Yes, I agree with Arash. Choose the model you like, but list its limitations and advantages. You can use more than one model, but make sure they show outcomes in different ways. For example, you could use GLM combined with canonical ordination to model your data :)

Thank you for valuable suggestions...

Aside from the suggestions of running multiple models, it your data type (presence-absence, presence-only) may influence model choice. MaxEnt is generally pretty robust for presence-only, but it makes a lot of assumptions and there's a lot of ways people try to work around the issues of pseudo-absences. Recently, Point Process Models have been suggested as a way of integrating presence-only and presence-absence data, but there has only been a couple studies that have done those with real systems. For the point point process models, I'd check out Koshkina et al. 2017 in Methods of Ecology and Evolution, Schank et al. 2017 in Diversity and Distributions, and Fletcher et al. 2016 in Landscape Ecology

" No silver bullets in correlative ecological niche modelling": Article No Silver Bullets in Correlative Ecological Niche Modeling: ...

There is no single best algorithm for all species. Maybe you can try the ensemble modelling, which can combine the predictions of several methods. You can use biomod2 R package.

I think the question about best model is wrong. You can choose the model according to your data (with expert opinion) or you can test your data with more than one model. But I would say that in recent years Maxent for wild animals, Random Forest and Generalized Addicted Model for plant species are seen as the methods giving the best results.

There is no single best model. the best way is to test all, evaluate them, and build the ensemble model from the best ones using a weighted averaging approach based on Area Under the Curve (AUC) statistics.

Cheers

Regression krigging