Document Type

Thesis

Abstract

Species distribution models are used to map and predict geographic distributions of animals based on environmental covariates. However, species distribution models often require high resolution habitat data and time-series data on animal locations. In data-limited regions with little animal survey data or habitat information, modeling is more challenging and often ignores important environmental attributes. For sea otters (Enhydra lutris), a federally protected keystone species with variable population trends across their range, predictive modeling of distributions has been successfully conducted in areas with an abundance of sea otter and habitat data. Here, we used open-access data across a single time step and leveraged a presence-only model, Maximum Entropy (MaxEnt), to investigate subtidal habitat associations (substrate and algal cover, bathymetry, and rugosity) of northern sea otters (E. lutris kenyoni) in a data-limited ecosystem, Kachemak Bay, Alaska. These habitat associations corroborated previous findings regarding the importance of bathymetry and understory kelp as predictors of sea otter presence. Novel associations were detected, as filamentous algae and shell litter were positively and negatively associated with sea otter presence, respectively. This study provides a quantitative framework for conducting species distribution modeling with limited temporal and spatial animal distribution and abundance data and utilized drop camera information as a novel approach to better understand habitat requirements of a stable sea otter population.

Publication Date

12-17-2022

Handle

http://hdl.handle.net/11122/13121

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