The importance of correcting for sampling bias in MaxEnt species distribution models

By S. Kramer‐Schadt, Jürgen Niedballa, J. Pilgrim, B. Schröder, Jana Lindenborn, Vanessa Reinfelder, Milena Stillfried, Ilja Heckmann, A. Scharf, Dave M. Augeri and 17 more. 2013

1,177 citations recorded by the source corpus.

Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c4499507432a836ff2a893a89fd26d20c9741323

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Citations in the corpus

Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “…v Article e03422 certain areas are inaccessible due to administrative reasons such as private property or governmental regulations but may also rise from imperfect or nondetection when occupied areas are sampled, yet no observations are obtained (Kramer-Schadt et al. 2013, Hefley et al. 2013).”
Mechanistic and Correlative Models of Ecological Niches (2015, background): “, spatial bias to accessible areas, incomplete sampling across an area, incomplete detectability of individuals) will have considerable potential to translate into new biases and problems in model outcomes (Hijmans 2012; Kramer-Schadt et al. 2013; Boria et al. 2014).”
Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias (2014, methodology): “[52]), we evaluated the correction performance by directly comparing the SDM outputs.”
A standard protocol for reporting species distribution models (2020, background): “For example, when presence records are spatially biased, one could sample the background data such that they reflect the same spatial bias (Phillips et al. 2009, Kramer-Schadt et al. 2013).”
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions (2014, methodology): “Our subsampling routine offers an alternative option of removing spatial bias in data availability, at least for cases where record numbers are very large and therefore a loss of sample size does not gravely affect model quality (cf. Kramer-Schadt et al., 2013).”
Crop wild relatives of pigeonpea [Cajanus cajan (L.) Millsp.]: Distributions, ex situ conservation status, and potential genetic resources for abiotic stress tolerance (2015, background): “are thus subject to sampling bias, they may not fully capture the possibility of occurrence of populations of CWR species in unique climates (Araújo and Guisan, 2006; Kramer-Schadt et al., 2013; Loiselle et al., 2007).”
Using species distribution models to inform IUCN Red List assessments (2014, methodology): “…study we conclude with some general findings and possible approaches for incorporating SDMs into Red List assessments: Control for sampling bias (Kramer-Schadt et al. 2013; Merow et al. 2013; Syfert et al. 2013) and consider background extent (e.g. apply an appropriate buffer to define a…”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, background): “This bias can lead to the pseudo-replication of points clustered around accessible areas and the over-representation of certain environmental conditions during model fitting, therefore negatively impacting ENM transferability (Boria et al., 2014; Kramer-Schadt et al., 2013).”
Habitat availability for amphibians and extinction threat: a global analysis (2015, background): “Inductive models are data intensive, as they require a large number of presence data to develop robust statistical relationships, and may be heavily biased if sampling is uneven (Kramer-Schadt et al., 2013; Ficetola et al., 2014a; LahozMonfort et al., 2014).”