Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment

Conor P Mcgowan ,Ryan Anthony ,Kyle Barrett ,James D M Tolliver ,Erin Rivenbark ,Jennifer K Wilson ,Christine E Hand ,Caitlin M Snyder ,Nicole Rankin ,Adam Smith ,Todd Schneider ,Whitney A Beisler ,Nicolette S Roach ,Fletcher M Smith ,Nicole F Angeli ,Jarrett Olen Woodrow ,Amy C Schwarzer ,Rusty K Griffin ,Amanda A Haverland ,Bryan D Watts

doi:10.3354/esr01063

Conor P Mcgowan , Ryan Anthony + Show 18 more

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https://doi.org/10.3354/esr01063

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Abstract

The US Fish and Wildlife Service (USFWS) has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of the current and predicted future status of a species. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, and recovery planning. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting conservation decisions in current data and cutting edge analytical and modeling techniques. Here, we describe a novel analysis of available data to assess the current condition of eastern black rail Laterallus jamaicensis jamaicensis across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future status under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units, and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With the results of these data analyses and predictive models, the USFWS concluded that protections of the Endangered Species Act were warranted for this subspecies.

Highlights

The emerging approach of the US Fish and Wildlife Service to decision making on protected and candidate species under the US Endangered Species Act of 1973 (ESA) uses a Species StatusAssessment (SSA) to gather and organize available data and information on a species of interest (Smith et al 2018)
We focused on surveys from each analysis unit that were repeated across years, so that we could use dynamic occupancy models to estimate site colonization and extinction over time (i.e. Southeast Coastal Plain, Southwest Coastal Plain, and the Great Plains; MacKenzie et al 2003, our Fig. 1)
We modeled a process that used a different base distribution for each year depending on whether it was a good year, an intermediate year, or a bad year for eastern black rails. Even though these year-specific dynamics were not supported in the dynamic occupancy analysis for the Southwest or the Great Plains, we applied this same dynamic to those analysis units in the simulation model because the data from the Southeast strongly supported year-specific dynamics, and had more years and far more sites sampled than the other analysis units

Summary

INTRODUCTION

The emerging approach of the US Fish and Wildlife Service (hereafter USFWS) to decision making on protected and candidate species under the US Endangered Species Act of 1973 (ESA) uses a Species Status. Smith et al (2018) presented an SSA framework that has 3 core components: (1) a review and description of the biology and ecological needs of the species; (2) an analysis and assessment of the current condition of these needs (i.e. availability of resources) and populations on the landscape (i.e. demographic, abundance, and/or trend estimation); (3) projection of future condition involving some type of probabilistic prediction about future status of needed resources and population metrics. We present the analysis of data to quantify ecological needs (SSA component 1) and estimate current status (SSA component 2) using a dynamic occupancy analysis (MacKenzie et al 2003) and a projection model to predict future status (SSA component 3; Table 1). Our projection model to predict future status outputs the proportion of sites remaining occupied each year, which we attempted to translate into resiliency and redundancy for describing probable future status (Table 1)

STUDY SPECIES

Model description

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DISCUSSION