Integrating variability in detection probabilities when designing wildlife surveys: a case study of amphibians from south-eastern Australia

Abstract

Occupancy-based monitoring programs rely on survey data to infer presence or absence of the target species. However, species may occupy a site and go undetected, leading to erroneous inference of absence ('false absence'). If detectability is influenced by the time of year or weather conditions, survey protocols can be adjusted to minimize the chance of false absences. In this study, detection probabilities for three amphibian species from south-eastern Australia were modelled using a Bayesian approach. For aural surveys, we compared basic models, which only included effects of survey date, duration and time of day on detection, to models including additional effects of weather. Model selection using deviance information criterion (DIC) suggested that the basic model was the most parsimonious for Crinia signifera, while models including relative humidity and water temperature were most supported for Limnodynastes dumerilii and L. tasmaniensis respectively. When predictive performance was assessed by cross validation, DIC results were largely matched for C. signifera and L. dumerilii, while models of detection for L. tasmaniensis were indistinguishable, AUC scores suggesting inadequate performance. We show how results such as these can be used to design surveys, developing protocols for individual surveys and estimating the number of surveys required under those protocols to achieve a threshold cumulative probability of detection. Conservation managers can use these models to maximize the efficiency of surveys. This will improve the accuracy of occupancy data, and reduce the risk of misdirected conservation actions resulting from false absences.

Interspecific variation in the phenology of advertisement calling in a temperate Australian frog community

“…Site selection was haphazard, including both randomized protocols (mostly for selecting pools along streams) and nonrandomized protocols that aimed to maximize geographic coverage while accounting for both access and logistical constraints (for further details, see Canessa et al. ; Canessa and Parris ; Heard et al. ).…”

“…After Canessa et al. (), we fitted up to fourth degree polynomial regressions between each meteorological variable and survey date using maximum likelihood in R version 3.0.3 (R Core Team ). The best‐fitting model for each meteorological variable was selected using Akaike's information criterion (Burnham and Anderson ), and the residuals from this model used as inputs for the logistic models.…”

“…; Hauselberger and Alford ; Canessa et al. ). Likewise, we are aware of only seven studies of the phenology of calling across an Australian frog community (Humphries ; Gillespie ; Morrison ; Lemckert and Mahony ; Lemckert and Grigg ; Dostine et al.…”

Multi-Scale, Direct and Indirect Effects of the Urban Stream Syndrome on Amphibian Communities in Streams

“…To optimize the accuracy of species surveys, we estimated a priori the probability of detecting adult male frogs using standardized aural surveys (methods and data described in [33,34]). For the species we expected to occur in the study area ( Crinia signifera , Limnodynastesdumerilii , Limnodynastes peroni , Limnodynastes tasmaniensis , Litoriaewingi , Litoria peroni , Litoriaraniformis , Litoria verreauxi ), we estimated that three 30-minute nocturnal visits per site, between late September and early December, would ensure a probability of detecting most species, if present, of more than 90% (no reliable estimates could be produced or sourced for Litoria peroni and Limnodynastes peroni ).…”

Designing screening protocols for amphibian disease that account for imperfect and variable capture rates of individuals

“…Finally, we estimated the daily individual probability of recapture as date-specific across the sampling season. We used a mixed-effect logistic regression with uninformative priors (Canessa et al 2012):…”