A novel approach for estimating densities of secretive species from road-survey and spatial-movement data
John D. Willson A E , Shannon E. Pittman B , Jeffrey C. Beane C and Tracey D. Tuberville D
+ Author Affiliations
- Author Affiliations
A Department of Biological Sciences, University of Arkansas, Fayetteville, Arkansas, 72701, USA.
B Department of Biology, Davidson College, Davidson, North Carolina, 28035-7118, USA.
C North Carolina State Museum of Natural Sciences, Raleigh, North Carolina, 27601, USA.
D University of Georgia’s Savannah River Ecology Laboratory, Aiken, South Carolina, 29802, USA.
E Corresponding author. Email: jwillson@uark.edu
Wildlife Research 45(5) 446-456 https://doi.org/10.1071/WR16175
Submitted: 21 September 2016 Accepted: 18 May 2018 Published: 29 August 2018
Abstract
Context: Accurate estimates of population density are a critical component of effective wildlife conservation and management. However, many snake species are so secretive that their density cannot be determined using traditional methods such as capture–mark–recapture. Thus, the status of most terrestrial snake populations remains completely unknown.
Aim: We developed a novel simulation-based technique for estimating density of secretive snakes that combined behavioural observations of snake road-crossing behaviour (crossing speed), effort-corrected road-survey data, and simulations of spatial movement patterns derived from radio-telemetry, without relying on mark–recapture.
Methods: We used radio-telemetry data to parameterise individual-based movement models that estimate the frequency with which individual snakes cross roads and used information on survey vehicle speed and snake crossing speed to determine the probability of detecting a snake, given that it crosses the road transect during a survey. Snake encounter frequencies during systematic road surveys were then interpreted in light of detection probabilities and simulation model results to estimate snake densities and to assess various factors likely to affect abundance estimates. We demonstrated the broad applicability of this approach through a case study of the imperiled southern hognose snake (Heterodon simus) in the North Carolina (USA) Sandhills.
Key results: We estimated that H. simus occurs at average densities of 0.17 ha–1 in the North Carolina Sandhills and explored the sensitivity of this estimate to assumptions and variation in model parameters.
Conclusions: Our novel method allowed us to generate the first abundance estimates for H. simus. We found that H. simus exists at low densities relative to congeners and other mid-sized snake species, raising concern that this species may not only have declined in geographic range, but may also occur at low densities or be declining in their strongholds, such as the North Carolina Sandhills.
Implications: We present a framework for estimating density of species that have traditionally been considered too secretive to study at the population level. This method will greatly enhance our ability to study and manage a wide variety of snake species and could be applied to other secretive wildlife species that are most frequently encountered during road surveys.
Additional keywords: abundance estimation, behaviour, Heterodon simus, method, radio-telemetry, southern hognose snake.
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