Literature DB >> 19486123

Biodiversity scales from plots to biomes with a universal species-area curve.

John Harte1, Adam B Smith, David Storch.   

Abstract

Classic theory predicts species richness scales as the quarter-power of area, yet species-area relationships (SAR) vary widely depending on habitat, taxa, and scale range. Because power-law SAR are used to predict species loss under habitat loss, and to scale species richness from plots to biomes, insight into the wide variety of observed SAR and the conditions under which power-law behavior should be observed is needed. Here we derive from the maximum entropy principle, a new procedure for upscaling species richness data from small census plots to larger areas, and test empirically, using multiple data sets, the prediction that up to an overall scale displacement, nested SAR lie along a universal curve, with average abundance per species at each scale determining the local slope of the curve. Power-law behaviour only arises in the limit of increasing average abundance, and in that limit, the slope approaches zero, not (1/4). An extrapolation of tree species richness in the Western Ghats to biome scale (60,000 km(2)) using only census data at plot scale ((1/4) ha) is presented to illustrate the potential for applications of our theory.

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Year:  2009        PMID: 19486123     DOI: 10.1111/j.1461-0248.2009.01328.x

Source DB:  PubMed          Journal:  Ecol Lett        ISSN: 1461-023X            Impact factor:   9.492


  35 in total

1.  Universal species-area and endemics-area relationships at continental scales.

Authors:  David Storch; Petr Keil; Walter Jetz
Journal:  Nature       Date:  2012-08-02       Impact factor: 49.962

2.  Integrating spatial and temporal approaches to understanding species richness.

Authors:  Ethan P White; S K Morgan Ernest; Peter B Adler; Allen H Hurlbert; S Kathleen Lyons
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-11-27       Impact factor: 6.237

3.  Energetic and ecological constraints on population density of reef fishes.

Authors:  D R Barneche; M Kulbicki; S R Floeter; A M Friedlander; A P Allen
Journal:  Proc Biol Sci       Date:  2016-01-27       Impact factor: 5.349

4.  Estimating biodiversity impacts without field surveys: A case study in northern Borneo.

Authors:  Justin Kitzes; Rebekah Shirley
Journal:  Ambio       Date:  2015-07-14       Impact factor: 5.129

5.  Quantifying and sustaining biodiversity in tropical agricultural landscapes.

Authors:  Chase D Mendenhall; Analisa Shields-Estrada; Arjun J Krishnaswami; Gretchen C Daily
Journal:  Proc Natl Acad Sci U S A       Date:  2016-10-24       Impact factor: 11.205

6.  A general framework for predicting delayed responses of ecological communities to habitat loss.

Authors:  Youhua Chen; Tsung-Jen Shen
Journal:  Sci Rep       Date:  2017-04-20       Impact factor: 4.379

7.  Turnover of soil bacterial diversity driven by wide-scale environmental heterogeneity.

Authors:  L Ranjard; S Dequiedt; N Chemidlin Prévost-Bouré; J Thioulouse; N P A Saby; M Lelievre; P A Maron; F E R Morin; A Bispo; C Jolivet; D Arrouays; P Lemanceau
Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

8.  Improving soil bacterial taxa-area relationships assessment using DNA meta-barcoding.

Authors:  S Terrat; S Dequiedt; W Horrigue; M Lelievre; C Cruaud; N P A Saby; C Jolivet; D Arrouays; P-A Maron; L Ranjard; N Chemidlin Prévost-Bouré
Journal:  Heredity (Edinb)       Date:  2014-10-08       Impact factor: 3.821

9.  Covariations in ecological scaling laws fostered by community dynamics.

Authors:  Silvia Zaoli; Andrea Giometto; Amos Maritan; Andrea Rinaldo
Journal:  Proc Natl Acad Sci U S A       Date:  2017-08-22       Impact factor: 11.205

10.  Field theory for biogeography: a spatially explicit model for predicting patterns of biodiversity.

Authors:  James P O'Dwyer; Jessica L Green
Journal:  Ecol Lett       Date:  2009-11-10       Impact factor: 9.492

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