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Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library.

ABSTRACT: New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i.e., a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases. Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain. Using this approach, we were able to increase the number of categorized specimens by a factor of 4.4 (from 794 to 3,497), and the number of categorized species by a factor of 4.8 from (147 to 707) at a rate much greater than chance (77.6% accuracy). The study identifies phylogenetic signatures of both native and nonnative species and suggests several practical applications for this approach including monitoring biodiversity and facilitating biosecurity.

SUBMITTER: Andersen JC

PROVIDER: S-EPMC7079013 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library.

Andersen Jeremy C JC Oboyski Peter P Davies Neil N Charlat Sylvain S Ewing Curtis C Meyer Christopher C Krehenwinkel Henrik H Lim Jun Ying JY Noriyuki Suzuki S Ramage Thibault T Gillespie Rosemary G RG Roderick George K GK

Ecological applications : a publication of the Ecological Society of America 20190612 5

New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological c ...[more]

PMID: 31050090

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Project description:To evaluate the regional biogeographical patterns of West Indian native and nonnative herpetofauna, we derived and updated data on the presence/absence of all herpetofauna in this region from the recently published reviews. We divided the records into 24 taxonomic groups and classified each species as native or nonnative at each locality. For each taxonomic group and in aggregate, we then assessed the following: (1) multiple species-area relationship (SAR) models; (2) C- and Z-values, typically interpreted to represent insularity or dispersal ability; and (3) the average diversity of islands, among-island heterogeneity, ?-diversity, and the contribution of area effect toward explaining among-island heterogeneity using additive diversity partitioning approach. We found the following: (1) SARs were best modeled using the Cumulative Weibull and Lomolino relationships; (2) the Cumulative Weibull and Lomolino regressions displayed both convex and sigmoid curves; and (3) the Cumulative Weibull regressions were more conservative than Lomolino at displaying sigmoid curves within the range of island size studied. The Z-value of all herpetofauna was overestimated by Darlington (Zoogeography: The geographic distribution of animals, John Wiley, New York, 1957), and Z-values were ranked: (1) native > nonnative; (2) reptiles > amphibians; (3) snake > lizard > frog > turtle > crocodilian; and (4) increased from lower- to higher-level taxonomic groups. Additive diversity partitioning showed that area had a weaker effect on explaining the among-island heterogeneity for nonnative species than for native species. Our findings imply that the flexibility of Cumulative Weibull and Lomolino has been underappreciated in the literature. Z-value is an average of different slopes from different scales and could be artificially overestimated due to oversampling islands of intermediate to large size. Lower extinction rate, higher colonization, and more in situ speciation could contribute to high richness of native species on large islands, enlarging area effect on explaining the between-island heterogeneity for native species, whereas economic isolation on large islands could decrease the predicted richness, lowering the area effect for nonnative species. For most of the small islands less affected by human activities, extinction and dispersal limitation are the primary processes producing low species richness pattern, which decreases the overall average diversity with a large among-island heterogeneity corresponding to the high value of this region as a biodiversity hotspot.

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Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library.

Publications

Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library.

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