Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection

The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.

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Main Authors: Botella, Christophe, Joly, Alexis, Monestiez, Pascal, Bonnet, Pierre, Munoz, François
Format: article biblioteca
Language:eng
Subjects:U10 - Informatique, mathématiques et statistiques, L60 - Taxonomie et géographie animales, F70 - Taxonomie végétale et phytogéographie, distribution des populations, modélisation environnementale, http://aims.fao.org/aos/agrovoc/c_6113, http://aims.fao.org/aos/agrovoc/c_9000056,
Online Access:http://agritrop.cirad.fr/595825/
http://agritrop.cirad.fr/595825/1/595825.pdf
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spelling dig-cirad-fr-5958252024-01-29T02:48:59Z http://agritrop.cirad.fr/595825/ http://agritrop.cirad.fr/595825/ Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection. Botella Christophe, Joly Alexis, Monestiez Pascal, Bonnet Pierre, Munoz François. 2020. PloS One, 15 (5):e0232078, 18 p.https://doi.org/10.1371/journal.pone.0232078 <https://doi.org/10.1371/journal.pone.0232078> Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection Botella, Christophe Joly, Alexis Monestiez, Pascal Bonnet, Pierre Munoz, François eng 2020 PloS One U10 - Informatique, mathématiques et statistiques L60 - Taxonomie et géographie animales F70 - Taxonomie végétale et phytogéographie distribution des populations modélisation environnementale http://aims.fao.org/aos/agrovoc/c_6113 http://aims.fao.org/aos/agrovoc/c_9000056 The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/595825/1/595825.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1371/journal.pone.0232078 10.1371/journal.pone.0232078 info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0232078 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1371/journal.pone.0232078
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic U10 - Informatique, mathématiques et statistiques
L60 - Taxonomie et géographie animales
F70 - Taxonomie végétale et phytogéographie
distribution des populations
modélisation environnementale
http://aims.fao.org/aos/agrovoc/c_6113
http://aims.fao.org/aos/agrovoc/c_9000056
U10 - Informatique, mathématiques et statistiques
L60 - Taxonomie et géographie animales
F70 - Taxonomie végétale et phytogéographie
distribution des populations
modélisation environnementale
http://aims.fao.org/aos/agrovoc/c_6113
http://aims.fao.org/aos/agrovoc/c_9000056
spellingShingle U10 - Informatique, mathématiques et statistiques
L60 - Taxonomie et géographie animales
F70 - Taxonomie végétale et phytogéographie
distribution des populations
modélisation environnementale
http://aims.fao.org/aos/agrovoc/c_6113
http://aims.fao.org/aos/agrovoc/c_9000056
U10 - Informatique, mathématiques et statistiques
L60 - Taxonomie et géographie animales
F70 - Taxonomie végétale et phytogéographie
distribution des populations
modélisation environnementale
http://aims.fao.org/aos/agrovoc/c_6113
http://aims.fao.org/aos/agrovoc/c_9000056
Botella, Christophe
Joly, Alexis
Monestiez, Pascal
Bonnet, Pierre
Munoz, François
Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection
description The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.
format article
topic_facet U10 - Informatique, mathématiques et statistiques
L60 - Taxonomie et géographie animales
F70 - Taxonomie végétale et phytogéographie
distribution des populations
modélisation environnementale
http://aims.fao.org/aos/agrovoc/c_6113
http://aims.fao.org/aos/agrovoc/c_9000056
author Botella, Christophe
Joly, Alexis
Monestiez, Pascal
Bonnet, Pierre
Munoz, François
author_facet Botella, Christophe
Joly, Alexis
Monestiez, Pascal
Bonnet, Pierre
Munoz, François
author_sort Botella, Christophe
title Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection
title_short Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection
title_full Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection
title_fullStr Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection
title_full_unstemmed Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection
title_sort bias in presence-only niche models related to sampling effort and species niches: lessons for background point selection
url http://agritrop.cirad.fr/595825/
http://agritrop.cirad.fr/595825/1/595825.pdf
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AT monestiezpascal biasinpresenceonlynichemodelsrelatedtosamplingeffortandspeciesnicheslessonsforbackgroundpointselection
AT bonnetpierre biasinpresenceonlynichemodelsrelatedtosamplingeffortandspeciesnicheslessonsforbackgroundpointselection
AT munozfrancois biasinpresenceonlynichemodelsrelatedtosamplingeffortandspeciesnicheslessonsforbackgroundpointselection
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