Code and data

Builds monthly habitat suitability models for high-abundance zooplankton patches ("tau-patches"), where a patch is defined by a species-specific abundance threshold. Point-and-click Shiny app or YAML-driven package.

remotes::install_github("chross22/taupatch")

config <- load_config("study.yaml")
run_taupatch_app()   # or use the config

Fits a set of candidate detection functions to line-transect distance data and compares them on the quantities that actually matter for abundance — effective strip half-width and its coefficient of variation — rather than on the Akaike information criterion alone. Includes the gamma key that Distance::ds() does not offer.

remotes::install_github("chross22/dsfit")

sw <- sweep_models(
  model_set(key = c("hn", "hr")),
  data = detections
)

Reads marine mammal survey data recorded in the North Atlantic Right Whale Consortium (NARWC) database format and puts it into a single, predictable shape, reconciling the column names that different survey programs use for the same thing.

remotes::install_github("chross22/narwcr")

dat <- read_narwc("extract.csv")
narwc_column_mapping(dat)

Turns aerial line-transect survey data from the NARWC database into effort segments ready for Distance and dsm — each with a location, an amount of effort, and counts of what was seen along it. Where the survey recorded declination angles, perpendicular distances come out in the shape a detection function wants.

remotes::install_github("chross22/distsamp")

dat  <- read_narwc(path)
validate_narwc(dat)   # never stops
segs <- segment_survey(dat, 5)

Fetches Copernicus Marine and other ocean and atmosphere data and joins it to point observations in space and time, at the source data's own resolution. The join is a general spatiotemporal nearest-feature match, so either side may be the species data.

remotes::install_github("chross22/datamatch")

sst <- accessCopernicus("thetao", bb)
pts <- matchData(sightings, sst)
source_of(pts)

Computes derived oceanographic covariates from gridded ocean data — spatial and temporal gradients, time-integrated variables, calendar-unit lags, eddy kinetic energy, Lyapunov exponents, and distances to fronts and shore.

remotes::install_github("chross22/derivoce")

grad  <- horizontal_gradient(sst)
shore <- distance_to_shore(grid)

Turns fitted models into manuscript-ready figures. Every effect curve is paired with a rug of the raw data stacked directly above it, so a bend in the curve can be read against how much data actually supports it. GAMs via gratia, everything else via marginaleffects.

remotes::install_github("chross22/fancyfx")

fit <- mgcv::gam(y ~ s(sst), data = d)
plotEffects(fit, d, "sst")