Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
Biodiversity Data Guide
Access, analyze, and visualize biodiversity data from global databases including GBIF, iNaturalist, and GenBank for ecological and evolutionary research.
Major Biodiversity Data Sources
Database Content Records API Cost GBIF Species occurrence records 2.4B+ Yes Free iNaturalist Citizen science observations 180M+ Yes Free GenBank (NCBI) Genetic sequences 250M+ Yes Free BOLD Systems DNA barcode records 15M+ Yes Free eBird Bird observations 1.3B+ Yes Free IUCN Red List Conservation status 160,000+ Yes Free (with key) OBIS Marine biodiversity 100M+ Yes Free Catalogue of Life Taxonomic backbone 2M+ species Yes Free TRY Plant Trait Plant functional traits 12M+ Request Free WorldClim Climate data (rasters) Global Download Free
Querying GBIF (Species Occurrences)
Python (pygbif)
from pygbif import species as sp
from pygbif import occurrences as occ
# Search for a species by name
name_result = sp.name_backbone(name="Panthera tigris", rank="species")
taxon_key = name_result["usageKey"]
print(f"GBIF taxon key: {taxon_key}")
print(f"Status: {name_result['status']}")
print(f"Kingdom: {name_result['kingdom']}")
# Get occurrence records
results = occ.search(
taxonKey=taxon_key,
hasCoordinate=True, # Only georeferenced records
country="IN", # India
limit=100,
year="2020,2024", # Year range
basisOfRecord="HUMAN_OBSERVATION"
)
print(f"Total records matching: {results['count']}")
for record in results["results"][:5]:
print(f" [{record.get('year')}] {record.get('decimalLatitude'):.4f}, "
f"{record.get('decimalLongitude'):.4f} - {record.get('datasetName', 'N/A')}")
R (rgbif) library(rgbif)
library(sf)
library(ggplot2)
# Get occurrence data
tiger_key <- name_backbone(name = "Panthera tigris")$usageKey
occurrences <- occ_search(
taxonKey = tiger_key,
hasCoordinate = TRUE,
limit = 500,
year = "2020,2024",
basisOfRecord = "HUMAN_OBSERVATION"
)
# Convert to spatial data
occ_df <- occurrences$data
coords <- occ_df[, c("decimalLongitude", "decimalLatitude")]
occ_sf <- st_as_sf(coords, coords = c("decimalLongitude", "decimalLatitude"),
crs = 4326)
# Map occurrences
world <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf")
ggplot() +
geom_sf(data = world, fill = "grey90") +
geom_sf(data = occ_sf, color = "red", size = 1, alpha = 0.5) +
coord_sf(xlim = c(60, 150), ylim = c(-10, 50)) +
labs(title = "Panthera tigris occurrences (2020-2024)") +
theme_minimal()
ggsave("tiger_map.pdf", width = 10, height = 6)
Species Distribution Modeling
MaxEnt Workflow library(dismo)
library(raster)
# 1. Get occurrence data
occ_data <- occ_search(taxonKey = tiger_key, hasCoordinate = TRUE,
limit = 1000)$data
occ_points <- occ_data[, c("decimalLongitude", "decimalLatitude")]
occ_points <- na.omit(occ_points)
# 2. Get environmental predictors (WorldClim bioclimatic variables)
bioclim <- getData("worldclim", var = "bio", res = 10)
# bio1 = Annual Mean Temperature
# bio12 = Annual Precipitation
# bio4 = Temperature Seasonality
# ... (19 bioclimatic variables total)
# 3. Extract environmental values at occurrence points
env_values <- extract(bioclim, occ_points)
# 4. Generate background (pseudo-absence) points
bg_points <- randomPoints(bioclim, n = 10000)
# 5. Fit MaxEnt model
me_model <- maxent(bioclim, occ_points, a = bg_points,
args = c("betamultiplier=1.5",
"responsecurves=true"))
# 6. Predict habitat suitability
prediction <- predict(me_model, bioclim)
plot(prediction, main = "Predicted Habitat Suitability")
points(occ_points, pch = 16, cex = 0.5)
# 7. Evaluate model
eval_result <- evaluate(me_model, p = occ_points, a = bg_points,
x = bioclim)
print(paste("AUC:", round(eval_result@auc, 3)))
Phylogenetic Analysis
Building a Phylogeny library(ape)
library(phytools)
# Read alignment (FASTA format)
alignment <- read.FASTA("aligned_sequences.fasta")
# Distance-based tree (Neighbor-Joining)
dist_matrix <- dist.dna(alignment, model = "TN93")
nj_tree <- nj(dist_matrix)
# Root the tree
rooted_tree <- root(nj_tree, outgroup = "outgroup_species")
# Plot phylogeny
plot(rooted_tree, type = "phylogram", cex = 0.8)
axisPhylo()
# Maximum likelihood tree (using phangorn)
library(phangorn)
data_phyDat <- phyDat(alignment, type = "DNA")
ml_tree <- pml_bb(data_phyDat, model = "GTR+G+I",
rearrangement = "NNI")
Comparative Methods library(caper)
# Phylogenetic independent contrasts
# Test whether body mass predicts home range size
# while accounting for phylogenetic relatedness
trait_data <- data.frame(
species = c("Sp_A", "Sp_B", "Sp_C", "Sp_D"),
body_mass = c(5.2, 12.1, 3.8, 45.0),
home_range = c(10, 25, 8, 120)
)
# Create comparative data object
comp_data <- comparative.data(
phy = rooted_tree,
data = trait_data,
names.col = species,
vcv = TRUE
)
# Phylogenetic Generalized Least Squares (PGLS)
pgls_model <- pgls(log(home_range) ~ log(body_mass),
data = comp_data,
lambda = "ML") # Estimate Pagel's lambda
summary(pgls_model)
Ecological Data Analysis
Diversity Metrics import numpy as np
from scipy.stats import entropy
def calculate_diversity(abundance_vector):
"""Calculate common biodiversity metrics."""
n = np.array(abundance_vector)
N = n.sum()
p = n / N # Relative abundances
p = p[p > 0] # Remove zeros
return {
"species_richness": len(n[n > 0]),
"shannon_H": entropy(p, base=np.e),
"simpson_D": 1 - np.sum(p**2),
"evenness_J": entropy(p, base=np.e) / np.log(len(p)),
"fisher_alpha": estimate_fisher_alpha(n),
"total_abundance": int(N)
}
def estimate_fisher_alpha(n):
"""Estimate Fisher's alpha diversity parameter."""
from scipy.optimize import brentq
S = len(n[n > 0])
N = n.sum()
def equation(alpha):
return alpha * np.log(1 + N/alpha) - S
try:
return brentq(equation, 0.1, 1000)
except ValueError:
return np.nan
# Example: Bird community survey
abundances = [45, 23, 12, 8, 5, 3, 2, 1, 1]
metrics = calculate_diversity(abundances)
for key, val in metrics.items():
print(f" {key}: {val:.4f}" if isinstance(val, float) else f" {key}: {val}")
Community Analysis library(vegan)
# Species abundance matrix (sites x species)
community <- matrix(c(
10, 5, 3, 0, 1,
8, 12, 0, 2, 3,
0, 1, 15, 8, 0,
2, 0, 12, 10, 1
), nrow = 4, byrow = TRUE,
dimnames = list(paste0("Site", 1:4), paste0("Sp", 1:5)))
# Alpha diversity
diversity(community, index = "shannon") # Shannon H
diversity(community, index = "simpson") # Simpson 1-D
# Beta diversity (Bray-Curtis dissimilarity)
bc_dist <- vegdist(community, method = "bray")
# NMDS ordination
nmds <- metaMDS(community, distance = "bray", k = 2)
plot(nmds, type = "t")
# PERMANOVA (testing group differences)
env_data <- data.frame(habitat = c("forest", "forest", "grassland", "grassland"))
adonis2(community ~ habitat, data = env_data, method = "bray")
Data Standards and Best Practices
Darwin Core Standard Darwin Core (DwC) is the standard schema for biodiversity data exchange:
Term Description Example scientificNameFull taxonomic name "Panthera tigris (Linnaeus, 1758)" decimalLatitudeLatitude in decimal degrees 27.1751 decimalLongitudeLongitude in decimal degrees 78.0421 eventDateDate of observation "2024-03-15" basisOfRecordType of record "HUMAN_OBSERVATION" coordinateUncertaintyInMetersSpatial precision 100 institutionCodeData provider "iNaturalist"
Data Quality Checks
Coordinate validation : Flag points in oceans for terrestrial species (and vice versa)Taxonomic verification : Match names against Catalogue of Life or GBIF backboneTemporal consistency : Remove records with impossible datesDuplicate detection : Remove spatial and temporal duplicatesEnvironmental outliers : Flag occurrences in climatically unsuitable areasSampling bias correction : Use spatial thinning or bias files in SDMs 