sesars: Sampling Effort Species-Area Relationship (SESARS)

In spacc: Fast Spatial Species Accumulation Curves

Sampling Effort Species-Area Relationship (SESARS)

Description

Model the joint effect of sampling effort and area on species richness. Corrects for unequal survey intensity across sites, common in atlas data and citizen science datasets.

Usage

sesars(object, effort, model = c("power", "additive"), ...)

Arguments

object	A spacc object.
effort	Numeric vector. Sampling effort per site (e.g., hours, visits, trap-nights). Must have length equal to number of sites.
model	Character. SESARS model: "power" (default): S = c * A^z * E^w (multiplicative power law) "additive": S = c + z * log(A) + w * log(E)
...	Additional arguments passed to stats::nls() or stats::lm().

Details

Standard SARs assume complete sampling within each area unit. SESARS incorporates sampling effort (E) alongside area (A) to provide unbiased richness estimates across regions with unequal survey intensity.

Value

An object of class spacc_sesars containing:

model	Model type
fit	Fitted model object
coef	Model coefficients
data	Data frame used for fitting

References

Dennstadt, F., Horak, J. & Martin, M.D. (2019). Predictive sampling effort and species-area relationship models for estimating richness in fragmented landscapes. Diversity and Distributions, 26, 1112-1123.

See Also

extrapolate(), spacc()

Examples

coords <- data.frame(x = runif(50), y = runif(50))
species <- matrix(rbinom(50 * 30, 1, 0.3), nrow = 50)
sac <- spacc(species, coords)
effort <- rpois(50, 10)
ses <- sesars(sac, effort, model = "power")
print(ses)

spacc documentation built on June 20, 2026, 5:07 p.m.