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

ISPRS International Journal of Geo-Information

Paul Holloway¹  Jennifer A. Miller²  Linda See² 

[1] Department of Geography and the Environment, The University of Texas at Austin, 305 E 23rd Street, Austin, TX 78712, USA; E-Mail

关键词: geographically weighted regression;    scale;    species richness;    birds;   

DOI  :  10.3390/ijgi4020783

来源: mdpi

【 摘 要 】

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 G_i*(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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.