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The application of GIS and spatiotemporal analyses to investigations of unusual marine mammal strandings and mortality events

Identifieur interne : 001E06 ( Main/Exploration ); précédent : 001E05; suivant : 001E07

The application of GIS and spatiotemporal analyses to investigations of unusual marine mammal strandings and mortality events

Auteurs : Stephanie A. Norman ; Jessie Huggins [États-Unis] ; Tim E. Carpenter [États-Unis] ; James T. Case [États-Unis] ; Dyanna M. Lambourn [États-Unis] ; Jim Rice [États-Unis] ; John Calambokidis [États-Unis] ; Joseph K. Gaydos [États-Unis] ; M. Bradley Hanson [États-Unis] ; Deborah A. Duffield [États-Unis] ; Sandra Dubpernell [États-Unis] ; Susan Berta [États-Unis] ; Matt Klope [États-Unis]

Source :

RBID : ISTEX:C8CD8540F7F6D694EF8E18E6C74C571B472DA2D1

English descriptors

Abstract

In 2006–2007, an unusually high number of harbor porpoises (Phocoena phocoena) stranded along the Washington and Oregon coastlines. Spatiotemporal analyses were used to examine their ability to detect clusters of porpoise strandings during an unusual mortality event (UME) in the Pacific Northwest using stranding location data. Strandings were evaluated as two separate populations, outer coast and inland waters. The presence of global clustering was evaluated using the Knox spatiotemporal test, and the presence of local clusters was investigated using a spatiotemporal scan statistic (space–time permutation). There was evidence of global clustering, but no local clustering, supporting the hypothesis that strandings were due to more varied etiologies instead of localized causes. Further analyses at subregional levels, and concurrently assessing environmental factors, might reveal additional geographic distribution patterns. This article describes the spatial analytical tools applied in this study and how they can help elucidate the spatiotemporal epidemiology of other UMEs and assist in determining their causes. More than one spatial analytical technique should be used if the study objective is to detect and describe clustering in time and space and to generate hypotheses regarding causation of marine mammal disease and stranding events.

Url:
DOI: 10.1111/j.1748-7692.2011.00507.x


Affiliations:


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Le document en format XML

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<term>Adult males</term>
<term>Aerial surveys</term>
<term>Analytical methods</term>
<term>Atmospheric administration</term>
<term>Bovine tuberculosis</term>
<term>British columbia</term>
<term>Cape flattery</term>
<term>Carcass drift</term>
<term>Cascadia research</term>
<term>Case data</term>
<term>Case pairs</term>
<term>Cluster detection</term>
<term>Coast stock</term>
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<term>Critical time</term>
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<term>Data collection</term>
<term>Disease outbreaks</term>
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<term>Entire study area</term>
<term>Epidemiology</term>
<term>Exploratory analyses</term>
<term>Fisheries science center</term>
<term>Fishery</term>
<term>Fourth avenue</term>
<term>Geographic information systems</term>
<term>Geographic location</term>
<term>Global clusters</term>
<term>Gulland</term>
<term>Harbor porpoise</term>
<term>Harbor porpoises</term>
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<term>Unusual mortality event</term>
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<div type="abstract" xml:lang="en">In 2006–2007, an unusually high number of harbor porpoises (Phocoena phocoena) stranded along the Washington and Oregon coastlines. Spatiotemporal analyses were used to examine their ability to detect clusters of porpoise strandings during an unusual mortality event (UME) in the Pacific Northwest using stranding location data. Strandings were evaluated as two separate populations, outer coast and inland waters. The presence of global clustering was evaluated using the Knox spatiotemporal test, and the presence of local clusters was investigated using a spatiotemporal scan statistic (space–time permutation). There was evidence of global clustering, but no local clustering, supporting the hypothesis that strandings were due to more varied etiologies instead of localized causes. Further analyses at subregional levels, and concurrently assessing environmental factors, might reveal additional geographic distribution patterns. This article describes the spatial analytical tools applied in this study and how they can help elucidate the spatiotemporal epidemiology of other UMEs and assist in determining their causes. More than one spatial analytical technique should be used if the study objective is to detect and describe clustering in time and space and to generate hypotheses regarding causation of marine mammal disease and stranding events.</div>
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