and ecosystem functions than other uses [ 7 ]. Moreover, the consumption of NTFPs fulfills multiple roles, frequently underpinning rural livelihoods and local economies, aiding food security, fostering trade, and preserving cultural traditions and knowledge [ 8 ]. These species are also excellent sources of micro and macronutrients [ 9 , 10 ].

Hence, some researchers argue that discouraging the commercialization of wild food plants may adversely affect the subsistence and income of local populations, potentially leading to greater reliance on other forest resources with more harmful consequences than food collection itself [ 11 ]. One of these harmful consequences is deforestation, caused using wood for firewood and the construction of fences and houses, which require substantial amounts of green wood. Conversely, the commercial value of NTFPs, coupled with the opportunity for income generation, may incentivize conservation efforts among local communities for the forests that supply these resources [ 12 ].

Since the 1990s, investigations into the sustainability of food plant use have sought to determine the impact on species populations without conclusively addressing whether such use confers protective benefits. In contrast, research on plant domestication supports the notion that significant food value may lead to conservation practices, such as tolerance, protection, and promotion [ 13 ]. Plants with desirable traits may be maintained during deforestation or other disturbances, promoted through distribution and dispersal, and specifically safeguarded against competitors and herbivory [ 13 ].

However, the extent to which a plant’s significance for one use can shield it from more destructive applications, Footnote 1 namely the interaction effects among different utilization types, remains underexamined. This gap hinders our comprehension of protective dynamics in socio-ecological systems and their economic benefits for humans. Such insights are vital for shaping biocultural conservation frameworks that recognize the multifaceted advantages of maintaining cultural practices intertwined with biodiversity.

It is conceivable that certain NTFP uses, including for food, may exert a protective effect against more damaging activities such as wood uses. Although overharvesting of fruits has been shown to affect the regeneration of wild fruit trees adversely [ 14 , 15 ], food use is typically seen as specialized, with minimal impact on plant populations, whereas wood uses is often deemed generalist, posing broader threats [ 3 ].

The classification of plant uses as specialized or generalist may vary depending on the social-ecological context. In the context of several South American communities, specialized uses are defined by a narrower range of suitable plants meeting specific requirements, with the specialization premise reinforced by observations that plant availability exerts little to no influence on such uses [ 16 , 17 ]. In contrast, generalist uses accommodate a broader spectrum of species, with the most utilized often being the most accessible, as with many wood uses practices [ 18 ]. For example, the use of wood for firewood (fuel category) is often classified as generalist because, although factors like durability and high calorific leads to a species’ preference, potentially all woody species would be useful for this purpose [ 19 , 20 ]. As fuelwood use often requires large amounts of wood and/or frequent collection, it commonly targets the most abundant species [ 21 ]. Despite the fact that the uses in the construction (e.g., house and fence construction) and technology (e.g., tools, kitchen utensils) categories often require quality wood and are considered less generalist uses compared to the fuel category [ 3 ], they are still more generalist than the food use.

For woody species with edible fruits, some requirements such as nutritional value and flavor [ 22 , 23 ] are needed, and a much smaller proportion of species meet these requirements. Thus, in general, regardless of how generalist the use of wood is, any tree can meet some wood application (fuel, construction, technology), but not every tree has edible fruits. Although quality may also be an important predictor of plant importance for generalist uses [ 20 ], the generalist nature of wood use is supported by studies investigating the apparency (availability) hypothesis, which posits a correlation between environmental availability and species utilization [ 18 , 24 , 25 ]. Therefore, for generalist applications, alternatives may spare certain species for specialized uses, such as food, where fewer species can act as substitutes.

Silva et al. [ 26 ] were the first to test the protection hypothesis. They analyzed woody plants from the Caatinga used domestically for medicinal and wood purposes to evaluate whether the importance of medicinal use (specialized use) had an impact on wood use (generalist use). Their findings revealed a modest yet significant medicinal use effect on wood uses, providing supportive evidence for the hypothesis as plants of greater medicinal value saw less wood utilization. Moreover, Silva et al  suggested that the protective effect could be more pronounced in species with high medicinal importance.

The use of plants for food is also considered a likely candidate for conferring protective effects against wood uses. Wild food plants are often crucial for providing essential nutrients or for supplementing diets, playing a vital role in ensuring food security and offering economic benefits through the trade of these resources. Given food use’s specialized nature, dietary importance, and economic potential, there is a presumption that communities may prefer to preserve these plants from irreversible harm, such as wood uses. For the latter, alternative species are available due to the generalist nature of wood use.

In this context, we investigate the protection hypothesis from two distinct possibilities. We hypothesize that food uses (specialized) protect plants from wood uses (generalist). We examined: (1) whether the protective effect is proportional to the intensity of a species’ use for food purposes, and (2) if a protective effect only targets key species for food purposes. Here, ‘key species’ refer to wild food plants of high regional importance, which are well-established within the local community for both consumption and income generation.

This study is the inaugural inquiry into the protection hypothesis concerning the protective effect stemming from food use. Moreover, unlike Silva et al. [ 26 ], our study incorporates the commercial relevance of woody plants (trees and shrubs), providing income for the local population. Methodologically, we refine hypothesis testing by employing the checklist-interview technique [ 27 ] to boost respondent recall, ensuring all associated uses (food and wood) are considered.

Materials and methods

The research was carried out in a rural community within the coastal “Restinga” vegetation of Piaçabuçu, situated on the southern coast of Alagoas state. Piaçabuçu spans an area of 243.686 km 2 , housing a population of 15,908 individuals [ 28 ]. It features a tropical ‘As’ climate in the Köppen and Geiger classification, with an average annual temperature of 25.3 °C and an annual rainfall average of 1283 mm [ 29 ]. Notably, the municipality is designated with two sustainable use Conservation Units: the federally instituted Piaçabuçu Environmental Protection Area, established in 1983, and the state-sanctioned Marituba do Peixe Environmental Protection Area, created in 1988.

The Marituba do Peixe Environmental Protection Area spans 18,556 hectares and extends over portions of the Alagoan municipalities of Piaçabuçu (45%), Feliz Deserto (43%), and Penedo (6%) [ 30 ]. This area boasts diverse vegetation, including native “Restinga”, “Várzea”, and other forest formations [ 30 ]. Within the Indirect Influence Area of Marituba do Peixe Environmental Protection Area lies the village of Retiro (depicted in Fig.  1 ), which was the focal point for the ethnobiological segment of this study.

Geographic Location of the Retiro Community in the Municipality of Piaçabuçu-Alagoas, Brazil

The Retiro community is structured with a residents’ association and a family farmers’ association. It is equipped with a primary healthcare unit and a municipal elementary school. The predominant faith among residents is Christianity, represented by two Catholic and two evangelical churches. Currently, the community comprises approximately 288 families, a decrease of 81 families since before the COVID-19 pandemic, as reported by Gomes et al. [ 31 ]. This discrepancy may be partly due to some families not being documented, a requirement for health unit registration.

Retiro was selected for this study due to the local reliance on plant resources for both food and wood. The community’s economy is significantly driven by the extraction and commercialization of wild food plant fruits [ 31 ], along with shrimp and fish [ 32 ]. Wood resource extraction for personal use and commerce, particularly firewood, charcoal, and materials for fencing, is also prevalent. These resources are marketed through open markets or direct orders in Piaçabuçu and Penedo, whereas wood products are solely distributed by order.

Firewood is the primary cooking fuel in the community, though some households use both cooking gas and firewood. Meals are typically prepared on traditional clay or makeshift brick stoves. Firewood also serves in roasting shrimp and baking cakes from rice straw, a common bait for shrimp in local fishing gear known as “cóvu.”

Architecturally, many “taipa” houses (rammed earth) are present within the community, often serving as dwellings for individuals from other regions staying temporarily in the area.

Ethical and legal aspects of the research

This research project received approval from the Research Ethics Committee by Federal University of Alagoas (UFAL), No. 1998673, securing authorization for studies involving human participants as per the stipulations of National Health Council Resolution 466/2012. Additionally, scientific activities involving the collection and transport of botanical specimens within the Marituba do Peixe Environmental Protection Area were duly registered with Chico Mendes Institute for Biodiversity Conservation/Biodiversity Authorization and Information System (ICMBio/SISBIO), No. 87,112-1.

To ensure ethical compliance, all community members aged 18 or over—to whom the objectives of the research were explained—and who consented to participate, were asked to provide a signature or thumbprint on the Informed Consent Form (ICF), as well as on the image use authorization form.

Data collection

Data collection was carried out in three distinct phases: a participatory workshop, a forest inventory, and checklist-interviews.

1st data collection stage: participatory workshops

Participatory workshops with the residents of the Retiro community aimed to identify significant wild food plant species for consumption and commercial use, as well as their harvesting locations. These workshops were facilitated by local leaders and a researcher from the Laboratory of Biocultural Ecology, Conservation, and Evolution (LECEB), who had previously interviewed community members. The residents were recruited through door-to-door invitations on the day before the workshop was held.

In the inaugural participatory workshop, participants were asked to list the wild plants they harvested for sale or consumption. They recorded the common names on a piece of cardboard, selecting eleven for further discussion. We then asked which of those species were most important for sale and consumption within the community, and they ranked the top five in order of importance . Additionally, the workshop served to note wood resources tied to food plants and their utilization for consumption and commerce within the community.

Thirteen women and three men, ranging in age from 31 to 82, contributed to this first workshop. While all were identified as gatherers, some also engaged in agriculture and fishing. A follow-up workshop sought to enrich this data with contributions from another set of gatherers ( n  = 17), including eight newcomers. This session, comprising thirteen women and four men from the same age bracket, validated the initial findings regarding species and harvesting sites.

Participants utilized a detailed satellite image from Google Earth to denote areas frequented for food and wood collection. An overlay of transparent acetate allowed them to make corrections directly on the map.

After pinpointing these areas, we selected those most frequented for the harvesting of both food plants and wood, prioritizing locations where the ranked key species were prevalent. Among the listed key species— Myrciaria floribunda (H. West ex Willd.) O. Berg (“cambuí”), Genipa americana L. (“jenipapo”), Psidium guineense Sw. (“araçá”), Spondias mombin L . (“cajá”), and Tamarindus indica L. (“tamarino”)—only the first three were represented in the forest surveys due to their presence in forests. Although S. mombin and T. indica are also key species in the region, the former occurred at the edges of roads or in backyards, while the last is found in fenced area with wire, with reports of increasing cultivation for pulp production by large landowners. Therefore, our study only included three out of the five key species, as well as the species that co-occur with them.

Three sites were thus chosen for the forest inventory: two with a natural predominance of key species and one characterized by a more generalized distribution of various plant species, including those bearing edible fruits.

Gomes et al. [ 31 ], the research design we adopted gave precedence to examining species that occurred alongside the key species; consequently, not all food and wood plants were included in our scope. Notably, Schinus terebinthifolia Raddi, while not a key species within Retiro, is a significant commercial species in the community and the most important commercial plant in neighboring areas, such as the Fazenda Paraíso settlement [ 31 ].

2nd data collection stage: forest inventory and field herbarium

The research included a forest inventory as part of a larger investigation of our research group into plant resource utilization within the region, although ecological data is not part of this study. For the purposes of this study, the forest inventory was only used to identify and collect species that co-occur with the key species. Without the forest inventory, we would have no baseline for the field herbarium (notebook with exsiccates of the species used as a visual stimulus during the application of the checklist-interview technique), since we would not know which woody plants co-occur with our key species. Therefore, although it is not our purpose to present results on forest structure and composition, the inventory was fundamental for the research. Exsiccates of these species were included in the field herbarium based on their abundance, as detailed below.

The sites selected for the inventory were privately owned yet accessible to local gatherers. Two of these sites fell within the Marituba do Peixe Environmental Protection Area boundaries in Piaçabuçu, while the third was in the municipality of Penedo, not included in this protection area but still proximal to the community.

We established five permanent plots, each measuring 50 × 20 m, and further divided these into 50 smaller subplots of 10 × 10 m situated within the primary native vegetation gathering sites designated during the workshops. This amounted to 0.5 hectares per area, with a total of 1.5 hectares surveyed across all areas.

During the inventory, we collected at least three reproductive samples of each plant species within the plots for identification and to assemble a field herbarium for use in subsequent interviews. Certain species, commonly referred to as “ingá” and “pau d’arco”, lacked fertile material at the time of collection, leading us to categorize them as ethnospecies for the purposes of this study. Consequently, in our identification records, we referred to these simply as “ingá” and “pau d’arco”, acknowledging that these common names might represent multiple botanical species. Furthermore, the ethnospecies “cambuí”, although biologically uniform—belonging to the species Myrciaria floribunda (H. West ex Willd.) O. Berg—was recognized by some residents as having different ethnovarieties—a distinction not universally acknowledged. In our analysis, we accounted for each mention of “cambuí” by participants, even though the general data summary did not differentiate between ethnovarieties. For instance, if interviewee A identified two types of “cambuí” (Yellow and Red) and Interviewee B referred to one (a general “cambuí”), we recorded two entries for A and one for B in our database.

For the field herbarium, we mounted exsiccates from species with more than 15 individuals in the surveyed areas onto duplex paper of dimensions 42 × 29.7 cm and stored them in folders of matching size. The herbarium included 24 species in total and 2 taxa that were treated as ethnospecies.

Photography of each species was conducted in situ, capturing images that emphasized the plants’ distinguishing features: overall appearance, flowers and/or fruits, branches, and stems. These photographs were compiled into folders on a tablet, which was employed to display the images during interviews. Both the exsiccates and the photo folders were numerically coded to correspond with the identifiers on the interview forms, ensuring that interviewees were unaware of the plant names and assisting the interviewer.

The botanical collection phase commenced in November 2021 and concluded in April 2023, an extended period due to intermittent interruptions from COVID-19 peaks and flooding that hindered fieldwork.

A local guide with extensive knowledge of the vegetation provided assistance for all fieldwork involving local vegetation access. We adhered to standard botanical collection protocols, and the exsiccate samples were deposited at the Dárdano de Andrade Lima herbarium of the Agronomic Institute of Pernambuco.

3rd data collection stage: checklist interview

Before commencing the interviews (third stage), we mapped all Retiro households in May 2023. This mapping was imperative for sample size calculation due to the absence of a census record; the health unit’s data was limited to registered families. We determined that household heads (one per household) aged 18 or older present during our visit would be interviewed. Considering that some individuals reside in the community only for short periods, we established an inclusion criterion that only families living in the area for more than one year would be eligible for the study.

We ascertained the number of residences, including both occupied and vacant, to be 361, initially yielding a sample size of 187 residences based on a 95% confidence level and a 5% margin of error. Subsequently, we conducted a simple random selection.

As every house in the community was recorded, including unoccupied ones, some selected residences were vacant. Additionally, given the research’s focus on potentially harmful wood resource use within the Environmental Protection Area, some families were reluctant to participate. Therefore, from the 187 chosen residences, we could only conduct interviews in 81 interviews of them. To overcome refusals, flood-affected houses, and temporary residents, additional draws were made.

After all draws, we excluded unoccupied houses ( n  = 82), residences on flood-impacted streets ( n  = 12), households temporary inhabitants ( n  = 18) and refusals or unavailability ( n  = 74) from the sample. After three unsuccessful attempts to locate a household head, we inferred their non-participation.

A notable number of individuals opted out of the study, a figure aligned with expectations for wood use research in protected areas, mirroring findings from Medeiros et al. [ 3 ]. The considerable number of unoccupied houses in the community can be primarily attributed to their use as summer residences by individuals from nearby municipalities, taking advantage of the community’s closeness to the beach. Additionally, a number of these houses are situated in areas susceptible to flooding during the rainy season, which also contributes to their vacancy.

The final sample consisted of 115 individuals—81 women and 34 men. Interviews were conducted from May to July 2023. During interviews, we applied the checklist-interview technique [ 27 ] to ensure uniform visual stimuli across all informants, enhancing recall of all plant-associated uses.

Interviewees were shown photos of each species and queried on whether they recognized the species. Affirmative responses led to further questions on the plant’s name, its uses (food and wood), whether the interviewee actually used the species, parts utilized, commercial harvesting, and collection and sale sites. For recognized plants, a Likert scale rated: perceived availability (only for those interviewees that often frequent vegetation areas), wood quality by use category (fuel, construction, technology), domestic use for wood and food, and commercial use.

In the fuel category, wood is used as firewood or charcoal for generating energy, cooking food, and heating water or spaces. The construction category encompasses the use of wood in structures for territorial demarcation, building homes, shelters for animals, and storage of items (e.g., fences, posts, house lines, rafters, battens, doors, windows). Technology refers to the use of wood in manipulated items that are not intended for demarcating spaces, such as tool handles, benches, tables, chairs, canoes, and oars, among others [ 33 ].

The ratings and responses in Likert scale are presented in Fig.  2 .

Information collected using a Likert scale on the variables considered in this study

This classification facilitated the synthesis of scoring for perceived wood quality, allowing individuals to assign ratings by category rather than for each specific use. If a participant identified a plant as useful for wood but did not personally use it, we probed for the reasons behind this choice. We also asked if there were any of the mentioned plants that, despite being good for wood uses, the interviewee did not harvested. These questions were included to gather information on self-conscious protective behaviors associated with the food use of woody species.

Only for the ethnospecies “ingá” and “pau d’arco”, instead of showing the photos and exsiccate, we asked directly if the person knew them for food or wood uses. In case of a positive answer, we asked the same cycle of questions conducted for the other species. This was done because we did not obtain sufficient fertile material for the taxonomic identification of all species of “ingá” and “pau d’arco” during the various collection events.

Additionally, we gathered socio-economic data from all informants through structured interviews, including gender, age, occupation, income, place of origin, education, and length of residence. This information enabled the characterization of the socio-economic profile of the interviewees.

In this sense, the primary livelihoods include gathering, particularly collecting edible fruits, as well as pension, fishing, and agriculture, with some engaging in multiple occupations. A variety of other professions are represented to a lesser extent. The age of interviewees spans ages 18 to 82, with an average age of 48.14 years.

Most interviewees are literate (76.65%) are literate, of whom 73.91% have completed or partially completed basic education, and 1.74% have higher education qualifications.

The number of people occupying the residences ranges from one to seven residents. However, the majority of houses are occupied by: two or three residents (29.57%), followed by one or four resident(s) (15.65%).

Household incomes show substantial variation: (a) under one minimum wage (28.70%), (b) exactly one minimum wage (14.78%), (c) one and a half to two minimum wages (41.74%), (d) up to three minimum wages (13.04%), with a minority exceeding five minimum wages (1.74%).

Data analysis

For statistical analyses, we removed from the database any instances where species were identified for food purposes but not for wood purposes, as the focus of the research was on criteria for selecting wood plants. Consequently, non-wood plants were disregarded. Similarly, we excluded data from individuals who did not frequent forest environments to ensure that our information on species availability came from realistic assessments.

Our response variable, domestic wood use, was ordinal, as depicted in Fig.  3 . Therefore, we utilized Cumulative Link Mixed Models (CLMMs), incorporating the interviewee as a random effect to account for the non-independence of information from the same individual. The CLMMs were executed using the clmm function from the R package ordinal .

Widespread species model and key species-based model with their variables and respective measures

To evaluate the stability of our models and check for multicollinearity, we used the omcdiag function from the mctest package in R. We determined an absence of multicollinearity if none or at most one of the six indicators were positive. To circumvent multicollinearity, we constructed two models. The first model, termed the widespread protection model, assigned domestic and commercial food use values on a 5-point Likert scale based on reported usage intensity. For the key species-based protection model, food use was a binary variable: it took the value of 1 if the mention included the use of a key species, and 0 if the mention involved a key species only known but not used, or non-key species, regardless of usage.

Model selection was based on the most parsimonious option, as indicated by the lowest Akaike Information Criterion corrected for small sample sizes (AICc). We interpreted a ΔAICc (difference from the lowest AICc) of less than 2 as substantial support for the model’s inclusion among the best set of models, following Burnham and Anderson [ 34 ]. Following model selection, we computed a model average, which considered the average beta of all variables within the parsimonious models. Since the variables were standardized via z-standardization, we compared the relative effect sizes of all variables.

The variable ‘commercial wood use’ was not included in the models due to its limited mentions ( n  = 5) within the community and only six citations of species that are commercially traded for wood, exclusive of domestic use.

In addition to the explanatory variables related to food use, both models incorporated control variables for availability and quality, as previously identified in the literature as predictors of wood use [ 20 , 24 , 25 ]. Our quality indicator was the maximum perceived quality. It was determined by the highest Likert scale quality rating given by an interviewee for a species across the three categories of wood use. For example, if, for a given species, values of 3, 4, and 5 were assigned by an interviewee to the categories of construction, technology, and fuelwood, respectively, the maximum perceived quality would be recorded as 5.

To analyze the qualitative data on protection behaviors, we examined the responses, categorized them, and used descriptive statistics.

Wood and food uses: general aspects

All plants were recognized to varying extents by the interviewees. The most recognized species/ethnospecies were: Genipa americana L. (“jenipapo"), Inga spp. (“ingá”), Myrciaria floribunda (H. West ex Willd.) O. Berg (“cambuí”), Manilkara salzmannii (A.DC.) H.J.Lam (Massaranduba), Psidium guineense Sw. (“araçá”), Mouriri sp. (“cruirí"), and Bignoniaceae spp. (“pau d’arco”), with recognition rates of 56.5% or higher during interviews. The first five species achieved high recognition levels, exceeding 80%. Notably, G. americana , M. floribunda , and P. guineeense were identified as key species during the workshops. A comprehensive list of all species included in the checklist, along with their recognition and citation frequencies, is presented in Table  1 .

Over half of the species on the checklist (57.69% or n  = 15) were recognized for both food and wood uses. Within the three categories of wood use addressed in this study, fuelwood (37.89%) and construction (36.93%) had the highest citation percentages. Technology accounted for only 25.18% of wood citations. Within the fuelwood category, firewood led with the highest percentage (62.82%) of citations relative to the total uses in the category, followed by charcoal (37.18%). The construction category comprised 25 wood uses, with over half (50.55%) the citations pertaining to fences, and the remainder divided among uses such as line (11.98%) and rafter (10.74%). The technology category included 67 wood uses, featuring lower usage percentages compared to the other categories. Uses such as hoe handle (11.92%) and hoe shaft (10.30%) were the only applications exceeding 10% of citations in relation to the total uses within this category. All wood uses attributed to the species are detailed in  Supplementary Material 1 .

Widespread protection model

In the widespread protection model, domestic and commercial food use did not significantly influence domestic wood use when controlling for availability and quality variables (Fig.  4 ). This means that there is no linear relationship between food use and domestic wood use.

Impact of Predictor Parameters (quality, availability, domestic food use, commercial food use, domestic use of key species, and commercial use of key species) on domestic wood utilization of wild edible plants. Left: widespread protection model. Right: key species-based protection model. The central circles indicate the median coefficient estimates of the associations, and the horizontal lines delineate the 95% credibility intervals. The parameter coefficient estimates are plotted along the x -axis, while the predictor levels are represented on the y -axis. The vertical line intersecting the zero point on the x -axis (indicating no effect) facilitates comparison of the sizes of positive, negative, and null effect coefficients. In the parameter level grouping, non-overlapping horizontal bars denote significant differences. Horizontal bars intersecting the zero line on the x -axis signify a non-significant effect

Quality and availability were significant predictors of domestic wood use in the model. This suggests that, within the local context, there is a tendency to use woody plants for wood purposes based on their higher quality and greater availability.

Key species-based protection model

We observed a pronounced protective effect on key species, where the domestic use variable was more influential than both perceived availability and wood quality (see Fig.  4 ). However, the variable indicating commercial use did not significantly affect the use of wood for domestic purposes.

Within the model focusing on key species, both availability and wood quality (considered as control variables) had a significant impact on wood use. Consequently, our findings imply the existence of a threshold level of importance for the protective effect of food use on wood uses. This indicates that only those plants with substantial domestic food importance are shielded from being utilized for wood by the local population. The complete statistical results are available in the Supplementary Material  2 .

Evidence of protection based on qualitative data

When inquiring whether individuals refrained from using any of the recognized plants for wood purposes, despite acknowledging their suitability for such use, we gathered responses that support a tendency to protect certain species with dual edible and wood functions. The key species identified during the participatory workshop as significant to the local community, and which garnered substantial recognition in the checklist, were notably prominent in this context.

Out of the 60 respondents to this question, 37 reported no restraint in using plants suitable for wood purposes. Among the 23 participants that chose not to collect certain plants, 12 indicated not collecting species had both edible and wood uses.

Of all mentions of plants with both edible and wood applications, seven pertained to key species (as shown in Table  2 ). The primary rationale for sparing these species from wood harvesting, or only using their dry branches, is their provision of edible fruits valued within the community. This rationale is illustrated by the testimonies concerning P. guineense , G. americana , and M. floribunda .

Manilkara salzmannii though having a limited role in commerce, garners mixed views on its suitability for consumption within the community. Nonetheless, six interviewees mentioned the species, with two specifically expressing their intent to conserve it from being used for wood purposes: (1) “I don’t take it, thinking about the fruits and the future. I don’t like to take it (wood) while it’s still green, I only pick up the dry branches that have fallen on the ground.” (2) "Because it’s a plant that bears fruit, and it doesn’t sprout again if you cut it."

Despite M. salzmannii not being designated as a key species during the participatory workshop, it nonetheless received noteworthy acknowledgment in the checklist-interview. This suggests that M. salzmannii may possess a certain degree of importance for food-related uses within the community.

In our widespread protection model, neither commercial nor domestic food use significantly explains domestic wood use. By contrast, in the key species-based protection model, domestic use emerges as the primary explanatory variable. In both models, perceived availability and quality significantly explain wood use, with quality being more important than availability.

Consistent with our hypothesis, we identify a protective effect of food use on wood use. This effect is not directly proportionate to the food use of the species but is confined to plants with considerable domestic food importance. Research conducted in the Brazilian Caatinga region, which initially tested the protection hypothesis using medicinal (specialized) and wood (generalist) use, suggested this possibility [ 26 ]. Although they observed a modest yet significant linear trend supporting the hypothesis, the authors graphically demonstrated that the protective effect intensified specifically among highly valued medicinal plants. This study furnishes statistical substantiation for what was previously inferred graphically.

Given that the protective effect is selective for key species, it indicates that merely having intermediate or low food importance is insufficient for wild food plants to evade wood use. Protection is afforded only to those species recognized as highly important. Indeed, key species not only receive high acknowledgment in the checklist (> 80%) but are also extensively consumed and increasingly traded within the community, in forms such as fresh fruit, juice pulp, and in the manufacture of alcoholic beverages, ice pops, among other products. Literature highlights that elevating the value of non-timber forest products for local populations acts as an incentive for forest species conservation [ 12 , 35 ].

Our findings suggest that protection is predominantly correlated with domestic consumption. The domestic use of non-timber forest products can be a way for poorer local populations to save money [ 36 ], as is the case with wild fruits that can replace commercially purchased foods. Although wild food plants serve only as supplementary food resources within the community—with staple crops like rice and beans constituting the primary plant food intake—the importance of key wild food plants likely motivates the observed protection behaviors. Moreover, the emotional connection with natural environments resulting from direct experiences with nature can lead to pro-environmental behaviors (actions that reduce negative environmental impacts or enhance the sustainable use of natural resources) or intentions to engage in nature protection, as environmental psychology research has demonstrated [ 37 , 38 ]. For example, Hinds and Sparks [ 39 ] found that individuals who grew up in rural areas tend to report more positive emotional connections, a stronger sense of identification, and more intense behavioral intentions regarding engagement with nature compared to those who were raised in urban environments. In this sense, the protective behaviors associated with the domestic consumption of key species may be related to the emotional bond linked to positive emotions built over a lifetime and across generations.

The low adherence to protective behaviors reported in interviews (see Evidence of protection based on qualitative data) could stem from various factors. Not all protective actions are necessarily conscious. Additionally, individuals may inadvertently omit mention of such behaviors in response to indirect inquiries like those posed in our study. Furthermore, protection may not be universally practiced within the community, and while the pressure to use wood from wild food plants may not be entirely eliminated, it could be reduced by fewer community members intensively exploiting key food plants for wood purposes.

Wood quality and species availability are significant determinants of wood use. It appears that, aside from key food species—whose utilization for wood is limited due to their value as food—other species are more likely to be used for wood purposes when they offer better trade-offs between availability and quality. Most studies that investigate the drivers of wood use tend to analyze quality or availability indicators separately, rather than in combination. These studies have found that either quality or availability can influence wood use [ 20 , 24 , 25 ].

Studies that consider multiple predictors of wood use have yielded divergent results. While availability seems to be a consistent predictor across different contexts, quality may or may not be a determinant of wood use [ 19 , 40 ].

In various social-ecological contexts, research has indicated that trade-offs between multiple variables act as drivers of plant resource use [ 19 , 41 ]. However, these trade-offs are often considered within a single use-category (e.g., the trade-off between quality and availability to explain fuelwood use). Therefore, the evidence of protection underscores the necessity of considering interactions between use-categories when evaluating criteria for plant resource selection (Fig.  5 ).

Hypothetical example of a trade-off between availability and quality explaining fuelwood use. In a simplified scenario where these are the only predictors of plant use within the fuelwood category, the most utilized species would be those exhibiting the highest trade-offs between availability and quality (represented by the blue dots in the right and left graphs). When considering the interaction with the food use-category under the key-species based protection model, the use of wood species with low to intermediate food importance would be proportional to the trade-off between quality and availability (graph on the right). However, for species that are considered key food plants (indicated by the dark green dot), their utilization for fuelwood would be less than what is predicted by their quality and availability alone

Recommendations for conservation strategies for plant species

The practical implication of a protective effect that acts solely on species of high food importance is that species recognized as having intermediate or lower importance remain unprotected, as do wood species without any associated food use. Moreover, if only a few species are highly valued for food, they might experience intense pressure from their use as food or be protected at the expense of other species. Therefore, we recommend that conservation strategies take into account the interactions between food and wood use-categories, i.e., the effects of one category on the other.

For species with intermediate or lower food importance, popularization strategies could prove beneficial to enhance their perceived value. Programs aimed at popularizing such species are crucial, as they may significantly contribute to food and nutritional security, while their use as food might concurrently protect them from being exploited for wood. These programs should establish incentives that encourage community members to use these resources sustainably. However, the effectiveness of this approach should be continuously monitored, as if importance is the primary factor driving protective behaviors, integrating other wild food plants into the set of key species may prove challenging.

Although the commercial importance of key species did not lead to protection in this study, the inclusion of certain species in local markets could also positively influence domestic use. Thus, popularization strategies could extend beyond local communities, emphasizing the importance of these plants for diet diversification and their potential nutritional value to generate demand for products sourced from local communities. One method to achieve this is through marketing campaigns that raise awareness about the significance of these plants in local markets and across social and conventional media platforms [ 42 ].

However, it is crucial to approach the popularization of highly important food species with caution to prevent the oversimplification of the plant community, as observed with açaí ( Euterpe oleracea Mart.), where management practices have simplified estuarine communities in the Amazon Rainforest [ 43 ].

For wood species that lack an associated food use, conservation strategies must be implemented to mitigate the pressure on their exploitation. Considering that the primary wood uses in the community are for fuel (firewood) and construction (fencing), conservation efforts should be tailored to these applications. Firewood is the most commonly cited use in the Retiro community, and due to its characteristics regarding short replenishment time and large volume of wood used, it poses a significant threat to species conservation, depending on the collection method (green or dry). For people with greater social vulnerability, firewood is an important resource for cooking. To address this, we recommend the use of efficient wood stoves. These stoves, through their structural configuration, reduce cooking time and, consequently, the daily volume of wood used and deforestation compared to traditional stoves [ 44 ].

Although there is controversy in the literature regarding the long-term economic costs and benefits of improved stove use in developing countries [ 45 ] and their efficiency[ 46 ], several studies have shown significant reductions in firewood use with the adoption of this technology [ 44 , 47 , 48 ]. For instance, a study based on an improved stove intervention in the Chalaco District, Northern Andes of Peru, recorded a 46% reduction in firewood consumption (approximately 650 kg of firewood per household throughout the rainy season) among households that properly used improved stoves during winter [ 48 ]. Similarly, Bensch and Peters [ 47 ], who evaluated the impact of these stoves in rural Senegal through a randomized clinical trial, found a total 31% reduction in firewood consumption over one week. Additionally, the use of efficient stoves can contribute to a higher quality of life for users by reducing smoke from wood combustion, which can cause respiratory diseases [ 49 ]. However, for successful implementation of efficient stoves, besides local community interest, factors influencing long-term adoption, such as maintenance costs, need to be considered.

An alternative to replacing firewood use is increased investment in public policies that ensure access to Liquefied Petroleum Gas (LPG). While families receiving the gas voucher through the federal government program (Bolsa Família) still use a mix of LPG and firewood in the community, education, health, and human well-being initiatives, combined with these public policies, may have a better response in the community during the transition from firewood to LPG use. This is especially important considering that the use of firewood, for the most part, spans generations. The same applies to the transition from traditional or makeshift brick stoves to efficient stoves.

To reduce the use of species employed in the construction of dead fences—where trunks and branches of woody plants are cut green for use—we recommend a gradual replacement with species used as living fences, which are kept alive. This strategy has been indicated as effective as it represents a gene bank of native species and contributes to the maintenance of these species [ 50 ]. “rompe gibão” ( Phyllanthus sp.) and “cruirí” ( Mouriri sp.) were mentioned by some interviewees as species used for living fences, and “peroba” ( Tabebuia elliptica (DC.) Sandwith) was mentioned as having the ability for its stake to remain green in a humid environment. They are considered hard and resistant woods (“fixe”) by the interviewees who recognized them on the checklist. These species could potentially be used for this purpose, but they need to be evaluated in terms of their characteristics and ecological status.

Finally, although our results admit that there is a protective effect on species with high food importance (key species) regarding wood uses, it is necessary to investigate the ecological status of these species to assess whether harvesting is being done sustainably and if overexploitation of these species is not occurring, as has been identified in other studies with non-timber forest products [ 5 , 14 , 15 ].

Recommendations for future ethnobiological studies

Some challenges for testing the protection hypothesis in future studies include:

Studies should account for the interactions not only between two use-categories but also among all use-categories associated with the plant species. For instance, a plant might be protected from wood uses not solely due to its food or medicinal value, but because it serves multiple purposes. Thus, protection may only become apparent when evaluating the full spectrum of plant use dynamics.

Gender and age variables ought to be incorporated into the tests of the protection hypothesis, given that individuals of different ages or genders may protect plants for varied purposes.

Studies could delve into the affective aspects of protection, as these may inspire individuals to spare certain species from wood uses due to resources that evoke positive affective memories. For example, a fruit that was greatly cherished during one’s childhood or that constituted the main sustenance for a person’s family might be protected. While affective reasons are personal, common patterns may surface, especially among individuals with similar cultural or community backgrounds who may share collective memories.

It is necessary to further investigate the influence of social organization on the protective behavior of local peoples toward wild food species. For instance, in contexts where there are associations of fruit gatherers or cooperatives, protective behavior may increase compared to rural communities where social organization is poorly established or absent. Alternatively, protective behavior may be directed on an individual basis.

Research designs should enhance the methodological approach concerning qualitative evidence for protection. The questioning should be crafted to elicit precise responses without leading the participant, yet still addressing the core issue effectively. Our study utilized indirect questions that may not have fully captured our main objective. We propose that future research adopting discourse analysis techniques (underpinned by multiple theoretical frameworks) would yield valuable insights.

Limitations of this study

For two groups of plants treated in this study as ethnospecies ( “ pau d’arco” and “ingá”), we were unable to elucidate their taxonomies despite our efforts. Our results suggest that these ethnospecies are not under the protective effect of food use, and the lack of botanical identification complicates the targeting of conservation strategies, especially for future studies in this region. Although we do not know the quantity and specific species, we suspect they are at risk of threat due to logging, especially for “pau d’arco” (Bignoniaceae spp.). At least two species of “pau d’arco” are listed in the International Union for Conservation of Nature Red List with concerning ecological statuses: Handroanthus impetiginosus (Mart. ex DC.) Mattos (“pau d’arco rosa”) listed as near-threatened and Handroanthus serratifolius (Vahl) S. Grose (“pau d’arco amarelo”), listed as endangered [ 51 ]. Both were assessed for the list in 2020. As respondents mentioned three types of “ pau d’arco” (“roxo”, “amarelo”, and “branco"), it is possible that species of this genus are included. Through botanical identification, we identified that a plant known in the community as “peroba” is the species Tabebuia elliptica (DC.) Sandwith (“pau d’arco branco”), specified on the Red List with a status of least concern. This makes this area an interesting occurrence for this plant group. In light of this, we acknowledge this limitation in our study and invite other researchers specializing in these plant groups to direct research efforts in this region and clarify the taxonomy of these species.

Our data on the perceived quality of wood were collected from a single Likert scale value considering all wood uses of the plant reported by the interviewee for each wood use category, instead of considering the quality for each reported wood use in each category. This optimized data collection. However, the heterogeneous nature of categories such as technology, where the wood quality of the plant can vary significantly among uses (e.g., tools, furniture, boat), can be challenging for the interviewee to assign a single rating considering various distinct uses. This may have biased our results with very generic perceptions of species quality. Given that wood use is diverse, future studies could consider a more meticulous design, such as focusing on the most relevant uses within each category in the local community and assessing their perceived quality independently.

In this study, we did not monitor the collection activity, so we were unable to differentiate between wood collected from fallen stems and branches (with less impact on plants) and wood removed directly from the plants (with greater impact). However, this does not compromise our results, as the aim was to assess general usage behaviors, and other studies have already recorded the predominance of cutting practices, whether for dry or green, live or dead wood [ 3 , 21 ].

The inclusion of species for the composition of the checklist interview was based on their availability in areas of co-occurrence with key species. Although greater availability of species is a potential indicator of higher use, it is not universal. There may be species in the sampled vegetation areas that are less available precisely because they are under greater use pressure or due to other environmental or intrinsic factors not considered in this study. Therefore, it is essential to also consider ecological approaches in research to have an overall assessment of the impact of such uses on the plant community structure, even if the focus of the research is on the most important plant species.

Overall, we found that there is a protective effect that acts primarily on plant species of high food importance (key species), rather than proportionally to the importance of the species . Consequently, we encourage future studies to test the protection hypothesis within various socio-environmental contexts and we suggest considering two distinct possibilities: generalized protection and protection targeted at key species.

In light of our findings, we advise that species demonstrating an overlap between food and wood uses, yet possessing intermediate or lower food importance should be prioritized in popularization strategies to raise their significance. Moreover, species solely used for timber, which do not benefit from food-related protection, also require attention through biocultural conservation strategies. Given that the protective effect is limited to a select number of plant species, these species warrant further ecological investigation to determine their conservation status within their natural habitats, to identify whether they face increased pressure from their use as food, and to ascertain if their prominence is leading to a reduction in plant diversity.

Availability of data and materials

Data are provided within the manuscript or supplementary information files.

Here, we consider destructive applications, those that compromise the individual plant and its population in the short term. For example, a shallow cut or a substantial cut of the branches of a tree, which can hinder its growth. In contrast to the fruit, which is collected, and the individual plant remains intact, it can cause damage to the population in the long term.

Abbreviations

Cumulative link mixed models

Non-timber forest products

Universidade Federal de Alagoas (Federal University of Alagoas)

Sistema de Autorização e Informação em Biodiversidade (Biodiversity Authorization and Information System)

Instituto Chico Mendes de Conservação da Biodiversidade (Chico Mendes Institute for Biodiversity Conservation)

Informed consent form

Laboratory of Biocultural Ecology, Conservation, and Evolution

Akaike information criterion

Liquefied Petroleum Gas

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Acknowledgements

We extend our gratitude to the residents of the Retiro community for their support in this research, particularly to Mr. José Antônio and Mrs. Maria das Dores, who warmly welcomed us and facilitated our requests within the community. Our appreciation also goes to Dr. Marcos Sobral, a Myrtaceae expert, who graciously identified the species of this family in our study. Likewise, we express our thanks to the researcher from the Agronomic Institute of Pernambuco, Maria Olivia de Oliveira Cano, for her receptiveness and efficiency in assisting us during all visits throughout the COVID-19 pandemic. We also extend our heartfelt thanks to the members of the Laboratory of Biocultural Ecology, Conservation and Evolution for their support in fieldwork activities.

This study was funded by the Brazilian Biodiversity Fund—FUNBIO, the HUMANIZE and Eurofins Foundations (FUNBIO—Fellowships Conserving the Future, granted to RAC, No. 025/2022), the National Council for Scientific and Technological Development (CNPq) (Doctoral fellowship for RAC, No 141873/2020-5 and productivity grant to PMM, No. 304866/2020-2) and the Research Support Foundation of the State of Alagoas (FAPEAL) (Granted to PMM, No. APQ2022021000027).

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Caetano, R.d., da Silva, E.L.G., Colin-Nolasco, L.F. et al. Conservation of wild food plants from wood uses: evidence supporting the protection hypothesis in Northeastern Brazil. J Ethnobiology Ethnomedicine 20 , 81 (2024). https://doi.org/10.1186/s13002-024-00719-3

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Reforestation to capture carbon could be done much more cheaply, study says

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• New research shows that a mix of natural forest regrowth and tree planting could remove up to 10 times more carbon at $20 per metric ton than previously estimated by the IPCC, the U.N.’s climate science panel.
• The study found that natural regeneration is more cost-effective in 46% of suitable areas, while tree planting is better in 54%, suggesting a tailored approach could maximize carbon capture.
• Researchers estimate that using the most cost-effective method in each location could remove 31.4 billion metric tons of CO2 over 30 years at less than $50 per metric ton.
• While the findings are promising, experts caution that reforestation alone can’t solve the climate crisis and emphasize the need to consider biodiversity and other ecological factors alongside cost-effectiveness.

Trees are allies in the struggle against climate change, and regrowing forests to capture carbon may be cheaper than we thought. According to new research published in Nature Climate Change , a strategic mix of natural regrowth and tree planting could be the most cost-effective way to capture carbon.

Researchers analyzed reforestation projects in 138 low- and middle-income countries to compare the costs of different reforestation approaches. They found it’s possible to remove 10 times more carbon at $20 per metric ton, and almost three times more at $50, compared to what the Intergovernmental Panel on Climate Change (IPCC) had previously estimated .

Neither natural regeneration nor tree planting consistently outperforms the other. Instead, the most cost-effective method varies depending on local conditions. Natural regeneration, which involves letting forests regrow on their own, is cheaper in about 46% of suitable areas. Tree planting, on the other hand, is more cost-effective in 54% of areas.

“Natural regeneration is more cost-effective in areas where tree planting is expensive, regrowing forests accumulate carbon more quickly, or timber infrastructure is distant,” said lead author Jonah Busch, who conducted the study while working for Conservation International. “On the other hand, plantations outperform in areas far from natural seed sources, or where more of the carbon from harvested wood is stored in long-lasting products.”

The research team estimates that by using the cheapest method in each location, we could remove a staggering 31.4 billion metric tons of carbon dioxide from the atmosphere over 30 years, at a cost of less than $50 per metric ton. This is about 40% more carbon removal than if only one method was used universally.

“It’s exciting that the opportunity for low-cost reforestation appears much more plentiful than previously thought; this suggests reforestation projects are worth a second look by communities that might have prejudged them to be cost prohibitive,” said Busch. “While reforestation can’t be the only solution to climate change, our findings suggest it should be a bigger piece of the puzzle than previously thought.”

To reach these conclusions, the research team gathered data from hundreds of reforestation projects and used machine-learning techniques to map costs across different areas at a 1-kilometer (0.6-mile) resolution. This detailed approach allowed them to consider crucial factors such as tree growth rates and potential species in different regions.

Ecologist Robin Chazdon, who wasn’t involved in the research, praised the comprehensive approach but highlighted important considerations beyond cost-effectiveness.

“These eye-opening findings add nuance and complexity to our understanding of the net costs of carbon storage for naturally regenerating forests and monoculture plantations,” Chazdon said. However, she emphasized that “the relative costs of carbon storage should not be the only factor to consider regarding spatial planning of reforestation.”

Chazdon pointed out some of the ecological trade-offs involved in different reforestation methods. Monoculture tree plantations, while potentially cost-effective in certain areas, often create excessive water demand and provide poor opportunities for native biodiversity conservation. In contrast, naturally regenerating forests typically offer a wider range of ecosystem services and better support local biodiversity.

“Ultimately, these environmental costs and benefits — which can be difficult to monetize — need to be incorporated in decisions regarding how and where to grow plantations or foster natural regeneration,” Chazdon said.

The study’s authors acknowledge these limitations and suggest several directions for future research. They propose extending the analysis to high-income countries and exploring other forms of reforestation, such as agroforestry or planting patches of trees and allowing the rest of an area to regrow naturally.

Additionally, the researchers emphasize the need to integrate their findings on cost-effectiveness with data on biodiversity, livelihoods and other societal needs to guide reforestation efforts in different contexts.

While the study’s findings are promising, the researchers caution that reforestation alone won’t solve the climate crisis. Even at its maximum potential, reforestation would only remove as much carbon dioxide in 30 years as eight months of current global emissions.

Reforestation is very important, but it won’t solve climate change on its own, Busch said. Ultimately, “we still need to reduce emissions from fossil fuels.”

Banner image of two men planting trees in the Yokadouma Council Forest, Cameroon. Image courtesy WWF.

Liz Kimbrough  is a staff writer for Mongabay and holds a Ph.D. in ecology and evolutionary biology from Tulane University, where she studied the microbiomes of trees. View more of her reporting  here .

How to pick a tree-planting project? Mongabay launches transparency tool to help supporters decide

Busch, J., Bukoski, J. J., Cook-Patton, S. C., Griscom, B., Kaczan, D., Potts, M. D., … Vincent, J. R. (2024). Cost-effectiveness of natural forest regeneration and plantations for climate mitigation.  Nature Climate Change , 1-7. doi: 10.1038/s41558-024-02068-1

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