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Dynamic models for longitudinal butterfly data

Dennis, Emily B., Morgan, Byron J. T., Freeman, Stephen N., Roy, David B., Brereton, Tom (2015) Dynamic models for longitudinal butterfly data. Journal of Agricultural, Biological, and Environmental Statistics, 21 (1). pp. 1-21. ISSN 1085-7117. E-ISSN 1537-2693. (doi:10.1007/s13253-015-0216-3) (KAR id:46264)

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Official URL:
http://dx.doi.org/10.1007/s13253-015-0216-3

Abstract

There has been recent interest in devising stochastic models for seasonal insects, which

respond rapidly to climate change. Fitted to count data, these models are used to construct

indices of abundance, which guide conservation and management. We build upon Dennis et

al. (2014, under review) to produce dynamic models, which provide succinct descriptions of

data from all years simultaneously. They produce estimates of key life-history parameters

such as annual productivity and survival.

Analyses for univoltine species, with only one generation each year, extend to bivoltine

species, with two annual broods. In the latter case we estimate the productivities of each

generation separately, and also devise extended indices which indicate the contributions

made from different generations.

We demonstrate the performance of the models using count data for UK butterfly species,

and compare with current procedures which use generalized additive models. We may incor-

orate relevant covariates within the model, and illustrate using northing and measures of

temperature. Consistent patterns are demonstrated for multiple species. This generates a

variety of hypotheses for further investigation, which have the potential to illuminate features

of butterfly phenology and demography which are at present poorly understood.

Item Type: Article
DOI/Identification number: 10.1007/s13253-015-0216-3
Uncontrolled keywords: abundance; auto-regression; butterflies; concentrated likelihood; indicators of biodiversity; indices; multi-stage life cycle; phenology
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QH Natural history > QH541 Ecology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Byron Morgan
Date Deposited: 19 Dec 2014 15:47 UTC
Last Modified: 08 Dec 2022 23:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/46264 (The current URI for this page, for reference purposes)
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