Dynamic Models for Longitudinal Butterfly Data

Dennis, Emily B.; Morgan, Byron J. T.; Freeman, Stephen N.; Roy, David B.; Brereton, Tom

doi:10.1007/s13253-015-0216-3

Cited by 22 publications

(29 citation statements)

References 35 publications

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“…Negative population changes may also be driven by various other influences. For example, warmer wetter winters (which are increasing under climate change) have been shown to negatively impact some moths (Conrad et al 2002;Hunter et al 2014) and butterflies (Dennis et al 2016b;McDermott Long et al 2017), and there is also growing evidence for negative impacts from light pollution (Macgregor et al 2015;van Langevelde et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Trends and indicators for quantifying moth abundance and occupancy in Scotland

et al. 2019

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Moths form an important part of Scotland's biodiversity and an up-to-date assessment of their status is needed given their value as a diverse and species-rich taxon, with various ecosystem roles, and the known decline of moths within Britain. We use long-term citizen-science data to produce species-level trends and multi-species indicators for moths in Scotland, to assess population (abundance) and distribution (occupancy) changes. Abundance trends for moths in Scotland are produced using Rothamsted Insect Survey count data, and, for the first time, occupancy models are used to estimate occupancy trends for moths in Scotland, using opportunistic records from the National Moth Recording Scheme. Species-level trends are combined to produce abundance and occupancy indicators. The associated uncertainty is estimated using a parametric bootstrap approach, and comparisons are made with alternative published approaches. Overall moth abundance (based on 176 species) in Scotland decreased by 20% for 1975-2014 and by 46% for 1990-2014. The occupancy indicator (based on 230 species) showed a 16% increase for 1990-2014. Alternative methods produced similar indicators and conclusions, suggesting robustness of the results, although rare species may be under-represented in our analyses. Species abundance and occupancy trends were not clearly correlated; in particular species with negative population trends showed varied occupancy responses. Further research into the drivers of moth population changes is required, but increasing occupancy is likely to be driven by a warming summer climate facilitating range expansion, whereas population declines may be driven by reductions in habitat quality, changes in land management practices and warmer, wetter winters.

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Section: Discussionmentioning

confidence: 99%

Trends and indicators for quantifying moth abundance and occupancy in Scotland

et al. 2019

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“…In bumblebees, successive castes are not necessarily the offspring of the previous caste; rather, each annual caste count obviously comprises the offspring of the old queens in that season. Our model also extends that of Dennis et al (2016b) in order to estimate these ecologically important rates of within-season productivity, which now index aspects of colony size.…”

Section: Introductionmentioning

confidence: 92%

“…We extend the model developed for successive broods of multivoltine butterflies by Matechou et al (2014) and its extensions presented in Dennis et al (2016a,b) in such a way that these uncertainties are explicitly modelled, without the need (for example) to decree a priori that queens are first or second generation using an arbitrary separation date. Dennis et al (2016b) model butterfly data using "productivity" parameters to link the successive generations. In bumblebees, successive castes are not necessarily the offspring of the previous caste; rather, each annual caste count obviously comprises the offspring of the old queens in that season.…”

Section: Introductionmentioning

confidence: 99%

Caste-Specific Demography and Phenology in Bumblebees: Modelling BeeWalk Data

Matechou

Freeman

Comont

2018

JABES

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We present novel dynamic mixture models for the monitoring of bumblebee populations on an unprecedented geographical scale, motivated by the UK citizen science scheme BeeWalk. The models allow us for the first time to estimate bumblebee phenology and within-season productivity, defined as the number of individuals in each caste per colony in the population in that year, from citizen science data. All of these parameters are estimated separately for each caste, giving a means of considerable ecological detail in examining temporal changes in the complex life cycle of a social insect in the wild. Due to the dynamic nature of the models, we are able to produce population trends for a number of UK bumblebee species using the available time-series. Via an additional simulation exercise, we show the extent to which useful information will increase if the survey continues, and expands in scale, as expected. Bumblebees are extraordinarily important components of the ecosystem, providing pollination services of vast economic impact and functioning as indicator species for changes in climate or land use. Our results demonstrate the changes in both phenology and productivity between years and provide an invaluable tool for monitoring bumblebee populations, many of which are in decline, in the UK and around the world.Supplementary materials accompanying this paper appear online.

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“…Fitting unimodal phenological data with a Gaussian curve is not new (e.g. Dennis et al 2015, Oke et al 2019, Stewart et al 2020), but in the past this required custom coding. By fitting Gaussian curves with liner models, it is easy to use out-of-the-box regression methods.…”

Section: Introductionmentioning

confidence: 99%

“Estimating abundance and phenology from transect count data with GLMs”

Edwards

Crone

2020

Preprint

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9Understanding organismal phenology has been an emerging interest in ecology, in part because 1 0 phenological shifts are one of the most conspicuous signs of climate change. While we are seeing 1 1 increased collection of phenological data and creative use of historical data sets, existing 1 2 statistical tools to measure phenology are generally either limited (e.g., first day of observation, 1 3 which has problematic biases) or are challenging to implement (often requiring custom coding, 1 4or enough data to fit many parameters). We present a method to fit phenological data with 1 5Gaussian curves using linear models, and show how robust phenological metrics can be obtained 1 6 using standard linear regression tools. We then apply this method to eight years of Baltimore 1 7 checkerspot data using generalized linear mixed models (GLMMs). This case study illustrates 1 8 the ability of years with extensive data to inform years with less data and shows that butterfly 1 9

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Dynamic Models for Longitudinal Butterfly Data

Cited by 22 publications

References 35 publications

Trends and indicators for quantifying moth abundance and occupancy in Scotland

Trends and indicators for quantifying moth abundance and occupancy in Scotland

Caste-Specific Demography and Phenology in Bumblebees: Modelling BeeWalk Data

“Estimating abundance and phenology from transect count data with GLMs”

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