Date:
2018/06/14
Time:
10:15
Room:
K306 Anton
Density-dependent detectability in dynamic occupancy survey: a case study on a vulnerable beetle species in hollow trees
(Oral and Poster)
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Conservation of threaten species living in fragmented habitats crucially relies on evaluating their occupancy and their ability to colonize and persist in habitat patches. Fitting stochastic patch occupancy models (SPOMs) to occupancy data can help assessing these features. However it is critical to account for the limited detectability of target species in this type of analysis to avoid severe biases in estimation.
Detectability of a population in a habitat patch often tightly depends on the local density of individuals. This connection between density and detectability has rarely been used in SPOM analysis, even when abundance data are available. The two quantities are often considered independent and estimated separately. Here, we propose a framework for using density-dependent detectability in the analysis of a SPOM.
We illustrate our approach with the example of T. opacus, a beetle inhabiting hollows in old trees. We use a 6-year survey of adults abundances in an woodland pasture area harbouring a high density of old oaks, in Östergötland, south-east Sweden. T. opacus is classified as "vulnerable" on the Swedish red list.
We first modeled abundance data in occupied trees as a function of tree and environmental features. We used this model to predict density-dependent estimates of detectability in all the trees of our study site. Importantly, we could explore how the environmental features affect carrying capacity and detectability of trees, with the latter aspect being rarely explored in metapopulation studies. Secondly, we showed the good match between our density-dependent estimates of detectability and those obtained from repeated occupancy surveys (Pearson r = 0.54, p<2E-16). Thirdly, we found that omitting density-dependent detectability had little effect on metapopulation parameter estimation in our example (except lowering the goodness of fit to data), probably because we performed many repeated surveys in sites. Ultimately, accounting for density-dependent limited detectability did not eliminate unstructured colonization from our study system, which may indicate the contribution of the cryptic larval stage of T. opacus.
Our study thus shows that density-dependent framework allows for a simple integration of limited detectability into metapopulations studies, based on a more thorough use of abundance data than classic approaches. In particular, it may be applied even with no or a limited number of repeated surveys, although a statistical survey would be neceassry to assess this point. More generally our work also suggests that accounting for limited detectability is only the first step towards deriving reliable metapopulation model estimates for conservation planning.