kuenm: an R package for detailed development of ecological niche models using Maxent
Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail...
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| Published in | PeerJ (San Francisco, CA) Vol. 7; p. e6281 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
PeerJ. Ltd
06.02.2019
PeerJ, Inc PeerJ Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way.
This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric.
Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes. |
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| AbstractList | Background Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way. Results This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric. Discussion Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes. Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way. This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric. Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes. Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way. This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric. Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes. Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way.BACKGROUNDEcological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way.This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric.RESULTSThis package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric.Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes.DISCUSSIONUse of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes. |
| ArticleNumber | e6281 |
| Audience | Academic |
| Author | Peterson, A. Townsend Osorio-Olvera, Luis Barve, Narayani Cobos, Marlon E. |
| Author_xml | – sequence: 1 givenname: Marlon E. surname: Cobos fullname: Cobos, Marlon E. organization: Biodiversity Institute and Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States of America – sequence: 2 givenname: A. Townsend surname: Peterson fullname: Peterson, A. Townsend organization: Biodiversity Institute and Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States of America – sequence: 3 givenname: Narayani surname: Barve fullname: Barve, Narayani organization: Biodiversity Institute and Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States of America, Florida Museum of Natural History, University of Florida, Gainesville, FL, United States of America – sequence: 4 givenname: Luis surname: Osorio-Olvera fullname: Osorio-Olvera, Luis organization: Biodiversity Institute and Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States of America, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, México, Mexico, Centro del Cambio Global y la Sustentabilidad A.C., Villahermosa, Tabasco, Mexico |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30755826$$D View this record in MEDLINE/PubMed |
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| References | Peterson (10.7717/peerj.6281/ref-21) 2008; 213 R Core Team (10.7717/peerj.6281/ref-24) 2018 Warren (10.7717/peerj.6281/ref-33) 2011; 21 Peterson (10.7717/peerj.6281/ref-17) 2014 Nylander (10.7717/peerj.6281/ref-15) 2004 Escobar (10.7717/peerj.6281/ref-6) 2014; 9 Peterson (10.7717/peerj.6281/ref-18) 2018; 1429 Warren (10.7717/peerj.6281/ref-34) 2014; 20 Jiménez-Valverde (10.7717/peerj.6281/ref-10) 2011; 13 Owens (10.7717/peerj.6281/ref-16) 2013; 263 Raghavan (10.7717/peerj.6281/ref-25) 2019; 14 Elith (10.7717/peerj.6281/ref-5) 2011; 17 Hijmans (10.7717/peerj.6281/ref-8) 2005; 25 Jiménez-Valverde (10.7717/peerj.6281/ref-9) 2012; 21 Phillips (10.7717/peerj.6281/ref-23) 2006; 190 Elith (10.7717/peerj.6281/ref-4) 2010; 1 Anderson (10.7717/peerj.6281/ref-3) 2003; 162 Searcy (10.7717/peerj.6281/ref-27) 2016; 187 Spear (10.7717/peerj.6281/ref-30) 1997; 12 Kass (10.7717/peerj.6281/ref-11) 2018 Soberón (10.7717/peerj.6281/ref-29) 2005; 2 Lobo (10.7717/peerj.6281/ref-12) 2007; 17 Saupe (10.7717/peerj.6281/ref-26) 2012; 237–238 Peterson (10.7717/peerj.6281/ref-19) 2008; 17 Warren (10.7717/peerj.6281/ref-32) 2010; 33 Aiello-Lammens (10.7717/peerj.6281/ref-1) 2015; 38 Alonso Bosch (10.7717/peerj.6281/ref-2) 2011 Franklin (10.7717/peerj.6281/ref-7) 2013; 19 Muscarella (10.7717/peerj.6281/ref-14) 2014; 5 Steele (10.7717/peerj.6281/ref-31) 2013; 64 Merow (10.7717/peerj.6281/ref-13) 2013; 36 Peterson (10.7717/peerj.6281/ref-20) 2007; 30 Peterson (10.7717/peerj.6281/ref-22) 2011 Sequeira (10.7717/peerj.6281/ref-28) 2018; 9 |
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| Snippet | Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging... Background Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a... |
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| SubjectTerms | Automation Biodiversity Biogeography Bioinformatics Calibration Climate change Computational Biology Disease transmission Ecology Extrapolation risks Model calibration Model projections Model selection Niches (Ecology) Software Species distribution models |
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| Title | kuenm: an R package for detailed development of ecological niche models using Maxent |
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