Species abundance distribution results from a spatial analogy of central limit theorem
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem, Research Support, U.S. Gov't, Non-P.H.S.
PubMed
19346488
PubMed Central
PMC2672478
DOI
10.1073/pnas.0810096106
PII: 0810096106
Knihovny.cz E-zdroje
- MeSH
- biodiverzita * MeSH
- biologické modely * MeSH
- počítačová simulace MeSH
- ptáci MeSH
- stromy MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
The frequency distribution of species abundances [the species abundance distribution (SAD)] is considered to be a fundamental characteristic of community structure. It is almost invariably strongly right-skewed, with most species being rare. There has been much debate as to its exact properties and the processes from which it results. Here, we contend that an SAD for a study plot must be viewed as spliced from the SADs of many smaller nonoverlapping subplots covering that plot. We show that this splicing, if applied repeatedly to produce subplots of progressively larger size, leads to the observed shape of the SAD for the whole plot regardless of that of the SADs of those subplots. The widely reported shape of an SAD is thus likely to be driven by a spatial parallel of the central limit theorem, a statistically convergent process through which the SAD arises from small to large scales. Exact properties of the SAD are driven by species spatial turnover and the spatial autocorrelation of abundances, and can be predicted using this information. The theory therefore provides a direct link between SADs and the spatial correlation structure of species distributions, and thus between several fundamental descriptors of community structure. Moreover, the statistical process described may lie behind similar frequency distributions observed in many other scientific fields.
Zobrazit více v PubMed
McGill BJ, et al. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecol Lett. 2007;10:995–1015. PubMed
Tokeshi M. Species Coexistence: Ecological and Evolutionary Perspectives. Oxford: Blackwell; 1999.
Engen S, Lande R. Population dynamic models generating the lognormal species abundance distribution. Math Biosci. 1996;132:169–183. PubMed
Hubbell SP. The Unified Theory of Biodiversity and Biogeography. Princeton: Princeton Univ Press; 2001.
Harte J, Kinzig AP, Green JL. Self-similarity in the distribution and abundance of species. Science. 1999;284:334–346. PubMed
Šizling AL, Storch D. In: Scaling Biodiversity. Storch D, Marquet PA, Brown JH, editors. Cambridge, UK: Cambridge Univ Press; 2007. pp. 77–100.
Storch D, et al. The quest for a null model for macroecological patterns: Geometry of species distributions at multiple spatial scales. Ecol Lett. 2008;11:771–784. PubMed
Nekola JC, Brown JH. The wealth of species: Ecological communities, complex systems and the legacy of Frank Preston. Ecol Lett. 2007;10:188–196. PubMed
May RM. In: Ecology and Evolution of Communities. Cody ML, Diamond JM, editors. Cambridge: The Belknap Press of Harvard Univ Press; 1975. pp. 81–120.
Williamson M, Gaston KJ. The lognormal distribution is not an appropriate null hypothesis for the species-abundance distribution. J Anim Ecol. 2005;74:409–422.
Šizling AL, Storch D, Reif J, Gaston KJ. Invariance in species-abundance distributions. Theoretical Ecology. 2008 doi 10.1007/s12080–008-0031–3. PubMed DOI
Storch D, Šizling AL. The concept of taxon invariance in ecology: Do diversity patterns vary with changes in taxonomic resolution? Folia Geobotanica. 2008 doi: 10.1007/s12224–008-9015–8. DOI
McGill BJ. Does mother nature really prefer rare species or are log-left-skewed SADs sampling artefact? Ecol Lett. 2003;6:766–773.
Marquet PA, Keymer JE, Cofré H. In: Macroecology: Concepts and consequences. Blackburn TM, Gaston KJ, editors. Oxford: British Ecological Society and Blackwell Science Ltd; 2003. pp. 64–81.
Kallenberg O. Foundations of Modern Probability. New York: Springer-Verlag; 1997.
Condit R. Tropical Forest Census Plots. Berlin, Germany, and Georgetown, Texas: Springer-Verlag and R. G. Landes Company; 1998.
Hubbell SP, et al. Light gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science. 1999;283:554–557. PubMed
Wasserman LA. All of Statistics: A Concise Course in Statistical Inference. Berlin: Springer-Verlag; 2004.
Gaston KJ, Evans KL, Lennon JJ. In: Scaling Biodiversity. Storch D, Marquet PA, Brown JH, editors. Cambridge: Cambridge Univ Press; 2007. pp. 181–222.
Dolman AM, Blackburn TM. A comparison of random draw and locally neutral models for the avifauna of an English woodland. BMC Ecol. 2004;4:8. PubMed PMC
Zillio T, Condit R. The impact of neutrality, niche differentiation and species input on diversity and abundance distributions. Oikos. 2007;116:931–940.
Allen CR. Patterns in body mass distribution: sifting among alternative hypotheses. Ecol Lett. 2006;9:630–643. PubMed
Storch D, Gaston KJ. Untangling ecological complexity on different scales of space and time. Basic Appl Ecol. 2004;5:389–400.
Green JL, Plotkin JB. A statistical theory for sampling species abundances. Ecol Lett. 2007;10:1037–1045. PubMed
Harte J, Kinzig AP. On the implications of species-area relationships for endemism, spatial turnover, and food web patterns. Oikos. 1997;80:417–427.