Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F24%3A00599097" target="_blank" >RIV/67985939:_____/24:00599097 - isvavai.cz</a>
Alternative codes found
RIV/60460709:41330/24:98847
Result on the web
<a href="https://doi.org/10.1111/ecog.07294" target="_blank" >https://doi.org/10.1111/ecog.07294</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/ecog.07294" target="_blank" >10.1111/ecog.07294</a>

Result language
angličtina
Original language name
Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter
Original language description
Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10618 - Ecology

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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