Species-Area Curve Calculator

The species-area relationship (SAR) describes how the number of species found in a habitat increases with the size of that habitat. It is one of the most fundamental patterns in ecology and biogeography, observed consistently across different taxa, regions, and spatial scales.

The classic formula is S = c × A^z, where S is the number of species, A is the area of the habitat, c is a constant that represents species richness per unit area (varies by ecosystem and taxon), and z is the slope of the relationship on a log-log scale. The log-linear form is log(S) = log(c) + z × log(A).

The z exponent typically falls between 0.1 and 0.5. For oceanic islands, z averages around 0.25–0.35. For mainland habitat patches (like forest fragments), z tends to be lower, around 0.12–0.25. A z of 0.3 is commonly used as a general rule of thumb.

The c constant is a taxon- and region-specific parameter that captures species richness in a unit area. It reflects factors like climate, productivity, evolutionary history, and dispersal ability. Higher c values indicate more species-rich systems for a given area.

The SAR is widely applied to estimate species loss from habitat destruction. By comparing S values at original and reduced habitat areas, ecologists can project how many species may go extinct as habitats shrink. It underpins many assessments of biodiversity risk associated with deforestation and land conversion.

Island SARs (sampling across true islands or isolated patches) tend to produce steeper z values (~0.25–0.35) because immigration is limited and extinction risk is higher. Mainland SARs (sampling nested areas within a continuous habitat) typically yield shallower slopes (~0.10–0.20) because species can recolonize from surrounding areas.

This calculator models the theoretical species-area power law (S = cA^z), which is distinct from empirical species accumulation curves (which are built from sequential sampling data). However, both describe how observed species richness increases with sampling effort or area, and the outputs here can serve as a theoretical baseline for comparison.

On a log-log plot, the species-area relationship becomes linear: log(S) = log(c) + z × log(A). The slope of this line is z, and the intercept is log(c). This transformation is useful because it linearizes the power law, making it easier to fit the model to empirical data using regression.