Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise.
A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of ‘collapsed data’. A case study of owls and gliders in Victoria, Australia, is also illustrated.
Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications.
To avoid biased estimates of inter‐species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter‐dependencies and occupancy.
SummaryMultiple-pass (i.e. removal) sampling and mark-recapture experiments were undertaken in the Sydenham River (Ontario, Canada) to assess the effectiveness of seining to detect and estimate the local abundance of blackstripe topminnow, Fundulus notatus (Rafinesque, 1820) as well as to compare catch characteristics from closed and open (with and without block nets) sample units. Probability of species detection using three-pass seining was estimated to be 0.58 in closed units, and 0.51 in open units. To be 95% confident of occupancy status, a minimum of five repeat surveys is required. A decline in catch occurred in only half of the sample units, population size estimates were often imprecise, and attempts to generate mark-recapture population estimates were unsuccessful. Mean capture probabilities were 0.48 in closed units and 0.65 in open units, when depletion occurred. For blackstripe topminnow and other fishes encountered, there were no significant differences between closed and open units in the frequency of depletion or capture probability. Compared to single-pass surveys, monitoring programs that employ three seine hauls are more likely to detect the presence of the blackstripe topminnow and any decline in local abundance.
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