Scaling spatial pattern metrics: impacts of composition and configuration on downscaling accuracy

Frazier, Amy E.; Kedron, Peter; Ovando-Montejo, Gustavo A.; Yun, Zhao

doi:10.1007/s10980-021-01349-w

Cited by 10 publications

(1 citation statement)

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“…The article "Scaling spatial pattern metrics: impacts of composition and configuration on downscaling accuracy" (Frazier et al 2021) similarly focuses on landscape composition and configuration, but the authors examine how the composition and configuration of fine-scale landscape information affect the performance of scaling functions created through coarse-graining. Using neutral models to control landscape structure, the authors demonstrate that predictions made with scaling functions are more sensitive to landscape configuration than composition, and that the prediction accuracy of the commonly observed power law scaling functions degrades rapidly as function exponents depart from a certain range.…”

Section: Contributions Of the Special Issuementioning

confidence: 99%

Advancing a science of scaling in landscape ecology

2023

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Yet, beyond the identification and description of consistent relationships, there has been little use of these scaling relationships to predict values at unmeasured scales (Frazier 2014) or uncover generalizable insights into landscape structure and function across ecosystems. In short, our understanding of how measurement scale affects landscape analyses has progressed, but we have limited understanding of what is driving these relationships or how we might build generalizable knowledge about the predictability of data or landscape patterns from these relationships. In fact, in the seminal paper "Key issues and research priorities in landscape ecology: An idiosyncratic synthesis", Wu and Hobbs (2002) identify scaling not scale as a key research priority,

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Section: Contributions Of the Special Issuementioning

confidence: 99%