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Statistics Gone Wild – Animals

As part of our Statistics gone wild event for schools on 23rd June 2017 we highlighted six animal species that our group has been working with. Below you can find out more information about the work the group has been doing on each of these animals.

Small Copper Butterfly (Lycaena phlaeas)

Emily Dennis (formerly University of Kent, currently Butterfly Conservation) and Byron Morgan (University of Kent)  working with Butterfly Conservation and the Centre for Ecology and Hydrology have been developing and improving statistical models for monitoring butterfly populations. This includes a recent paper that was published in the journal Ecological Indicators with Tom Brereton (Butterfly Conservation) and David Roy (Centre for Ecology and Hydrology),  and the articles in several newspapers:

https://www.theguardian.com/environment/2017/feb/16/urban-butterfly-declines-69-compared-to-45-drop-countryside

http://www.dailymail.co.uk/sciencetech/article-4229306/Paving-gardens-hits-city-butterflies.html

http://butterfly-conservation.org/48-14855/butterflies-declining-faster-in-urban-areas.html

 

Great Crested Newt (Triturus cristatus)

The Durrell Institute for Conservation and Ecology (DICE) at the University of Kent has a long running project that collects data on newt populations breeding in ponds located near the Canterbury campus. Several of the group have helped to collect this data (https://www.kent.ac.uk/smsas/statistics/research/seak-news.html?view=429) . Richard Griffiths, David Sewell and Rachel McCrea looked at statistical models that examine the effect the climate has on the survival of newts, which has been published in Biological Conservation (http://www.sciencedirect.com/science/article/pii/S0006320709004820).

Other studies on Great Crested Newts have looked at removing (and relocating) newts from sites that are being developed. Recent work published in the Annals of Applied Statistics  by Eleni Matechou, Rachel McCrea, Byron Morgan, Darryn Nash and Richard Griffiths has developed better statistical models for this type of data (https://kar.kent.ac.uk/55734/7/AOAS949.pdf).

 

Early Bumblebee (Bombus pratorum)

 

Eleni Matechou has been collaborating with the Bumblebee Conservation Trust and the Centre for Ecology and Hydrology to develop new models for monitoring bumblebee populations using data collected as part of the citizen science scheme BeeWalk. The newly developed models allow us for the first time to estimate bumblebee  phenology, defined  as the study of cyclic and seasonal natural phenomena, especially in relation to climate and plant and animal life, and within-season productivity, defined as the number of individuals in each caste per colony in the population in that year. The results give a means of considerable ecological detail in examining temporal changes in the complex life-cycle of a social insect in the wild.

 

Semipalmated Sandpiper (Calidris pusilla)

Eleni Matechou in collaboration with researchers from the Department of Statistics, University of Oxford in the UK, the North Carolina Cooperative Fish and Wildlife Research Unit and the Patuxent Wildlife Research Center in the USA, has developed models for estimating the number of migratory birds present at a site each day of the season as well as the total population size.  The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers as it can distinguish between birds that use multiple stopover sites for brief periods of time and birds that visit fewer sites but have longer stopovers. The work has resulted in two published papers in the Journal of Agricultural, Environmental and Ecological Statistics  and in the journal Environmental and Ecological Statistics.

 

 

Galapagos Penguin (Spheniscus mendiculus)

Marina Jimenez-Munoz, with Eleni Matechou and Diana Cole, have been working on modeling data from the Charles Darwin Foundation on the Galapagos Penguin collected by Gustavo Jimenez (Charles Darwin Foundation). The Galapagos Penguin is an endangered species which is very vulnerable weather fluctuations (particularly to strong El Niño events), and human activities. These penguins nest in islands, often in cavities which are of difficult access to biologists. For this reason data collection may be at times challenging, resulting in low capture probabilities and/or misleading counts. The aim is to build an integrated analysis which combines multilevel occupancy and mark-recapture data in order to estimate the change in abundance and survival for this species. ​​

Alpine Ibex (Capra ibex)

Rachel McCrea has been working with Achaz von Hardenberg from University of Chester on modeling data on the Alpine Ibex. The Alpine ibex population in Gran Paradiso National Park (Northwestern Italian Alps) has suffered a dramatic decline over the last 20 years. Previous models, based on total count data available since 1956, identified density dependence and winter snow cover as the main drivers of the population dynamics until it reached its peak in 1993, but were unable to predict the subsequent decline. The population fall-off is associated with a strong decline in kid survival which passed from an average of 0.58 (rate of kids which reach the yearling stage in 1981-1990) to an average of 0.35 in the last 10 years. Two main hypotheses have been proposed to explain this decline: i) Ageing of the population: in ungulates older females are known to have lower fertility and produce less viable kids; ii) Mismatch between trophic and breeding phenology due to climate change. Current research involves fitting integrated population models to determine which of these hypotheses drives the observed dynamics. Integrated population modeling involves combining two or more different types of data, with different models, in one integrated analysis.

 

 

 

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Paper: Temporally varying natural mortality: Sensitivity of a virtual population analysis and an exploration of alternatives

The paper

Temporally varying natural mortality: Sensitivity of a virtual population analysis and an exploration of alternatives

by

Shanae Allen, William Satterthwaite, David Hankin, Diana Cole and Michael Mohr

will appear in Fisheries Science, and is available online early at http://www.sciencedirect.com/science/article/pii/S0165783616302958

Abstract:

Cohort reconstructions (CR) currently applied in Pacific salmon management estimate temporally variantexploitation, maturation, and juvenile natural mortality rates but require an assumed (typically invariant)adult natural mortality rate (dA), resulting in unknown biases in the remaining vital rates. We exploredthe sensitivity of CR results to misspecification of the mean and/or variability of dA, as well as the potentialto estimate dAdirectly using models that assumed separable year and age/cohort effects on vital rates(separable cohort reconstruction, SCR). For CR, given the commonly assumed dA= 0.2, the error (RMSE) inestimated vital rates is generally small (≤ 0.05) when annual values of dAare low to moderate (≤ 0.4). Thegreatest absolute errors are in maturation rates, with large relative error in the juvenile survival rate. Theability of CR estimates to track temporal trends in the juvenile natural mortality rate is adequate (Pearson’scorrelation coefficient > 0.75) except for high dA(≥ 0.6) and high variability (CV > 0.35). The alternativeSCR models allowing estimation of time-varying dAby assuming additive effects in natural mortality,fishing mortality, and/or maturation rates did not outperform CR across all simulated scenarios, and areless accurate when additivity assumptions are violated. Nevertheless an SCR model assuming additiveeffects on fishing and natural (juvenile and adult) mortality rates led to nearly unbiased estimates of allquantities estimated using CR, along with borderline acceptable estimates of the mean dAunder multiplesets of conditions conducive to CR. Adding an assumption of additive effects on the maturation ratesallowed nearly unbiased estimates of the mean dAas well. The SCR models performed slightly betterthan CR when the vital rates covaried as assumed. These separable models could serve as a partial checkon the validity of CR assumptions about the adult natural mortality rate, or even a preferred alternativeif there is strong reason to believe the vital rates, including juvenile and adult natural mortality rates,covary strongly across years or age classes as assumed.

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Statistical Ecology Research Festival (SERF)

The Statistical Ecology Research Festival (SERF) is a one day event for postgraduate students in Statistics and/or Ecology. SERF is a great opportunity for  statistics students to learn more about ecological data, how they are collected, what specific questions ecologists are trying to answer by collecting the data and the practical problems that can be encountered in the field. Ecology students will have the chance to learn more about the wide array of statistical techniques used to analyse ecological data, the challenges faced from a statistician’s perspective and emerging areas of statistical research.

SERF will comprise of oral presentations and posters, a round table discussion which will allow attendees to share their research and establish better connections, a discussion on future funding opportunities and a networking reception at the end of the day.

It will be a wonderful opportunity for research postgraduates from both statistical and ecological disciplines to interact with each other and showcase their research. As a result of sharing knowledge among attendees, SERF will generate ideas and approaches for future research in statistical ecology as well as partnerships among participants.

SE@K

We are the Statistical Ecology @Kent (SE@K) group at the School of Mathematics, Statistics & Actuarial Science at University of Kent. We are members of the National Centre for Statistical Ecology (NCSE). SERF is fully funded by Eastern Academic Research Consortium (Eastern ARC) in order to encourage interdisciplinary research.

Submitting an abstract

SERF is open to postgraduate research students/postdocs in statistical ecology or quantitative ecology, but priority will be given to those from Eastern ARC partner institutions (University of Kent, Essex and East Anglia).

Abstracts of no more than 500 words for a talk of 15 minutes including questions must be submitted using the online submission form accompanied by the name of the applicant and their institution and contact details by the 8th May 2016.

The abstracts will be judged on quality, novelty and relevance to SERF. The abstracts selected for oral presentation will be chosen to ensure a wide range of topics are covered during the day. Successful applicants will be notified by the 16th May.

Registration

Registration is free. Lunch and refreshments will be provided free of charge to all participants.

Please note that a limited number of travel expenses (up to £50) are available for presenters whose abstracts were accepted for oral presentations.

Programme for the day

Coming soon…

Location and directions

SERF  will take place on the 7th June at the University of Kent in Canterbury. The talks will take place in  Cornwallis Octagon Lecture Theatre 3 and the round table discussion and networking  in Cornwallis East Seminar Room 2. Both venues are one-minute away from Darwin College bus stop. The link below gives more information on travel and direction. Direction Guidelines

Follow-up material

Presentation slides and posters will be published here. Coming soon…

 Contact information

If you have further enquiries, please contact us at SERF@kent.ac.uk

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