Author
Jana Lindenborn
Bio: Jana Lindenborn is an academic researcher from Leibniz Association. The author has contributed to research in topics: Sampling bias & Environmental niche modelling. The author has an hindex of 2, co-authored 2 publications receiving 617 citations.
Papers
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Leibniz Association1, University of Potsdam2, Technische Universität München3, Colorado State University4, University of Oxford5, Wildlife Conservation Society6, University of California, Davis7, Indonesian Institute of Sciences8, Mulawarman University9, Universiti Malaysia Sabah10, Kyoto University11, International Union for Conservation of Nature and Natural Resources12, Mississippi State University13
TL;DR: 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.
822 citations
01 Jan 2013
TL;DR: In this article, two common 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, and tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.
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.
4 citations
Cited by
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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.
Abstract: Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.
1,016 citations
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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.
Abstract: 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.
775 citations
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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.
424 citations
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Humboldt University of Berlin1, University of California2, University of St Andrews3, University of Freiburg4, University of Arizona5, Harvard University6, University of Kansas7, California Academy of Sciences8, Agro ParisTech9, University of Savoy10, University of Salford11, École Polytechnique Fédérale de Lausanne12, University of Connecticut13
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.
Abstract: Species distribution models (SDMs) constitute the most common class of models
across ecology, evolution and conservation. The advent of ready-to-use software pack
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ages and increasing availability of digital geoinformation have considerably assisted
the application of SDMs in the past decade, greatly enabling their broader use for
informing conservation and management, and for quantifying impacts from global
change. However, models must be fit for purpose, with all important aspects of their
development and applications properly considered. Despite the widespread use of
SDMs, standardisation and documentation of modelling protocols remain limited,
which makes it hard to assess whether development steps are appropriate for end use.
To address these issues, we propose a standard protocol for reporting SDMs, with an
emphasis on describing how a study’s objective is achieved through a series of model
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ing decisions. We call this the ODMAP (Overview, Data, Model, Assessment and
Prediction) protocol, as its components reflect the main steps involved in building
SDMs and other empirically-based biodiversity models. The ODMAP protocol serves
two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it 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. We detail all elements of ODMAP, and explain
how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata
and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an
interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and
adoption by the scientific community.
309 citations
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Humboldt University of Berlin1, Sapienza University of Rome2, University of Vermont3, European Forest Institute4, University of Freiburg5, Danish Nature Agency6, University of Forestry, Sofia7, University of Turin8, Forest Research Institute9, Mediterranean University10, University of Lisbon11, University of Zagreb12, Czech University of Life Sciences Prague13, Aleksandras Stulginskis University14, University of Trás-os-Montes and Alto Douro15, Saints Cyril and Methodius University of Skopje16, Swiss Federal Institute for Forest, Snow and Landscape Research17, University of Agriculture, Faisalabad18, University of Eastern Finland19, Bulgarian Academy of Sciences20
TL;DR: In this article, Sabatini et al. discuss the importance of gender diversity in soccer and discuss the role of gender in the sport of soccer in terms of sportswriting.
Abstract: Francesco Maria Sabatini1 | Sabina Burrascano2 | William S. Keeton3 | Christian Levers1 | Marcus Lindner4 | Florian Pötzschner1 | Pieter Johannes Verkerk5 | Jürgen Bauhus6 | Erik Buchwald7 | Oleh Chaskovsky8 | Nicolas Debaive9 | Ferenc Horváth10 | Matteo Garbarino11 | Nikolaos Grigoriadis12 | Fabio Lombardi13 | Inês Marques Duarte14 | Peter Meyer15 | Rein Midteng16 | Stjepan Mikac17 | Martin Mikoláš18 | Renzo Motta11 | Gintautas Mozgeris19 | Leónia Nunes14,20 | Momchil Panayotov21 | Peter Ódor10 | Alejandro Ruete22 | Bojan Simovski23 | Jonas Stillhard24 | Miroslav Svoboda18 | Jerzy Szwagrzyk25 | Olli-Pekka Tikkanen26 | Roman Volosyanchuk27 | Tomas Vrska28 | Tzvetan Zlatanov29 | Tobias Kuemmerle1
258 citations