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Environmental DNA (eDNA) surveys have become a popular tool for assessing the distribution of species. However, it is known that false positive and false negative observation error can occur at both stages of eDNA surveys, namely the field sampling stage and laboratory analysis stage. We present an RShiny app that implements the Griffin et al. (2020) statistical method, which accounts for false positive and false negative errors in both stages of eDNA surveys that target single species using quantitative PCR methods. Following Griffin et al. (2020), we employ a Bayesian approach and perform efficient Bayesian variable selection to identify important predictors for the probability of species presence as well as the probabilities of observation error at either stage. We demonstrate the RShiny app using a data set on great crested newts collected by Natural England in 2018, and we identify water quality, pond area, fish presence, macrophyte cover and frequency of drying as important predictors for species presence at a site. The state‐of‐the‐art statistical method that we have implemented is the only one that has specifically been developed for the purposes of modelling false negative and false positive observation error in eDNA data. Our RShiny app is user‐friendly, requires no prior knowledge of R and fits the models very efficiently. Therefore, it should be part of the tool‐kit of any researcher or practitioner who is collecting or analysing eDNA data.

ContextTropical forest loss and fragmentation and the associated loss in species diversity are increasing in both magnitude and scope. Much attention has been paid to how attributes of forest fragments, such as area and forest structure, impact the diversity and functional composition of vertebrate communities, while more recent work has begun to consider the importance of landscape-level variables, such as surrounding tree cover. Yet, the relative impacts of these factors on species diversity and functional composition remain unclear, particularly among under-studied taxonomic groups.ObjectivesWe quantified how species richness, community composition, and functional traits of terrestrial birds and mammals are associated with variation in fragment area, elevation, habitat structure and surrounding tree cover. Our goal was to determine the degree to which these diverse explanatory variables contribute to species diversity.MethodsWe used motion-activated camera traps to sample terrestrial birds and mammals in 22 forest fragments in northwestern Ecuador. We used a hierarchical multi-species occupancy model accounting for imperfect species detection to estimate species richness and species composition differences among fragments, weighted multiple regression and distance matrix regression to assess covariates of richness and composition, and an RLQ ordination to assess co-variation of environmental conditions and species traits.ResultsTerrestrial mammals and birds exhibited similar relationships to key environmental variables, but also showed guild-specific differences. Elevation was significantly associated with differences in species richness and community composition for both groups. Forest cover in the surrounding matrix was associated with higher species richness and changes in community composition in mammals, but not terrestrial birds. Canopy openness showed a positive association with mammalian species richness but no relationship with bird species richness. There was no association between density of large trees and richness for either group. We found no significant associations between environmental variation and functional composition among forest fragments.ConclusionsThis work highlights the general importance of elevation and forest cover in shaping patterns of species diversity and composition in forest fragments and suggests heightened sensitivity to matrix conditions in mammals relative to terrestrial birds.