RESUMEN
Rangeland ecosystems are changing worldwide with the abandonment of extensive pastoralism practices and greater interest for species coexistence. However, the lack of compiled data on current changes in the abundance and distribution of herbivores challenges rangeland management decisions. Here we gathered and made available for the first time the most extensive set of occurrence data for rangeland herbivores in Iceland in an Open Access framework for transparent and repeatable science-based decisions. We mapped fine scale species distribution overlap to identify areas at risk for wildlife-livestock conflict and overgrazing. Nationwide and long term (1861-2021) occurrence data from 8 independent datasets were used alongside 11 predictor raster layers ("Big Data") to data mine and map the distribution of the domestic sheep (Ovis aries), feral reindeer (Rangifer tarandus tarandus), pink-footed geese (Anser brachyrhynchus), and rock ptarmigan (Lagopus muta islandorum) over the country during the summer. Using algorithms of Maxent in R, RandomForest, TreeNet (stochastic gradient boosting) and MARS (Splines) in Minitab-SPM 8.3, we computed 1 km pixel predictions from machine learning-based ensemble models. Our high-resolution models were tested with alternative datasets, and Area Under the Curve (AUC) values that indicated good (reindeer: 0.8817 and rock ptarmigan: 0.8844) to high model accuracy (sheep: 0.9708 and pink-footed goose: 0.9143). Whenever possible, source data and models are made available online and described with ISO-compliant metadata. Our results illustrate that sheep and pink-footed geese have the greatest overlap in distribution with potential implication for wildlife-livestock conflicts and continued ecosystem degradation even under diminishing livestock abundance at higher elevation. These nationwide models and data are a global asset and a first step in making available the best data for science-based sustainable decision-making about national herbivores affecting species coexistence and environmental management.