The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches
Abstract
Biodiversity studies rely heavily on estimates of species' distributions often obtained through ecological niche modelling. Numerous software packages exist that allow users to model ecological niches using machine learning and statistical methods. However, no existing package with a graphical user interface allows users to perform model calibration and selection based on convex forms such as ellipsoids, which may match fundamental ecological niche shapes better, incorporating tools for exploring, modelling, and evaluating niches and distributions that are intuitive for both novice and proficient users. Here we describe an r package, N iche T ool B ox (ntbox), that allows users to conduct all processing steps involved in ecological niche modelling: downloading and curating occurrence data, obtaining and transforming environmental data layers, selecting environmental variables, exploring relationships between geographic and environmental spaces, calibrating and selecting ellipsoid models, evaluating models using binomial and partial ROC tests, assessing extrapolation risk, and performing geographic information system operations via a graphical user interface. A summary of the entire workflow is produced for use as a stand‐alone algorithm or as part of research reports. The method is explained in detail and tested via modelling the threatened feline species Leopardus wiedii. Georeferenced occurrence data for this species are queried to display both point occurrences and the IUCN extent of occurrence polygon (IUCN, 2007). This information is used to illustrate tools available for accessing, processing and exploring biodiversity data (e.g. number of occurrences and chronology of collecting) and transforming environmental data (e.g. a summary PCA for 19 bioclimatic layers). Visualizations of three‐dimensional ecological niches modelled as minimum volume ellipsoids are developed with ancillary statistics. This niche model is then projected to geographic space, to represent a corresponding potential suitability map. Using ntbox allows a fast and straightforward means by which to retrieve and manipulate occurrence and environmental data, which can then be implemented in model calibration, projection and evaluation for assessing distributions of species in geographic space and their corresponding environmental combinations.
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Very high resolution interpolated climate surfaces for global land areas
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005) · International Journal of Climatology · doi:10.1002/joc.1276 · Cited by 19,825
Maximum entropy modeling of species geographic distributions
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.03.026 · Cited by 15,396
A review of methods for the assessment of prediction errors in conservation presence/absence models
FIELDING, A. H., & BELL, J. F. (1997) · Environmental Conservation · doi:10.1017/s0376892997000088 · Cited by 6,956
Hutchinson, G. E. (1957) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.1957.022.01.039 · Cited by 6,228
Climatologies at high resolution for the earth’s land surface areas
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017) · Scientific Data · doi:10.1038/sdata.2017.122 · Cited by 4,171
Climatologies at high resolution for the earth’s land surface areas
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-139381 · Cited by 3,598
The art of modelling range-shifting species: The art of modelling range-shifting species
Elith, J., Kearney, M., & Phillips, S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00036.x · Cited by 2,813
BIOMOD – a platform for ensemble forecasting of species distributions
Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009) · Ecography · doi:10.1111/j.1600-0587.2008.05742.x · Cited by 2,749
Muscarella, R., Galante, P. J., Soley‐Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12261 · Cited by 1,962
Metadata
- Authors
- Osorio‐Olvera, L., Lira‐Noriega, A., Soberón, J., Peterson, A. T., Falconi, M., Contreras‐Díaz, R. G., Martínez‐Meyer, E., Barve, V., & Barve, N.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2020-08-09
- Volume
- 11
- Issue
- 10
- Pages
- 1199-1206
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W3042537209
- Cited by
- 371
- References
- 38
- FWCI
- 83.81
- Type
- journal article
- Language
- en
- Open access
- closed
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- Osorio‐Olvera, L., Lira‐Noriega, A., Soberón, J., Peterson, A. T., Falconi, M., Contreras‐Díaz, R. G., Martínez‐Meyer, E., Barve, V., & Barve, N. (2020). ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13452
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