A distribution study on the subtribe
Euptychiina
The main goal of this project is to produce distribution maps for each euptychiine species using museum and other data sources, and to create a repeatable process of map development with the goal of answering questions about euptychiine biogeography and evolution.
Background
Euptychiina, a subtribe of satyrines in the family Nymphalidae, has over 400 described species and is estimated to contain over 500. These butterflies are diverse and widespread across the Neotropics, present in both forest and grassland habitats, and their biogeography might therefore show general patterns of evolution that could apply to other Neotropical groups. However, relatively few studies have been conducted on their origins and distribution. Many broad-scale ecological hypotheses of insects- and, by extension, euptychiines- remain untested, largely due to incomplete or very coarse spatial distribution knowledge (Ballesteros‐Mejia et al., 2016). This project will tackle this information deficit by compiling distribution data to create individual range maps for all euptychiine species, providing a valuable resource for studies of Neotropical biogeography, and apply them to investigate the evolution of euptychiines.
Methods
The first step in this project was to compile existing data from collections at the Florida Museum of Natural History. Although data from labels of many specimens have been recorded, to use the locality information requires georeferencing localities: finding the geographical coordinates at which individual specimens were collected. My first step was to prioritize georeferencing localities for species with few existing georeferenced localities (less than 10-20 localities). I completed georeferencing for over one hundred localities of specimens in the Florida Museum’s collections. These localities were georeferenced by using locality descriptions along with other recorded information (such as elevation, nearby towns, kilometers along a road, or other notable landmarks), and searching for localities online and through Google Earth to identify the collection site and record the likely coordinates, within a specified margin of error.
In previous semesters, I also worked with the online citizen science project iNaturalist to identify species from priority genera with limited records, using prepared identification guides to species provided by my mentor. In iNaturalist, there are roughly 40,000 euptychiine records in the Neotropics. For each genus of interest, we found unidentified specimens and used field guides to add their identification. For those specimens in iNaturalist with an existing identification, we ensured the current identification was correct using the provided field guides, then downloaded the iNaturalist record data. In this stage of the project, we regularly encountered known undescribed species. For these, we recorded the iNaturalist record data, the undescribed species temporary name, and the current genus they belong to in a separate Excel file. My identifications were reviewed by Keith Willmott before entering our dataset. During this semester, we worked with a team of expert collaborators to identify images of euptychiines in iNaturalist.org. Resulting records, including locality information, were downloaded.
Once data for a genus had been collected, we used the program QGIS to locate georeferencing or locality errors. This was accomplished by mapping points and extracting elevation data from GIS layers by point and species. Outliers were evaluated and the data updated. Next, we used an R script that we created to generate extent of occurrence regions for each species as a batch process. This script calculated the nearest neighbor distance between points, then found the 90th percentile of the distance data to generate a buffered minimum convex polygon (MCP). Buffered MCPs were then evaluated in QGIS and manually edited to account for the unique geography of the Andes mountains, which are a dispersal barrier for many species, to remove regions where species do not occur.
Next, we used the Wallace Ecological Modelling App (“Wallace”), a program that utilizes R and RStudio, to model species distributions. One goal of this project, as mentioned, is to develop a repeatable method for modeling distributions, and Wallace is user-friendly and provides diverse options to explore data and develop models. We selected 4 of the 19 BioClim bioclimatic variables (Fick et al., 2017) that are known to be significant for butterflies. Because Euptychiina has a coarse spatial distribution knowledge, additional variables result in unrealistic overfitting of models:
To generate presence-absence maps from the model’s suitability maps we selected the Minimum Threshold - due to the relatively sparse locality data for euptychiines, this threshold includes all known data points and is conservative with respect to predicted regions of occurrence. Additionally, Wallace provides two methods to model the potential distribution of species based on environmental variables, BioClim and MAXENT, which differ in their approaches and complexity.
Utilizing Wallace with specified parameters, we were able to create individual range maps for the subtribe Euptychiina, and richness maps for respective genera. Additional details of this process are withheld in anticipation of the upcoming publication.
Outcomes
The individual range maps for euptychiine species, and the richness maps for their respective genera, have applications for studying both the historical biogeography and macroecology of Euptychiina. For example, in combination with the existing phylogeny for the group (Espeland et al., 2023), these distribution maps will allow future studies to address whether species evolve in allopatry or sympatry, and how community diversity varies across the Neotropics, and which factors could potentially be influencing it. After extracting climate niche information for species, we could also test hypotheses about species diversification (Peña et al., 2010). In addition, future studies will be able to utilize the process developed here for modeling Neotropical butterfly distributions and expand this project to other neotropical groups.
Citations
Ballesteros‐Mejia, L., Kitching, I. J., Jetz, W., & Beck, J. (2016). Putting insects on the map:
Near‐global variation in sphingid moth richness along spatial and environmental gradients. Ecography, 40(6), 698–708. https://doi.org/10.1111/ecog.02438
Espeland et al. A global phylogeny of butterflies reveals their evolutionary history, ancestral
hosts and biogeographic origins. Nat Ecol Evol 7, 903–913 (2023).
https://doi.org/10.1038/s41559-023-02041-9
Fick, S. E., & Hijmans, R. J. 2017. WorldClim 2: new 1km spatial resolution climate surfaces for
global land areas. International Journal of Climatology, 37(12), 4302-4315.
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, ME, Anderson, R. P. (2023). wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions. Ecography, 2023(3), e06547.
Kass, J. M., Vilela, B., Aiello‐Lammens, M. E., Muscarella, R., Merow, C., Anderson, R. P. (2018).
Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion. Methods in Ecology and Evolution, 9, 1151–1156.
Peña, C., Nylin, S., Freitas, A. V., & Wahlberg, N. 2010. Biogeographic history of the butterfly
subtribe Euptychiina (Lepidoptera, Nymphalidae, Satyrinae). Zoologica Scripta, 39(3), 243–258.