I'm trying to predict shorebird distributions using habitat variables, using Maxent to model. I have 4 categorical data (wetlands, waterbodies, tilled, beach) and 1 continuous data layer (topographic roughness).

My categorical data layers are binary (1 = habitat is present, 0 = habitat not present). This is because 3 of these layers were extracted from a larger dataset called SOLRIS and I didn't want everything in the dataset, which had 30 different habitat classes. However after extracting the layers I wanted, I ended up having many zeros in these layers because only few cells were classified as beach habitat, for example. All my layers can be seen here, where D1-D4 consist of the binary categorical layers. Cells that are zero appear to be blank, where there is no colour on the maps. However topographic roughness can be seen covered across the entire extents of the study area:

Habitat variables - categorical vs continuous

Additionally, Maxent produces variable percent contributions, that show how topo roughness contributed to majority of the model.

Variable Contributions

As a result, the model prioritized the topographic roughness layer because each cell in this layer had a value indicating how rough the terrain was, as opposed to the rest of the habitat categorical layers which had a few patches of the defined habitat, and when the habitat was not present, the cell was coded as zero. Here is one of the models: Probability Distribution of Arctic Shorebirds

How do I make sure Maxent runs on the categorical habitat variables across the entire study area?

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