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ISPRS Int. J. Geo-Inf. 2015, 4(2), 783-798; doi:10.3390/ijgi4020783

Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns

Department of Geography and the Environment, The University of Texas at Austin, 305 E 23rd Street, Austin, TX 78712, USA
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Author to whom correspondence should be addressed.
Academic Editors: Linda See and Wolfgang Kainz
Received: 30 November 2014 / Revised: 19 April 2015 / Accepted: 24 April 2015 / Published: 5 May 2015
(This article belongs to the Special Issue Spatial Analysis for Environmental Applications)
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Abstract

In this paper we explore relationships between bird species richness and environmental factors in New York State, focusing particularly on how spatial scale, autocorrelation and nonstationarity affect these relationships. We used spatial statistics, Getis-Ord Gi*(d), to investigate how spatial scale affects the measurement of richness “hot-spots” and “cold-spots” (clusters of high and low species richness, respectively) and geographically weighted regression (GWR) to explore scale dependencies and nonstationarity in the relationships between richness and environmental variables such as climate and plant productivity. Finally, we introduce a geovisualization approach to show how these relationships are affected by spatial scale in order to understand the complex spatial patterns of species richness. View Full-Text
Keywords: geographically weighted regression; scale; species richness; birds geographically weighted regression; scale; species richness; birds
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Holloway, P.; Miller, J.A. Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns. ISPRS Int. J. Geo-Inf. 2015, 4, 783-798.

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