Marshall et al. Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type. Ecology and Evolution 2015; 5(19): 4426–4436. DOI: 10.1002/ece3.1579
Pollinators are ecologically and economically important, but have been in decline. Some conservation initiatives have been implemented, but the effectiveness depends on the characteristics of the surrounding landscape and other environmental variables. Creating species distribution models (SDM) for wild bees can be challenging given their high mobility. Additionally, SDMs the often data aggregated over number of years and are rarely validated with external data. Authors examine the performance of SDMs in correctly predicting wild bee occurrences from field surveys. Furthermore, they attempt to identify species and/or traits that are better suited to SDMs.
They expect species with highly specialized habitat needs or rare species to have higher predicted habitat suitability by the SDM. Additionally, the authors expect better performance in agriculture areas that are stable such as orchards rather than agriculture subject to crop rotation.
The distribution of wild bees in the Netherlands was modeled using a total of 43,989 observations including for 193 species across 25 genera. Records dated back to as early as 1990. The MAXENT model included 13 variables: seven land use, five climate and elevation. Background points were sampled from areas where wild bee species had been found since 1990. AUC values recalculated from a 10 fold cross validation scheme, in the final model was validated with independent field surveys from agricultural sites.
The performance of SDM to predict wild bee occurrences in field surveys depended on species trait, target habitat, and sampling technique. Generally, the model performed better for highly specialized species with restricted habitats. This is promising, given that most species identified for conservation purposes are often specialists. M onany species were found in predicted unsuitable habitats, but this is most likely due to the seasonal changes in crop flowering or crop rotation that is not captured in the SDM. This study demonstrates the need to incorporate more specific information about landscape type, crop type, including fine-scale vegetation and information on flower availability by seasons into SDMs used for conservation purposes.