Species Distribution Modeling Philosophy

In species distribution modeling (SDM), we often focus on improving predictive performance — but rarely pause to ask a more fundamental question: what kind of model are we actually using, and why?

In this video, I explore two major modeling philosophies in SDM: correlative and mechanistic approaches.

Using Random Forest (a correlative, data-driven method) and HMSC (a more structure-informed Bayesian framework incorporating species traits), I compare how different assumptions shape our understanding of species-environment relationships.

Rather than asking which model is “better,” the more important question is:
What is your model actually trying to explain — patterns or processes?

This is a reflection on model choice as a conceptual decision, not just a technical one.

If you’re working with ecological data, spatial modeling, or species distribution models, this perspective may help you rethink how you approach your own analyses.

Feel free to share your thoughts or current modeling challenges in the comments — especially if you’ve had to choose between speed and ecological interpretability in your work.

#SpeciesDistributionModeling
#SDM
#Ecology
#StatisticalModeling
#SpatialAnalysis
#RandomForest
#HMSC
#EnvironmentalInformatics

Видео Species Distribution Modeling Philosophy канала Clear Flow Analytics