DOI: 10.5061/dryad.vq83bk3vp
title: Data from: Improving inferences and predictions of species environmental responses with occupancy data

description: Occupancy models represent a useful tool to estimate species distribution
 throughout the landscape. Among them, MacKenzie et al.’s model (2002, MC),
 is frequently used to infer species environmental responses. However, the
 assumption that detection probability is homogeneous or fully explained by
 covariates may limit its performance. Species should be more easily
 observed at sites with a higher number of individuals. We simulated data
 following Royle and Nichols (2003) occupancy model (RN) that accounts for
 abundance-driven heterogeneous detection and two variants with
 overdispersion in the detection probability and local abundances. Then, we
 compared the performance of the MC model against that of RN.  In
 addition to model misspecifications, insufficient information in data
 (i.e. infrequent detections) can limit our ability to detect existing
 effects with affordable sampling designs. To deal with this source of
 error, we extended RN approach to a community-level joint species model
 (RN-JSM), where species responses and detectability depended on their
 traits and phylogeny. Then, we tested RN-JSM performance in simulated and
 out-of-sample field data. High abundance-driven heterogeneity in detection
 (i.e. common and secretive species) limited the ability of the MC model to
 quantify covariate effects; especially, when the number of visits was low.
 Both models (MC and RN), often failed to detect existing effects when data
 were overdispersed. Moreover, the RN model consistently lacked sufficient
 power when analyzing data from uncommon species (even when simulations and
 model specifications perfectly matched). This problem was solved by our
 RN-JSM, which yielded more precise and accurate estimates of species
 environmental responses. Increased accuracy in rare species held when the
 RN-JSM was tested with real and out-of-sample datasets. In the light of
 our results, we propose: (i) for common and secretive species analyze
 occupancy data with the RN model and prioritize revisiting sites; (ii) for
 species that may have overdispersed detectability or local abundances
 (e.g. with correlated behaviors or occurring in clusters), apply RN
 extensions that account for this extra variation (e.g. Poisson-beta or
 zero-inflated models). Finally, (iii) for uncommon species (mean
 abundances &lt; 1), whenever possible, gather data at the community
 level and apply joint-species modeling techniques.
description: Dataset is divided in three different sections: 
 1) Literature search of field studies using occupancy models to
 estimate species responses to covariate effects. We performed our search
 using web of knowledge database. For a full description please read
 Appendix A of the paper. 2)
 MC_RN_single_spp_Simulations.R -- Scripts for simulating data following a
 Royle-Nichols (2003) approach (RN) in which site-specific species
 detectabiltiy depends on local abundances and two variants with
 overdispersed local abundances and detectability of individuals. Scripts
 also contain fit of models (and error calculation) for Royle and Nichols
 (2003), McKenzie (2002) and a McKenzie variant in which species
 detectability is modeled as a function of local covariates. 
 RN_single_JSM_Simulations.R-- Scripts of simulation of multiple
 species RN model and model fit according to RN and JSM-RN model.
 3) Illustration of single-species Royle-Nichols (2003) and JSM-RN
 model performance with a dataset from bird communities in uruguay
 grasslands. A full description of data collection can be found in section
 3.2 of  material and methods section. In the case of RN
 we also performed simulations of a community followirng a joint-species
 distribution approach. 
description: READ_ME.txt contains an overview of all files contained in this
 dataset Description_Simulations.txt contains detailed
 information of scripts of comparisons of RN vs MC model performance as
 well as RN vs RN-JSM model performance in simulted datasets. 
 Uruguay_grassland_data.txt contains detailed information about
 files related to field data analyses.