DOI: 10.5061/dryad.nk98sf7v3
title: Global distribution of polymorphism in ants

description: Biologists have long been fascinated by the processes that give rise to
 the phenotypic complexity of organisms, yet whether there exist geographic
 hotspots of phenotypic complexity remains poorly explored. Phenotypic
 complexity can be readily observed in ant colonies, which are
 superorganisms with morphologically differentiated queen and worker castes
 analogous to the germline and soma of multicellular organisms. Several ant
 species have evolved ‘worker polymorphism’, where workers in a single
 colony show quantifiable differences in size and head-to-body scaling.
 Here, we use 256,754 occurrence points from
 8,990 ant species to investigate the geography
 of worker polymorphism. We show that arid regions of the world are the
 hotspots of superorganism complexity. Tropical savannas and deserts, which
 are typically species-poor relative to tropical or even temperate forests,
 harbor the highest densities of polymorphic ants. We discuss the possible
 adaptive advantages that worker polymorphism provides in arid
 environments. Our work may provide a window into the
 environmental conditions that promote the emergence
 of highly complex phenotypes.
description: We reviewed the literature and classified 15,518 species of ants
 as either 'polymorphic’ according to a broad definition of worker
 polymorphism or as ‘monomorphic’ (Table S1, S2). Species lacking
 significant size or allometric variation in the worker caste were
 classified as monomorphic whereas all others were classified as
 polymorphic (see Fig. S1 for more details). To classify ant species as
 polymorphic or monomorphic, we first systematically searched AntWiki [34]
 and AntWeb [35], two open-access repositories curated by myrmecologists
 that collate information and list studies on each ant species, for
 relevant literature. These databases were chosen because resources are
 easily accessible and the taxonomic upkeep of the species pages is
 consistent [34, 35]. Using the primary literature cited on these pages, we
 searched for explicit mention of the terms monomorphic, polymorphic, or
 any of the categories of polymorphism defined by Wilson [21]. When
 multiple sources were available for a given species, we used the most
 recent article. We used the species occurrence data
 from the Global Ant Biodiversity Informatics(hereafter referred to as
 GABI) database [43]. This occurrence database is a synthesis of data from
 published literature (~10,000 publications), as well as from online,
 open-source databases, museum records (including 87 online open-source
 databases, such as  Antweb), and personal collection databases. The
 taxonomy of legacy records is placed in a common framework and
 continuously updated with new data. It also includes records extracted
 from 87 online, open-source databases, such as Antweb, as well as
 museum records. Species name validity is being checked for each record and
 follows the Bolton Catalogue available from AntCat.org. Species
 identification errors are corrected on the basis of taxonomic revisions
 and biodiversity literature (e.g. species checklist),  contact with
 experts, and direct decisions based on biogeographic knowledge.  extracted
 all spatially validated occurrence points from the database (accessed in
 May  2018), and after excluding occurrence data from oceanic islands, we
 retained 256,754points matching 8,990 ant species for our
 analyses.  
description: # Global distribution of polymorphism in ants Dataset DOI:
 [10.5061/dryad.nk98sf7v3](https://doi.org/10.5061/dryad.nk98sf7v3) ##
 Description of the data and file structure # **Warm and arid regions of
 the world are hotspots of superorganism complexity** ## Description This
 dataset contains georeferenced occurrence records for the global ant
 species derived from the GABI (Global Ant Biodiversity Informatics)
 database and enriched with spatial, biogeographic, and climatic
 covariates. Each row represents a unique occurrence record, including
 geographic coordinates, taxonomic classification, spatial aggregation
 identifiers, point density metrics, and climate variables extracted at the
 occurrence location. The dataset is intended for use in biodiversity
 analyses, species distribution modeling, macroecology, and biogeographic
 synthesis. 
 
 #### File: polymorphism_data_final.csv ## Data structure Each row
 corresponds to a point occurrence record. Columns are described below. ##
 Column descriptions 
 
 | Column name | Description | | :------------------- |
 :---------------------------------------------------------------------- |
 | X | row id | | gabi\_acc\_number | Unique accession number from the GABI
 database | | valid\_species\_name | Taxonomically validated species name
 associated with the record | | country | Country where the occurrence was
 recorded | | dec\_lat | Decimal latitude | | dec\_lon | Decimal longitude
 | | bentity2\_name | Standardized entity for spatial joins | | poly\_id |
 Polymorphism ID (1\|0) | | Continent | Continent where the occurrence is
 located | | new\_id | Derived id used for analyses | | MAP.point | Total
 Annual Precipitation at the spatial point, extracted from BIOCLIM | |
 point\_density | Density of occurrences within the spatial point
 neighborhood | | MAT.point | Mean annual temperature at the spatial point,
 extracted from BIOCLIM | | genus | Genus-level taxonomic classification |
 | subFamily | Subfamily level taxonomic classification | 
 
 ## Spatial reference * Coordinate system: WGS84 (EPSG:4326) * Coordinates
 are provided in decimal degrees. ## Taxonomic scope * **Kingdom:**
 Animalia * **Phylum:** Arthropoda * **Class:** Insecta * **Order:**
 Hymenoptera * **Family:** Formicidae ## Geographic scope * Global ##
 Limitations * Occurrence records may reflect sampling bias toward
 accessible or well-surveyed areas. * Climatic variables are
 point-extracted and reflect the resolution and assumptions of the
 underlying climate dataset. (BIOCLIM v1.0) * Taxonomic validation reflects
 the status at the time of data extraction (2021). ## Citation If you use
 this dataset, please also cite the original GABI data source and BIOCLIM
 climatic source.  * Guénard, B., Weiser, M. D., Gomez, K., Narula, N.,
 & Economo, E. P. (2017).\ **The Global Ant Biodiversity Informatics
 (GABI) database: Synthesizing data on the geographic distribution of ant
 species (Hymenoptera: Formicidae).**\ *Myriapodologica*, 14, 83–89. *
 Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis,
 A. (2005).\ **Very high resolution interpolated climate surfaces for
 global land areas.**\ *International Journal of Climatology*, 25(15),
 1965–1978.\
 [https://doi.org/10.1002/joc.1276](https://doi.org/10.1002/joc.1276) ##
 Contact For questions regarding data processing, spatial aggregation, or
 climate variable extraction, please contact
 [jp.lessard@concordia.ca](mailto:jp.lessard@concordia.ca) ### ####