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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F14%3A10283359" target="_blank" >RIV/00216208:11310/14:10283359 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1111/j.1600-0587.2013.00441.x" target="_blank" >http://dx.doi.org/10.1111/j.1600-0587.2013.00441.x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/j.1600-0587.2013.00441.x" target="_blank" >10.1111/j.1600-0587.2013.00441.x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 madewith 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).

Název v anglickém jazyce

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

Popis výsledku anglicky

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 madewith 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).

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EH - Ekologie – společenstva

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Ecography

ISSN

0906-7590

e-ISSN

—

Svazek periodika

37

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

DK - Dánské království

Počet stran výsledku

8

Strana od-do

1084-1091

Kód UT WoS článku

000344645100008

EID výsledku v databázi Scopus

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