The importance of correcting for sampling bias in MaxEnt species distribution models
Stephanie Kramer-Schadt,Jürgen Niedballa,John D. Pilgrim,Boris Schröder,Boris Schröder,Jana Lindenborn,Vanessa Reinfelder,Milena Stillfried,Ilja Heckmann,Anne K. Scharf,Dave M. Augeri,Susan M. Cheyne,Andrew J. Hearn,Joanna Ross,David W. Macdonald,John Mathai,James A. Eaton,Andrew J. Marshall,Gono Semiadi,Rustam Rustam,Henry Bernard,Raymond Alfred,Hiromitsu Samejima,J. W. Duckworth,Christine Breitenmoser-Wuersten,Jerrold L. Belant,Heribert Hofer,Andreas Wilting +27 more
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TLDR
It is concluded that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.Abstract:
Aim
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.
Location
Borneo, Southeast Asia.
Methods
We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas.
Results
Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased.
Main Conclusions
We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.read more
Citations
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Journal ArticleDOI
spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models
Matthew E. Aiello-Lammens,Matthew E. Aiello-Lammens,Robert A. Boria,Aleksandar Radosavljevic,Bruno Vilela,Robert P. Anderson,Robert P. Anderson +6 more
TL;DR: This work provides a worked example of spatial thinning of species occurrence records for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.
Journal ArticleDOI
Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
TL;DR: The ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species, but 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.
Journal ArticleDOI
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
TL;DR: A subsampling routine is used as an exemplar taxon to provide evidence that range model quality is decreasing due to the spatial clustering of distributional records in GBIF and shows that data with less spatial bias produce better predictive models even though they are based on less input data.
Journal ArticleDOI
A standard protocol for reporting species distribution models
Damaris Zurell,Janet Franklin,Christian König,Phil J. Bouchet,Carsten F. Dormann,Jane Elith,Guillermo Fandos,Xiao Feng,Gurutzeta Guillera-Arroita,Antoine Guisan,José J. Lahoz-Monfort,Pedro J. Leitão,Daniel S. Park,A. Townsend Peterson,Giovanni Rapacciuolo,Dirk R. Schmatz,Boris Schröder,Josep M. Serra-Diaz,Wilfried Thuiller,Katherine L. Yates,Niklaus E. Zimmermann,Cory Merow +21 more
TL;DR: This work proposes a standard protocol for reporting SDMs, and introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses.
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Where are Europe's last primary forests?
Francesco Maria Sabatini,Sabina Burrascano,William S. Keeton,Christian Levers,Marcus Lindner,Florian Pötzschner,Pieter Johannes Verkerk,Jürgen Bauhus,Erik Buchwald,Oleh Chaskovsky,Nicolas Debaive,Ferenc Horváth,Matteo Garbarino,Nikolaos Grigoriadis,Fabio Lombardi,Inês Duarte,Peter Meyer,Rein Midteng,Stjepan Mikac,Martin Mikoláš,Renzo Motta,Gintautas Mozgeris,Leónia Nunes,Leónia Nunes,Momchil Panayotov,Péter Ódor,Alejandro Ruete,Bojan Simovski,Jonas Stillhard,Miroslav Svoboda,Jerzy Szwagrzyk,Olli-Pekka Tikkanen,Roman Volosyanchuk,Tomáš Vrška,Tzvetan Zlatanov,Tobias Kuemmerle +35 more
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References
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Book
Applied Logistic Regression
David W. Hosmer,Stanley Lemeshow +1 more
TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
Journal ArticleDOI
Applied Logistic Regression.
TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Journal ArticleDOI
Very high resolution interpolated climate surfaces for global land areas.
Robert J. Hijmans,Susan E. Cameron,Susan E. Cameron,Juan L. Parra,Peter G. Jones,Andy Jarvis +5 more
TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Journal ArticleDOI
Maximum entropy modeling of species geographic distributions
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Journal ArticleDOI
Novel methods improve prediction of species' distributions from occurrence data
Jane Elith,Catherine H. Graham,Robert P. Anderson,Miroslav Dudík,Simon Ferrier,Antoine Guisan,Robert J. Hijmans,Falk Huettmann,John R. Leathwick,Anthony Lehmann,Jin Li,Lúcia G. Lohmann,Bette A. Loiselle,Glenn Manion,Craig Moritz,Miguel Nakamura,Yoshinori Nakazawa,Jacob C. M. Mc Overton,A. Townsend Peterson,Steven J. Phillips,Karen Richardson,Ricardo Scachetti-Pereira,Robert E. Schapire,Jorge Soberón,Stephen E. Williams,Mary S. Wisz,Niklaus E. Zimmermann +26 more
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
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