e-journal

A comparison ofMaxlike and Maxent for modelling species distributions

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1. 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
modellingmethod, Maxlike, with themorewidely usedMaxent.
2. 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.
3. 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.
4. The choice of whichmodel 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 themodels are specified carefully.

Key-words: occurrence model, presence-only data, probability of presence, resource selection function, species distributionmodelling

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Informasi Detail

Judul Seri

Methods in Ecology and Evolution

No. Panggil

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Penerbit

: British Ecological Society., 2014

Deskripsi Fisik

Methods in Ecology and Evolution 2014, 5, 215–225

Bahasa

English

ISBN/ISSN

doi: 10.1111/2041-21

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

Bilogi

Info Detail Spesifik

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Pernyataan Tanggungjawab

Wati/Agus

Versi lain/terkait

Lampiran Berkas

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