Ecological traits predict population changes in moths

A - Papers appearing in refereed journals

Coulthard, E., Norrey, J., Shortall, C. R. and Harris, W. E. 2019. Ecological traits predict population changes in moths. Biological Conservation. 233, pp. 213-219.

AuthorsCoulthard, E., Norrey, J., Shortall, C. R. and Harris, W. E.

Understanding the ecological traits which predispose species to local or global extinction allows for more effective pre-emptive conservation management interventions. Insect population declines are a major facet of the global biodiversity crisis, yet even in Europe they remain poorly understood. Here we identify traits linked to population trends in ‘common and widespread’ UK moths. Population trend data from the Rothamsted Research Insect Survey spanning 40 years was subject to classification and regression models to identify common traits among species experiencing a significant change in occurrence. Our final model had an accuracy of 76% and managed to predict declining species on 90% of occasions, but was less successful with increasing species. By far the most powerful predictor associated for declines was moth wingspan with large species declining more frequently. Preference for woody or herbaceous larval food sources, nocturnal photoperiod activity, and richness of habitats occupied also proved to be significantly associated with decline. Our results suggest that ecological traits can be reliably used to predict declines in moths, and that this model could be used for Data Deficient species, of which there are many.

KeywordsEcological traits; Extinction-risk; Lepidoptera; Moths
Year of Publication2019
JournalBiological Conservation
Journal citation233, pp. 213-219
Digital Object Identifier (DOI)
Web address (URL)
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe Rothamsted Insect Survey - National Capability [2017-2022]
Publisher's version
Output statusPublished
Publication dates
Online05 Apr 2019
Publication process dates
Accepted14 Feb 2019
PublisherElsevier Sci Ltd
Copyright licenseCC BY

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