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Moths and butterflies on alien shores – global biogeography of non-native Lepidoptera

Citation

Mally, Richard et al. (2022), Moths and butterflies on alien shores – global biogeography of non-native Lepidoptera, Dryad, Dataset, https://doi.org/10.5061/dryad.31zcrjdnw

Abstract

Lepidoptera is a highly diverse, predominantly herbivorous insect order, with species transported to outside their native range largely facilitated by the global trade of plants and plant-based goods. Analogous to island disharmony, we examine invasion disharmony, where species filtering during invasions increases systematic compositional differences between native and non-native species assemblages, and test whether some families are more successful at establishing in non-native regions than others. We compared numbers of non-native, unintentionally introduced Lepidoptera species with the land area of 11 regions worldwide (Hawaii, North America, Galapagos, Europe, South Africa, South Korea, Japan, Nansei Islands, Ogasawara Islands, Australia, New Zealand). Differences among native and non-native assemblages in the distribution of species among families were investigated using ordination analysis. We tested whether invasion disharmony is explained by propagule pressure (proxied by species richness in border interceptions) and if families were associated with specific trade commodities. In total, 741 non-native Lepidoptera species, accounting for 0.47% of the global diversity of lepidopterans, are established in at least one of the 11 regions. Crambidae, Pyralidae, Tineidae and Gracillariidae were particularly successful invaders, whereas the two most species-rich families, Erebidae and Geometridae, were under-represented among non-native Lepidoptera. Much of the variation in species numbers in the native, and less so in the non-native assemblages could be attributed to land area. Although native assemblages were similar among nearby regions, non-native assemblages were not, suggesting geography had little effect on invasion disharmony. Comparison of established with intercepted species revealed that macromoth families were generally under-represented in establishments, whereas several micromoth families were under-represented in interceptions. This discrepancy may relate to greater detectability of larger species or high propagule pressure via associations with specific invasion pathways. Invasion disharmony in Lepidoptera appears to be driven by processes unrelated to the success of native assemblages. While native assemblages developed through long-term evolutionary radiation, the composition of non-native assemblages is driven by differential invasion pathways and traits affecting the establishment of founder populations that vary among families.

Methods

Lists of native and non-native established Lepidoptera species were compiled from 11 different regions (North America (Canada, continental USA), the Hawaiian Archipelago, the Galapagos Archipelago, Europe (including its major islands and the European part of Russia), South Africa, South Korea, Japan (excluding the following two regions), the Nansei Islands, the Ogasawara Islands, Australia, New Zealand). The data on non-native established Lepidoptera are part of a larger database, "International Non-native Insect Establishment Data", that is periodically updated and freely available (Turner et al. 2021a).

Taxonomic delimitation of Lepidoptera families and total species numbers per family follows recently published works (van Nieukerken et al. 2011; Zahiri et al. 2011, 2012, 2013; Kaila et al. 2013, 2020; Sohn et al. 2013; Heikkilä et al. 2014; Regier et al. 2014, 2015; Kristensen et al. 2015). The polyphyletic Batrachedridae (Heikkilä et al., 2014) were kept in the circumscription of van Nieukerken et al. (2011). Chrysodeixis chalcites and Ch. eriosoma (Noctuidae) were treated as one species, as current species delimitation methods (morphology, DNA Barcode data) cannot separate them into distinct species.

Taxonomic “cleaning” using the GBIF taxonomic database (GBIF Secretariat 2019) and the P package ‘taxize’, version 2 (Chamberlain & Szöcs 2013) ensured standardization of the datasets from the different regions and avoid duplication through synonyms and misspellings. Code used for this taxonomic cleaning is available in the Zenodo repository (Blake & Turner 2021). The small number of names that was not recognized in the GBIF backbone taxonomy was searched for manually via searches in alternative databases (Beccaloni et al. 2003; Nuss et al. 2003–2022; De Prins & De Prins 2006–2022, 2011–2022; Gilligan et al. 2018) and manual online researching of names.

Data on Lepidoptera species intercepted at sea- and airports were sourced from regions that largely overlapped with the regions investigated for the establishments: North America (mainland USA, Canada), Hawaii, the western countries of the European and Mediterranean Plant Protection Organization (EPPO), UK, South Africa, Japan, South Korea, Australia and New Zealand. These data were pooled to quantify species richness for Lepidoptera families. The border interception data are described in detail in Brockerhoff et al. (2014), Turner et al. (2021b) and Saccaggi et al. (2021).

Data on trade commodities associated with Lepidoptera intercepted during inspections and identified at least to genus level were derived from the interception data described above. The commodity dataset represented a geographical and temporal subset, with commodities not recorded for all interceptions, and with the commodity data from New Zealand from an earlier period (19602000) (see Appendix S1 in Mally et al. 2022). Data were pooled from interceptions at sea- and airports of six regions (USA incl. Hawaii; Canada; EPPO; Japan; Australia; New Zealand), and span different timeframes from 1950 to the late 2010s. Data from each region were pooled to quantify species richness for each Lepidoptera family. The data span 14 classes of commodities, based on the Harmonized Commodity Description and Coding System developed by the World Customs Organization (https://www.trade.gov/harmonized-system-hs-codes): Plant products, Animal products, Wood products, Foodstuffs, Transport, Metal products, Machinery/Electrical, Mineral products, Stone/Glass, Chemical products, Plastics/Rubber, Textiles, Footwear/Headgear, and Miscellaneous. To limit the impact of stochastic effects, analyses were restricted to families with more than 100 commodity records. Plant product commodities of these families were itemised to 10 subclasses for a more fine-scaled analysis of pathways: Cereals (HS-10), Coffee/tea/spices (HS-09), Flours (HS-11), Fruit/nuts (HS-08), Gum/resin (HS-13), Live plants/cut flowers (HS-06), Vegetable fibres (HS-53), Seeds/grains/medicinal (HS-12), Vegetable products (HS-14), and Vegetables (HS-07). Data collection and processing of commodities data is described in detail in Fenn-Moltu et al. (in review).

 

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Funding

OP RDE, Award: CZ.02.1.01/0.0/0.0/16_019/0000803

New Zealand Ministry of Business, Innovation and Employment, Award: C09X1501

U.S. Forest Service, Award: 21-IG-11132762-241

Te Pūnaha Matatini

Programme de la famille Sandoz-Monique de Meuron pour la relève universitaire, canton Vaud

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Award: SNF 310030_192619

National Socio-Environmental Synthesis Center, Award: DBI-1639145

Horizon 2020, Award: 771271