library(climniche)
case_path <- system.file("extdata/mediterranean_anchovy", package = "climniche")
case_cells <- read.csv(
file.path(case_path, "anchovy_climniche_input_sample.csv"),
check.names = FALSE
)
layer_manifest <- read.csv(
file.path(case_path, "anchovy_biooracle_layer_manifest.csv")
)
sdm_settings <- read.csv(
file.path(case_path, "anchovy_presence_background_sdm_settings.csv")
)
metric_weights <- read.csv(
file.path(case_path, "anchovy_climniche_sensitivity_weights.csv")
)
Climatic data
The six retained variables describe temperature, salinity, pH and water movement. Current and future layers have identical variables, resolution and cell order.
variable_table <- subset(
layer_manifest,
retained_for_climniche,
c("variable", "label", "depth", "current_time", "future_time")
)
variable_table[["future_scenario"]] <- "SSP2-4.5"
variable_table
#> variable label depth
#> 1 temperature_mean Mean temperature surface (depthsurf)
#> 2 temperature_range Temperature range surface (depthsurf)
#> 3 salinity_range Salinity range surface (depthsurf)
#> 5 ph_mean Mean pH surface (depthsurf)
#> 6 sea_water_speed_mean Mean current speed surface (depthsurf)
#> 7 sea_water_speed_range Current speed range surface (depthsurf)
#> current_time future_time
#> 1 Bio-ORACLE v3 baseline time layers averaged 2050-01-01T00:00:00Z
#> 2 Bio-ORACLE v3 baseline time layers averaged 2050-01-01T00:00:00Z
#> 3 Bio-ORACLE v3 baseline time layers averaged 2050-01-01T00:00:00Z
#> 5 Bio-ORACLE v3 baseline time layers averaged 2050-01-01T00:00:00Z
#> 6 Bio-ORACLE v3 baseline time layers averaged 2050-01-01T00:00:00Z
#> 7 Bio-ORACLE v3 baseline time layers averaged 2050-01-01T00:00:00Z
#> future_scenario
#> 1 SSP2-4.5
#> 2 SSP2-4.5
#> 3 SSP2-4.5
#> 5 SSP2-4.5
#> 6 SSP2-4.5
#> 7 SSP2-4.5
The diagonal climatic metric uses the ratio of current-domain variance to occurrence-cell variance, bounded to 0.25-4 and normalised to mean one. These weights define the relative distance scale for this case study; they are not physiological sensitivity estimates.
weight_table <- merge(
variable_table[c("variable", "label")],
metric_weights[c("variable", "sensitivity_weight")],
by = "variable",
sort = FALSE
)
names(weight_table) <- c(
"Variable", "Climate variable", "Climatic metric weight"
)
weight_table
#> Variable Climate variable Climatic metric weight
#> 1 temperature_mean Mean temperature 0.7709378
#> 2 temperature_range Temperature range 0.9848117
#> 3 salinity_range Salinity range 2.0457639
#> 4 ph_mean Mean pH 0.7755083
#> 5 sea_water_speed_mean Mean current speed 0.6458254
#> 6 sea_water_speed_range Current speed range 0.7771529
The package contains a 2,000-cell extract of the prepared raster inputs so that the central calculation runs when this vignette is built. The maps later in the vignette come from the full Mediterranean raster analysis.
climate_variables <- variable_table[["variable"]]
current_columns <- paste0(climate_variables, "_current")
future_columns <- paste0(climate_variables, "_future")
current_climate <- as.matrix(case_cells[current_columns])
future_climate <- as.matrix(case_cells[future_columns])
colnames(current_climate) <- climate_variables
colnames(future_climate) <- climate_variables
weight_row <- sdm_settings[["setting"]] == "sdm_threshold"
sdm_threshold <- as.numeric(sdm_settings[weight_row, "value"])
climatic_weights <- setNames(
metric_weights[["sensitivity_weight"]],
metric_weights[["variable"]]
)
Climate exposure relative to the current niche
Suitability values at or below the SDM threshold receive zero reference weight. Values above the threshold retain their continuous values.
anchovy_fit <- fit_climniche(
current = current_climate,
future = future_climate,
occupied = case_cells[["sdm_suitability"]],
occupied_threshold = sdm_threshold,
sensitivity = climatic_weights,
preprocess = FALSE
)
climniche_summary(anchovy_fit)
#> Scope Cells Reference weight sum Boundary quantile Boundary distance
#> current 1200 825.3044 0.95 5.241434
#>
#> Metric Mean Median 90th percentile
#> Climatic Displacement 3.519480 3.509524 3.7255453
#> Niche Distance Shift 2.184474 2.530491 2.9668506
#> Climatic Reconfiguration 2.553753 2.376605 3.4869382
#> Niche Boundary Exceedance 0.140777 0.000000 0.2373889
Let current and future climatic conditions at cell \(i\) be \(c_i\) and \(f_i\), the centre of the current climatic niche reference be \(\mu\), and the fitted climatic distance be \(d_A\). The reported quantities are
\[
\begin{aligned}
D_i &= d_A(f_i,c_i),\\
R_i &= d_A(f_i,\mu)-d_A(c_i,\mu),\\
C_i &= \sqrt{\max(0,D_i^2-R_i^2)},\\
E_i &= \max(0,d_A(f_i,\mu)-B_q).
\end{aligned}
\]
Climatic Displacement (\(D_i\)) and Niche Distance Shift (\(R_i\)) form the primary geometric pair. Climatic Reconfiguration (\(C_i\)) is derived from them as the non-radial component of Climatic Displacement; it is not an independently estimated process. Niche Boundary Exceedance (\(E_i\)) compares the future distance with the weighted empirical radial boundary \(B_q\).
The identity can be checked directly from the fitted object.
geometry_error <- with(
anchovy_fit,
climate_change_amount^2 -
niche_distance_change^2 -
climate_reconfiguration^2
)
max(abs(geometry_error))
#> [1] 3.552714e-15
Niche Boundary Exceedance describes the future boundary-relative state. A positive value is an excess distance beyond \(B_q\); it does not by itself show that the cell crossed the boundary between current and future conditions.
Mediterranean maps
The full raster workflow uses the same climatic variables and reference weights.
spatial_fit <- fit_climniche_terra(
current = current_layers[[climate_variables]],
future = future_layers[[climate_variables]],
occupied = anchovy_suitability,
occupied_threshold = sdm_threshold,
domain = mediterranean_sea_mask,
sensitivity = climatic_weights,
preprocess = FALSE
)
metric_maps <- plot_climniche_maps(
spatial_fit,
occupied = anchovy_suitability,
occupied_only = TRUE,
occupied_threshold = sdm_threshold,
study_region = mediterranean_boundary,
degree_labels = "hemisphere",
legend_title = FALSE,
legend_position = "bottom"
)
metric_maps
