Data from: "Unequal greening and urbanization trajectories in Santiago de Chile, and their consequences for an urban-adapted woodpecker"

Data Description This repository contains the geospatial and tabular data underlying the analysis of unequal greening trajectories and their consequences for the black-cheeked woodpecker (Veniliornis lignarius) in Santiago de Chile. grid_hex_40ha_santiago.zip — Shapefile of the regular hexagonal spatial grid (40 ha per cell) used as the primary spatial aggregation unit for covariate extraction, model fitting, and change detection, restricted to hexagons intersecting the 2017 census urban boundary of Santiago. model_data_40ha.rds — R data file (RDS) containing the model-ready dataset used to fit the candidate generalized additive models (GAMs) at the 40-ha hexagonal grain, including the zero-filled, effort-filtered, and spatio-temporally thinned eBird detection/non-detection records for V. lignarius joined to their corresponding hexagon-level environmental covariates (NDVI, EVI, Dynamic World land-cover fractions) and survey effort variables (checklist duration, distance traveled, number of observers, hour of day, year, and coordinates). dynamic_world.zip — GeoTIFF rasters (10-m resolution) of per-pixel Dynamic World V1 land-cover class fractions (trees, built, bare, grass, crops, shrub and scrub), comprising annual median composites for each year from 2015 to 2025 (centered on the austral winter solstice, July 1st–June 30th) and a single decadal median composite spanning the full study period, clipped to the Santiago urban boundary. vegetation_indices.zip — GeoTIFF rasters (10-m resolution) of Sentinel-2-derived spectral vegetation indices (NDVI, EVI), computed from cloud- and shadow-masked surface reflectance bands after compositing, comprising annual median composites for each year from 2015 to 2025 and a single decadal median composite spanning the full study period, clipped to the Santiago urban boundary. All raster layers were generated in Google Earth Engine and exported at native 10-m resolution; the hexagonal grid and model dataset were produced in R (v4.3.3) using the sf, mgcv, auk, and exactextractr packages.