A comparison of Maxlike and Maxent for modelling species distributions
- Understanding species spatial occurrence patterns and their environmental dependence is one of the fundamental goals in ecology and evolution. Often, occurrence models are built with presence-only data because absence data are unavailable. We compare the strengths and limitations of the recently developed presence-only modelling method, Maxlike, with the more widely used Maxent.
- In spite of disparities highlighted by the developers of Maxlike and Maxent, we show approximate formal relationships between the parameters of Maxlike and Maxent for two scenarios to illustrate their similarity. Using case studies based on real and simulated data, we show how these similarities manifest in practice.
- We find more similarities than differences between Maxlike and Maxent, including coefficient values, predicted spatial distributions, similarity to presence–absence models, predictive performance and ranking the predicted suitability of cells. Maxlike reliably predicted absolute occurrence probabilities for very large data sets on landscapes where occurrence probability approximately spanned [0,1]. For smaller data sets, the uncertainty in predicted occurrence probability by Maxlike was very large, due to the inherent limitations of presence-only data. In contrast, Maxent is constrained to predicting relative occurrence probabilities or relative occurrence rates unless it is provided with additional information from presence–absence data. Both models can reliably predict relative differences in occurrence probability.
- The choice of which model to use depends partly on sampling assumptions, which we discuss in detail. Due to limitations of presence-only data, ecologists should typically focus on interpretations relying on relative differences in occurrence probability or relative occurrence rates. We discuss how to remedy a number of concerns about the use of Maxent and how to avoid some potential pitfalls with Maxlike – particularly related to high variance predictions. We conclude that both methods are similarly valuable for understanding and predicting species’ distributions in terms of relative differences in occurrence probability when the models are specified carefully.