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Full-Text Articles in Life Sciences

Spatially Explicit Population Estimates Of The Florida Black Bear, Jacob Michael Humm May 2017

Spatially Explicit Population Estimates Of The Florida Black Bear, Jacob Michael Humm

Masters Theses

The Florida black bear (Ursus americanus floridanus) is currently comprised of 7 isolated subpopulations: Apalachicola, Eglin, Osceola, Ocala/St. Johns, Chassahowitzka, Highlands/Glades, and Big Cypress. The last statewide assessment of Florida black bear population dynamics was conducted by Simek et al. (2005) using traditional capture-markrecapture methods. The subspecies was removed from Florida’s List of State Threatened Species in 2012 contingent upon the formulation of a management plan that would maintain viable subpopulations of black bears in suitable habitat. Accurate population estimates for each of the remaining black bear subpopulations in Florida were needed to achieve the management goals of …


A Bayesian Method For Assessing Multi-Scale Species-Habitat Relationships, Erica F. Stuber, Lutz F. Gruber, Joseph J. Fontaine Jan 2017

A Bayesian Method For Assessing Multi-Scale Species-Habitat Relationships, Erica F. Stuber, Lutz F. Gruber, Joseph J. Fontaine

Nebraska Cooperative Fish and Wildlife Research Unit: Staff Publications

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.

Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.

Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian …