Dataset Information

Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry.

ABSTRACT: Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and generalized linear mixed-effects models to relate camera trap probabilities for marked animals to independent space use from telemetry relocations using 2 years of data for fishers (Pekania pennanti) as a case study. We evaluated (1) camera trap efficacy by estimating how camera detection probabilities are related to nearby telemetry relocations and (2) whether home range utilization density estimated from telemetry data adequately predicts camera detection probabilities, which would indicate consistency of the two data types. The number of telemetry relocations within 250 and 500 m from camera traps predicted detection probability well. For the same number of relocations, females were more likely to be detected during the first year. During the second year, all fishers were more likely to be detected during the fall/winter season. Models predicting camera detection probability and photo counts solely from telemetry utilization density had the best or nearly best Akaike Information Criterion (AIC), suggesting that telemetry and camera traps provide consistent information on space use. Given the same utilization density, males were more likely to be photo-captured due to larger home ranges and higher movement rates. Although methods that combine data types (spatially explicit capture-recapture) make simple assumptions about home range shapes, it is reasonable to conclude that in our case, camera trap data do reflect space use in a manner consistent with telemetry data. However, differences between the 2 years of data suggest that camera efficacy is not fully consistent across ecological conditions and make the case for integrating other sources of space-use data.

SUBMITTER: Popescu VD

PROVIDER: S-EPMC3997311 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry.

Popescu Viorel D VD Valpine Perry P Sweitzer Rick A RA

Ecology and evolution 20140224 7

Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and generalized linear mixed-effects models to relate camera trap probabilities for marked animals to independent space use from telemetry relocations ...[more]

PMID: 24772272

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Project description:Network analysis of infectious disease in wildlife can reveal traits or individuals critical to pathogen transmission and help inform disease management strategies. However, estimates of contact between animals are notoriously difficult to acquire. Researchers commonly use telemetry technologies to identify animal associations; but such data may have different sampling intervals and often captures a small subset of the population. The objectives of this study were to outline best practices for telemetry sampling in network studies of infectious disease by determining (1) the consequences of telemetry sampling on our ability to estimate network structure, (2) whether contact networks can be approximated using purely spatial contact definitions, and (3) how wildlife spatial configurations may influence telemetry sampling requirements.We simulated individual movement trajectories for wildlife populations using a home range-like movement model, creating full location datasets and corresponding "complete" networks. To mimic telemetry data, we created "sample" networks by subsampling the population (10-100% of individuals) with a range of sampling intervals (every minute to every three days). We varied the definition of contact for sample networks, using either spatiotemporal or spatial overlap, and varied the spatial configuration of populations (random, lattice, or clustered). To compare complete and sample networks, we calculated seven network metrics important for disease transmission and assessed mean ranked correlation coefficients and percent error between complete and sample network metrics.Telemetry sampling severely reduced our ability to calculate global node-level network metrics, but had less impact on local and network-level metrics. Even so, in populations with infrequent associations, high intensity telemetry sampling may still be necessary. Defining contact in terms of spatial overlap generally resulted in overly connected networks, but in some instances, could compensate for otherwise coarse telemetry data.By synthesizing movement and disease ecology with computational approaches, we characterized trade-offs important for using wildlife telemetry data beyond ecological studies of individual movement, and found that careful use of telemetry data has the potential to inform network models. Thus, with informed application of telemetry data, we can make significant advances in leveraging its use for a better understanding and management of wildlife infectious disease.

Project description:Artificial prey techniques-wherein synthetic replicas of real organisms are placed in natural habitats-are widely used to study predation in the field. We investigated the extent to which videography could provide additional information to such studies. As a part of studies on aposematism and mimicry of coral snakes (Micrurus) and their mimics, observational data from 109 artificial snake prey were collected from video-recording camera traps in three locations in the Americas (terra firme forest, Tiputini Biodiversity Station, Ecuador; premontane wet forest, Nahá Reserve, Mexico; longleaf pine forest, Southeastern Coastal Plain, North Carolina, USA). During 1,536 camera days, a total of 268 observations of 20 putative snake predator species were recorded in the vicinity of artificial prey. Predators were observed to detect artificial prey 52 times, but only 21 attacks were recorded. Mammals were the most commonly recorded group of predators near replicas (243) and were responsible for most detections (48) and attacks (20). There was no difference between avian or mammalian predators in their probability of detecting replicas nor in their probability of attacking replicas after detecting them. Bite and beak marks left on clay replicas registered a higher ratio of avian:mammalian attacks than videos registered. Approximately 61.5% of artificial prey monitored with cameras remained undetected by predators throughout the duration of the experiments. Observational data collected from videos could provide more robust inferences on the relative fitness of different prey phenotypes, predator behavior, and the relative contribution of different predator species to selection on prey. However, we estimate that the level of predator activity necessary for the benefit of additional information that videos provide to be worth their financial costs is achieved in fewer than 20% of published artificial prey studies. Although we suggest future predation studies employing artificial prey to consider using videography as a tool to inspire new, more focused inquiry, the investment in camera traps is unlikely to be worth the expense for most artificial prey studies until the cost:benefit ratio decreases.

Dataset Information

Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry.

Publications

Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry.

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