DOI: 10.5068/d1v097
title: Multi-species occupancy models as robust estimators of community richness

description: 1. Understanding patterns of diversity is central to ecology and
 conservation, yet estimates of diversity are often biased by imperfect
 detection. In recent years, multi-species occupancy models (MSOM) have
 been developed as a statistical tool to account for species-specific
 heterogeneity in detection while estimating true measures of diversity.
 Although the power of these models has been tested in various ways, their
 ability to estimate gamma diversity – or true community size, N – is a
 largely unrecognized feature that needs rigorous evaluation. 2. We use
 both simulations and an empirical dataset to evaluate the bias, precision,
 accuracy, and coverage of estimates of N from MSOM compared to the widely
 applied iChao2 non-parametric estimator. We simulated 5,600 datasets
 across 7 scenarios of varying average occupancy and detectability
 covariates, as well as varying numbers of sites, replicates, and true
 community size. Additionally, we use a real dataset of surveys over 9
 years (where species accumulation has asymptoted, indicating true N), to
 estimate N from each annual survey. 3. Simulations showed that both MSOM
 and iChao2 estimators are generally accurate (i.e., unbiased and precise)
 except under unideal scenarios where mean species occupancy is low. In
 such scenarios, MSOM frequently overestimated N. Across all scenarios,
 MSOM estimates were less certain than iChao2, but this led to
 over-confident iChao2 estimates that showed poor coverage. Results from
 the real dataset largely confirmed the simulation findings, with MSOM
 estimates showing greater accuracy and coverage than iChao2. 4. Community
 ecologists have a wide choice of analytical methods, and both iChao2 and
 MSOM estimates of N are substantially preferable to raw species counts.
 The simplicity of non-parametric estimators has obvious advantages, but
 our results show that in many cases, MSOM may provide superior estimates
 that also account more accurately for uncertainty. Both methods can show
 strong bias when average occupancy is very low, and practitioners should
 show caution when using estimates derived from either method under such
 conditions.
description: Dataset is a mix of empirical data on bird communities in burned
 forest in California, as well as simulated data to estimate community
 size. 
description: Simulations include one R code file
 ("Code_Simulation.R") and one JAGS model code file
 ("Simulation_JAGS_model.txt"). R code re-produces all results
 and figures as presented in the manuscript. Empirical
 analysis includes two R code files, 1 raw data file
 ("Data_EmpiricalRaw.Rdata"), and nine model-result files (e.g.,
 "CommunityResults_2010.Rdata"), one for each year of data
 2010–2018. The first R code file ("Code_EmpiricalFit.R") takes 1
 year of data and fits a multi-species occupancy model with data
 augmentation, extracting information on the true community size, creating
 a model-result file for each year (as provided in this archive). The
 second R code file ("Code_EmpiricalPlot.R") takes all 9 years of
 model results and re-creates the figures and tables as presented in the
 manuscript.