The day I have been warning students and collaborators of has finally come. rgdal has been archived. Why? The maintainer, Roger Bivand, has retired, and no one else will maintain it to keep it working smoothly as computing continues to evolve. You can read more about that here.
What does this mean? If you did not have the time, resources, know-how, etc to make the switch from rgdal, raster, and sp to terra, stars, sf, etc. sooner, there's no reason to panic. As long as you don't need to update anything, your raster and rgdal code will probably still work for a while, but it will be increasingly difficult to maintain. You will also not be able to submit any new R packages that depend on rgdal, etc. Additionally, if you teach using R for spatial data or have students that do spatial analysis as part of their research, it's time to update course materials. I'm right there with you. I teach spatial analysis at a number of different levels of expertise and detail, and I am working on updating the material from which I pull to generate course content when I have a little time to spare. The best resource I've found for distilling the differences between raster and terra especially is here. In honor of the archiving of rgdal, and with the hope that maybe it saves at least a few people a few minutes of headaches, I present to you a freshly-updated introduction to some very basic GIS in R. Ta da! I am one of a long list of co-authors on a paper that came out in Nature Ecology and Evolution recently, titled “A global phylogeny of butterflies reveals their evolutionary history, ancestral hosts and biogeographic origins”. This is the most recent result of the ButterflyNet project, which began while I was a postdoc in the Kawahara and Guralnick labs at the Florida Museum of Natural History. It is incredibly global in its collaborative scope, not just in terms of data collection, but also in analysis and interpretation. The project’s modest goal was to generate the largest butterfly phylogeny to date, and assemble a massive database of morphological, ecological, and geographic information to pair it with. The phylogeny presented in the recently-published paper represents 92% of all butterfly genera and is based on 391 genes; it provided the backbone for several comparative phylogenetic analyses, that focused on biogeographic and host-plant interaction reconstructions, which was largely where my contribution fit in. Honestly, I felt a bit spoiled being able to swan in after the hard work of data collection to consult on the analyses. Based on the available data, it appears butterflies originated in the Americas and first fed on plants in the family Fabaceae. However, the true message of the paper could just as easily be “we’ve just scratched the surface here, there is so much more to do!!”, as there are a lot more data that have been collected and not yet analyzed. Look forward to continuing downstream papers in the coming years! Here is the citation: Kawahara, A.Y., Storer, C., Carvalho, A.P.S. et al. A global phylogeny of butterflies reveals their evolutionary history, ancestral hosts and biogeographic origins. Nat Ecol Evol (2023). https://doi.org/10.1038/s41559-023-02041-9 And here's reporting on the paper by National Public Radio in the US! One of the base units of analysis for biogeography and conservation science is the species range map. Once we know where a species is, we can ask questions like "Why is it there?", "How did it get there?", or "What can we do to make this place better for it?" Especially these days, I am very interested in mapping marine fish distributions, which, it turns out, is not as simple as mapping terrestrial species. The problem is that ecological niche modeling and species distribution modeling methods and theory were largely developed by people working in terrestrial systems. Specifically, correlative modeling approaches were developed based on environmental conditions extracted at two-dimensional coordinates across a horizontal landscape. Sure, birds and insects fly (and some of them at very high altitudes), but their interactions with the upper troposphere are generally brief and unlikely to directly effect the distributions of these species. In comparison, ocean fishes may live their whole lives at the ocean's surface or the seafloor, but they may also swim freely somewhere between the two. The largest migration in the world occurs DAILY, when a million tons of mesopelagic fishes travel between the cold, dark safety of the deep sea and warm, food-rich surface waters. If you modeled the ecological niche of one of these pelagic species based on surface conditions where it was caught at night, you might drastically underestimate its temperature tolerances. If distribution maps based on these inaccurate models are used in downstream analyses, they may bias results and cause all kinds of other problems. This is where voluModel comes in. voluModel is an R package I developed in collaboration with my advisor and co-author, Carsten Rahbek, during my Marie Curie Fellowship postdoc. In our new paper, out in Methods in Ecology and Evolution this week, we introduce a workflow to extract environmental conditions based on the three-dimensional coordinates where fishes are observed, as well as their background environments. These data can then be used in any typical ecological niche modeling algorithm that accepts data in a points-with-data format; the model can then be projected back into three-dimensional space using a simple loop. While three-dimensional modeling has been done before, this is the first set of tools to efficiently move through a 3-D workflow instead of using case-specific custom code. Accompanying the package are vignettes that provide 1) an overview of how voluModel works, 2) raster processing tools, 3) 3D environmental data sampling, 4) visualization tools, and 5) a basic overview of how to generate a generalized linear model with 3D data. Development of voluModel is ongoing--there have been three major updates since we first submitted the paper. Largely, this is due to the rapidly-developing landscape of faster, more efficient geoprocessing R tools using terra and other successors to the raster and rgeos packages, which are being phased out. However, we have also implemented several suggestions from manuscript reviewers and early package adopters. If you have suggestions for improvements or missing features, or if you have found bugs, you may report them here, or even better, send me a (detailed) pull request! My aspiration is that voluModel helps niche modelers (including me) to efficiently generate more accurate estimates of pelagic species distributions for downstream biogeographic analyses and conservation assessments. This is especially useful for data-poor species that may not be subject to the same exhaustive study as target fisheries species, but which are nonetheless important pieces in the puzzle both for biogeographers and conservation scientists. Read our new paper in Methods in Ecology and Evolution HERE. Access the voluModel website HERE. View voluModel on CRAN HERE. I’m thrilled to have been part of a paper out in Ecography today: “wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions”. It updates the original wallace R package, which is a really useful tool for teaching niche modeling in the R ecosystem without requiring students to be exceptionally proficient at coding first. All a person needs do is execute two lines of code, and a graphical user interface pops up that walks them through the steps of a niche modeling analysis, while documenting the decisions made along the way in R code file that can re-run to repeat the analyses.
There is a lot of potential for this approach to be used to teach students the workflows and methods involved in fairly complex niche modeling analyses. I have incorporated it into teaching materials for Masters' and PhD level courses, as well as non-academic workshops, and the new features really expand the scope of applications one can cover. wallace 2.0 also makes it easier to add custom modules to the wallace workflow (essentially analysis options, like specific statistics or data sources), and adds several such modules (including my occCite occurrence citation package). Here's the package website, which includes links to tutorials in multiple languages, including English, Spanish, and Japanese: wallaceecomod.github.io Here’s the citation: Kass, J.M., Pinilla-Buitrago, G.E., Paz, A., Johnson, B.A., Grisales-Betancur, V., Meenan, S.I., Attali, D., Broennimann, O., Galante, P.J., Maitner, B.S., Owens, H.L., Varela, S., Aiello-Lammens, M.E., Merow, C., Blair, M.E. and Anderson, R.P. (2023), wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions. Ecography e06547. https://doi.org/10.1111/ecog.06547 |
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Hannah L. Owens | What's Going On? |
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