Sampling Effort Species-Area Relationship (SESARS)

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().

Value

An object of class spacc_sesars containing:

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

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.

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.

Examples

# \donttest{
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)
# }