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Dennis EB, Morgan BJT, Freeman SN, Roy DB, Brereton T. Dynamic Models for Longitudinal Butterfly Data. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2015. [DOI: 10.1007/s13253-015-0216-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Abstract
We present models which provide succinct descriptions of longitudinal seasonal insect count data. This approach produces, for the first time, estimates of the key parameters of brood productivities. It may be applied to univoltine and bivoltine species. For the latter, the productivities of each brood are estimated separately, which results in new indices indicating the contributions from different generations. The models are based on discrete distributions, with expectations that reflect the underlying nature of seasonal data. Productivities are included in a deterministic, auto-regressive manner, making the data from each brood a function of those in the previous brood. A concentrated likelihood results in appreciable efficiency gains. Both phenomenological and mechanistic models are used, including weather and site-specific covariates. Illustrations are provided using data from the UK Butterfly Monitoring Scheme, however the approach is perfectly general. Consistent associations are found when estimates of productivity are regressed on northing and temperature. For instance, for univoltine species productivity is usually lower following milder winters, and mean emergence times of adults for all species have become earlier over time, due to climate change. The predictions of fitted dynamic models have the potential to improve the understanding of fundamental demographic processes. This is important for insects such as UK butterflies, many species of which are in decline. Supplementary materials for this article are available online.
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Matechou E, Dennis EB, Freeman SN, Brereton T. Monitoring abundance and phenology in (multivoltine) butterfly species: a novel mixture model. J Appl Ecol 2014. [DOI: 10.1111/1365-2664.12208] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Eleni Matechou
- Department of Statistics; University of Oxford; Oxford OX1 3TG UK
| | - Emily B. Dennis
- School of Mathematics, Statistics and Actuarial Science; University of Kent; Canterbury CT2 7NZ UK
| | - Stephen N. Freeman
- Centre for Ecology and Hydrology; Crowmarsh Gifford Wallingford OX10 8BB UK
| | - Tom Brereton
- Butterfly Conservation; East Lulworth Wareham Dorset BH20 5QP UK
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