Spatial autocorrelation and the scaling of species-environment relationships

Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently a...

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Veröffentlicht in:Ecology (Durham) Jg. 91; H. 8; S. 2455 - 2465
Hauptverfasser: de Knegt, H. J, van Langevelde, F, Coughenour, M. B, Skidmore, A. K, de Boer, W. F, Heitkönig, I. M. A, Knox, N. M, Slotow, R, van der Waal, C, Prins, H. H. T
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington, DC Ecological Society of America 01.08.2010
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ISSN:0012-9658, 1939-9170
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Abstract Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.
AbstractList Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.
Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena. [PUBLICATION ABSTRACT]
Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.
Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.
Author de Knegt, H. J.
van Langevelde, F.
Prins, H. H. T.
Knox, N. M.
Slotow, R.
Coughenour, M. B.
Skidmore, A. K.
de Boer, W. F.
van der Waal, C.
Heitkönig, I. M. A.
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  organization: Resource Ecology Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands
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ContentType Journal Article
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ISSN 0012-9658
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IsPeerReviewed true
IsScholarly true
Issue 8
Keywords Landscape
Variable
omitted variable bias
spatially lagged predictor
Bias
landscape context
Regression
scale
spatial autocorrelation
species-environment relationships
Environment
spatial regression
Autocorrelation
Language English
License http://onlinelibrary.wiley.com/termsAndConditions#vor
CC BY 4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a6195-6ba5ce7f515d5581cefbedc08ccff7d96630c7ff5e688c82990ab2ecf10e19943
Notes Corresponding Editor: H. H. Wagner.
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Snippet Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many...
Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species–environment relationships, because RSA invalidates many...
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StartPage 2455
SubjectTerms account
Animal and plant ecology
Animal, plant and microbial ecology
Animals
Autocorrelation
beta diversity
Biological and medical sciences
Computer Simulation
Correlation analysis
distributional data
Ecological modeling
Ecology
Ecosystem
Ecosystems
Estimation bias
et-al. 2007
Fundamental and applied biological sciences. Psychology
General aspects
geographical ecology
habitat
Heterogeneity
landscape context
Landscape ecology
Landscapes
Modeling
Models, Biological
omitted variable bias
patterns
Population Dynamics
Rain
red herrings
Regression analysis
Regression coefficients
regression-models
scale
selection
Spatial analysis
spatial autocorrelation
Spatial models
spatial regression
spatially lagged predictor
Species
species-environment relationships
Statistical analysis
Trees
Urban ecology
Title Spatial autocorrelation and the scaling of species-environment relationships
URI https://www.jstor.org/stable/27860810
https://onlinelibrary.wiley.com/doi/abs/10.1890%2F09-1359.1
https://www.ncbi.nlm.nih.gov/pubmed/20836467
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Volume 91
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