The Anthropocene has heralded an unprecedented loss of biodiversity, primarily due to changes in land use and land cover, climate change, pollution, (over-)exploitation, and biological invasions. Our results confirm the utility of satellite-derived land cover maps in supporting high-resolution species distribution modeling and suggest there is scope to monitor changes in species distributions over time given the dense time series provided by products such as DW. Partial dependencies in our models showed that increasing the proportion of grassland habitat is positively associated with wild bee species richness, thereby justifying bee conservation schemes that aim to enhance semi-natural grassland habitat. While the differences in RMSE we observed between models were small, they may be significant when such models are used to prioritize grassland patches within a landscape for conservation subsidies or management policies. When validating the model predictions of bee species richness against citizen science data on solitary bee occurrences using generalized linear models, we found that ELC10 performed best ( AIC = 2278 ± 4), followed by WC ( AIC = 2367 ± 3), and DW ( AIC = 2376 ± 3).

All satellite-derived maps outperformed a manually mapped Norwegian land cover dataset called AR5 ( RMSE = 3.02 ± 0.02). Models using grassland habitat from DW performed best ( RMSE = 2.8 ± 0.03 average ± standard deviation across models), followed by ELC10 ( RMSE = 2.85 ± 0.03) and WC ( RMSE = 2.87 ± 0.02). The proportion of grassland habitat within 250 m (derived from the land cover maps), along with temperature and distance to sandy soils, were used as predictors in both Bayesian regularized neural network and random forest models.

Here we compare the performance of three popular Sentinel-2 (10-m) land cover maps, including dynamic world (DW), European land cover (ELC10), and world cover (WC), in predicting wild bee species richness over southern Norway.

Recent advances in remote sensing and machine learning have allowed for high-resolution species distribution modeling that can inform landscape-level decision-making.

Mapping the spatial and temporal dynamics of species distributions is necessary for biodiversity conservation land-use planning decisions.

Find address on satellite map