Sampling bias in geographic and environmental space and its effect on the predictive power of species distribution models

By N. Bystriakova, M. Peregrym, R. Erkens, Olesya O. Bezsmertna, Harald Schneider. 2012

85 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/e1722e025e660a3216653b6105a4ed2906c66da0

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Where is positional uncertainty a problem for species distribution modelling (2014, background): “2-EV not automatically have to be a reason to discard the data (Chapman 2005). In this case, it is important to know whether and where the error is problematic. For example, Graham et al. (2008) compared different models to see if they were affected by an introduced random error (up to 5 km) to the ”
Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias (2014, background): “The quality of the model can be strongly affected if entire parts of the environmental space suitable to a species are absent or poorly represented in the survey dataset [36,37], or alternatively, if some areas are overrepresented due to locally high sampling efforts.”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “…bias are problematic for SDM: (1) incomplete sampling and (2) oversampling; when unknown, both result in unquantifiable spatial heterogeneity in the sampling intensity across a landscape and its environmental space (Freitag et al. 1998, Bystriakova et al. 2012, Guillera-Arroita et al. 2015).”
Predicting into unknown space? Estimating the area of applicability of spatial prediction models (2020, background): “Second, (cross-) validation estimates are based319 on the sample data only, but spatial sampling is usually biased (e.g. Kadmon et al. 2004, Bystriakova et al. 2012) and is320 unlikely to cover the entire environmental conditions of heterogeneous environments even when a sampling design321 is…”