Estimating the spatial scales of landscape effects on abundance

Context Spatial variation in abundance is influenced by local- and landscape-level environmental variables, but modeling landscape effects is challenging because the spatial scales of the relationships are unknown. Current approaches involve buffering survey locations with polygons of various sizes...

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Bibliographic Details
Published in:Landscape ecology 2016-08, Vol.31 (6), p.1383-1394
Main Authors: Chandler, Richard, Hepinstall-Cymerman, Jeffrey
Format: Article
Language:English
Subjects:
Biodiversity
Biomedical and Life Sciences
Ecology
Environmental Management
Estimating techniques
Landscape Ecology
Landscape/Regional and Urban Planning
Life Sciences
Nature Conservation
Research Article
Sustainable Development
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Summary:Context Spatial variation in abundance is influenced by local- and landscape-level environmental variables, but modeling landscape effects is challenging because the spatial scales of the relationships are unknown. Current approaches involve buffering survey locations with polygons of various sizes and using model selection to identify the best scale. The buffering approach does not acknowledge that the influence of surrounding landscape features should diminish with distance, and it does not yield an estimate of the unknown scale parameters. Objectives The purpose of this paper is to present an approach that allows for statistical inference about the scales at which landscape variables affect abundance. Methods Our method uses smoothing kernels to average landscape variables around focal sites and uses maximum likelihood to estimate the scale parameters of the kernels and the effects of the smoothed variables on abundance. We assessed model performance using a simulation study and an avian point count dataset. Results The simulation study demonstrated that estimators are unbiased and produce correct confidence interval coverage except in the rare case in which there is little spatial autocorrelation in the landscape variable. Canada warbler abundance was more highly correlated with site-level measures of NDVI than landscape-level NDVI, but the reverse was true for elevation. Canada warbler abundance was highest when elevation in the surrounding landscape, defined by an estimated Gaussian kernel, was between 1300 and 1400 m. Conclusions Our method provides a rigorous way of formally estimating the scales at which landscape variables affect abundance, and it can be embedded within most classes of statistical models.
ISSN:0921-2973
1572-9761
DOI:10.1007/s10980-016-0380-z