Ming presented a poster titled “Optimal Design for Removal Sampling” at the postgraduate research festival that took place at the University of Kent.
She presented her work in which she investigates removal models accounting for temporary emigration analytically and examines how to optimally allocate a fixed level of total sampling effort in terms of maximising the Fisher information.
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.
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:
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
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).
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.
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.
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.
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.
The paper Hidden Markov Models for Extended Batch Data by Laura L. E. Cowen, Panagiotis Besbeas, Byron J. T. Morgan and Carl J. Schwarz has been published online early in Biometrics
Summary. Batch marking provides an important and efficient way to estimate the survival probabilities and population sizes of wild animals. It is particularly useful when dealing with animals that are difficult to mark individually. For the first time, we provide the likelihood for extended batch-marking experiments. It is often the case that samples contain individuals that remain unmarked, due to time and other constraints, and this information has not previously been analysed. We provide ways of modelling such information, including an open N-mixture approach. We demonstrate that models for both marked and unmarked individuals are hidden Markov models; this provides a unified approach, and is the key to developing methods for fast likelihood computation and maximisation. Likelihoods for marked and unmarked individuals can easily be combined using integrated population modelling. This allows the simultaneous estimation of population size and immigration, in addition to survival, as well as efficient estimation of standard errors and methods of model selection and evaluation, using standard likelihood techniques. Alternative methods for estimating population size are presented and compared. An illustration is provided by a weather-loach data set, previously analysed by means of a complex procedure of constructing a pseudo likelihood, the formation of estimating equations, the use of sandwich estimates of variance, and piecemeal estimation of population size. Simulation provides general validation of the hidden Markov model methods developed and demonstratestheir excellent performance and efficiency. This is especially notable due to the large numbers of hidden states that may be typically required
Byron has been awarded a 24-month Leverhulme Emeritus Fellowship to enable him to conduct research on “Environmental modelling for moths and butterflies”.
Well done Byron!
On Thursday 11th May Diana gave a talk on Parameter Redundancy and Identifiability in Ecology Models at the University of Sheffield as part of their statistics group seminar series.
The talk slides are available below:
First year student Alex Diana and his supervisor, Dr Eleni Matechou, took part in field work sampling for newts.
The long-running project, coordinated by the Durrell Institute for Conservation and Ecology (DICE) at the University of Kent, collects data on newt populations breeding in ponds located near the Canterbury campus.
They saw and identified male and female great crested, palmate and smooth newts. They also learned where newts place their eggs and what male newts do to attract the females (thanks to a very vivid description by Professor Richard Griffiths from DICE!)
Three SE@K students (Ming, Marina and Alex) took part in masterclasses organised by the SMSAS outreach officer, Joe Watkins. For details about master classes and other outreach events at SMSAS see here https://www.kent.ac.uk/smsas/outreach/on-campus.html.
The classes took place on two days and involved four sessions: i) Introduction to probability and statistics ii) Removal modelling, iii) Occupancy modelling and iv) Capture-recapture modelling. They were attended by some of the most enthusiastic and engaged year 9 students in the local area.
All sessions were interactive and the participating year 9 students had the opportunity to replicate real-life sampling techniques for monitoring populations of lizards, penguins and birds. These involved digging for lizards in the sand, looking for hidden penguins and marking birds. No animals were hurt in the process as they were all made out of plastic!
The following paper has been recently published in PLOS ONE and is available online at: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174433
Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.
Emily and Byron (with David Roy and Tom Brereton) have had the paper Urban indicators for UK butterflies published in Ecological Indicators. The paper is available at:
http://www.sciencedirect.com/science/article/pii/S1470160X17300092
The paper has been covered in the press including:
http://www.dailymail.co.uk/sciencetech/article-4229306/Paving-gardens-hits-city-butterflies.html
https://www.kent.ac.uk/news/environment/12510/the-statistics-behind-the-urban-decline-of-butterflies
Abstract:
Most people live in urban environments and there is a need to produce abundance indices to assist policy and management of urban greenspaces and gardens. While regional indices are produced, with the exception of birds, studies of the differences between urban and rural areas are rare. We explore these differences for UK butterflies, with the intention to describe changes that are relevant to people living in urban areas, in order to better connect people with nature in support of conservation, provide a measure relevant to human well-being, and assess the biodiversity status of the urban environment.
Transects walked under the UK Butterfly Monitoring Scheme are classified as urban or rural, using a classification for urban morphological zones. We use models from the Generalised Abundance Index family to produce urban and rural indices of relative abundance for UK butterfly species. Composite indices are constructed for various subsets of species. For univoltine and bivoltine species, where we are able to fit phenomenological models, we estimate measures of phenology and identify urban/rural differences. Trends in relative abundance over the period 1995–2014 are more negative in urban areas compared to rural areas for 25 out of 28 species. For the composite indices, all trends are negative, and they are significantly more negative for urban areas than for rural areas. Analysis of phenological parameters shows butterflies tend to emerge earlier in urban than in rural areas. In addition, some fly longer in urban than in rural areas, whereas in other cases the opposite is the case, and hypotheses are proposed to account for these features.
Investigating new urban/rural indicators has revealed national declines that are stronger for urban areas. For continued monitoring, there is a need for an urban butterfly indicator, and for this to be evaluated and reported annually. We explain how this may be interpreted, and the relevance for other monitoring schemes. The results of this paper, including the phenological findings, shed new light on the potentially deleterious effects of urbanisation and climate change, which require suitable monitoring and reporting to support policy and management, for example of urban greenspaces and gardens.
Rachel, Diana and Eleni with Richard Griffiths have been awarded £53,584.00 funding for a NERC Advanced Training Short Course `Statistical models for wildlife population assessment and conservation’ for 2018 and 2019, following the successful course in January 2017.
