A simple framework for maximizing camera trap detections using experimental trials.

Camera trap data are biased when an animal passes through a camera's field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship between a sensor array and an animal. Here, we describe a general, experimental approach to evaluate detection errors that arise from the interaction between cameras and animals. We adapted distance sampling models and estimated the combined effects of distance, camera model, lens height, and vertical angle on the probability of detecting three different body sizes representing mammals that inhabit temperate, boreal, and arctic ecosystems. Detection probabilities were best explained by a half-normal-logistic mixture and were influenced by all experimental covariates. Detection monotonically declined when proxies were ≥6 m from the camera; however, models show that body size and camera model mediated the effect of distance on detection. Although not a focus of our study, we found that unmodeled heterogeneity arising from solar position has the potential to bias inferences where animal movements vary over time. Understanding heterogeneous detection probabilities is valuable when designing and analyzing camera trap studies. We provide a general experimental and analytical framework that ecologists, citizen scientists, and others can use and adapt to optimize camera protocols for various wildlife species and communities. Applying our framework can help ecologists assess trade-offs that arise from interactions among distance, cameras, and body sizes before committing resources to field data collection.

Estimating the intensity of use by interacting predators and prey using camera traps.

Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white-tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200-m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three-year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator-prey interactions. Our approach is easily applied to other continuous time point-based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes.

Nutritional state reveals complex consequences of risk in a wild predator-prey community.

Animal populations are regulated by the combined effects of top-down, bottom-up and abiotic processes. Ecologists have struggled to isolate these mechanisms because their effects on prey behaviour, nutrition, security and fitness are often interrelated. We monitored how forage, non-consumptive effects (NCEs), consumptive predation and climatic conditions influenced the demography and nutritional state of a wild prey population during predator recolonization. Combined measures of nutrition, survival and population growth reveal that predators imposed strong effects on the prey population through interacting non-consumptive and consumptive effects, and forage mechanisms. Predation was directly responsible for adult survival, while declining recruitment was attributed to predation risk-sensitive foraging, manifested in poor female nutrition and juvenile recruitment. Substituting nutritional state into the recruitment model through a shared term reveals that predation risk-sensitive foraging was nearly twice as influential as summer forage conditions. Our findings provide a novel, mechanistic insight into the complex means by which predators and forage conditions affect prey populations, and point to a need for more ecological studies that integrate behaviour, nutrition and demography. This line of inquiry can provide further insight into how NCEs interactively contribute to the dynamics of terrestrial prey populations; particularly, how predation risk-sensitive foraging has the potential to stabilize predator-prey coexistence.

Estimating plant abundance using inflated beta distributions: Applied learnings from a lichen-caribou ecosystem.

Quantifying abundance and distribution of plant species can be difficult because data are often inflated with zero values due to rarity or absence from many ecosystems. Terrestrial fruticose lichens (Cladonia and Cetraria spp.) occupy a narrow ecological niche and have been linked to the diets of declining caribou and reindeer populations (Rangifer tarandus) across their global distribution, and conditions related to their abundance and distribution are not well understood. We attempted to measure effects related to the occupancy and abundance of terrestrial fruticose lichens by sampling and simultaneously modeling two discrete conditions: absence and abundance. We sampled the proportion cover of terrestrial lichens at 438 vegetation plots, including 98 plots having zero lichens. A zero-inflated beta regression model was employed to simultaneously estimate both the absence and the proportion cover of terrestrial fruticose lichens using fine resolution satellite imagery and light detection and ranging (LiDAR) derived covariates. The probability of lichen absence significantly increased with shallower groundwater, taller vegetation, and increased Sphagnum moss cover. Vegetation productivity, Sphagnum moss cover, and seasonal changes in photosynthetic capacity were negatively related to the abundances of terrestrial lichens. Inflated beta regression reliably estimated the abundance of terrestrial lichens (R2 = .74) which was interpolated on a map at fine resolution across a caribou range to support ecological conservation and reclamation. Results demonstrate that sampling for and simultaneously estimating both occupancy and abundance offer a powerful approach to improve statistical estimation and expand ecological inference in an applied setting. Learnings are broadly applicable to studying species that are rare, occupy narrow niches, or where the response variable is a proportion value containing zero or one, which is typical of vegetation cover data.

Predators choose prey over prey habitats: evidence from a lynx-hare system.

Resource selection is grounded in the understanding that animals select resources based on fitness requirements. Despite uncertainty in how mechanisms relate to the landscape, resource selection studies often assume, but rarely demonstrate, a relationship between modeled variables and fitness mechanisms. Using Canada lynx (Lynx canadensis) and snowshoe hare (Lepus americanus) as a model system, we assess whether prey habitat is a viable surrogate for encounters between predators and prey. We simultaneously collected winter track data for lynx and hare in two study areas. We used information criteria to determine whether selection by lynx is best characterized by a hare resource selection probability function (RSPF) or by the amount of hare resource use. Results show that lynx selection is better explained by the amount of hare use (SIC = -21.9; Schwarz's Information Criterion) than by hare RSPF (SIC = -16.71), and that hare RSPF cannot be assumed to reveal the amount of resource use, a primary mechanism of predator selection. Our study reveals an obvious but important distinction between selection and use that is applicable to all resource selection studies. We recommend that resource selection studies be coupled with mechanistic data (e.g., metrics of diet, forage, fitness, or abundance) when investigating mechanisms of resource selection.