Biology subjects

Royle, J.

Publications and source records attributed to Royle, J..

3 recordsLinked to original sources

bioRxiv2024

Experimental evaluation of Eastern box turtle (Terrapene carolina carolina) detectability in visual search surveys

Understanding how detection probability varies over time, space, or in response to measurable covariates is important to inform the monitoring and assessment of many species. A standard model to understand detectability - the availability/perception model - admits that detection probability is the composite of two components: availability and perception. Availability is largely affected by environmental and behavioral factors, whereas perception is primarily affected by attributes of individual observers and survey protocols, and thus can potentially be partially controlled by survey design. We designed and implemented a field study to understand the perception component of detection for Eastern box turtles (Terrapene carolina carolina) using visual encounter surveys. We obtained and deployed museum specimens of Eastern box turtle shells and subjected them to visual search surveys by observers in realistic field situations. Overall, about 50% of the box turtle shells were detected by observers including, 41.5% in the partially visible state and 63% in the fully visible state. There were significant differences among observers, which may be due to observer-specific variation in search technique -- the observers varied in how well they achieved the protocol guidance.

ecology↗

bioRxiv2020

Towards optimal sampling design for spatial capture-recapture

Spatial capture-recapture (SCR) has emerged as the industry standard for estimating population density by leveraging information from spatial locations of repeat encounters of individuals. The precision of density estimates depends fundamentally on the number and spatial configuration of traps. Despite this knowledge, existing sampling design recommendations are heuristic and their performance remains untested for most practical applications. To address this issue, we propose a genetic algorithm that minimizes any sensible, criteria-based objective function to produce near-optimal sampling designs. To motivate the idea of optimality, we compare the performance of designs optimized using three model-based criteria related to the probability of capture. We use simulation to show that these designs out-perform those based on existing recommendations in terms of bias, precision, and accuracy in the estimation of population size. Our approach allows conservation practitioners and researchers to generate customized and improved sampling designs for wildlife monitoring.

ecology↗

bioRxiv2020

Spatial Proximity Moderates Genotype Uncertainty in Genetic Tagging Studies

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower quality samples cannot be used, and they do not allow for errors in individual identification. We introduce the Genotype Spatial Partial Identity Model (SPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of samples were originally discarded because of uncertainty in individual identity. The Genotype SPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected 3 errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63% and 42%, respectively, using 3 PCRs per genetic sample. Further, the simulations showed that the Genotype SPIM model parameters are identifiable with only one PCR per sample, and that accuracy and precision are relatively insensitive to the number of PCRs for high quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high quality samples, but when using the Genotype SPIM, genotyping protocols could be more efficient by devoting more resources to low quality samples. SignificanceWe present a new statistical framework for the estimation of animal demographic parameters, such as abundance, density, and growth rate, from noninvasive genetic samples (e.g., hair, scat). By integrating a genetic classification model with a spatial population model, we show that accounting for spatial proximity of samples reduces genotype uncertainty and improves parameter estimation. Our method produces a fundamentally different approach to genetic capture-recapture by sharing information between the normally disjunct steps of assigning individual identities to genetic samples and modeling population processes. Further, it leads to more efficient protocols for processing genetic samples, which can lower project costs and expand opportunities for applying noninvasive genetics to conservation and management problems.

ecology↗