Supplement 1. WinBUGS code for fitting the multi-species hierarchical community model which does not include detection.

<h2>File List</h2><blockquote> <p><a href="WinBUGS_NODET.txt">WinBUGS_NODET.txt</a> -- (MD5: 1ec428b4176bc2c5e067531b7685d523)</p> </blockquote><h2>Description</h2><blockquote> <p>WinBUGS model code and specifications for the multi-species hierarchical community model which does not include detection, based on survey-specific detection/non-detection records combined across three sampling occasions for five years. </p> </blockquote

Great Lakes piping plover population data

Great Lakes piping plover population data from 1993 - 2016 used to build a coupled integrated population model-Bayesian population viability analysis (IPM-BPVA). The first tab includes capture-mark-resight (CMR) data for hatch-year and after hatch-year birds. Note that the CMR data included here represent a partial list (proprietary records omitted) because of the endangered status of Great Lakes piping plovers, but are sufficient for conducting the IPM-BPVA analysis. The second tab includes census data (number of breeding pairs annually). The third tab includes 2 columns on productivity: FLEDGE is the number of fledglings observed annually and SURV_BROOD is the number of surveyed broods annually. The last tab includes merlin census data collected at 2 observatories annually: Hawk Mountain (HM) and Whitefish Point (WP)

Appendix A. Species- and community-level true positive rate (TPR) and true negative rate (TNR) values for the habitat-only and autologistic models.

Species- and community-level true positive rate (TPR) and true negative rate (TNR) values for the habitat-only and autologistic models

Yue et al 2015 Data

Camera trap and site data used for the stud

Number of species in which the species-specific parameter estimate was positive or negative.

<p>Values in parenthesis indicate the subset of species in which the posterior intervals do not overlap zero<b>.</b></p

Conservation of Avian Diversity in the Sierra Nevada: Moving beyond a Single-Species Management Focus

<div><p>Background</p><p>As a result of past practices, many of the dry coniferous forests of the western United States contain dense, even-aged stands with uncharacteristically high levels of litter and downed woody debris. These changes to the forest have received considerable attention as they elevate concerns regarding the outcome of wildland fire. However, attempts to reduce biomass through fuel reduction (i.e., thinning of trees) are often opposed by public interest groups whose objectives include maintaining habitat for species of concern such as the spotted owl, <i>Strix occidentalis</i>, the northern goshawk, <i>Accipiter gentilis,</i> and the Pacific fisher, <i>Martes pennanti</i>. Whether protection of these upper-trophic level species confers adequate conservation of avian forest diversity is unknown.</p><p>Methodology and Principal Findings</p><p>We use a multi-species occurrence model to estimate the habitat associations of 47 avian species detected at 742 sampling locations within an 880-km² area in the Sierra Nevada. Our approach, which accounts for variations in detectability of species, estimates occurrence probabilities of all species in a community by linking species occurrence models into one hierarchical community model, thus improving inferences on all species, especially those that are rare or observed infrequently. We address how the avian community is influenced by covariates related to canopy cover, tree size and shrub cover while accounting for the impacts of abiotic variables known to affect species distributions.</p><p>Conclusions and Significance</p><p>Environmental parameters estimated through our approach emphasize the importance of within and between stand-level heterogeneity in meeting biodiversity objectives and suggests that many avian species would increase under more open canopy habitat conditions than those favored by umbrella species of high conservation concern. Our results suggest that a more integrated approach that emphasizes maintaining a diversity of habitats across environmental gradients and minimizing urbanization may have a greater benefit to ecosystem functioning then a single-species management focus.</p></div

Location of the study area in California/Nevada, USA with 172 primary sample points indicated.

<p>Sampling points were distributed across forested areas (green) using systematic random sampling. Cluster sampling was conducted within 200 m of each primary sample point. Each of these 742 sites was sampled multiple times within a year for a total 2021 data points over the four-year study.</p

Appendix A. Analysis of population models with compensatory and overcompensatory recruitment.

Analysis of population models with compensatory and overcompensatory recruitment

Mean parameter estimates and posterior intervals for the effect of A) percent canopy cover, B) standard deviation in canopy cover, C) mean DBH, D) standard deviation in DBH, E) percent shrub cover for each of the 47 species included in our analysis.

<p>Values indicate the change in occurrence predicted as a function of the change in one standard deviation of change in each response variable. For purposes of illustration, species were classified as very common (occupancy probability ≥85%, N = 6), common (≥50% and <85%, N = 8), uncommon (≥25% and <50%, N = 13), rare (≥10% and <25%, N = 8) and very rare (<10%, N = 11) based on their mean probability of occurrence for average environmental and habitat conditions in the Tahoe Basin (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063088#pone.0063088.s001" target="_blank">Appendix S1</a>). Panels F-J show comparisons of the covariate estimates across these groups.</p

Strength of autocorrelation for individual species across the bird community at Patuxent National Wildlife Refuge.

<p>Gray bars represent a reduced model with only spatial effects included; unfilled bars represent a global model with landcover and spatial effects included. Bar heights represent means from the posterior distribution of a Bayesian analysis based on three independent MCMC chains (n = 20,000). All of the Bayesian credibility intervals overlapped and are not shown for clarity.</p