Population Size Influences Amphibian Detection Probability: Implications for Biodiversity Monitoring Programs

Abstract

Monitoring is an integral part of species conservation. Monitoring programs must take imperfect detection of species into account in order to be reliable. Theory suggests that detection probability may be determined by population size but this relationship has not yet been assessed empirically. Population size is particularly important because it may induce heterogeneity in detection probability and thereby cause bias in estimates of biodiversity. We used a site occupancy model to analyse data from a volunteer-based amphibian monitoring program to assess how well different variables explain variation in detection probability. An index to population size best explained detection probabilities for four out of six species (to avoid circular reasoning, we used the count of individuals at a previous site visit as an index to current population size). The relationship between the population index and detection probability was positive. Commonly used weather variables best explained detection probabilities for two out of six species. Estimates of site occupancy probabilities differed depending on whether the population index was or was not used to model detection probability. The relationship between the population index and detectability has implications for the design of monitoring and species conservation. Most importantly, because many small populations are likely to be overlooked, monitoring programs should be designed in such a way that small populations are not overlooked. The results also imply that methods cannot be standardized in such a way that detection probabilities are constant. As we have shown here, one can easily account for variation in population size in the analysis of data from long-term monitoring programs by using counts of individuals from surveys at the same site in previous years. Accounting for variation in population size is important because it can affect the results of long-term monitoring programs and ultimately the conservation of imperiled species.

Multi-event models reveal the absence of interaction between an invasive frog and a native endangered amphibian

“…This relationship between detection and pond area may be due to a link between pond area and the abundance of each species. This conclusion would be in accordance with previous studies that have shown that abundance is one of the most determinant factors in detection probability (Mackenzie et al 2002;Royle and Nichols 2003), especially in amphibians (Dodd and Dorazio 2004;Tanadini and Schmidt 2011). Secondly, this approach revealed that the presence of PR negatively influenced BV detection, whereas the occurrence of BV had a positive effect on PR detection.…”

Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends

“…We believe that occasional detections of these dispersing individuals is the cause of our unexpectedly-low detection probabilities. The consequences of false-positive errors are most extreme for rare species (Miller et al, 2011), which may also be hard to detect due to low abundances (McCarthy et al, 2013;Tanadini & Schmidt, 2011). The usual approach "pond = population" may be wrong because within-season movements between sites appear to be common in amphibians (Kopecky, Vojar, & Denoel, 2010;Petranka et al, 2004;Schmidt, 2005;Tournier, Besnard, Tournier, & Cayuela, 2017).…”

“…When surveyed, each site was visited on three occasions during predefined time windows coinciding with the amphibian breeding season. Further information on the survey protocol can be found in Tanadini and Schmidt (2011) and Roth, Bühler, and Amrhein (2016). Volunteers were asked to record complete checklists; we therefore were able to infer non-observation of species not explicitly reported during a visit (Kéry, Gardner, & Monnerat, 2010).…”

Monitoring Strategy for Eight Amphibian Species in French Guiana, South America

“…The design recommendations are based on the probability of detection and occupancy rates estimated in this pilot study and should therefore be re-evaluated as these change. Abundance-induced heterogeneity in detection probability can induce bias in the occupancy estimator [37]. If such heterogeneity is suspected, a model that accounts for it would be more appropriate.…”