Best practices and software for themanagement and sharing of camera trap data for small and large scales studies

Camera traps typically generate large amounts of bycatch data of non-target species that are secondary to the study’s objectives. Bycatch data pooled from multiple studies can answer secondary research questions; however, variation in field and data management techniques creates problems when pooling data from multiple sources. Multi-collaborator projects that use standardized methods to answer broad-scale research questions are rare and limited in geographical scope. Many small, fixed-term independent camera trap studies operate in poorly represented regions, often using field and data management methods tailored to their own objectives. Inconsistent data management practices lead to loss of bycatch data, or an inability to share it easily. As a case study to illustrate common problems that limit use of bycatch data, we discuss our experiences processing bycatch data obtained by multiple research groups during a range-wide assessment of sun bears Helarctos malayanus in Southeast Asia. We found that the most significant barrier to using bycatch data for secondary research was the time required, by the owners of the data and by the secondary researchers (us), to retrieve, interpret and process data into a form suitable for secondary analyses. Furthermore, large quantities of data were lost due to incompleteness and ambiguities in data entry. From our experiences, and from a review of the published literature and online resources, we generated nine recommendations on data management best practices for field site metadata, camera trap deployment metadata, image classification data and derived data products. We cover simple techniques that can be employed without training, special software and Internet access, as well as options for more advanced users, including a review of data management software and platforms. From the range of solutions provided here, researchers can employ those that best suit their needs and capacity. Doing so will enhance the usefulness of their camera trap bycatch data by improving the ease of data sharing, enabling collaborations and expanding the scope of research

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

Introducing a unique animal ID and digital life history museum for wildlife metadata

Funding: C.R. acknowledges funding from the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82515R-20). G.B., R.K., S.C.D. and D.E.-S. acknowledge funding from NASA. A.S. and F.I. acknowledge support from the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions Individual Fellowships (grant no. 101027534 and no. 101107666, respectively). S.C.D. acknowledges funding from NASA Ecological Forecasting Program Grant 80NSSC21K1182. A.M.M.S. was supported by an ARC DP DP210103091. This project is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF10539 to M.W., as well as the Academy for the Protection of Zoo Animals and Wildlife e.V., Germany.1. Over the past five decades, a large number of wild animals have been individually identified by various observation systems and/or temporary tracking methods, providing unparalleled insights into their lives over both time and space. However, so far there is no comprehensive record of uniquely individually identified animals nor where their data and metadata are stored, for example photos, physiological and genetic samples, disease screens, information on social relationships. 2. Databases currently do not offer unique identifiers for living, individual wild animals, similar to the permanent ID labelling for deceased museum specimens. 3. To address this problem, we introduce two new concepts: (1) a globally unique animal ID (UAID) available to define uniquely and individually identified animals archived in any database, including metadata archived at the time of publication; and (2) the digital ‘home’ for UAIDs, the Movebank Life History Museum (MoMu), storing and linking metadata, media, communications and other files associated with animals individually identified in the wild. MoMu will ensure that metadata are available for future generations, allowing permanent linkages to information in other databases. 4. MoMu allows researchers to collect and store photos, behavioural records, genome data and/or resightings of UAIDed animals, encompassing information not easily included in structured datasets supported by existing databases. Metadata is uploaded through the Animal Tracker app, the MoMu website, by email from registered users or through an Application Programming Interface (API) from any database. Initially, records can be stored in a temporary folder similar to a field drawer, as naturalists routinely do. Later, researchers and specialists can curate these materials for individual animals, manage the secure sharing of sensitive information and, where appropriate, publish individual life histories with DOIs. The storage of such synthesized lifetime stories of wild animals under a UAID (unique identifier or ‘animal passport’) will support basic science, conservation efforts and public participation.Peer reviewe

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Addressing challenges in camera-trap studies: Survey designs for multiple species, serial dependence, and site-to-site variability when estimating activity patterns

University of Minnesota Ph.D. dissertation. July 2020. Major: Wildlife Conservation. Advisors: John Fieberg, Todd Arnold. 1 computer file (PDF); xvii, 103 pages.Camera traps are widely used to collect information on the behavior, abundance, and occurrence of wild animals living in all parts of the world. Their low cost and ease of use makes it possible to collect information on many species simultaneously over large areas and long periods of time (Burton et al. 2015). These devices record the presence and activity of the animals that travel in front of their sensors. Most modern cameras rely on passive infrared sensors that are automatically triggered when a change in temperature is detected (Welbourne et al. 2016). They also have very low energy requirements and can be active for weeks without battery replacement, recording thousands of images along with the date and time each picture was taken. These characteristics allow users to continuously survey an area for long periods of time with minimal or no disturbance to wildlife, and make cameras extremely useful for surveying remote locations or studying animals that are particularly wary of human presence (e.g., carnivores). Decrease in cost per unit, technological advancements (e.g., switch from film to digital format, increase in trigger speed and storage capability), and development of statistical methods for estimating (relative) abundance and density have resulted in an exponential increase in the number of camera trap studies, especially those targeting secretive and elusive terrestrial vertebrates (Rovero and Zimmermann 2016). Additionally, the educational and outreach potential of the images collected, paired with the ease of use of camera devices, have led to the development of citizen science programs aimed at engaging the public in image processing and data collection (e.g., Snapshot Safari: snapshotsafari.org; eMammals: https://emammal.si.edu/). Camera traps were initially used for species checklists (Tobler et al. 2008) and for estimating population density for species in which individuals could be individually identified (Karanth and Nichols 1998), but they are now routinely used to quantify relative abundance (Rovero and Marshall 2009), occupancy (Pettorelli et al. 2010, Rich et al. 2016, Scotson et al. 2017), and density (Sun et al. 2017), to study animal behavior (Caravaggi et al. 2017) and diel activity patterns (Wang et al. 2015, Frey et al. 2017, 2020), and to investigate species richness and community composition at a global scale (Ahumada et al. 2011). Despite their widespread use, the field of camera trapping is still young and evolving, and several questions related to the use of this technology require further investigation (Meek et al. 2015). In this dissertation, I focus on two aspects related to survey design and analysis of camera-trap data: differential response across species to survey design strategies in studies aimed at simultaneously collecting data on multiple species (chapter 1), and statistical approaches to minimize or account for correlation when analyzing camera-trap data (chapter 2 and 3). In Chapter 1, I focus on differential responses of North-American carnivores to survey-design strategies often used in studies that target multiple species (i.e., multi-species studies). Cameras allow investigators to collect massive amounts of fine-resolution information on a large number of species simultaneously and have been described as an ideal, cost-effective tool for large-scale multi-species monitoring programs (Steenweg et al. 2017, Ahumada et al. 2019). Nevertheless, researchers have only recently started to quantify consequences of species-specific responses to survey-design strategies when the aim of the study goes beyond estimating species richness (Pease et al. 2016, Evans et al. 2019, Mills et al. 2019, Buyaskas et al. 2020, Holinda et al. 2020). Species’ characteristics, such as movement patterns, behavior and home range size affect their response to survey design; hence, in multi-species studies, a survey design that is appropriate for one species, might not work for another. This chapter was motivated by the Minnesota Department of Natural Resources’ (MN DNR) need for an approach capable of simultaneously monitoring multiple species of carnivores for informing conservation and management decisions. In collaboration with MN DNR biologists, I carried out a multi-year camera-trap study to assess species-specific responses to different survey strategies for 10 species of carnivores. I evaluated responses to two different survey-design frameworks (random- versus road-based), two different lure types (salmon versus fatty acid scent oil), two different placement strategies (completely random versus randomly-selected sites with feature-based placement), and survey timing (spring versus fall); I also assessed temporal trends in daily encounter probabilities within each season. I collected data at 100 locations in northern Minnesota during each of five 6-week long surveys between spring 2016 and spring 2018, accumulating more than 2 million pictures. Using Generalized Linear Mixed Models (GLMMs), I quantified species-specific responses to the different design strategies and found that differences were particularly strong for choice of survey-design framework and lure type. In Chapter 2, I illustrate how the lorelogram, a statistical tool for quantifying correlation in binary responses (Heagerty and Zeger 1998), can be used in ecological contexts to explore spatial and temporal correlation in binary data. Camera traps collect images near-continuously, resulting in data that are temporally correlated (Meek et al. 2014). A common strategy to minimize dependence among observations of a species collected at a certain site is data aggregation; images of a species are assumed to belong to the same encounter event whenever they occur at the same site and within some temporal threshold (e.g., 30 min apart). The temporal interval between subsequent pictures collected at the same site is often chosen arbitrarily (Burton et al. 2015) because methods to explore correlation in binary data (e.g., camera-trap detection/non-detection) are rare in the ecological literature. In this chapter, I show how the lorelogram, a statistical method commonly applied in the biomedical literature, can be used to describe correlation in camera-trap data and help with selecting appropriate time intervals to minimize serial correlation. Although the initial motivation behind this section of my dissertation was to provide a statistical tool for robustly selecting temporal intervals to minimize serial dependency in camera-trap data, the lorelogram can also be applied to other binary data exhibiting spatial or temporal correlation (e.g., distributional data). I demonstrate how the lorelogram can quantify spatial/temporal correlation using data from chapter 1 and data from the North-American Breeding Bird Survey (Pardieck et al. 2018). To facilitate the use of the lorelogram by analysts, I created an R-package that is freely available for download at https://github.com/FabiolaIannarilli/lorelogram. In Chapter 3, I illustrate how GLMMs can be used to describe diel activity patterns from camera-trap data. Understanding how individuals change their patterns of activity in response to the presence of competitors and natural and anthropogenic stressors is the goal of many ecological studies, and is important for predicting how species will adapt to future changes (e.g., climate change and land-use modifications). Camera-trap data collected at multiple sites are likely to be affected by site-to-site variability due to animals’ responses to differences in the biotic (e.g., presence of competitors; levels of human disturbance) and abiotic (e.g., proximity to roads; sampling treatments) characteristics of the sites sampled. Minimizing serial correlation by aggregating the data, as described in chapter 2, does not remove dependencies from repeated measures or account for site-to-site variability in frequency of site-use or in the timing of peak activity levels. Kernel Density Estimators (KDEs) are the most used approach to describe diel activity patterns from camera-trap data; circular KDEs, in particular, can be used to account for periodicities in diel activity patterns. This method, however, assumes independence among observations and, thus, ignores site-to-site variability common in camera-trap studies. In contrast, the GLMM approaches presented in this chapter can account for several types of variability (e.g., in frequency of site-use and correlation among repeated measures) using covariates and random effects; they also provide measures of the level of activity at sites characterized by different conditions. Like KDEs, the GLMM approach can accommodate the periodic nature of activity-pattern data, and can test hypotheses about changes in diel activity patterns due to biotic and abiotic factors, but they do so in a more direct way than KDEs (e.g., using AIC or Likelihood Ratio Tests). Using a simulation study, I compare the accuracy of GLMMs and KDEs when estimating activity patterns from camera-trap data. Using data from chapter 1, I also provide examples of how GLMMs can be used to explore diel activity patterns under different conditions (e.g., sampling treatments, seasons, or presence of competitors). Throughout the rest of the dissertation, I will use the first-person plural voice, ‘we’, to reflect the collaborative nature of the work presented. The second chapter of this dissertation is published in Methods in Ecology and Evolution (Iannarilli et al. 2019) and the material in chapter 1 is currently under review for publication in Wildlife Biology

Livestock activity shifts large herbivore temporal distributions to their crepuscular edges

<p>Wildlife species are transitioning to greater crepuscular and nocturnal activity in response to high human densities. This plasticity in temporal niches may partially mitigate the impacts of human activity but may also result in underestimating human effects on species foraging, predator-prey relationships, and community level interactions. We deployed remote cameras to characterize shifts in herbivore diel activity in protected habitat vs pastoralist landscapes. We then compared species traits including body mass, dietary preferences, and behavioral characteristics as potential predictors of species sensitivity to livestock. Our data capture a significant temporal shift away from core cattle activity for nearly every herbivore species in our study, leading to more crepuscular activity patterns. As livestock were primarily diurnal and predators primarily nocturnal in pastoralist habitat, species that decreased their overlap with livestock were more likely to increase their overlap with potential predators. Other than species' typical daytime activity levels, we found no evidence that any particular trait significantly predicted temporal shifts in response to livestock. Instead, species generally trended toward greater activity levels at dawn, suggesting that cattle have a homogenizing effect on community-wide activity patterns. Our findings highlight how cohabitation with livestock can profoundly alter the temporal niches of wild herbivores. Shifts in diel activity patterns may reduce herbivore foraging time or efficiency and potentially have cascading shifts on predator-prey dynamics. Given that species traits could not predict responses to livestock, our analysis suggests that conservation strategies should consider each species separately when designing interventions for wildlife management.</p><p>Funding provided by: Carol and Wayne Pletcher Fellowship*<br>Crossref Funder Registry ID: <br>Award Number: </p><p>Funding provided by: Dayton Bell Museum Fellowship*<br>Crossref Funder Registry ID: <br>Award Number: </p><p>Funding provided by: Charles P. Sigerfoos Graduate Fellowship*<br>Crossref Funder Registry ID: <br>Award Number: </p><p><strong>Camera-Trap Data Collection</strong></p> <p>This study was a comparative assessment of species activity patterns in the presence (Mara conservancies) and absence (Serengeti National Park) of cattle. Species activity patterns were inferred from camera image data. Serengeti camera data was previously published on dataDryad (doi: 10.1038/sdata.2015.26). For Mara conservancy data, we deployed unpaired, unbaited remote cameras along systematic grids in accrodance with Snapshot Serengeti methodology. In the conservancies, a total of 63 Cuddeback cameras (Models 1347 and 1279; Cuddeback Inc., Green Bay, WI, USA) were spaced at ~1.15 km intervals. Enonkishu housed 21 cameras from Nov. 2018 to 2022, and the grid was expanded into Ol Choro and Lemek conservancies in Aug. 2021. For optimal species detection and camera efficiency, cameras captured 3 images per trigger during the day and 1 image per trigger at night. Each trigger was considered an individual "capture event", remaining dormant for 1-minute in between captures. To insure temporal independence. capture events were filtered to remove repeats of the same species at the same camera within 30-minute intervals.</p&gt

Data, R Code, and Output Supporting: Evaluating species-specific responses to camera-trap survey designs

These files contain data, R code and associated output supporting results presented in "Iannarilli, F., Erb, J., Arnold, T. W., and Fieberg, J. R. (2020). Evaluating species-specific responses to camera-trap survey designs. Wildlife Biology". In this paper, we assess species-specific responses by ten medium-to-large North-American carnivores to different survey design strategies commonly applied in camera-trap studies. Data were collected in northern Minnesota, USA, between 2016 and 2018 (23 337 active trap-days). We compared responses to: 1) two different survey-design frameworks (random- versus road-based), 2) two different lure types (salmon oil versus fatty acid scent oil), 3) two different placement strategies (completely random versus randomly-selected sites with feature-based placement), 4) survey timing (spring versus fall) and 5) temporal trends in daily encounter probabilities. Our results show that even species morphologically and taxonomically similar respond differently to survey-design strategies, and, thus, species-specific responses to design choices should be carefully considered in camera trap studies focused on multiple species.Minnesota Department of Natural ResourcesWildlife Restoration Program (Pittman- Robertson