Abstract:Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a representative sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we… Show more
“…Accounting for geographic sampling bias in biodiversity science is an active field of research. The high flexibility of neural networks in IUCNN will make it easy for users to apply different approaches to factor geographic sampling biases into automated conservation assessments, for instance, by scrutinizing data structure (Boyd et al, 2021), spatial thinning (Aiello-Lammens et al, 2015) and modelling (Bruelheide et al, 2020;Varela et al, 2014).…”
Section: F I G U R Ementioning
confidence: 99%
“…In particu- that if records are split by time intervals, it may be relevant to explicitly consider temporal sampling bias (differences in sampling effort at different times; e.g. Boyd et al, 2021;Zizka, Rydén, et al, 2021).…”
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
“…Accounting for geographic sampling bias in biodiversity science is an active field of research. The high flexibility of neural networks in IUCNN will make it easy for users to apply different approaches to factor geographic sampling biases into automated conservation assessments, for instance, by scrutinizing data structure (Boyd et al, 2021), spatial thinning (Aiello-Lammens et al, 2015) and modelling (Bruelheide et al, 2020;Varela et al, 2014).…”
Section: F I G U R Ementioning
confidence: 99%
“…In particu- that if records are split by time intervals, it may be relevant to explicitly consider temporal sampling bias (differences in sampling effort at different times; e.g. Boyd et al, 2021;Zizka, Rydén, et al, 2021).…”
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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