Appendix A. A description of the simulation design.

A description of the simulation design

Appendix B. Tables presenting simulation results.

Tables presenting simulation results

Appendix A. Simulation study for uncertain species analysis.

Simulation study for uncertain species analysis

Appendix A. Simulation study.

Simulation study

Appendix B. Modeling of the multiple detection methods design.

Modeling of the multiple detection methods design

Appendix B. Calculation of standard errors, superpopulation estimates, and confidence intervals for Alley North samples.

Calculation of standard errors, superpopulation estimates, and confidence intervals for Alley North samples

Supplement 1. R and BUGS code to fit all the models presented.

<h2>File List</h2><div> <p><a href="BUGS-Models.bug">BUGS-Models.bug</a> (MD5: bbc0c6df0dc6e68ff25a3172ad349f07)</p> <p><a href="FormatData_forBUGS.r">FormatData_forBUGS.r</a> (MD5: cb618b5c9dae1c6a5c19730541f8a74a)</p> <p><a href="Likelihoods.r">Likelihoods.r</a> (MD5: 0e56760d67549e4e6efc587d94097907)</p> <p><a href="SimulateData_Calibration.r">SimulateData_Calibration.r</a> (MD5: cf2a083f6a73bdb71db9d6c4966d8976)</p> <p><a href="SimulateData_ObservationConf.r">SimulateData_ObservationConf.r</a> (MD5: a08687fcc8a826dbebb64b7e57647b78)</p> <p><a href="SimulateData_SiteConf-MM.r">SimulateData_SiteConf-MM.r</a> (MD5: 885ee8cb22dea9bee08ea53d7cacaede)</p> <p><a href="SimulateData_SiteConf-MS.r">SimulateData_SiteConf-MS.r</a> (MD5: f826d94e8c110481c544696e94d5cf60 )</p> </div><h2>Description</h2><div> <p>The Four files named as SimulateData_xxx.r are used to simulate data for each of the four corresponding study design described in the text. Note that the site confirmation design has two variants: (1) the Multiple-Method design (MM) and (2) the Multiple-State design (MS). The file Likelihoods.r contains the likelihoods for the four models, and it can be run in R, as is. To run the Bayesian models in OpenBUGS (or WinBUGS), run the script FormatData_forBUGS first. It will transform the simulated data in the appropriate from for BUGS. Then, you can use the code in BUGS-Models.bug to run each of the four models presented in the paper.</p> </div

When habitat matters: Habitat preferences can modulate co-occurrence patterns of similar sympatric species

<div><p>Disentangling the role of competition in regulating the distribution of sympatric species can be difficult because species can have different habitat preferences or time use that introduce non-random patterns that are not related to interspecific interactions. We adopted a multi-step approach to systematically incorporate habitat preferences while investigating the co-occurrence of two presumed competitors, morphologically similar, and closely related ground-dwelling birds: the brown tinamou (<i>Crypturellus obsoletus</i>) and the tataupa tinamou (<i>C</i>. <i>tataupa</i>). First, we used single-species occupancy models to identify the main landscape characteristics affecting site occupancy, while accounting for detection probability. We then used these factors to control for the effect of habitat while investigating species co-occurrence. In addition, we investigated species present-time partitioning by measuring the degree of overlap in their activity time. Both species were strictly diurnal and their activity time highly overlapped (i.e., the species are not present-time partitioning). The distribution of the two species varied across the landscape, and they seemed to occupy opposite portions of the study area, but co-occurrence models and species interaction factors suggested that the tinamous have independent occupancy and detection. In addition, co-occurrence models that accounted for habitat performed better than models without habitat covariates. The observed co-occurrence pattern is more likely related to habitat preferences, wherein species segregated by elevation. These results provide evidence that habitat characteristics can play a bigger role than interspecific interactions in regulating co-existence of some species. Therefore, exploring habitat preferences while analyzing co-occurrence patterns is essential, in addition to being a feasible approach to achieve more accurate estimation of parameters reflecting species interactions. Occupancy models can be a valuable tool in such modeling.</p></div

Appendix A. Superpopulation model output (POPAN data type) for all colonies.

Superpopulation model output (POPAN data type) for all colonies

Estimated site occupancy probability of brown tinamou (<i>Crypturellus obsoletus</i>, left) and tataupa tinamou (<i>C</i>. <i>tataupa</i>, right) at an Atlantic forest remnant in Brazil based on camera trap survey data.

<p>Darker colors indicated lower occupancy probabilities. This map was obtained from interpolation of model-averaged site occupancy probabilities.</p