Single Night Surveys of Moth Communities Can Serve as Ultra-Rapid Biodiversity Assessments.

Geometridae Lepidoptera Tortricidae conservation umbrella species

Journal

Insects
ISSN: 2075-4450
Titre abrégé: Insects
Pays: Switzerland
ID NLM: 101574235

Informations de publication

Date de publication:
09 Dec 2022
Historique:
received: 15 11 2022
revised: 02 12 2022
accepted: 07 12 2022
entrez: 23 12 2022
pubmed: 24 12 2022
medline: 24 12 2022
Statut: epublish

Résumé

Biodiversity conservation decisions are typically based on limited data and resources. For this reason, there is great interest in surveying taxa that may allow for a rapid assessment of the biodiversity at a site. Numerous taxa have been proposed and utilized for rapid assessments that allow for such a survey in a matter of weeks or less. Herein, we test the idea that nocturnal moths have many of the characteristics that make them ideal for such surveys, such as relative ease of identification, strong ecological association with specific plant species and habitats, high alpha diversity, extended seasonal activity, and ease of trapping. We demonstrate that even in a few hours of sampling during single night surveys, moth communities are predictive of regional forest types at sampling sites in New Jersey. We sampled moths in five different forest habitats in New Jersey, USA: Pine Barrens, Upland Deciduous Forest, Palustrine Deciduous Forest, Maritime Forest, and Ruderal/Disturbed Forests, at four sites per forest type. Non-metric multidimensional scaling (NMDS) analyses revealed that moth communities differ significantly across these four forest types (p < 0.01). We used Analysis of Similarity (ANOSIM) R tests to quantify the degree of differentiation among moth communities, and found that Tortricidae (R = 0.657) and Geometridae (R = 0.637) predict forest communities nearly as well as the total moth diversity (R = 0.668). Uncommon species (R = 0.665) were better predictors than common species (R = 0.500). Host plant generalists (R = 0.654) were better predictors than specialists (0.538), which was a surprising find.

Identifiants

pubmed: 36555045
pii: insects13121135
doi: 10.3390/insects13121135
pmc: PMC9781280
pii:
doi:

Types de publication

Journal Article

Langues

eng

Références

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Auteurs

Daniel P Duran (DP)

Department of Environmental Science, Rowan University, Glassboro, NJ 08210, USA.

Matthew Timar (M)

Telemitra Incorporated, Haddonfield, NJ 08033, USA.

Blaine Rothauser (B)

GZA Geoenvironmental, Inc., Fairfield, NJ 07004, USA.

Classifications MeSH