Detection biases yield misleading patterns of species persistence and colonization in fragmented landscapes

Abstract

Abstract. Species occurrence patterns, and related processes of persistence, colonization and turnover, are increasingly being used to infer habitat suitability, predict species distributions, and measure biodiversity potential. The majority of these studies do not account for observational error in their analyses despite growing evidence suggesting that the sampling process can significantly influence species detection and subsequently, estimates of occurrence. We examined the potential biases of species occurrence patterns that can result from differences in detectability across species and habitat types using hierarchical multispecies occupancy models applied to a tropical bird community in an agricultural fragmented landscape. Our results suggest that detection varies widely among species and habitat types. Not incorporating detectability severely biased occupancy dynamics for many species by overestimating turnover rates, producing misleading patterns of persistence and colonization of agricultural habitats, and misclassifying species into ecological categories (i.e., forest specialists and generalists). This is of serious concern, given that most research on the ability of agricultural lands to maintain current levels of biodiversity by and large does not correct for differences in detectability. We strongly urge researchers to apply an inferential framework which explicitly account for differences in detectability to fully characterize species-habitat relationships, correctly guide biodiversity conservation in human-modified landscapes, and generate more accurate predictions of species responses to future changes in environmental conditions.

Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities

“…Disregarding imperfect detection can lead to biased inference and predictions about range dynamics. For instance, false absences can induce negative bias in the estimators of occupancy and survival (1 – extinction) probabilities, and the slope of covariate relationships (Kéry , Kéry et al , MacKenzie et al p. 41, Ruiz‐Gutiérrez and Zipkin ). Kéry et al () show maps of discrepancies between predictions of extinction and colonization obtained assuming perfect detection and those obtained accounting for false absence records in an analysis of crossbill data in Switzerland.…”

Vertebrates are poor umbrellas for invertebrates: cross‐taxon congruence in an Australian tropical savanna

“…However, another factor contributing to low congruence may be insufficient sampling intensity, leading to data that may not adequately represent the biological communities of interest (see de Solla et al 2005). Failure to account for imperfect detectability can bias commonly used metrics of occurrence and richness and hence contribute to inaccurate study conclusions and uncertainty in management and policy decisions (Driscoll 2010, Ruiz-Guti errez and Zipkin 2011, Kellner and Swihart 2014. Failure to account for imperfect detectability can bias commonly used metrics of occurrence and richness and hence contribute to inaccurate study conclusions and uncertainty in management and policy decisions (Driscoll 2010, Ruiz-Guti errez and Zipkin 2011, Kellner and Swihart 2014.…”

Differential matrix use by Neotropical birds based on species traits and landscape condition

“…Our findings are based on occurrence patterns that included detection probabilities that differed by matrix type. Had we not corrected for differential detectability, our comparisons of richness and occurrences between matrix types would have been biased and potentially produced spurious patterns (as also found by Ruiz‐Gutiérrez and Zipkin ). One caveat is that, unlike for agricultural matrices, we modeled the effects of bauxite and suburban areas on detection to be uniform across all species in each of these two matrices.…”