The power of monitoring: optimizing survey designs to detect occupancy changes in a rare amphibian population

Abstract

Biodiversity conservation requires reliable species assessments and rigorously designed surveys. However, determining the survey effort required to reliably detect population change can be challenging for rare, cryptic and elusive species. We used a tropical bromeliad-dwelling frog as a model system to explore a cost-effective sampling design that optimizes the chances of detecting a population decline. Relatively few sampling visits were needed to estimate occupancy and detectability with good precision, and to detect a 30% change in occupancy with 80% power. Detectability was influenced by observer expertise, which therefore also had an effect on the sampling design – less experienced observers require more sampling visits to detect the species. Even when the sampling design provides precise parameter estimates, only moderate to large changes in occupancy will be detected with reliable power. Detecting a population change of 15% or less requires a large number of sites to be surveyed, which might be unachievable for range-restricted species occurring at relatively few sites. Unless there is high initial occupancy, rare and cryptic species will be particularly challenging when it comes to detecting small population changes. This may be a particular issue for long-term monitoring of amphibians which often display low detectability and wide natural fluctuations.

Spatially explicit power analysis for detecting occupancy trends for multiple species

“…), (2) the level of sampling effort required to detect an effect size with a desired degree of confidence (Barata et al. ), and (3) which sampling regime will likely have the highest chance at detecting a specified level of change (Sewell et al. ).…”

“…It is calculated by specifying the change of interest that one wishes to detect (known as the effect size), the acceptable type 1 error rate (false alarm rate), and the "natural" or background variation in the observed data, which is composed of stochastic environmental variation and observation error (counting error or detection error). Power analysis can inform (1) how likely it is that monitoring will detect important changes in a species distribution and/or abundance (Thorn et al 2011, Loos et al 2015, (2) the level of sampling effort required to detect an effect size with a desired degree of confidence (Barata et al 2017), and (3) which sampling regime will likely have the highest chance at detecting a specified level of change (Sewell et al 2012).…”

Power to detect trends in abundance within a distance sampling framework

“…A number of studies have provided insights and guidance on how to allocate sampling effort efficiently for monitoring programmes where occupancy is the focal population attribute (Barata, Griffiths, & Ridout, ; Field et al, ; Gálvez, Guillera‐Arroita, Morgan, & Davies, ; Guillera‐Arroita & Lahoz‐Monfort, ; MacKenzie & Royle, ; Sanderlin, Block, & Ganey, ). In settings where surveys yield only detection–nondetection data, occupancy provides a useful framework for monitoring changes in distributional attributes of animal populations.…”

Optimization of an ecological integrity monitoring program for protected areas: Case study for a network of national parks

“…The calculation was also done with a degree of significance of 0.1 in order to evaluate the impact of this parameter on the statistical power. These two values were tested as they are commonly used in survey design optimization studies [ 26 ],[ 29 ],[ 30 ]. The average and standard deviation values for each sampling site were calculated using the field data collected since the implementation of PSIE, in 2004.…”