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Jon C. Gering

Researcher at Truman State University

Publications -  11
Citations -  2061

Jon C. Gering is an academic researcher from Truman State University. The author has contributed to research in topics: Species richness & Species diversity. The author has an hindex of 11, co-authored 11 publications receiving 1962 citations. Previous affiliations of Jon C. Gering include Miami University.

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Partitioning Species Diversity across Landscapes and Regions: A Hierarchical Analysis of α, β, and γ Diversity

TL;DR: A statistical approach for testing null hypotheses that observed partitions of species richness or diversity indices differed from those expected by chance is developed, and these tests are illustrated using data from a hierarchical study of forest‐canopy beetles.
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The additive partitioning of species diversity: recent revival of an old idea

TL;DR: The additive partitioning of diversity is nearly as old as the multiplicative concept, yet ecologists are only just now beginning to use additive partitions to examine patterns of species diversity as discussed by the authors.
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Additive Partitioning of Species Diversity across Multiple Spatial Scales: Implications for Regional Conservation of Biodiversity

TL;DR: This article applied additive partitioning to data on arboreal beetle diversity (richness, Shannon, Simpson) collected from a nested design consisting of four hierarchical levels (trees, forest stands, sites, and ecoregions) that corresponded to increasingly broader spa- tial scales within the eastern deciduous forest of Ohio and Indiana.
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Predation on artificial bird nests along an urban gradient: predatory risk or relaxation in urban environments?

TL;DR: In this article, the authors investigated the influence of urbanization on predation by conducting an artificial nest experiment along an urban gradient of six sites ranging from natural to urbanized ecosystems.
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The alpha–beta–regional relationship: providing new insights into local–regional patterns of species richness and scale dependence of diversity components

TL;DR: This work applied the ABR approach to four local– regional relationships based on data from 583 arboreal beetle species collected in a hierarchically nested sampling design and analysed the scale dependence of alpha and beta using a hierarchical linear model.