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.

It's official! Starting today, I am a Marie Skłodowska-Curie Fellow, with a project titled "MARDIGRAS: Elucidating MARine DIversity GRAdients with Empirical and Theoretical ModelS". I'm thrilled for the opportunity to take my experiences of the last few years working on biodiversity patterns in butterflies and birds, and puzzle through how to infer biodiversity patterns and their underlying mechanisms in marine fishes.

The aim of the project is to take a deep dive (sorry not sorry for the pun) into understanding worldwide biodiversity patterns for three groups of marine fishes (my beloved Gadiformes, aka codfishes; Scombriformes, aka mackerels and tunas; and Beloniformes, aka flyingfishes), develop a mechanistic model of how biodiversity patterns arise in marine systems, and then contrast diversity patterns among the three groups of fishes and with the mechanistic model.

While there is extensive macroecological literature on diversity gradients in terrestrial systems, especially regarding latitude, marine systems seem to be following a different set of rules. First, geographic patterns of diversity appear to be neither clear nor ubiquitous. Recently, Chaudhary et al. (2016) found a bimodal diversity curve with respect to latitude (that is, more species were found at middle latitudes than at the equator), whereas Rabosky et al (2018) found a unimodal curve with diversity concentrated at the equator (which is the expected pattern for terrestrial groups). Admittedly, these studies had different organismal foci and employed different methods, but this disparity is striking.

Second, the mechanisms underlying diversity patterns in marine systems appear to be quite different. Generally, in terrestrial systems it is thought that speciation is highest in the tropics, as this is where the most energy is concentrated (among other explanations). However, the aforementioned study by Rabosky and colleagues found that speciation (in marine fishes) was highest at high latitudes! Some of this may be attributable to the unique properties of ocean ecosystems compared to terrestrial ones. As such, one of my project goals is to adapt a mechanistic model that was developed for terrestrial tropical biodiversity (Rangel et al. 2018) and adapt it for the marine context, using diversity patterns in codfishes, mackerels, and flyingfishes to evaluate how realistic the model is.

Stay tuned as I make progress in this exciting area, either here or by following #mardigrasProj on Twitter!