Imperfect detection biases extinction‐debt assessments

Abstract

Freshwater ecosystems have been substantially altered, threatening the survival and recovery of aquatic species at risk. Estimating the likelihood and magnitude of future extinctions (extinction debt; ED) is integral for conserving biodiversity and requires accurate species composition lists. Using species-area relationships, we estimated ED for fishes in historically disturbed wetlands in the Lake Erie basin. Then, we used simulated data sets to assess how ED varied when species lists used to derive species-area relationships had an increasing proportion of undetected species. When species lists were incomplete, ranging from 0.99 to 0.75, 15% fewer wetlands were estimated to have species in ED and, on average, 50% fewer species were expected to go extinct per wetland. Imperfect detection ultimately biased conservation prioritization among wetlands. Our findings suggest that if imperfect detection is not accounted for when projecting future extinctions, the severity of future species loss across a landscape, and the subsequent need for immediate restorative action, can be greatly underestimated.

Accounting for imperfect detection when estimating species‐area relationships and beta‐diversity

“…This highlights an important limitation in SAR research, potentially calling into question the accuracy of assessments of the impacts of habitat fragmentation on species richness. In empirical settings, a systematic negative bias in SAR z‐value estimates would result in underestimates of the number of local species extinctions that result from increasing habitat fragmentation, potentially leading to recommendations of minimum habitat patch sizes below those required for the persistence of species with large area requirements (Cam et al., 2002; Montgomery et al., 2021). Furthermore, although observed species counts were generally able to provide accurate pairwise Sørensen similarity estimates, this did not translate into accurate estimates of β‐diversity model coefficients, and such inaccuracies were only worsened by the application of iNEXT.3D.…”

“…( 2021 ) represents an artefact of estimation error (Type I Error), rather than underlying patterns of species richness (Gwinn et al., 2015 ). Further investigation is thus required to determine how sampling effort influences the estimation of biodiversity trends within fragmented landscapes, and the circumstances where statistical estimators may be able to reliably correct for deficiencies in sampling (Montgomery et al., 2021 ).…”

“…3D and MSOMs, tend to perform poorly when sample sizes are small and species detection probabilities are low (McNew & Handel, 2015;Tingley et al, 2020), and it is thus possible that the dramatic changes in SAR slopes observed by Palmeirim et al (2021) represents an artefact of estimation error (Type I Error), rather than underlying patterns of species richness (Gwinn et al, 2015). Further investigation is thus required to determine how sampling effort influences the estimation of biodiversity trends within fragmented landscapes, and the circumstances where statistical estimators may be able to reliably correct for deficiencies in sampling (Montgomery et al, 2021).…”