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Camera-trapping for conservation: a guide to best-practices
Abstract and Figures
100 YEARS OF HISTORY
Over the last decade, millions of people around the world have become aware of the camera trap. The candid images and videos that camera traps produce have been featured in countless documentaries, are widely shared on social media, and have been the focus of hugely popular citizen science projects. Less well known is the fact that the camera trap has a long history that extends back more than 100 years. Over this time, they have gone from being an experimental technology used by just a handful of people to a commercialised technology being used by many thousands of photographers, hobbyists, hunters and biologists.
THE MODERN CAMERA TRAP
The modern camera trap is simply a digital camera connected to an infrared sensor which can “see” warm objects that are moving, like animals. When an animal moves past the sensor it causes the camera to fire, recording an image or video to the memory card for later retrieval. Camera traps can be left in the field to continuously watch an area of habitat for weeks or even months, recording the rarest events which occur in nature. This can include everything from a big cat patrolling its territory, to the raiding of a bird´s nest by a predator. Camera traps are also “wildlife friendly”, in that they cause little or no disturbance to wildlife. At the same time, they produce permanent and verifiable records of animals, akin to traditional museum voucher specimens.
HIGHLY EFFECTIVE TOOLS
Camera traps provide data on exactly where species are, what they are doing, and how large their populations are. They can be used to build up a picture of whole communities of species, including how they are structured and how species are interacting over space and time. Camera traps are also being deployed to understand how humans and livestock interact with wildlife. The development of networked camera traps, capable of sending images over phone or satellite networks in near real-time, has provided a new tool in the fight against poaching. New software tools and statistical models are also making it much easier and faster to obtain high quality information from the thousands of images that camera traps can quickly generate. This is improving our understanding of human impacts on wildlife, and helping land managers make better decisions at both small and large scales.
CHALLENGES
Despite the great potential of camera traps, there are a number of significant challenges involved in working with them. This can be frustrating for first-time users of the technology and can lead to wasted time and resources. Here we provide all the information needed to get up and running with camera traps as quickly as possible. Our aim is to maximise the effectiveness of camera traps for conservation and ecological research. We introduce the technology, help you decide if camera traps are right for your needs, provide the information you need when shopping for camera traps, and then give detailed recommendations on how exactly to deploy camera traps in the field.
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... The camera trap site was used as a random effect. To control for differences in camera sampling effort, the total number of days per 30-day-long interval that each camera was operational was used as an offset in the model (Wearn and Glover-Kapfer 2017). The total number of independent baboon detections recorded across all cameras in the Gorongosa National Park camera trap survey for each month analyzed was also used as an offset, effectively accounting for annual changes in activity, and thus detectability, which may have been due to seasonal patterns or interannual changes in population density. ...
Objectives
With contemporary, human‐induced climate change at a crisis point, extreme weather events (e.g., cyclones, heatwaves, floods) are becoming more frequent, intense, and difficult to predict. These events can wreak rapid and significant changes on ecosystems; thus, it is imperative to understand how wildlife communities respond to these disruptions. Primates are perceived as being a largely adaptable order, but we often lack the quantitative data to rigorously assess how they are impacted by extreme environmental change. Leveraging detections from a long‐term camera trap survey, this opportunistic study reports the effects of an extreme weather event on a little‐studied population of free‐ranging primates in Gorongosa National Park, Mozambique.
Materials and Methods
We examined shifts in gray‐footed chacma baboon ( Papio ursinus griseipes ) and vervet monkey ( Chlorocebus pygerythrus ) spatial distribution and relative abundance following Cyclone Idai—a category four tropical cyclone that struck Mozambique in March 2019.
Results
Baboon spatial distributions were impacted in the first month after the cyclone, with more detections in areas where flooding was less severe. Spatial distributions renormalized once floodwaters began to recede. We describe vervet monkey spatial distribution trends, though sample size limitations inhibited statistical analysis. Primate relative abundance did not appear to substantially decrease following the cyclone, suggesting troops were able to adopt behavioral adjustments to evade rising floodwaters.
Discussion
These findings highlight the behavioral flexibility of Gorongosa's primates and their ability to adapt to extreme—if temporary—disruptions, with implications for primate conservation in the Anthropocene and research into how rapid climatic events may have shaped primate evolution.
... We installed the camera traps at 1 to 3 m depending on the focus point (i.e., fruits) on the ground or on the cactus. We programmed the camera traps with a maximum trigger speed of 0.1 ′′ , without flash or infrared warning light, with a recovery speed of 0.5 ′′ , at a maximum detection angle, in image capture format [27]. ...
Mining is an indispensable activity that threatens biodiversity globally. However, assessments of key ecological processes for the maintenance of plants threatened by mining, such as the effectiveness of frugivory and seed dispersal, are almost non-existent. We evaluated the effectiveness of fruit and seed dispersal in the threatened cactus Browningia candelaris at two different sites: one distant and one close to a mining company currently in operation. Unfortunately, in the study area, B. candelaris is only present in the two evaluated sites, which makes it impossible to have replications for the distant and nearby sites. With this caveat in mind, we evaluated the different parameters of dispersal effectiveness by comparing both sites, far and close to the copper mine. Fruit abundance was significantly higher near the mine. By contrast, animal richness was lower near the mine. However, animal visitation rates for consumption of immature and mature fruits did not differ significantly between sites. Of the 15 animals observed, only four consumed and defecated live seeds: the fox Lycalopex culpaeus, the lizard Microlophus theresioides, and the mouse Octodontomys gliroides and Phyllotis xantopygus. Seed dispersal effectiveness was higher near the mine, but extremely low at both sites. In fact, population recruitment of new cacti was null at both sites, near and far from the mine, due to the scarcity of water in the environment. This is probably due to the increasing aridity of the Atacama Desert due to global warming. Therefore, new studies to assess synergies between potential local threats, such as mining operations, and global threats, such as global warming, are essential to carry out relevant conservation actions on endangered species such as cacti.
... Habitat variation, climatic conditions, human influences, research methodologies, and seasonal shifts in species habitat use contribute to the variability of RAI values across geographic regions (Foster and Harmsen 2012;Sollmann et al. 2013). Various habitat types and structures can influence species abundance, while climatic conditions and human activities can exacerbate this influence (Wearn and Glover-Kapfer 2017). Additionally, the use of diverse research methods and protocols across studies can lead to variations in results (O'Brien 2011). ...
Türkiye, due to its position as a bridge between Asia and Europe, encompassing three distinct biogeographic regions and its diverse climatic conditions and geographical features, exhibits the characteristics of a small continent in terms of biodiversity, hosting a very high number of mammalian species. However, information on these mammals' activity patterns and co‐occurrence, specifically in Türkiye, is limited. Our study aimed to reveal the daily activity patterns and temporal overlaps of mammalian species detected using camera traps in Sülüklü Lake Nature Park. The white‐breasted hedgehog was strictly nocturnal, while the European badger, gray wolf, European hare, wild boar, and beech marten tended to be nocturnal. The Caucasian squirrel was strictly diurnal, and the roe deer tended to be diurnal. The highest temporal overlap was found between the white‐breasted hedgehog and the beech marten (∆4 = 0.84, 95% CI), followed by the red fox and roe deer (∆1 = 0.77, 95% CI). The lowest temporal overlap (∆1 = 0.081, 95% CI) was found between the white‐breasted hedgehog and the Caucasian squirrel. The second lowest overlap (∆1 = 0.136, 95% CI) occurred between the Caucasian squirrel and the European badger. Our findings have provided new and detailed insights into the diversity of mammalian species within the nature park located in Northwestern Anatolia. These data will support and facilitate future research aimed at understanding the mechanisms of species coexistence in this ecosystem. The results obtained will enable a deeper examination of ecosystem dynamics and contribute to developing strategies for biodiversity conservation.
... Possibly the most critical task in conservation consists of being able to identify individual animals over time. Through Capture-Recapture techniques, researchers can evaluate their fitness and track their demographics [30]. For example, in the case of leopards, monitoring their populations in an ever-growing urban landscape in India is the focus of intensive research [10], [11]. ...
Accurate identification of individual leopards across camera trap images is critical for population monitoring and ecological studies. This paper introduces a deep learning framework to distinguish between individual leopards based on their unique spot patterns. This approach employs a novel adaptive angular margin method in the form of a modified CosFace architecture. In addition, I propose a preprocessing pipeline that combines RGB channels with an edge detection channel to underscore the critical features learned by the model. This approach significantly outperforms the Triplet Network baseline, achieving a Dynamic Top-5 Average Precision of 0.8814 and a Top-5 Rank Match Detection of 0.9533, demonstrating its potential for open-set learning in wildlife identification. While not surpassing the performance of the SIFT-based Hotspotter algorithm, this method represents a substantial advancement in applying deep learning to patterned wildlife identification. This research contributes to the field of computer vision and provides a valuable tool for biologists aiming to study and protect leopard populations. It also serves as a stepping stone for applying the power of deep learning in Capture-Recapture studies for other patterned species.
... We employed dynamic occupancy models (MacKenzie et al. 2003) to conduct an eight-year evaluation of large mammal habitat use within the Peneda-Gerês National Park in northern Portugal. These models, previously used for wildlife monitoring using camera traps (Wearn andGlover-Kapfer 2017, Gould et al. 2019), improve the understanding of the dynamics of species occupancy (MacKenzie et al. 2003). Our study, conducted on a relatively small scale covering only 16 km 2 , offers insights into local habitat preferences and ecological interactions, extending beyond mere occupancy assessments. ...
The issue of agricultural land abandonment in southern Europe has raised concerns about its impact on biodiversity. While abandoned areas can lead to positive developments like creating new habitats and restoring native vegetation, they can also result in human–wildlife conflicts, particularly in areas with extensive farming and free‐ranging livestock. To understand habitat selection and use of livestock and wild ungulates, it is essential to study their spatial and temporal distribution patterns. In this context, we conducted a long‐term large mammal monitoring project using camera traps in the Peneda‐Gerês National Park in northern Portugal. Our primary focus was on exploring habitat preferences, occupancy dynamics, and potential spatial use correlations between domestic and wild species, utilizing dynamic occupancy models. Most wild species exhibited stable area use patterns, while domestic species experienced marginal declines, and the Iberian ibex displayed signs of repopulation. We observed distinct effects of habitat variables on occupancy, colonization, and extinction, revealing species‐specific patterns of habitat utilization. Human disturbance had a notable impact on domestic species but did not affect wild ones. Camera sensitivity emerged as a critical factor, enhancing detection probability for all species. Additionally, habitat and weather variables exerted varying effects on detection probabilities, underscoring the necessity of accounting for these factors in modeling the detection process. We found shared habitat preferences between cattle and horses, both positively correlated with wolves, suggesting potential human–wildlife conflicts. Despite extensive spatial overlap, domestic and wild species seem to exhibit ecological independence possibly due to distinct strategies and low predation pressure. Overall, the study emphasizes the multifaceted factors influencing habitat use. The observed species associations contribute to understanding ecological relationships and potential resource competition, emphasizing the importance of considering environmental variables for effective wildlife conservation and management.
The Himalayas, including Nepal, are a biodiversity hotspot. However, records on mammalian richness remain incomplete due to resource limitations, inadequate training, and the remote location of study areas. The unprotected forest area of the Panchthar-Ilam-Taplejung region in eastern Nepal is a vital corridor connecting India and Nepal. Using a structured methodology we aimed to increase our knowledge of mammalian diversity in this area. Camera traps were deployed throughout the Panchthar-Ilam-Taplejung area in 53 locations in winter and 54 in spring, accumulating 3014 camera trap days and generating 93,336 images, with a positive trigger rate of 29.8%. The survey revealed 17 species of medium to large-sized mammals and an additional six species of smaller unidentified mammals, including two melanic variations and two previously undocumented species. Activity patterns were calculated for species with more than five image records in both seasons. The findings contribute essential information about the Kangchenjunga Landscape, which can be used to further conservation efforts in this critical ecosystem corridor.
Chimpanzees ( Pan troglodytes ) in Senegal may use nocturnality to mitigate hyperthermia risk in semi-arid environments but the degree of nocturnality for such chimpanzees also in sympatry with large carnivores remains uncertain. We compared diel activity among chimpanzees and their potential predators at Assirik in Niokolo-Koba National Park and contextualized these findings relative to other unit-groups in savanna landscapes. From 2015-2018, we generated a predator inventory using multi-modal methods and monitored the diel activity of chimpanzees and predators with camera traps [ camera trap (CT) days]. From 2015-2023, we also surveyed for evidence of predation during recce walks. Six potential nonhuman predators occur at Assirik, including lions ( Panthera leo ), leopards ( Panthera pardus ), spotted hyenas ( Crocuta crocuta ), African wild dogs ( Lycaon pictus ), Nile crocodiles ( Crocodylus niloticus ), and rock pythons ( Python sebae ). We documented one suspected case of a predator killing a chimpanzee. Nocturnality comprised 12.7% of CT events for chimpanzees and these events were more concentrated at twilight. Chimpanzees were more active during the day, predators were more active at night, and there was substantial temporal overlap among chimpanzees and potential predators during twilight intervals. Our findings support the hypothesis that savanna chimpanzees in Senegal are active at night in response to the extremely hot environment. We hypothesize that Assirik chimpanzees experience a tension between decreasing hyperthermia and increasing predation risk during nocturnality.
Background:
Ixodes ricinus (Linnaeus 1758) vectors several important diseases in Europe, and the nymphal abundance in an area is an important factor determining tick bite risk. While interactions between abiotic, habitat, and vertebrate host factors and this tick species are generally well understood in continental Europe, this is not the case in Ireland, a highly fragmented and vertebrate depauperate region of Europe. This study examines the abiotic, habitat and host factors predicting nymphal abundance in such a setting. Our findings may provide insights for possible future changes in I. ricinus vector ecology on continental Europe given current predictions of future vertebrate diversity loss.
Methods:
15 woodland sites in Ireland were surveyed over three years (2020-2022) wherein abiotic and habitat factors were determined and tick abundance recorded. Concurrently, mammal and birdsong activity data were collected for each site across multiple visits. Generalised linear mixed models were used to identify the most important factors predicting I. ricinus abundance.
Results:
Nymphal I. ricinus abundance was driven by seasonality, with peak abundance occurring in April. Abiotic and habitat factors featured less than expected in models predicting nymphal abundance, but mean minimum winter temperature was found to have an inverse predictive relationship with adult tick abundance. While I. ricinus nymphs were significantly more abundant at sites where deer were present, at visit level, there was an inverse predictive relationship between deer activity events the week of a site visit and nymphal abundance. Modelling individual host species as predictors of nymphal abundance also identified increased mean robin birdsong events for the previous year to be a predictor of decreased nymphal abundance.
Conclusions:
Seasonality predicted nymphal tick abundance more robustly than any other abiotic variable. Seasonality was also the driving factor behind the relationships seen between deer activity and nymphal abundance. This highlights the importance of understanding the seasonal changes in dynamics between I. ricinus abundance and host activity, a less well-studied area. Furthermore, the identification of European robin as a predictor of nymphal abundance in woodland sites confirms the important relationship between passerine bird species and I. ricinus in Ireland.
A comprehensive manual for camera trapping wildlife populations for conservation. Includes technical details covering equipment, practical advice, survey types and data management and analysis.
Contemporary methods for sampling wildlife populations include the use of remotely triggered wildlife cameras (i.e., camera traps). Such methods often result in the collection of hundreds of thousands of photos that must be identified, archived, and transformed into data formats required for statistical analyses.
Cpw Photo Warehouse is a freely available software based in Microsoft Access ® that has been customized for this purpose using Visual Basic ® for Applications ( VBA ) code. Users navigate a series of point‐and‐click menu items that allow them to input information from camera deployments, automatically import photos (and image data stored within the photos) related to those deployments, and store data within a relational database. Images are seamlessly incorporated into the database windows, but are stored separately from the database.
The database includes menu options that (i) facilitate identification of species within the images, (ii) allow users to view and filter any subset of the databased on study area, species, season, etc., and (iii) produce input files for common analyses such as occupancy, abundance, density and activity patterns using Programs mark , presence , density and the r packages ‘secr’ and ‘overlap’.
Our database makes explicit use of multiple observers, which greatly enhances the efficiency and accuracy with which a large number of photos can be identified. Modular subsets of the data can be distributed to an unlimited number of observers on or off site for identification. Modules are then re‐incorporated into the database using a custom import function.
Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM out-performs other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and in the ocelot example, individual sex determined from photographs is used to further resolve partial identities, one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard 2 camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.
Camera traps set to monitor target species generate large amounts of bycatch data of non-target species, which are secondary to the study’s objectives. Bycatch data pooled from multiple studies can answer additional questions that were not the objective of the primary studies. Variation in field and data management techniques creates logistical and statistical problems when pooling data from multiple sources. Successful multi-collaborator projects use standardized field and data management methods, and combine their data to answer valuable broad-scale research questions (e.g. monitoring threatened species). Long term, multi-collaborator projects, however, are rare and limited in geographical scope. Hundreds of small, fixed-term independent camera trap studies operate in otherwise un-represented regions, often using field and data management methods tailored to their own study’s objectives. Inconsistent data management practices may lead to loss of bycatch data, or an inability to easily share it. To maximize the benefit of camera trapping, small studies should anticipate that their bycatch data will be useful to others in research of non-target species. During a range-wide assessment of sun bears Helarctus malayanus in Southeast Asia we documented a range of common data management problems encountered when processing data from multiple research groups. From our experiences, and from a review of the published literature and online resources, we generated nine key recommendations on data management best practices. Following these practices can further the usefulness of camera trap by-catch data, by improving the ease of sharing, enabling collaborations, and expanding the scope of research.
Vandalism and theft of camera traps is common, imposing financial and data losses on wildlife professionals. Like many ‘victims’, our response to a spate of thefts was to attempt to install camera traps at heights we suspected would reduce detection and interference by vandals. We sought to determine if placing camera traps above humans’ eye line, to reduce the likelihood of detection and theft by vandals, would compromise predator detection in road-based surveys. Our efforts to resolve this problem led us to discover the importance of placing camera traps at a height commensurate with the height of the animals being studied. Monitoring stations comprised of two camera traps, one at 0.9 m and another at 3 m above ground level, were established at regular intervals along trails during two survey periods. We also conducted a pilot trial to compare vertical (facing downwards) to horizontal (facing across) orientation of camera traps to detect medium-sized mammals. We compared images recorded by the pairs of camera to consider whether height made a significant difference to detections of predators. We found that cameras placed 3 m high and those facing downwards reduced the detection rate of all species compared to those at 0.9 m, so placing camera traps higher than normal significantly compromised our survey data. It is important to note that such data loss would not necessarily be apparent without a robust comparison between deployment strategies. Saving camera traps but concurrently sacrificing data quality is unlikely to be an acceptable outcome for many wildlife professionals. This study reports that placing camera traps too high will reduce the detection of animals and compromise the quality of the survey data. © 2016 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Motivation
Several spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.
Model and data analysis
We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.
Benefits
Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.
The challenges associated with monitoring low‐density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non‐invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools.
For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions.
Here, we used a large‐scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013–2015 to sample across a 70 096‐km ² region of western New York, USA . We fit occupancy and Royle–Nichols models to species detection–non‐detection data collected by cameras, and spatial capture–recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15‐km ² grid cells was modelled as a function of landscape attributes known to influence fisher distribution.
We found a close relationship between grid cell estimates of fisher state variables from the models using detection–non‐detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous‐mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection–non‐detection data.
Synthesis and applications . Our work provides empirical evidence that models using detection–non‐detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost‐effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non‐detection data will be affected by aspects of study design and species ecology.
Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling.
Snow tracking is often used to inventory carnivore communities, but species identification using this method can produce ambiguous and misleading results. DNA can be extracted from hair and scat samples collected from tracks made in snow. Using DNA analysis could allow positive track identification across a broad range of snow conditions, thus increasing survey accuracy and efficiency. We investigated the efficacy of DNA identification using hairs and scats collected during the winter along putative Canada lynx (Lynx canadensis) snow tracks and compared our findings to those obtained using hair-snaring techniques during the summer. We were able to positively identify 81% and 98% of the hair and scat samples, respectively, that were collected in or near snow tracks. Samples containing amplifiable lynx DNA were collected at rates of 1.2–1.3 per km of lynx tracks followed. These amplification rates and encounter frequencies validate the collection and use of DNA samples from snow tracks as a feasible technique for identifying Canada lynx and possibly other rare carnivores. We recommend that biologists include the collection of hairs and scats for DNA analysis as part of snow-tracking surveys whenever species identification is a high priority.
Recent work in the tropics has advanced our understanding of the local impacts of land-use change on species richness. However, we still have a limited ability to make predictions about species abundances, especially in heterogeneous landscapes. Species abundances directly affect the functioning of an ecosystem and its conservation value. We applied a hierarchical model to camera- and live-trapping data from a region in Borneo, and estimated the relative abundance (controlling for imperfect detection) of 57 terrestrial mammal species, as a function of either categorical or continuous metrics of land-use change. We found that mean relative abundance increased (by 28%) from old-growth to logged forest, but declined substantially (by 47%) in oil palm plantations compared to forest. Abundance responses to above-ground live tree biomass (a continuous measure of local logging intensity) were negative overall, whilst they were strongly positive for landscape forest cover. From old-growth to logged forest, small mammals increased in their relative abundance proportionately much more than large mammals (169% compared to 13%). Similarly, omnivores and insectivores increased more than other trophic guilds (carnivores, herbivores and frugivores). From forest to oil palm, species of high conservation concern fared especially poorly (declining by 84%). Invasive species relative abundance consistently increased along the gradient of land-use intensity. Changes in relative abundance across nine functional effects groups based on diet were minimal from old-growth to logged forest, but in oil palm only the vertebrate predation function was maintained. Our results show that, in the absence of hunting, even the most intensively logged forests can conserve the abundance and functional effects of mammals. Recent pledges made by companies to support the protection of High Carbon Stock logged forest could therefore yield substantial conservation benefits. Within oil palm, our results support the view that “wildlife-friendly” practices offer a low potential for reducing biodiversity impacts.