Understanding the connections between species distribution models

TL;DR

This paper explores the relationships among various species distribution modeling methods for presence-background data, clarifies their equivalences, and introduces a new unified constrained LK method for improved estimation.

Contribution

It unifies multiple existing methods for species distribution modeling, clarifies their connections, and proposes a new, easier-to-implement constrained LK approach.

Findings

All methods estimate relative probability of presence similarly.

Methods can estimate absolute probability with known species prevalence.

The new CLK method simplifies implementation and broadens applicability.

Abstract

Models for accurately predicting species distributions have become essential tools for many ecological and conservation problems. For many species, presence-background (presence-only) data is the most commonly available type of spatial data. A number of important methods have been proposed to model presence-background (PB) data, and there have been debates on the connection between these seemingly disparate methods. The paper begins by studying the close relationship between the LI (Lancaster & Imbens, 1996) and LK (Lele & Keim, 2006) models, which were among the first developed methods for analysing PB data. The second part of the paper identifies close connections between the LK and point process models, as well as the equivalence between the Scaled Binomial (SB), Expectation-Maximization (EM), partial likelihood based Lele (2009) and LI methods, many of which have not been noted in the literature. We clarify that all these methods are the same in their ability to estimate the relative probability (or intensity) of presence from PB data; and the absolute probability of presence, when extra information of the species' prevalence is known. A new unified constrained LK (CLK) method is also proposed as a generalisation of the better known existing approaches, with less theory involved and greater ease of implementation.