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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Academic Editors: Linda See and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2015, 4(2), 783-798; https://doi.org/10.3390/ijgi4020783
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)
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.
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Keywords:
geographically weighted regression; scale; species richness; birds
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MDPI and ACS Style
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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