2020 ESA Annual Meeting (August 3 - 6)

COS 146 Abstract - Global patterns of ant diversity and congruence with other taxa

Jamie M. Kass1, Benoit Guenard2, Clinton N. Jenkins3, Kenneth Dudley1, Fumika Azuma1, Anne Chao4, Robert R. Dunn5, Brian L. Fisher6, Heloise Gibb7, Catherine Parr8, Nathan J. Sanders9, Michael Weiser10 and Evan P. Economo1, (1)Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Japan, (2)Biological Sciences, University of Hong Kong, Hong Kong, China, (3)Instituto de Pesquisas Ecológicas, (4)Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan, (5)Applied Ecology, North Carolina State University, Raleigh, NC, (6)California Academy of Sciences, San Francisco, CA, (7)Department of Ecology, Environment and Evolution, La Trobe University, Melbourne, VIC, Australia, (8)School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom, (9)Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, (10)Department of Biology, University of Oklahoma, Norman, OK
Background/Question/Methods

Although fine-scale, global biodiversity estimates are available for vertebrates, such estimates for hyper-diverse groups with great ecological importance such as insects are largely missing or only possible at a coarse, regional scale. This is mainly due to digitized data deficiencies for most known species and insufficient data representation across biogeographic regions. As patterns of diversity for insects are likely different from those for vertebrates, identifying insect diversity hotspots can lead to future conservation decisions that better protect insect populations from further decline. In particular, ants have dominant roles and complex interaction networks in the wide variety of ecosystems they inhabit, and thus patterns of ant richness should serve as good proxies for other invertebrate groups. We synthesized diverse data sources including literature, museum records, and personal collections and assembled a combined dataset on ant geographic distributions. As expert range maps do not exist for a majority of the over 15,000 known ant species, we focused on modeling ranges from occurrence records, which we cleaned and georeferenced using a data-checking pipeline we developed for large occurrence datasets. We used these occurrence data to make species range estimates based on 1) range polygons and 2) species distribution model (SDM) predictions based on bioclimatic variables for species with sufficient data. We then overlaid range polygons, binary SDM predictions, and continuous SDM predictions to make different global species richness estimates, including those that weight species with small ranges higher. We compared these results with global richness estimates for different vertebrate groups. In addition, we made alternate estimates of ant richness by extrapolating based on Hill numbers using a moving window and making predictions based on genus richness.

Results/Conclusions

The model-based richness estimates were more conservative than the polygon-based ones, though they had the same general patterns. Estimated ant richness was highest in the Neotropics, with hotspots also in North America, Central Africa, Madagascar, the Mediterranean, South Asia, Southeast Asia, and Australia. Small-ranged species had the highest richness in the Neotropics, Madagascar, the Mediterranean, Southeast Asia, Papua New Guinea, and eastern Australia. Vertebrate richness patterns had areas of congruence and difference with those of ants, and we identified areas where relative richness was higher for either. These new global ant richness estimates are the most spatially comprehensive and fine-scale to date and should aid the additional prioritization of regions that are important for insect conservation.