Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

By Yoan Fourcade, J. Engler, D. Rödder, J. Secondi. PLoS ONE, 2014

1,112 citations recorded by the source corpus.

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

Subfields: Ecological Modeling, Ecology, Epidemiology

View Paper: https://www.semanticscholar.org/paper/60c50bb38cc99ac54c3b3ca01bb839ea55b1cab0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Robert A. Boria
2. D. Yeo
3. Yiwen Zeng
4. B. Low
5. H. Tan
6. Janet Franklin
7. Francis Gilbert
8. Kenneth E. Nussear

In Ecology

1. Robert A. Boria
2. Yiwen Zeng
3. D. Yeo
4. B. Low
5. H. Tan
6. Janet Franklin
7. Francis Gilbert
8. Kenneth E. Nussear

In Epidemiology

1. R. P. Anderson

Citations in the corpus

• Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “…however, with some studies suggesting that G-Filter methods may outperform the FactorBiasOut method in some instances (KramerSchadt et al. 2013, Fourcade et al. 2014, Stolar and Nielsen 2015), but others showing E-Filter methods as superior to G-Filter methods when using climatic variables…”
• Invasive weed species’ threats to global biodiversity: Future scenarios of changes in the number of invasive species in a changing climate (2020, background): “We must consider the potential bias imposed by the undersampling (Fourcade et al., 2014) inherent in large databases of herbarium samples (González-Orozco et al.”
• Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): “To reduce the effect of sampling bias, spatial filtering (Boria et al., 2014; Fourcade et al., 2014) was carried out on the occurrences datasets.”
• spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models (2015, background): “…thinning (i.e. in geographical space), which has been applied frequently and can result in species occurrence data that yield better performing ENMs (Pearson et al. 2007, Veloz 2009, Kramer-Schadt et al. 2013, Syfert et al. 2013, Verbruggen et al. 2013, Boria et al. 2014, Fourcade et al. 2014).”
• A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, methodology): “Yet it is the ease and simplicity of its implementation that has propelled MaxEnt to be the most prominent, widely-used SDM technique in scientific research (Fitzpatrick et al., 2013; Fourcade et al., 2014; Hijmans and Elith, 2013).”