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Grinnellian ecological niches and ellipsoids in R
Species ecological niches understood from a Grinnellian perspective are widely studied in distributional ecology. Conservation biology, disease risk mapping, and prediction of potential impacts of climate change are among the most common applications in which ecological niches are characterized. Ecological niche modeling represents a set of methodological tools that allow researchers to characterize and analyze species niches (Peterson et al. 2011). This field has grown rapidly during the last decades, especially thanks to theoretical and computational advances, and several software solutions have been developed during this period (Peterson et al. 2018). Evidence in the scientific literature suggests that Grinnellian ecological niches are convex in nature and they may probably have an ellipsoidal form when multiple dimensions are considered (Jiménez et al. 2019). Following these ideas, some authors have successfully used ellipsoids to study the relationship between the distances to species’ niche centroid with their abundances (Yañez-Arenas et al. 2012; Ureña-Aranda et al. 2015). However, among the diverse available software to characterize ecological niches, algorithms to model these niches as ellipsoids in the environmental space are scarce. Given the need for more solutions that allow analyses of ecological niches as ellipses, this project aims for developing specialized tools to perform multiple ecological niche modeling-related analyses.
Few options exist to perform ecological niche modeling-related analyses using ellipsoids in R. To our knowledge only the ntbox package include some options to perform similar analyses. However, plenty of other analyses can be performed assuming ellipsoidal niches, especially if recent related literature is considered. That is why developing new tools would be an important contribution to the field.
In general, we expect to obtain tools to perform the analyses listed below. Analyses are expected to be performed using environmental and species occurrence data, and the geographic component of niches should be considered. Visualizations in geographic and environmental space as well as appropriate documentation and some vignettes will also be required.
- Characterization of ecological niches based on ellipsoids (models).
- Projection of ellipsoid based models to other scenarios.
- Transformation of suitability layers from other algorithms into niche centroid distance models
- Projection of transformed niche centroid distance models to other scenarios
- Analyses of overlap among ellipsoidal ecological niche models
- Plots of results from analyses performed with other functions
Mentors, please explain how this project will produce a useful package for the R community. A broad community of researchers and students of distinct fields related to distributional ecology that are currently using R will be benefited by the existence of new tools to perform interesting analyses. The toolbox to be generated will facilitate complicated analyses not currently available, which would help in performing descriptive analyses as well as in testing a variety of hypothesis related to species ecological niches and geographic distributions.
Students, please contact mentors below after completing at least one of the tests below.
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Luis Osorio-Olvera luismurao@gmail.com is a scientist focused on distributional ecology and has participated as a GSoC student in 2016 and now as a mentor with the R project organization. Luis has contributed to various R packages related to biodiversity analysis and visualizations.
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Vijay Barve vijay.barve@gmail.com is a biodiversity data scientist that has been a GSoC student and mentor since 2012 with the R project organization. Vijay has contributed to several packages on CRAN.
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Narayani Barve narayani.ku@gmail.com is a biodiversity informatics scientist and was a GSoC student (2015) as well as a mentor (2016-2018). She developed the package ENMGadgets and has contributed to various other R packages. She has extensive experience working with spatial information.
Students, please do one or more of the following tests before contacting the mentors above.
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Easy: Write and script to download species occurrences from GBIF and bioclimatic layers from WorldClim and plot them in a map.
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Medium: Reduce the geographic extent of the raster layers to a buffer of 200 km from the occurrences (this will be reduced geographic area). Extract data from two bioclimatic layers using the occurrences and plot the data in a scatterplot. Calculate an ellipsoid using the centroid and covariance matrix of the extracted data and add the ellipsoid on the previous plot.
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Hard: Using environmental data from three bioclimatic layers, identify regions of the reduced geographic area that are inside and outside an ellipse that contains 95% of the data extracted to the species occurrences.
Students, please post a link to your test results here.
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Student name: Marlon E. Cobos
Email: manubio13@gmail.com, marloncobos@ku.edu
University: University of Kansas
Program: Ph.D. in Ecology and Evolutionary Biology
Solution to All Tests: Test solutions
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Jiménez A.,J Soberón, A. Christen, Desiereé Soto. 2019. On the problem of modeling a fundamental niche from occurrence data. Ecological Modelling DOI: https://doi.org/10.1016/j.ecolmodel.2019.01.020
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Peterson, A. T., M. E. Cobos, and D. Jiménez‐García. 2018. Major challenges for correlational ecological niche model projections to future climate conditions. Annals of the New York Academy of Sciences 1429:66–77.
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Peterson, A. T., J. Soberón, R. G. Pearson, R. P. Anderson, E. Martínez-Meyer, M. Nakamura, and M. B. Araújo. 2011. Ecological Niches and Geographic Distributions. Princeton University Press, Princeton.
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Ureña-Aranda, C. A., O. Rojas-Soto, E. Martínez-Meyer, C. Yáñez-Arenas, R. L. Ramírez, and A. E. de los Monteros. 2015. Using Range-Wide Abundance Modeling to Identify Key Conservation Areas for the Micro-Endemic Bolson Tortoise (Gopherus flavomarginatus). PLOS ONE 10:e0131452.
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Yañez‐Arenas, C., E. Martínez‐Meyer, S. Mandujano, and O. Rojas‐Soto. 2012. Modelling geographic patterns of population density of the white-tailed deer in central Mexico by implementing ecological niche theory. Oikos 121:2081–2089.