Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 372421
Title Avian spatial responses to forest spatial heterogeneity at the landscape level: conceptual and statistical challenges
Author(s) Fortin, M.J.; Melles, S.J.
Source In: Real World Ecology: large-scale and long-term case studies and methods / Miao, S., Carsten, S., Nungesser, M., New York : Springer - ISBN 9780387779416 - p. 137 - 160.
DOI https://doi.org/10.1007/978-0-387-77942-3_6
Department(s) Land Dynamics
PE&RC
Publication type Peer reviewed book chapter
Publication year 2009
Abstract An explicit consideration of spatial structure in ecological studies plays an increasingly important role in attempts to better understand and manage ecological processes, such as deforestation, forest homogenization, and escalating landscape heterogeneity. The goal of this chapter is to quantify the relationship between forest cover data and ovenbird (Seiurus aurocapilla) abundance¿a ground nesting passerine that breeds in contiguous forests¿in southern Ontario (Canada). To quantify this relationship, we use the Ontario Breeding Bird Atlas 2001¿2005 and compare two spatially explicit modeling methods: geographically weighted regression (GWR) and regression kriging (RK). We show how GWR and RK account for residual spatial autocorrelation in models of forest cover and ovenbird abundance, and we examine the insights they provide. Based on regression kriging, we found that 68 % (adjusted R 2 ) of ovenbird abundance was explained by forest cover, which was an improvement over ordinary least-square regression (adjusted R 2 = 43%), but was not uniformly better than variance explained by GWR in different subregions. These results emphasize the importance of both performing spatial data exploration prior to statistical analyses and accounting for spatial structure during the analysis
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