The Think Piece, titled “Understanding ecosystems and resilience using DNA“, explores opportunities for applying advances in DNA and RNA technologies to improve understanding of ecosystem function and resilience. Eleni’s contribution in particular, written together with Doug Yu, UEA, looks at “The contribution of DNA-based methods to achieving socio-ecological resilience”.
Diana is co-author on the paper: A guide to state–space modeling of ecological time series, which was published in Ecological Monographs.
Abstract: State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics, animal movement, and capture–recapture data, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological variation and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. We provide the knowledge for ecologists to create SSMs that are robust to common, and often hidden, estimation problems, and the model selection and validation tools that can help them assess how well their models fit their data. We present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models.
SE@K members Oscar and Rachel have published a paper reviewing the current state-of-the-art in modelling removal data. This work is part of the the EPSRC project EP/S020470/1 “Modelling removal and re-introduction data for improved conservation”.
The paper is available in full here: https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0229965
Removal modelling in ecology: A systematic review
Oscar Rodriguez de Rivera and Rachel McCrea
Removal models were proposed over 80 years ago as a tool to estimate unknown population size. More recently, they are used as an effective tool for management actions for the control of non desirable species, or for the evaluation of translocation management actions. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important features of the data and we discuss the shift in the required assumptions for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique.
Former SE@K PhD student Anita Jeyam, Rachel and Roger Pradel (Montpellier) have had a paper published presenting a new test for determining the underlying state structure of Hidden Markov models. This is an exciting piece of work which provides the foundation for addressing a complex topic for general HMMs.
The full paper is available open access here: https://www.frontiersin.org/articles/10.3389/fevo.2021.598325/full
A Test for the Underlying State-Structure of Hideen Markov Models – Partially Observed Capture-Recapture Data
Hidden Markov models (HMMs) are being widely used in the field of ecological modeling,
however determining the number of underlying states in an HMM remains a challenge.
Here we examine a special case of capture-recapture models for open populations,
where some animals are observed but it is not possible to ascertain their state (partial
observations), whilst the other animals’ states are assigned without error (complete
observations). We propose a mixture test of the underlying state structure generating
the partial observations, which assesses whether they are compatible with the set of
states observed in the complete observations. We demonstrate the good performance
of the test using simulation and through application to a data set of Canada Geese.
Rachel McCrea and co-authors from St Andrews and Edinburgh have published a paper on modelling the recruitment process of gray seals.
The full paper is available open access here: https://www.frontiersin.org/articles/10.3389/fevo.2021.600967/full
Modeling Recruitment of Birth Cohorts to the Breeding Population: A Hidden Markov Approach
Worthington, King, McCrea, Smout and Pomeroy
Long-term capture-recapture studies provide an opportunity to investigate the population dynamics of long-lived species through individual maturation and adulthood and/or
time. We consider capture-recapture data collected on cohorts of female gray seals
(Halichoerus grypus) born during the 1990s and later observed breeding on the Isle
of May, Firth of Forth, Scotland. Female gray seals can live for 30+ years but display
individual variability in their maturation rates and so recruit into the breeding population
across a range of ages. Understanding the partially hidden process by which individuals
transition from immature to breeding members, and in particular the identification of
any changes to this process through time, are important for understanding the factors
affecting the population dynamics of this species. Age-structured capture-recapture
models can explicitly relate recruitment, and other demographic parameters of interest,
to the age of individuals and/or time. To account for the monitoring of the seals from
several birth cohorts we consider an age-structured model that incorporates a specific
cohort-structure. Within this model we focus on the estimation of the distribution of
the age of recruitment to the breeding population at this colony. Understanding this
recruitment process, and identifying any changes or trends in this process, will offer
insight into individual year effects and give a more realistic recruitment profile for the
current UK gray seal population model. The use of the hidden Markov model provides an
intuitive framework following the evolution of the true underlying states of the individuals.
The model breaks down the different processes of the system: recruitment into the
breeding population; survival; and the associated observation process. This model
specification results in an explicit and compact expression for the model with associated
efficiency in model fitting. Further, this framework naturally leads to extensions to more
complex models, for example the separation of first-time from return breeders, through
relatively simple changes to the mathematical structure of the model.
Diana along with Wei Cai, Stephanie Yurchak and Laura Cowen have published the paper:
in Ecology and Evolution
1. Capture–recapture experiments are conducted to estimate population parameters such as population size, survival rates, and capture rates. Typically, individuals are captured and given unique tags, then recaptured over several time periods with the assumption that these tags are not lost. However, for some populations, tag loss cannot be assumed negligible. The Jolly‐Seber tag loss model is used when the no‐tag‐loss assumption is invalid. Further, the model has been extended to incorporate group heterogeneity, which allows parameters to vary by group membership. Many mark–recapture models become overparameterized resulting in the inability to independently estimate parameters. This is known as parameter redundancy.
2. We investigate parameter redundancy using symbolic methods. Because of the complex structure of some tag loss models, the methods cannot always be applied directly. Instead, we develop a simple combination of parameters that can be used to investigate parameter redundancy in tag loss models.
3. The incorporation of tag loss and group heterogeneity into Jolly‐Seber models does not result in further parameter redundancies. Furthermore, using hybrid methods we studied the parameter redundancy caused by data through case studies and generated tag histories with different parameter values.
4. Smaller capture and survival rates are found to cause parameter redundancy in these models. These problems resolve when applied to large populations.
Emily and Byron, along with Marc Kery, Armin Coray, Michael Schaub and Bruno Baur have published the paper:
in Ecological Modelling.
Population size of species with birth-pulse life-cycles varies both within and between seasons, but most population dynamics models assume that a population can be characterised adequately by a single number within a season. However, within-season dynamics can sometimes be too substantial to be ignored when modelling dynamics between seasons. Typical examples are insect populations or migratory animals. Numerous models for only between-season dynamics exist, but very few have combined dynamics at both temporal scales.
In a new approach, we extend appreciably the models of Dennis et al. (2016b): we show how to adapt them for a generation time year and fit an integrated population model for multiple data types, by maximising a joint likelihood for population counts of unmarked individuals and capture–recapture data from a study with marked individuals. We illustrate the approach using annual monitoring data for the endangered flightless beetle Iberodorcadion fuliginator from 18 populations in the Upper Rhine Valley for 1998–2016, with a 2-year life cycle. Standard likelihood methods are used for model fitting and comparison, and a concentrated (profile) likelihood approach provides computational efficiency.
Additional information from the capture–recapture data makes the population model more robust and, importantly, enables true, rather than relative, abundance to be estimated. A dynamic stopover model provides estimates of both survival and phenology parameters within a season, and also of productivity between seasons. For I. fuliginator, we demonstrate a population decline since 1998 and how this links with productivity, which is affected by temperature. A delayed mean emergence date in recent years is also shown.
A main point of interest is the focus on the two temporal scales at which perhaps most animal populations vary: in the short-term, a population is seldom truly closed within a single season, and in the long-term (between seasons) it never is. Hence our models may serve as a template for a general description of population dynamics in many species. This includes rare species with limited data sets, for which there is a general lack of population dynamic models, yet conservation actions may greatly benefit from this kind of models.
Analysing recording data of this nature presents unique challenges relating to their vast quantity but also associated sampling biases. Using cutting edge modelling, this project will maximise these valuable datasets to enhance our understanding of species’ phenology (flight periods), distribution and range dynamics to help inform future conservation delivery and policy for UK butterflies and moths.
The candidate will undertake new statistical model developments applied to citizen science data. The research will involve:
- Critically assessing sampling design to determine how much data are needed to reliably estimate species’ occurrence trends – can occupancy models be used for rare species with small ranges?
- Modelling species’ phenology from citizen science data to provide new insights on variation over space and time.
- Applying state-of-the-art variable selection techniques to better describe drivers of species’ range and distribution change through suitable spatial and environmental covariates.
This project has been shortlisted for Aries funding. More details can be found at:
Oscar and Alex presented their work at the (virtual) RSS conference 2020 during the invited session on “Challenges and advances of spatial modelling in ecology” organised by Rachel.
Oscar’s talk, titled “The Importance of spatio-temporal modelling in Ecology” described the importance spatio-temporal models to understand the relationship between species in a common area. Oscar explains the problem caused due to the wolf eradication in Yellowstone National Park in 1920’s and how the landscape changed from this eradication to the reintroduction in 1990’s.
Alex’s talk, titled “Interaction point processes in spatially explicit capture-recapture models” described his work on a spatial capture-recapture model incorporating interactions within and between individuals of two species. The model relies on the theory of interaction point processes. As inference for these processes cannot be performed using standard techniques due to the intractability of the likelihood, specific MCMC methods have to be used. The model is applied to a capture-recapture data-set of leopards and tigers collected in India.
The paper: Predicting potential cambium damage and fire resistance in Pinus nigra Arn. ssp. salzmannii by: ESPINOSA, J.; RODRÍGUEZ DE RIVERA, O.; MADRIGAL, J.; GUIJARRO, M; HERNANDO, C. , has just been published in Forest Ecology and Management.
Fire management can play a key role in ensuring stand maintenance in future scenarios of global change, particularly in Pinus nigra stands, which are known to be adapted to low-intensity surface fires through characteristics such as thick bark. In this study, laboratory tests were carried out to quantify cambium damage and fire resistance in P. nigra, by using a mass loss colorimeter device in a vertical configuration for the first time. In addition, low-intensity prescribed burning treatments were conducted in the field, and the field and laboratory data were compared. The following variables were used as proxy measures to assess cambium damage: time that temperature remained above 60 °C, heating rate and maximum absolute temperature in the inner bark area. The data were analysed using a Bayesian hierarchical approach (generalized linear mixed model). A threshold heat flux (25 kW m-2) for the time to ignition of bark was identified. A critical temperature of 60 °C was reached in the cambium during the combustion phase, after the flame was extinguished. The laboratory experiments showed, for the first time, the influence of flame residence time on the potential cambium damage. A bark thickness of 17 mm can be considered the threshold level for preventing critical temperatures being reached in Pinus nigra stands. The influence of bark thickness on protection against fire was confirmed, as was the importance of the coefficient of variation of bark thickness. The field results showed that flame characteristics (maximum temperature and residence time) were the most significant predictors of cambium damage. The combination of fire intensity and exposure time at low heat fluxes is more important than bark in determining cambium damage and may have important implications in the field of forest fuel management and in the ecology of pine forests.