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Point process models for presence‐only analysis
Author(s) -
Renner Ian W.,
Elith Jane,
Baddeley Adrian,
Fithian William,
Hastie Trevor,
Phillips Steven J.,
Popovic Gordana,
Warton David I.
Publication year - 2015
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12352
Subject(s) - computer science , point process , data mining , variety (cybernetics) , software , process (computing) , clarity , point (geometry) , econometrics , statistics , mathematics , artificial intelligence , operating system , biochemistry , chemistry , geometry , programming language
Summary Presence‐only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence‐only data. Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudo‐absences or ‘background points’) objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; and ways forward regarding difficult issues such as accounting for sampling bias. Point process models are related to some common approaches to presence‐only species distribution modelling, which means that a variety of different software tools can be used to fit these models, including maxent or generalised linear modelling software.