Volume 10, Issue 2
RESEARCH ARTICLE

Measurement of Biodiversity (MoB): A method to separate the scale‐dependent effects of species abundance distribution, density, and aggregation on diversity change

Daniel J. McGlinn

Corresponding Author

E-mail address: danmcglinn@gmail.com

Biology Department, College of Charleston, Charleston, South Carolina

Correspondence

Daniel J. McGlinn

Email: danmcglinn@gmail.com

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Xiao Xiao

School of Biology and Ecology, and Senator George J. Mitchell Center of Sustainability Solutions, University of Maine, Orono, Maine

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Felix May

Leuphana University Lüneburg, Lüneburg, Germany

German Centre for Integrative Biodiversity Research (iDiv), Halle‐Jena‐Leipzig, Leipzig, Germany

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Nicholas J. Gotelli

Department of Biology, University of Vermont, Burlington, Vermont

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Thore Engel

German Centre for Integrative Biodiversity Research (iDiv), Halle‐Jena‐Leipzig, Leipzig, Germany

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Shane A. Blowes

German Centre for Integrative Biodiversity Research (iDiv), Halle‐Jena‐Leipzig, Leipzig, Germany

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Tiffany M. Knight

German Centre for Integrative Biodiversity Research (iDiv), Halle‐Jena‐Leipzig, Leipzig, Germany

Institute of Biology, Martin Luther University Halle‐Wittenberg, Halle (Saale), Germany

Department of Community Ecology, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), Germany

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Oliver Purschke

German Centre for Integrative Biodiversity Research (iDiv), Halle‐Jena‐Leipzig, Leipzig, Germany

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Jonathan M. Chase

German Centre for Integrative Biodiversity Research (iDiv), Halle‐Jena‐Leipzig, Leipzig, Germany

Department of Computer Science, Martin Luther University, Halle‐Wittenberg, Leipzig, Germany

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Brian J. McGill

School of Biology and Ecology, and Senator George J. Mitchell Center of Sustainability Solutions, University of Maine, Orono, Maine

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First published: 04 October 2018
Citations: 20

Abstract

en

  1. Little consensus has emerged regarding how proximate and ultimate drivers such as productivity, disturbance and temperature may affect species richness and other aspects of biodiversity. Part of the confusion is that most studies examine species richness at a single spatial scale and ignore how the underlying components of species richness can vary with spatial scale.
  2. We provide an approach for the measurement of biodiversity that decomposes changes in species rarefaction curves into proximate components attributed to: (a) the species abundance distribution, (b) density of individuals and (c) the spatial arrangement of individuals. We decompose species richness by comparing spatial and nonspatial sample‐ and individual‐based species rarefaction curves that differentially capture the influence of these components to estimate the relative importance of each in driving patterns of species richness change.
  3. We tested the validity of our method on simulated data, and we demonstrate it on empirical data on plant species richness in invaded and uninvaded woodlands. We integrated these methods into a new r package (mobr).
  4. The metrics that mobr provides will allow ecologists to move beyond comparisons of species richness in response to ecological drivers at a single spatial scale toward a dissection of the proximate components that determine species richness across scales.

Zusammenfassung

de

  1. Es herrscht nur wenig Konsens darüber, auf welche Weise unmittelbare und mittelbare Faktoren wie Produktivität, Störung und Temperatur die Artenzahl und andere Aspekte der Biodiversität beeinflussen. Zum Teil rührt diese Unklarheit daher, dass die meisten Studien die Artenzahl nur auf einer einzigen räumlichen Skala betrachten und dabei außer Acht lassen, wie die zugrundeliegenden Komponenten der Artenzahl mit der räumlichen Skala variieren können.
  2. Hier stellen wir unseren Ansatz “measurement of biodiversity” vor, mit dem Unterschiede zwischen Rarefaction‐Kurven auf die unmittelbaren Komponenten der Artenzahl zurückgeführt werden können. Dies sind: (a) Die Abundanzverteilung der Arten, (b) die Individuendichte und (c) die räumliche Anordnung der Individuen. Um den relativen Beitrag dieser Komponenten an der Änderung der Artenzahl einzuschätzen, teilen wir diese mithilfe von räumlichen und nicht‐räumlichen, Stichproben‐ und Individuen‐basierten Rarefaction‐Kurven auf, die den Einfluss der Komponenten auf unterschiedliche Weise widerspiegeln.
  3. Wir haben unsere Methode mit simulierten Daten validiert und zeigen ihre Anwendung an einem empirischen Fallbeispiel zur Artenzahl in Wäldern mit und ohne invasive Arten. Unsere Methode wird in einem neuen r‐paket (mobr) zur Verfügung gestellt.
  4. Die Biodiversitätsmetriken, die von mobr ausgegeben werden, erlauben es Ökologen, einen differenzierteren Blick auf Biodiversitätsmuster zu werfen: Statt die Änderung von Artenzahl auf einer einzigen räumlichen Skala zu betrachten, kann der Effekt von ökologischen Faktoren auf die unmittelbaren Komponenten der Artentenzahl skalenübergreifend analysiert werden.

Number of times cited according to CrossRef: 20

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