My species of interest is a marine animal, and my study area is the Mediterranean Sea. All previous occurrence points are within the Mediterranean Sea, and my climate data has been clipped down to the size of my study area. When I run MaxEnt with WORLDCLIM BioClim data, the model output gives me only predicted distribution on LAND (mostly coastlines, some inland areas surrounding the Mediterranean Sea). Why is my model generating land-based occurrence points instead of occurrence points in the Med. Sea if my previous occurrence points and climate data occur in/over a waterbody? How can I fix this issue?
Dear Caroline,
Are you sure that the worldclim data correspond to marine climatologies ? of what I found, it corresponds to land data.
If that is correct :
1) You have very coastal occurrence point. At your working resolution, MaxEnt might consider your occurrence point as matching with worldclim land data, thus it is able to re-construct the environmental response.
2) Because your environmental maps are land data, the model can't project the environmental response on sea, but only on land (i.e., where you have environmental values).
Check again your environmental data, if its land or sea. You may have done a confusion with the "world ocean atlas" climatologies, that are ocean data.
Best,
Alexandre
Dear Caroline,
WorldClim contains data only for the terrrestial raster cells. So I completely agree with Alexandre Schickele and Pietro Milanesi . You should download marine datasets, such as Bio-ORACLE (https://www.bio-oracle.org/).
HTH,
Ákos
Dear Caroline,
Your question is a very important one. Although it's not advisable to do that, there are some possibilities. I'll list two for the simplicity of the answer:
1) if your points were located in a mangrove or very coastal (e.g., a couple of meters from the shore) and your focus was the atmospheric influence on marine animals, then yes, it should be possible.
2) if the species you were mapping was an invasive one or lived in a transient environment, also yes, it should be possible.
But as Alexandre Schickele Pietro Milanesi and Ákos Bede-Fazekas pointed out, you will benefit more if your models are trained using ocean-specific data. If I can suggest something for you, I'd say to group WorldClim data with Bio-Oracle into a single dataset (remember to keep all files with the same extension and spatial resolution) and then do a good statistical analysis to remove multicollinearity (for that a PCA or a VIF analysis should suffice) to select the variables for model training.
Since you are using Maxent for that purpose, I'd also like to point out that it's important that you define what are you modeling (the environmental suitability or the potential distribution) and also refer to Phillips et al. 2006 to review what is and how Maxent works and, also, this brief tutorial by Phillips himself about how to use Maxent GUI to model species: https://biodiversityinformatics.amnh.org/open_source/maxent/Maxent_tutorial2017.pdf
Hope this helps and feel free to contact me if you need further help.
Best wishes,
Gustavo
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