About: Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models

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About: Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://dx.doi.org/10.1111/j.1600-0587.2013.00441.x
Description	Ecological niche models represent key tools in biogeography but the effects of biased sampling hinder their use. Here, we address the utility of two forms of filtering the calibration data set (geographic and environmental) to reduce the effects of sampling bias. To do so we created a virtual species, projected its niche to the Iberian Peninsula and took samples from its binary geographic distribution using several biases. We then built models for various sample sizes after applying each of the filtering approaches. While geographic filtering did not improve discriminatory ability (and sometimes worsened it), environmental filtering consistently led to better models. Models made with few but climatically filtered points performed better than those made with many unfiltered (biased) points. Future research should address additional factors such as the complexity of the species' niche, strength of filtering, and ability to predict suitability (rather than focus purely on discrimination). Ecological niche models represent key tools in biogeography but the effects of biased sampling hinder their use. Here, we address the utility of two forms of filtering the calibration data set (geographic and environmental) to reduce the effects of sampling bias. To do so we created a virtual species, projected its niche to the Iberian Peninsula and took samples from its binary geographic distribution using several biases. We then built models for various sample sizes after applying each of the filtering approaches. While geographic filtering did not improve discriminatory ability (and sometimes worsened it), environmental filtering consistently led to better models. Models made with few but climatically filtered points performed better than those made with many unfiltered (biased) points. Future research should address additional factors such as the complexity of the species' niche, strength of filtering, and ability to predict suitability (rather than focus purely on discrimination). (en)
Title	Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models (en)
skos:prefLabel	Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models (en)
skos:notation	RIV/00216208:11310/14:10283359!RIV15-MSM-11310___
http://linked.open...avai/riv/aktivita	I
http://linked.open...avai/riv/aktivity	I
http://linked.open...iv/cisloPeriodika	11
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Varela, Sara
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Univerzita Karlova v Praze / Přírodovědecká fakulta
http://linked.open...dnocenehoVysledku	14763
http://linked.open...ai/riv/idVysledku	RIV/00216208:11310/14:10283359
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	region; maxent; absence; abundance; evolution; limitations; performance; geographic distributions; species distribution models (en)
http://linked.open.../riv/klicoveSlovo	geographic distributions maxent evolution performance region species distribution models absence abundance limitations
http://linked.open...odStatuVydavatele	DK - Dánské království
http://linked.open...ontrolniKodProRIV	[DF80E3C3492D]
http://linked.open...i/riv/nazevZdroje	Ecography
http://linked.open...in/vavai/riv/obor	EH
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	37
http://linked.open...iv/tvurceVysledku	Varela, Sara Fernandez-Gonzalez, Federico Anderson, Robert P. Garcia-Valdes, Raul
http://linked.open...ain/vavai/riv/wos	000344645100008
issn	0906-7590
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.1111/j.1600-0587.2013.00441.x
http://localhost/t...ganizacniJednotka	11310

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