Species–Area Relationship


The species–area relationship (SAR) describes the increase in species numbers with increasing area and is often referred to as one of ecology's few genuine laws. Different (mathematical) regression models can be fitted to species–area data to generate a species–area curve. The power model, S = cAz, is the most commonly used, but more than 20 models have been proposed. Community ecologists of the early nineteenth century were the first to propose a mathematical model. Subsequently, the SAR has become an important part of biogeography, macroecology and conservation. Two main types of SARs are described: those generated from sample areas (mainlands) and those generated from isolates (islands), more or less isolated from each other. There are many applications of the SAR: to identify, explain and compare patterns in nature, to extrapolate (upscale) species numbers and to forecast changes in species numbers – for example extinctions from habitat loss.

Key Concepts

  • Species‐area curves result from graphing the model, typically either the power model or the logarithmic model, fitted to species‐area data.
  • Isolate species–area relationships (iSARs) result from a comparison of the number of species on oceanic islands or other types of isolates, for example mountain tops (‘sky islands’) or forest remnants.
  • Sample‐area species–area relationships (saSARs) result by accumulating the number of species when adding new, typically continuous, sample areas (or increase the surveyed area) on mainlands (also referred to as ‘mainland SARs’).
  • Nested sampling areas are spatially organised so that each smaller area is completely contained within the next area, larger than the previous.
  • The z‐value is the exponent of the power‐law SAR and the most preferred SAR measurement. It is often described as the ‘slope’, because it becomes the slope in log–log space, although in reality, z is the rate at which the species–area curve decelerates.
  • Self‐similarity (or scale invariance), resulting if the species–area relationship is power law, causes the same proportional (or percentage) increase in species number for each doubling of area size. The z‐value controls this proportion.
  • Minimum‐area effects (MAEs) result from resource restrictions, when the isolate (or island) becomes too small to sustain viable populations of some species.
  • The equilibrium theory of island biogeography was proposed by MacArthur and Wilson in 1967 to explain species richness of oceanic islands, and also applies to other isolates.

Keywords: species diversity; species area; sample area; isolate; island biogeography; community ecology; power law; z‐value; species extinction; extrapolation

Figure 1. Species–area relationships (SARs) in log–log space, where power‐law SARs become straight lines and logarithmic SARs become convex. We see that one of Gleason's data sets is power law, contrary to his claims. The data points from Gleason's ‘scattered’ (continuous) sample areas fits the logarithmic model, and the data points from the continuous (nested) sample areas fit the power model.
Figure 2. (a) The expected difference between the curve shapes of sample‐area (mainland) and isolate (island) SARs. Islands and other isolates typically have fewer species than same‐size sample areas, because of minimum‐area effects (represented by grey shading). (b) The two processes of species extinction from habitat loss: (1) the original number of species, (2) the number after the immediate extinctions from decreased area and (3) the number after relaxation of species numbers down to equilibrium.
Figure 3. (a) The P2‐model fitted in transformed (log–log) space to Deshaye and Morisset's (1988) data of plants on Canadian island (Richmond Gulf). This SAR is distinctly sigmoid (though convex upward in log–log space, wherefore the inflection point is indicated), and the sigmoid P2‐model fits better than for example the convex power model. (b) Illustration of how log‐transforming the dependent variable (S) can affect the shape of the fitted SAR, based on Wright's data from the West Indies. The unbroken species–area curve is fitted with log S and the dashed line with (untransformed) S.
Figure 4. (a) The expected variation of the z‐value (slope in log–log space) between small (local), provincial (regional) and interprovincial (continental) scales to form a triphasic sample‐area SAR. From Preston's original bird data set. (b) A logarithmic breakpoint regression fitted to Nierings data on plants of the Kapingamaringi Atoll, illustrating how researchers have chosen to identify what is called the small‐island effect. Note that the curve in (b) is plotted in log‐linear space (whereas the curves in (a) are plotted in log–log space).
Figure 5. (a) The differences between power‐law species–area curves (plotted in log–log space) for birds (open squares and dashed lines) and reptiles (filled circles and unbroken line), recalculated from Wright's data of the West Indies. (b) The difference between mainland (filled squares and dashed line) and island (open circles and unbroken line) power‐law species–area curves, calculated from Heaney's data for mammalians in parts of Central Asia.


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Further Reading

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Tjørve, Even, and Tjørve, Kathleen MC(Jan 2017) Species–Area Relationship. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0026330]