DOI: 10.5061/dryad.b8gtht7b5
title: Multi-species occupancy modeling provides novel insights into amphibian metacommunity structure and wetland restoration

description: A fundamental goal of community ecology is to understand species-habitat
 relationships and how they shape metacommunity structure. Recent advances
 in occupancy modeling enable habitat relationships to be assessed for both
 common and rare species within metacommunities using multi-species
 occupancy models (MSOM). These models account for imperfect species
 detection and offer considerable advantages over other analytical tools
 commonly used for community analyses under the elements of metacommunity
 structure (EMS) framework. Here, we demonstrate that MSOM can be used to
 infer habitat relationships and test metacommunity theory, using
 amphibians. Repeated frog surveys were undertaken at 55 wetland sites in
 eastern Australia. We detected 11 frog species from three families
 (Limnodynastidae, Myobatrachidae and Pelodryadidae). The rarest species
 was detected at only one site whereas the most common species was detected
 at 42 sites (naïve occupancy rate: 0.02 – 0.76). Two models were assessed
 representing two competing hypotheses; the best-supported model included
 the covariates distance to the nearest site (connectivity), wetland area,
 presence of the non-native eastern mosquitofish (Gambusia holbrooki),
 proportion cover of emergent vegetation, an interaction term between
 Gambusia and emergent vegetation cover, and the proportion canopy cover
 over a site. Hydroperiod played no detectable role in metacommunity
 structure. We found species-habitat relationships that fit with current
 metacommunity theory – occupancy increased with wetland area and
 connectivity. There was a strong negative relationship between occupancy
 and the presence of predatory Gambusia, and a positive interaction between
 Gambusia and emergent vegetation. The presence of canopy cover strongly
 increased occupancy for several tree frog species, highlighting the
 importance of terrestrial habitat for amphibian community structure. We
 demonstrated how responses by amphibians to environmental covariates at
 the species level can be linked to occupancy patterns at the metacommunity
 scale. Our results have clear management implications – wetland
 restoration projects for amphibians and likely other taxa should maximize
 wetland area and connectivity, establish partial canopy cover, and
 eradicate Gambusia or provide aquatic vegetation to mitigate the impact of
 this non-native fish. We strongly advocate the use of MSOM to elucidate
 the habitat drivers behind animal occupancy patterns and to derive
 unbiased occupancy estimates for monitoring programmes.
description: <strong>R code to run data through OpenBUGS using the
 package R2WinBUGS</strong><br> R code used to run hierarchical
 Bayesian species richness model from Hamer et al. "Multi-species
 occupancy modeling provides novel insights into amphibian metacommunity
 structure and wetland restoration".
 <strong>Model 1_MSOM_Hamer et al_Ecol
 Appl.r. </strong>Data file containing model code
 <strong>crisig.csv.</strong> Data file for species
 1 <strong>limper.csv.</strong> Data file
 for species 2 <strong>limtas.csv.</strong>
 Data file for species 3
 <strong>litaur.csv.</strong> Data file for species
 4 <strong>litcae.csv.</strong> Data file
 for species 5 <strong>litden.csv.</strong>
 Data file for species 6
 <strong>litfal.csv.</strong> Data file for species
 7 <strong>litjer.csv.</strong> Data file
 for species 8 <strong>litlat.csv.</strong>
 Data file for species 9
 <strong>litper.csv.</strong> Data file for species
 10 <strong>plaorn.csv.</strong> Data file
 for species 11 <strong>days.csv.</strong>
 Data file for detection covariate "days"
 <strong>days2.csv.</strong> Data file for detection
 covariate "days<sup>2"</sup>
 <strong>temp.csv.</strong> Data file for detection
 covariate "temperature"
 <strong>wet.csv.</strong> Data file for detection
 covariate "wetness"
 <strong>year.csv.</strong> Data file for detection
 covariate "survey year"
 <strong>Kooragang site covariates.csv.</strong> Data
 file for site covariates