Byron Morgan gave presentations in Maynooth and Swansea last month:
Applications of hidden Markov models in ecology, 12 April, 2019, Swansea.
Forming multi-species indicators: behind the scenes, 29 April, 2009, Maynooth.
New paper – Trends and indicators for quantifying moth abundance and occupancy in Scotland
Byron Morgan and Emily Dennis have had a paper published in Journal of Insect Conservation.
The full paper can be accessed here.
Trends and indicators for quantifying moth abundance and occupancy in Scotland
E. B. Dennis, T. M. Brereton, B. J. T. Morgan, R. Fox, C. R. Shortall, T. Prescott, S. Foster
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.
New paper – Exact inference for integrated population modelling
Takis Besbeas and Byron Morgan have recently published a paper in Biometrics developing an approach for exact inference for integrated modelling.
The paper can be accessed here.
Exact inference for integrated population modelling
Integrated population modelling is widely used in statistical ecology. It allows data from population time series and independent surveys to be analysed simultaneously. In classical analysis the time‐series likelihood component can be conveniently approximated using Kalman filter methodology. However, the natural way to model systems which have a discrete state space is to use hidden Markov models (HMMs). The proposed method avoids the Kalman filter approximations and Monte Carlo simulations. Subject to possible numerical sensitivity analysis, it is exact, flexible, and allows the use of standard techniques of classical inference. We apply the approach to data on Little owls, where the model is shown to require a one‐dimensional state space, and Northern lapwings, with a two‐dimensional state space. In the former example the method identifies a parameter redundancy which changes the perception of the data needed to estimate immigration in integrated population modelling. The latter example may be analysed using either first‐ or second‐order HMMs, describing numbers of one‐year olds and adults or adults only, respectively. The use of first‐order chains is found to be more efficient, mainly due to the smaller number of one‐year olds than adults in this application. For the lapwing modelling it is necessary to group the states in order to reduce the large dimension of the state space. Results check with Bayesian and Kalman filter analyses, and avenues for future research are identified
New paper – Functional data analysis of multi-species abundance and occupancy data sets
Emily Dennis (Butterfly Conservation) and Byron Morgan (SE@K) have recently published a paper in Ecological Indicators exploring multi-species abundance and occupancy indices using Functional Data Analysis tools.
The full paper can be accessed here.
Functional data analysis of multi-species abundance and occupancy data sets
Emily B.Dennis, Byron J.T.Morgan, RichardFox, David B.Roy and Tom M.Brereton
Multi-species indicators are widely used to condense large, complex amounts of information on multiple separate species by forming a single index to inform research, policy and management. Much detail is typically lost when such indices are constructed. Here we investigate the potential of Functional Data Analysis, focussing upon Functional Principal Component Analysis (FPCA), which can be easily carried out using standard R programs, as a tool for displaying features of the underlying information. Illustrations are provided using data from the UK Butterflies for the New Millennium and UK Butterfly Monitoring Scheme databases. The FPCAs conducted result in a huge simplification in terms of dimensional reduction, allowing species occupancy and abundance to be reduced to two and three dimensions, respectively. We show that a functional principal component arises for both occupancy and abundance analyses that distinguishes between species that increase or decrease over time, and that it differs from percentage trend, which is a simplification of complex temporal changes. We find differences in species patterns of occupancy and abundance, providing a warning against routinely combining both types of index within multi-species indicators, for example when using occupancy as a proxy for abundance when insufficient abundance data are available. By identifying the differences between species, figures displaying functional principal component scores are much more informative than the simple bar plots of percentages of significant trends that often accompany multi-species indicators. Informed by the outcomes of the FPCA, we make recommendations for accompanying visualisations for multi-species indicators, and discuss how these are likely to be context and audience specific. We show that, in the absence of FPCA, using mean species occupancy and total abundance can provide additional, accessible information to complement species-level trends. At the simplest level, we suggest using jitter plots to display variation in species-level trends. We encourage further application to other taxa, and recommend the routine augmentation of multi-species indicators in the future with additional statistical procedures and figures, to serve as an aid to improve communication and understanding of biodiversity metrics, as well as reveal potentially hidden patterns of behaviour and guide additional directions for investigation.
National centre leadership role for Dr Rachel McCrea
Dr Rachel McCrea of the Statistical Ecology @ Kent group from the School of Mathematics, Statistics and Actuarial Science is taking on the role of Director of the National Centre for Statistical Ecology (NCSE).
The NCSE is a joint research centre which aims to “develop, apply and communicate innovative statistical methods for collecting and analysing ecological data, thereby improving the understanding and management of wild populations and their environment”. It was established in 2005 and the current consortium unites research excellence across the Universities of Kent, St Andrews, Bath, Bristol, Edinburgh, Essex, Exeter, Glasgow and Sheffield, together with the Centre for Ecology and Hydrology. A number of project partners are actively involved with the centre, including Biomathematics and Statistics Scotland, the Centre for Environment, Fisheries and Aquaculture Science, the Game and Wildlife Conservation Trust and Marine Scotland. The centre also has a considerable number of international members spanning the globe and initiated the world-leading biennial International Statistical Ecology Conference, the next of which will be hosted in Sydney in June 2020.
Rachel commenced this exciting role on 1st April, and together with the new Deputy Director, Professor Ruth King from the University of Edinburgh, looks forward to driving new NCSE initiatives. They have secured funding, jointly with the previous co-directors of the NCSE Professors Steve Buckland and Byron Morgan, to host an International Centre for Mathematical Sciences meeting in Edinburgh in June 2019 on the topic of Addressing Statistical Challenges of Modern Technological Advances. This meeting will be used to identify future directions and associated thematic areas of the NCSE to ensure that the centre remains at the forefront of internationally leading statistical ecology research.
Congratulation to Marina whose poster won silver at STEM for Britain.
On 13th March Marina went to Parliament to present her poster on How do bird populations vary across Britain? Spatially-explicit integrated population models, as part of STEM for Britain competition. As stated on the STEM for Britain website, “STEM for BRITAIN Awards are made on the basis of the very best research work and results by an early-stage or early-career researcher together with their ability to communicate their work to a lay audience.” Marina’s poster won silver in the Mathematics category. Well done Marina on this amazing achievement.
Marina to present her work at STEM for Britain
Congratulations to Marina who has been selected to display her poster at the House of Commons on Wednesday 13th March in the Mathematical Sciences Session.
Well done Marina!
Congratulations to Marina who will present her research at parliament in the STEM for Britain 2019 competition
Congratulations to Marina who has been selected to present her research at parliament in the STEM for Britain 2019 competition. How do bird population vary across Britain? Spatially-Explicit Integrated Population Models.
Paper by Alex Diana, Eleni Matechou and Jim Griffin accepted for publication
The paper, titled “A Polya Tree based model for unmarked individuals in an open wildlife population”, has been accepted for the conference “Bayesian Statistics: New Challenges and New Generations – BAYSM 2018”.
This is Alex’s first paper, well done Alex!
Paper – Estimating age‐dependent survival from age‐aggregated ringing data
The paper Estimating age‐dependent survival from age‐aggregated ringing data—extending the use of historical records, by Marina, Diana, Stephen Freeman (Centre for Ecology and Hydrology), Rob Robinson (BTO), Stephen Baillie (BTO) and Eleni, has been published in Ecology and Evolution.
Abstract:
Bird ring‐recovery data have been widely used to estimate demographic parameters such as survival probabilities since the mid‐20th century. However, while the total number of birds ringed each year is usually known, historical information on age at ringing is often not available. A standard ring‐recovery model, for which information on age at ringing is required, cannot be used when historical data are incomplete. We develop a new model to estimate age‐dependent survival probabilities from such historical data when age at ringing is not recorded; we call this the historical data model. This new model provides an extension to the model of Robinson, 2010, Ibis, 152, 651–795 by estimating the proportion of the ringed birds marked as juveniles as an additional parameter. We conduct a simulation study to examine the performance of the historical data model and compare it with other models including the standard and conditional ring‐recovery models. Simulation studies show that the approach of Robinson, 2010, Ibis, 152, 651–795 can cause bias in parameter estimates. In contrast, the historical data model yields similar parameter estimates to the standard model. Parameter redundancy results show that the newly developed historical data model is comparable to the standard ring‐recovery model, in terms of which parameters can be estimated, and has fewer identifiability issues than the conditional model. We illustrate the new proposed model using Blackbird and Sandwich Tern data. The new historical data model allows us to make full use of historical data and estimate the same parameters as the standard model with incomplete data, and in doing so, detect potential changes in demographic parameters further back in time.