MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review
Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s populari...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2017-03-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/3093.pdf |
| _version_ | 1797420740810637312 |
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| author | Narkis S. Morales Ignacio C. Fernández Victoria Baca-González |
| author_facet | Narkis S. Morales Ignacio C. Fernández Victoria Baca-González |
| author_sort | Narkis S. Morales |
| collection | DOAJ |
| description | Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool.” Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes. |
| first_indexed | 2024-03-09T07:05:58Z |
| format | Article |
| id | doaj.art-1146fde31cda4645bfbdbcb65b2d903e |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:05:58Z |
| publishDate | 2017-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-1146fde31cda4645bfbdbcb65b2d903e2023-12-03T09:30:11ZengPeerJ Inc.PeerJ2167-83592017-03-015e309310.7717/peerj.3093MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic reviewNarkis S. Morales0Ignacio C. Fernández1Victoria Baca-González2Department of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales, AustraliaFundación Ecomabi, Santiago, Región Metropolitana, ChileFacultad de Ciencias Biológicas, Universidad Complutense de Madrid, Madrid, SpainEnvironmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool.” Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.https://peerj.com/articles/3093.pdfUser-defined featuresAuto-featuresRegularization multiplierSpecies distributionEnvironmental niche modellingParameters configuration |
| spellingShingle | Narkis S. Morales Ignacio C. Fernández Victoria Baca-González MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review PeerJ User-defined features Auto-features Regularization multiplier Species distribution Environmental niche modelling Parameters configuration |
| title | MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review |
| title_full | MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review |
| title_fullStr | MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review |
| title_full_unstemmed | MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review |
| title_short | MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review |
| title_sort | maxent s parameter configuration and small samples are we paying attention to recommendations a systematic review |
| topic | User-defined features Auto-features Regularization multiplier Species distribution Environmental niche modelling Parameters configuration |
| url | https://peerj.com/articles/3093.pdf |
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