General indicators at 1 km resolution

Observed richness is calculated by summing the number of unique species observed for each year or each cell. Observed richness is highly dependent on the comprehensiveness of the dataset it is being applied to. If some regions are more intensively, carefully or systematically sampled than others, this will likely reflect as higher observed richness. Observed richness also depends on the relative abundance and spatial aggregation of each species, with less abundant and less aggregated species less likely to be discovered during surveys (Hillebrand et al. in Journal of Applied Ecology, 2018), as well as the detectability of each species.

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step (TEST)
OSR_map <- obs_richness_map(Angiosperm_data_cube,
                            cell_size="grid")

OSR_ts <- obs_richness_ts(Angiosperm_data_cube, 
                          cell_size="grid")

plot_map(OSR_map,
         visible_gridlines = FALSE)

plot_ts(OSR_ts)

The total number of occurrences is calculated by summing the occurrences of all species observed for each cell or year. While not itself an indicator, this variable provides an overview of the comprehensiveness and distribution of data in the cube being analysed, and may be helpful, or even vital, for interpreting the results of calculated indicators.

Show the code 
#load library
library(b3gbi)

TO_map <- total_occ_map(Angiosperm_data_cube,
                            cell_size="grid")

TO_ts <- total_occ_ts(Angiosperm_data_cube, 
                          cell_size="grid")

plot_map(TO_map, 
         visible_gridlines = FALSE)

plot_ts(TO_ts)

Species evenness is a commonly used indicator that measures how uniformly individuals are distributed across species in a region or over time. It provides a complement to richness by taking relative abundance into account. Although GBIF provides information about abundances as individual counts, the majority of entries lack this information. Hence, evenness can only be calculated using the proportions of observations rather than proportions of individuals. Strictly speaking, the evenness measures therefore indicate how uniformly species are represented in the respective data set rather than the true evenness of the ecological community.

Pielou’s Evenness

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step
PEM_map <-  pielou_evenness_map(Angiosperm_data_cube,
                            cell_size="grid")

PEM_ts <-  pielou_evenness_ts(Angiosperm_data_cube,
                            cell_size="grid")

plot_map(PEM_map, visible_gridlines = FALSE)

plot_ts(PEM_ts)

Williams’ Evenness

Show the code 
#load library
library(b3gbi)

WEM_map <- williams_evenness_map(Angiosperm_data_cube,
                            cell_size="grid")

WEM_ts <- williams_evenness_ts(Angiosperm_data_cube,
                            cell_size="grid")

plot_map(WEM_map,
         visible_gridlines = FALSE)

plot_ts(WEM_ts)

Rarity quantifies the scarcity or infrequency of species, and when summed over multiple species, serves as a crucial biodiversity indicator for conservation.A rare species might have a small population size, a limited distribution, or a unique ecological niche (Maciel, 2021; Rabinowitz, 1981). Rarity can also be a biodiversity indicator when summed over multiple species in an area, and may provide important insight for determining conservation priorities. When measured over time, rarity may indicate potential threats or changes in the environment. b3gbi offers two distinct measures: Abundance-Based Rarity (based on species’ proportional occurrences) and Area-Based Rarity (based on species’ spatial occupancy). These can be mapped (indicator_map) to identify areas with a higher presence of rare species, or tracked over time (indicator_ts) to observe changes in overall rarity.

Abundance - Based Rarity

Show the code 
#load library
library(b3gbi)

AbRM_map <- ab_rarity_map(Angiosperm_data_cube,
                            cell_size="grid")

AbRM_ts <- ab_rarity_ts(Angiosperm_data_cube,
                            cell_size="grid")

plot_map(AbRM_map)

plot_ts(AbRM_ts)

Area-based rarity

Show the code 
#load library
library(b3gbi)

ArRM_map <-  area_rarity_map(Angiosperm_data_cube,
                            cell_size="grid")

ArRM_ts <-  area_rarity_ts(Angiosperm_data_cube,
                           cell_size="grid")

plot_map(ArRM_map, visible_gridlines = FALSE)

plot_ts(ArRM_ts)

Hill Diversity provides a unified framework for various diversity measures, allowing for different emphases on rare versus common species through a single parameter, q. b3gbi calculates three common forms: q=0 (approximates Species Richness, weighing all species equally), q=1 (emphasizes common species, like Hill-Shannon), and q=2 (emphasizes very common species, like Hill-Simpson). These indicators represent the “effective number of species” and can be mapped (indicator_map) or tracked over time (indicator_ts) to provide multi-faceted insights into biodiversity patterns.

Species richness (q=0)

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step
SR_map <-  hill0_map(Angiosperm_data_cube,
                            cell_size="grid")

SR_ts <-  hill0_ts(Angiosperm_data_cube,
                            cell_size="grid")

plot_map(SR_map, visible_gridlines = FALSE)

plot_ts(SR_ts)

Hill - Shanon (q=1)

Show the code 
#load library
library(b3gbi)

HSh_map <- hill1_map(Angiosperm_data_cube,
                            cell_size="grid")



HSh_ts <- hill1_ts(Angiosperm_data_cube,
                           cell_size="grid")

plot_map(HSh_map,
         visible_gridlines = FALSE)

plot_ts(HSh_ts)

`

Hill - Simpson (q=2)

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step
HSi_map <- hill2_map(Angiosperm_data_cube,
                            cell_size="grid")


HSi_ts <- hill2_ts(Angiosperm_data_cube,
                            cell_size="grid")

plot_map(HSi_map,
         visible_gridlines = FALSE)

plot_ts(HSi_ts)

This indicator tracks the total number of unique species observed from the beginning of a specified time period up to a given year. It provides an estimation of how many new species are still being recorded over time within a region, helping to evaluate sampling effort and assess the overall recorded biodiversity over the study duration. This is an inherently temporal indicator, presented as a time series (indicator_ts).

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step
CR_ts <- cum_richness_ts(Angiosperm_data_cube, 
                         cell_size="grid")

plot_ts(CR_ts)

This variable calculates the average year of occurrence for all records within a given spatial unit (e.g., grid cell, for indicator_map) or temporal unit (e.g., year, for indicator_ts), providing an estimation of the relative recency of observations. Maps can highlight areas with more recent average records, while time series show the average observation date over cumulative data.

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step
NN_map <- newness_map(Angiosperm_data_cube,
                            cell_size="grid")

#TODO;check dat NA buiten Vlaanderen resultaat niet beïnvloedt?
NN_ts <- newness_ts(Angiosperm_data_cube, 
                          cell_size="grid")

plot_map(NN_map,
         visible_gridlines = FALSE)

plot_ts(NN_ts)

Occurrence Density measures the spatial concentration of records by calculating the total number of occurrences per square kilometre. This allows for more meaningful comparisons between spatial units of different sizes. It can be displayed as a map (indicator_map) illustrating areas with higher concentrations of records, or as a time series (indicator_ts) showing changes in the rate of recording over time for the study area.

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step
OD_map <- occ_density_map(Angiosperm_data_cube,
                            cell_size="grid")

plot_map(OD_map,
         visible_gridlines = FALSE)

Occupancy turnover measures the change in species composition over time, reflecting the rate at which species appear or disappear from a given area. It provides insights into the dynamic nature of ecological communities, highlighting shifts in species distributions and potential environmental changes. High turnover rates may indicate rapid community restructuring, potentially driven by factors such as habitat alteration, climate change, or invasive species. Analyzing occupancy turnover can be crucial for understanding ecosystem stability, identifying areas of conservation concern, and assessing the effectiveness of management strategies.

Show the code 
#load library
library(b3gbi)

#Execute preprocessing step
OT_ts <- occ_turnover_ts(Angiosperm_data_cube,
                         cell_size="grid")

plot_ts(OT_ts)
Warning: Removed 1 row containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).