Conditional logistic regression

[Gareth Russell]

Hello group: new poster here. Apologies if my question has been discussed before, but a search doesn't pull up anything.

I am coming over from home range and movement modeling, in which conditional logistic regression (CLR) is a standard tool for fitting resource selection functions and step selection functions. Looking at SDMs, it appears that both things have the same task of modeling presence points vs 'background'. So I immediately wonder: why is CLR not also the tool for SDMs? But *then* I wonder: maybe it is? The very helpful Merow et al. Ecography paper says this:

"When the aim is to predict counts of occurrences, it is helpful to think of MaxEnt as a Poisson model where the number of counts is a function of environmental variables. Such models are commonly referred to as the resource selection functions in the habitat suitability literature (Manly et al. 2002, Keating and Cherry 2004, Johnson et al. 2006)."

Indeed, resource selection CLR models can be fit as Poisson models. So if CLR = Poisson and MaxEnt = Poisson, then MaxEnt = CLR?

Am I getting this wrong? Missing some nuance?

Gareth

Ref although I assume everyone know it: Merow, C., Smith, M.J. and Silander, J.A., Jr (2013), A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography, 36: 1058-1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x