spacc

species accumulating in the neighbourhood

Spatial species accumulation curves built by nearest-neighbour expansion in C++.

Pick a starting site, then walk outward to its nearest neighbours, counting new species as you go. spacc does that from many starting points (Rcpp/RcppParallel backend), so the curve carries a confidence ribbon showing how richness depends on where you begin. The classical curve in vegan::specaccum() shuffles sites in random order and never sees the map. spacc reproduces that random-order curve and adds the spatial one, so you can read the gap between them.

library(spacc)

coords  <- data.frame(x = runif(100), y = runif(100))
species <- matrix(rbinom(100 * 50, 1, 0.3), nrow = 100)

# spatial accumulation: walk outward by nearest neighbour
sac <- spacc(species, coords, method = "knn", n_seeds = 100)
plot(sac)

Spatial order, not random order

vegan::specaccum() accumulates sites in random or collector order, so geography drops out. spacc() keeps it: each curve follows nearest-neighbour expansion from a real seed site, and the spread across seeds is the spatial signal. Run both and compare them directly.

sac        <- spacc(species, coords, method = "knn",    n_seeds = 100)
sac_random <- spacc(species, coords, method = "random", n_seeds = 100)

comp <- compare(sac, sac_random)   # permutation, bootstrap, or AUC test
plot(comp)

Seven expansion methods are available: knn and kncn (nearest-neighbour and nearest-centroid walks), radius, gaussian, and cone (distance- and direction-weighted growth), random (the classical null), and collector (data order). Geographic coordinates use distance = "haversine" with no projection step.

Backend selection

Nearest-neighbour queries pick a backend by site count, overridable with backend.

Backend	Distance	Cost per query	When
Exact (brute force)	any	O(n)	<= 500 sites (auto)
k-d tree (nanoflann)	Euclidean	O(log n)	> 500 sites (auto)
Ball tree	Haversine	O(log n)	> 500 sites, `distance = "haversine"`

sac <- spacc(species, coords_lonlat, distance = "haversine", n_seeds = 50)
sac <- spacc(species, coords, backend = "kdtree", n_seeds = 50)

Diversity beyond richness

Accumulation works for more than species counts. Each function carries the spatial ordering through to a different diversity measure, with map = TRUE and as_sf() for GIS output.

spaccHill(): Hill numbers (q = 0, 1, 2) along the accumulation, extending the iNEXT framework to spatial order.
spaccBeta(): beta diversity with turnover and nestedness components (Baselga 2010).
spaccCoverage(): coverage-based rarefaction via the Good-Turing estimator (Chao & Jost 2012).
spaccPhylo() / spaccFunc(): phylogenetic (MPD, MNTD) and functional (FDis, FRic) accumulation.
alphaDiversity() / gammaDiversity() / diversityPartition(): local, regional, and alpha-beta-gamma decomposition (Jost 2007).

hill <- spaccHill(species, coords, q = c(0, 1, 2), n_seeds = 50, map = TRUE)
plot(hill)                       # accumulation curves
plot(hill, type = "map", q = 0)  # per-site richness map

beta <- spaccBeta(species, coords, index = "sorensen", n_seeds = 50)
plot(beta, partition = TRUE)     # turnover vs nestedness

Grouped and spatiotemporal accumulation

Split species into groups (native vs alien, families, any factor) and accumulate each under the same spatial site ordering, or add a temporal axis to weight space against time.

# same spatial walk, separate curve per group
sac_grouped <- spacc(species, coords, groups = status, seed = 42)
plot(sac_grouped, facet = TRUE)

# sites sampled across years: weighted space-time distance
sac_st <- spacc(species, coords, method = "knn",
                time = site_years, w_space = 1, w_time = 0.5)

Fitting and standardizing

extrapolate(): asymptotic richness models (Michaelis-Menten, Lomolino, Weibull, logistic, EVT).
compare(): permutation, bootstrap, or AUC tests between curves.
rarefy() / subsample(): rarefaction to common effort, spatial subsampling.
coleman() / mao_tau() / spatialRarefaction(): analytical (non-simulation) curves.
as_spacc(): bring an existing vegan::specaccum() object into the same plotting and comparison machinery.

Installation

install.packages("pak")
pak::pak("gcol33/spacc")

Documentation

Support

“Software is like sex: it’s better when it’s free.” — Linus Torvalds

I’m a PhD student who builds R packages in my free time because I believe good tools should be free and open. I started these projects for my own work and figured others might find them useful too.

If this package saved you some time, buying me a coffee is a nice way to say thanks. It helps with my coffee addiction.

License

MIT (see the LICENSE.md file)

Citation

@software{spacc,
  author = {Colling, Gilles},
  title = {spacc: Fast Spatial Species Accumulation Curves},
  year = {2025},
  url = {https://github.com/gcol33/spacc}
}