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Using virtual species to study species distributions and model performance
Author(s) -
Meynard Christine N.,
Kaplan David M.
Publication year - 2013
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.12006
Subject(s) - occupancy , probabilistic logic , computer science , sampling (signal processing) , process (computing) , threshold model , sample (material) , convergence (economics) , probability distribution , species distribution , machine learning , artificial intelligence , data mining , ecology , statistics , mathematics , biology , physics , thermodynamics , filter (signal processing) , habitat , economics , computer vision , economic growth , operating system
Simulations of virtual species (i.e. species for which the environment–occupancy relationships are known) are increasingly being used to test the effects of different aspects of modelling and sampling strategy on performance of species distribution models ( SDM s). Here we discuss an important step of the simulation process: the translation of simulated probabilities of occurrence into patterns of presence and absence. Often a threshold strategy is used to generate virtual occurrences, where presence always occurs above a specific simulated probability value and never below. This procedure effectively translates any shape of simulated species response into a threshold one and eliminates any stochasticity from the species occupancy pattern. We argue that a probabilistic approach should be preferred instead because the threshold response can be treated as a particular case within this framework. This also allows one to address questions relating to the shape of functional responses and avoids convergence issues with some of the most common SDM s. Furthermore, threshold‐based virtual species studies generate over‐optimistic performance measures that lack classification error or incorporate error from a mixture of sampling and modelling choices. Incorrect use of a threshold approach can have significant consequences for the practising biogeographer. For example, low model performance may be interpreted as due to sample bias or poor model choice, rather than being related to fundamental biological responses to environmental gradients. We exemplify these shortcomings with a case study where we compare results from threshold and probabilistic simulation approaches.

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