The best of two worlds: using stacked generalisation for integrating expert range maps in species distribution models

Aim
Species distribution models (SDMs) are powerful tools for assessing suitable habitats across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species' realised distributions is often limited since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species' range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species' ranges and thereby range limits that are complementary to information offered by SDMs.

Innovation
Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalisation. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our app..., , , # The best of two worlds: using stacked generalization for integrating expert range maps in species distribution models

[https://doi.org/10.5061/dryad.6q573n65m](https://doi.org/10.5061/dryad.6q573n65m)

This repository contains Supporting Information for the article \"The best of two worlds: using stacked generalization for integrating expert range maps in species distribution models\" ([https://doi.org/10.1111/geb.13911](https://doi.org/10.1111/geb.13911)).

It contains three files:

1. \"model_df.csv\": CSV table containing modeling data frame with occurrence information (presence/background) for 49 bat species alongside values of predictors used for building SDMs
2. \"predictor_stack_agg.tif\": GeoTIFF containing raster stack of predictor variables used in SDMs, re-sampled to a reduced spatial resolution of 10km for demonstration purposes
3. \"iucn_dists.tif\": GeoTIFF containing raster stack of distance layers describing the distance of raster cells to the boundary of IUCN ranges for 49 ...