Rahel Sollmann

camtrapR: an R package for efficient camera trap data management

Summary Camera trapping is a widely applied method to study mammalian biodiversity and is still gaining popularity. It can quickly generate large amounts of data which need to be managed in an efficient and transparent way that links data acquisition with analytical tools. We describe the free and open-source R package camtrapR, a new toolbox for flexible and efficient management of data generated in camera trap-based wildlife studies. The package implements a complete workflow for processing camera trapping data. It assists in image organization, species and individual identification, data extraction from images, tabulation and visualization of results and export of data for subsequent analyses. There is no limitation to the number of images stored in this data management system; the system is portable and compatible across operating systems. The functions provide extensive automation to minimize data entry mistakes and, apart from species and individual identification, require minimal manual user input. Species and individual identification are performed outside the R environment, either via tags assigned in dedicated image management software or by moving images into species directories. Input for occupancy and (spatial) capture–recapture analyses for density and abundance estimation, for example in the R packages unmarked or secr, is computed in a flexible and reproducible manner. In addition, survey summary reports can be generated, spatial distributions of records can be plotted and exported to gis software, and single- and two-species activity patterns can be visualized. camtrapR allows for streamlined and flexible camera trap data management and should be most useful to researchers and practitioners who regularly handle large amounts of camera trapping data.

Ocelot ( Leopardus pardalis ) Density in Central Amazonia

Ocelots (Leopardus pardalis) are presumed to be the most abundant of the wild cats throughout their distribution range and to play an important role in the dynamics of sympatric small-felid populations. However, ocelot ecological information is limited, particularly for the Amazon. We conducted three camera-trap surveys during three consecutive dry seasons to estimate ocelot density in Amanã Reserve, Central Amazonia, Brazil. We implemented a spatial capture-recapture (SCR) model that shared detection parameters among surveys. A total effort of 7020 camera-trap days resulted in 93 independent ocelot records. The estimate of ocelot density in Amanã Reserve (24.84 ± SE 6.27 ocelots per 100 km2) was lower than at other sites in the Amazon and also lower than that expected from a correlation of density with latitude and rainfall. We also discuss the importance of using common parameters for survey scenarios with low recapture rates. This is the first density estimate for ocelots in the Brazilian Amazon, which is an important stronghold for the species.

Genetic sampling for estimating density of common species

Abstract Understanding population dynamics requires reliable estimates of population density, yet this basic information is often surprisingly difficult to obtain. With rare or difficult‐to‐capture species, genetic surveys from noninvasive collection of hair or scat has proved cost‐efficient for estimating densities. Here, we explored whether noninvasive genetic sampling (NGS) also offers promise for sampling a relatively common species, the snowshoe hare (Lepus americanus Erxleben, 1777), in comparison with traditional live trapping. We optimized a protocol for single‐session NGS sampling of hares. We compared spatial capture–recapture population estimates from live trapping to estimates derived from NGS, and assessed NGS costs. NGS provided population estimates similar to those derived from live trapping, but a higher density of sampling plots was required for NGS. The optimal NGS protocol for our study entailed deploying 160 sampling plots for 4 days and genotyping one pellet per plot. NGS laboratory costs ranged from approximately

Mesocarnivore activity patterns in the semiarid Caatinga: limited by the harsh environment or affected by interspecific interactions?

670 to

Fully Spatial Capture-Recapture Models

3000 USD per field site. While live trapping does not incur laboratory costs, its field costs can be considerably higher than for NGS, especially when study sites are difficult to access. We conclude that NGS can work for common species, but that it will require field and laboratory pilot testing to develop cost‐effective sampling protocols.

Diversity of small mammals in the Sierra Nevada: filtering by natural selection or by anthropogenic activities?

Activity patterns reflect adaptations to local biological and physical conditions. We estimated the activity patterns of 3 mesocarnivore species in a semiarid environment in northeastern Brazil: the ocelot (Leopardus pardalis), crab-eating fox (Cerdocyon thous), and oncilla (Leopardus tigrinus). We compared the overlap of daily activity among these species and to apex predators. We also estimated nighttime activity of these mesocarnivores during 2 years and compared activity peaks with those of apex predators and potential prey. All 3 mesocarnivores were nocturnal, with ocelots having only 1 record during daytime. Coefficients of overlap with larger predators were high (Δ1 > 0.7) for all pairwise comparisons, since all species were very nocturnal. Nighttime-only activity comparisons (Kolmogorov–Smirnov tests) showed that activity of oncillas differed from that of both larger mesocarnivores and jaguar activity, suggesting temporal segregation. Contrary to our expectations, rodent activity was dissimilar from that of ocelots and crab-eating foxes, but activity of rodents and oncillas was relatively synchronous. Activity of both cat species seems limited to the cooler nighttime, and nocturnal behavior of oncillas more likely reflects activity of potential prey rather than regulation by intraguild predators. Future studies in arid regions should consider climatic factors when estimating activity patterns.

2012: A Spatial Capture-Recapture Odyssey

In this chapter we investigate the basic spatial capture-recapture model, which we refer to as “model SCR0.” The model is a hierarchical model composed of two conditionally-related components: (1) a spatial point process describing the number and location of animal activity centers and (2) an observation model specifying capture or encounter probability as a function of the distance between individual activity centers and traps. As with all models, it includes several assumptions that must be understood and critically evaluated. We list the basic assumptions of SCR models and we provide some basic tools for simulating and analyzing spatial capture-recapture data in R, so that the assumptions and properties of the model can be fully appreciated. The chapter also focuses on practical issues such as how to format data and how to choose certain model settings. For inference, we focus on Bayesian methods using Markov chain Monte Carlo simulation and data augmentation. An example analysis is presented using wolverine data collected in southern Alaska, and this example demonstrates how to summarize posterior output for the purposes of producing posterior density maps and computing derived quantities such as the effective sample area.

Chapter 10 – Sampling Design

Both historical and contemporary factors may influence the structure and composition of biotas. Small mammal faunas in the Sierra Nevada of California, United States, are strongly dominated by generalist species; however, whereas 1 recent study argues that this is a product of recent anthropogenic influences, another provides a deeper evolutionary explanation based on historic fire frequencies. We summarize these patterns and proposed mechanisms, and we integrate data from 2 other studies—1 in the Sierra Nevada and 1 from an evolutionarily related mountain range in Baja California—to provisionally conclude that evolutionary adaptation, and possibly climatic warming in the Holocene, likely are the primary drivers of this faunal structure. However, we agree with work elsewhere in North America that recent anthropogenic filtering likely has amplified the effects of adaptation and climatic warming; one result of this is that the Sierra Nevada currently supports very limited areas of older (decadent) forests, and species dependent on these habitats may require special attention by resource managers.

Shifting up a gear with iDNA: from mammal detection events to standardized surveys

In this chapter, we briefly review the major themes that tie the book together, emphasizing the importance of space in both ecological and sampling processes. We discuss the future of spatial capture-recapture models in ecological research and suggest that they will increase in importance and largely replace non-spatial capture-recapture models. This prediction stems from the fact that, although we have demonstrated many potential uses for SCR models, many more await to be developed. For instance, we foresee models to describe and predict the consequences of individual interactions, such as competition and territoriality. Another exciting extension is to embed explicit movement models into SCR models for inference about movement behavior, and to improve the description of the observation process. SCR models are also lagging behind non-spatial models in some important ways. For instance, at the time of this writing, no one has devised methods for accomodating misidentification, and very few methods exist for combining different sources of data. Other important extensions that we briefly cover include models for gregarious species and models for single-catch traps. Each of these extensions should be achievable, and headway has already been made in some cases. We hope this book will inspire more rapid progress, and motivate new ideas and perspectives that will ultimately increase the usefulness of SCR models for addressing important problems in ecology and conservation.

State space and movement specification in open population spatial capture-recapture models

In the context of spatial sampling problems, where populations of mobile animals are sampled by an array of traps (or other sampling devices), there are a number of critical design elements. Two of the most important ones are the spacing and configuration of traps within the array. In this chapter we recommend a general framework for evaluating design choices for SCR studies, such as trap spacing, using Monte Carlo simulation of specific design scenarios—what we call scenario analysis. We evaluate trade-offs between trap spacing and the total area covered by traps (considerations that are related to available funding and logistics), including designs based on regular, clustered and rotating traps, and show that SCR models are flexible and perform well under a range of spatial design scenarios. While we recommend scenario analysis as a general tool to understand your expected data before carrying out a spatial capture-recapture study, it is possible to develop some heuristics and even analytic results related to the broader problem of model-based spatial design using an explicit objective function based on the inference objective. We outline an approach where we identify a variance criterion, namely, the variance of an estimator of N for the prescribed state-space. An ideal spatial design should minimize this variance. We show that this depends on the configuration of trap locations, and we provide a framework for optimizing the variance criterion over the design space (the collection of all possible designs of a given size).