DOI: 10.5061/dryad.b8gtht7c1
title: Data from: Effectiveness of joint species distribution models in the presence of imperfect detection

description: 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.
description: We gratefully acknowledge the assistance of Dr. Matt White and
 the Arthur Rylah Institute, Department of Environment, Land, Water and
 Planning (Victoria) for providing their field data for the Victorian
 Central Highland Owl and Glider Study.   The species
 data was collected over 202 sites in the Central Highland area of
 Gippsland in Victoria, using call playback and spotlighting with a visual
 search along a 100m transect, by teams from the former Victorian
 Department of Environment, Land, Water and Planning (DEPI)
 \autocite{Lumsden2013}.  Surveys took place from March to August 2012 with
 two to three survey replications at each site. 
description: <strong>OwlGlider_SurveyData</strong>: 
 Site occupancy data. The archive contains the values of the
 covariates at the survey sites used in the analysis of the 4 species
 (Powerful Owls, Sooty Owls, Greater Gliders and Yelow bellied
 gliders.),  (so_occupancy_cov.csv and so_detection_cov.csv) and contains
 the results of the 2 to 3 surveys and observations for 4 species
 (so_species_data.csv).  All data is in csv format.
 SurveyDataProcessing: R code and
 instructions to extra data to format required for analysis as described in
 the associated paper.