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      Modeling Behavior by Coastal River Otter ( Lontra Canadensis) in Response to Prey Availability in Prince William Sound, Alaska: A Spatially-Explicit Individual-Based Approach

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          Abstract

          Effects of climate change on animal behavior and cascading ecosystem responses are rarely evaluated. In coastal Alaska, social river otters ( Lontra Canadensis), largely males, cooperatively forage on schooling fish and use latrine sites to communicate group associations and dominance. Conversely, solitary otters, mainly females, feed on intertidal-demersal fish and display mutual avoidance via scent marking. This behavioral variability creates “hotspots” of nutrient deposition and affects plant productivity and diversity on the terrestrial landscape. Because the abundance of schooling pelagic fish is predicted to decline with climate change, we developed a spatially-explicit individual-based model (IBM) of otter behavior and tested six scenarios based on potential shifts to distribution patterns of schooling fish. Emergent patterns from the IBM closely mimicked observed otter behavior and landscape use in the absence of explicit rules of intraspecific attraction or repulsion. Model results were most sensitive to rules regarding spatial memory and activity state following an encounter with a fish school. With declining availability of schooling fish, the number of social groups and the time simulated otters spent in the company of conspecifics declined. Concurrently, model results suggested an elevation of defecation rate, a 25% increase in nitrogen transport to the terrestrial landscape, and significant changes to the spatial distribution of “hotspots” with declines in schooling fish availability. However, reductions in availability of schooling fish could lead to declines in otter density over time.

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          Most cited references28

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          Random walk models in biology.

          Mathematical modelling of the movement of animals, micro-organisms and cells is of great relevance in the fields of biology, ecology and medicine. Movement models can take many different forms, but the most widely used are based on the extensions of simple random walk processes. In this review paper, our aim is twofold: to introduce the mathematics behind random walks in a straightforward manner and to explain how such models can be used to aid our understanding of biological processes. We introduce the mathematical theory behind the simple random walk and explain how this relates to Brownian motion and diffusive processes in general. We demonstrate how these simple models can be extended to include drift and waiting times or be used to calculate first passage times. We discuss biased random walks and show how hyperbolic models can be used to generate correlated random walks. We cover two main applications of the random walk model. Firstly, we review models and results relating to the movement, dispersal and population redistribution of animals and micro-organisms. This includes direct calculation of mean squared displacement, mean dispersal distance, tortuosity measures, as well as possible limitations of these model approaches. Secondly, oriented movement and chemotaxis models are reviewed. General hyperbolic models based on the linear transport equation are introduced and we show how a reinforced random walk can be used to model movement where the individual changes its environment. We discuss the applications of these models in the context of cell migration leading to blood vessel growth (angiogenesis). Finally, we discuss how the various random walk models and approaches are related and the connections that underpin many of the key processes involved.
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            Uses and misuses of bioclimatic envelope modeling.

            Bioclimatic envelope models use associations between aspects of climate and species' occurrences to estimate the conditions that are suitable to maintain viable populations. Once bioclimatic envelopes are characterized, they can be applied to a variety of questions in ecology, evolution, and conservation. However, some have questioned the usefulness of these models, because they may be based on implausible assumptions or may be contradicted by empirical evidence. We review these areas of contention, and suggest that criticism has often been misplaced, resulting from confusion between what the models actually deliver and what users wish that they would express. Although improvements in data and methods will have some effect, the usefulness of these models is contingent on their appropriate use, and they will improve mainly via better awareness of their conceptual basis, strengths, and limitations.
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              Analyzing animal movements using Brownian bridges.

              By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal's mobility. We describe two critical developments that enable widespread use of the BBMM, including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After the BBMM is fitted to location data, an estimate of the animal's probability of occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide three examples: estimating animal home ranges, estimating animal migration routes, and evaluating the influence of fine-scale resource selection on animal movement patterns.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                10 June 2015
                2015
                : 10
                : 6
                : e0126208
                Affiliations
                [1 ]Wyoming Geographic Information Science Center, University of Wyoming, Laramie, Wyoming, United States of America
                [2 ]Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, United States of America
                [3 ]Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming, United States of America
                University of Girona, SPAIN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SEA NPN MB. Performed the experiments: SEA. Analyzed the data: SEA NPN MB. Contributed reagents/materials/analysis tools: SEA NPN MB. Wrote the paper: SEA NPN MB. Designed the software used in analysis: SEA.

                Article
                PONE-D-14-37501
                10.1371/journal.pone.0126208
                4489515
                26061497
                e7f6e6c8-0ebe-4564-bc82-aba04dfce969
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 26 August 2014
                : 30 March 2015
                Page count
                Figures: 8, Tables: 3, Pages: 27
                Funding
                Funding for this project was provided by the National Science Foundation ( http://www.nsf.gov/) DEB #0454474, the University of Georgia ( http://uga.edu) Warnell School of Forestry and Natural Resources ( http://www.warnell.uga.edu/), and the University of Wyoming ( http://uwyo.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                The raw simulation data can be obtained from the University of Wyoming Data archive at the following DOI: http://dx.doi.org/10.15786/M2159X. The zipped file contains 20 tab-delimited text files containing the raw simulated data, 1 Excel spreadsheet describing the content within each data table, 1 PDF describing the relational data structure of the database, and 1 VB.NET application directory housing the entire code, GIS and database products required to run an individual simulation model. These data are 17.1 GB in size.

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