Diversity of Nearctic Dragonflies and Damselflies (Odonata)

Abstract

Rarely have studies assessed Odonata diversity for the entire Nearctic realm by including Canada, the United States, and Mexico. For the first time, we explored Odonata diversity in this region according to a definition of natural community assemblages and generated species distribution models (SDMs). Species occurrence data were assembled by reviewing databases of specimens held by significant Odonata repositories and through an extensive search of literature references. Species were categorized as forest-dependent or non-forest-dependent, as lentic or lotic-dependent, and according to conservation status. Predicted distributions were stacked for all species across their entire ranges, including areas outside of the Nearctic. Species richness and corrected weighted endemism (CWE) were then calculated for each grid cell. We found a pattern of greater species richness in the eastern portion of the Nearctic, which can be explained by the higher aquatic habitat diversity at micro and macroscales east of the Rocky Mountains, promoting niche partitioning and specialization. In the Nearctic region, the southeastern US has the highest number of endemic species of dragonflies and damselflies; this degree of endemism is likely due to glacial refuges providing a foundation for the evolution of a rich and unique biota.

Spatial phylogenetics of Fagales: Investigating the history of temperate forests

“…Niche modeling approach-We built an ecological niche modeling pipeline in R to predict the ecological niche of 1,013 species that had at least five occurrence records after undergoing the coordinate cleaning described above. This pipeline adapts the workflow outlined in Abbott et al (2022) and was designed to automate the building of ecological niche models, while including steps that customize models for each species. First, accessible area, which was the area where the distribution model was fit and projected, was determined by buffering an alpha hull around occurrence records that passed all automated and manual filtering steps.…”

Investigating the potential of social media and citizen science data to track changes in species' distributions

“…Monthly minimum and maximum temperature as well as monthly precipitation for the period 1990 to 2020 were accessed from the Met Office at a 1‐km resolution (Met Office et al, 2022 ) and used to generate a series of monthly average bioclimate variables using the biovars function in the R package dismo (Hijmans et al, 2021 ), under the assumption that species' ranges respond to the long‐term averages of climate conditions (Taheri et al, 2020 ). These climate variables represent annual trends, seasonality, and limiting environmental factors and as such are designed to be biologically meaningful, being widely used for SDMs (Manzoor et al, 2018 ), and informative for Odonatan distributions (Abbott et al, 2022 ; Collins et al, 2017 ).…”

“…These climate variables represent annual trends, seasonality, and limiting environmental factors and as such are designed to be biologically meaningful, being widely used for SDMs (Manzoor et al, 2018), and informative for Odonatan distributions (Abbott et al, 2022;Collins et al, 2017). Predictor variables were reprojected to the British National Grid and aggregated to a 1-km resolution where needed using the functions projectRaster and aggregate in R package raster (Hijmans & van Etten, 2012).…”

Phylogenetic diversity and regionalization of root nodule symbiosis

“…Niche modeling approach-Here we used a semi-automated niche modeling approach implemented in R with an extensive manual curation strategy that has been reported previously (Abbott et al, 2022;Folk et al, 2023). Importantly, several aspects of modeling are customized on a per-species basis, including accessible areas and predictor variable set.…”

“…Then tuning parameters were optimized using combinations of range multipliers and feature classes available in Maxent using the R package ENMeval (Kass et al, 2021) with specifics reported in (Folk et al, 2023) and model choice either minimizing AICc (almost all models) or maximizing AUC (only models with AUC < 0.7). We converted continuous niche model outputs into binary (presence/absence) outputs by adapting a true skill statistic approach (Abbott et al, 2022), which selects for a thresholding value that balances commission and omission errors.…”