1,721,005 research outputs found
Bayesian Nonparametric Models for Modelling Ecological Data and Stochastic Processes for Modelling Species Interactions
Get PDFIn this thesis, we present four manuscripts, described in the second to fifth chapter. Chapter 2 presents a Bayesian nonparametric model for capture-recapture (CR) data collected at different sites and for several years. To estimate arrival and departure patterns at the different sites and years, we build an extension of the Dirichlet process, the Hierarchical Dependent Dirichlet process, which allows us to perform density estimation jointly across different sites and in the presence of covariates. In this case, we use a year-specific covariate, and model the correlation structure of the covariate across years using a multivariate Gaussian process. In Chapter 3, we present a model for estimating entry and exit patterns, as well as the population size, using count data (CD), by employing a Polya Tree (PT) prior. In Chapter 4 we present several extensions of chapter 3. More specifically, we extend the model to CR and to ring-recovery data and develop a joint model for CR and CD. In addition, we consider the case when multiple data-sets are modelled at the same time, by defining a hierarchical extension of the PT, which we define as Hierarchical Logistic PT. Finally, we extend the model to the case of long time series, by borrowing ideas from the Optional PT. Chapter 5 presents a spatial model to estimate interactions between multiple species using CR data. The model uses a vector of interaction point process (IPP), which allows us to estimate interactions between and within species. The use of an IPP leads to an intractable ratio of normalising constants (RNC), and hence we use the Monte Carlo Metropolis Hastings algorithm to approximate the RNC with an importance sampling estimate. The supplementary material for each paper is presented in the appendix
Bayesian hierarchical models for ecological data: estimating population size, spatial and temporal patterns.
Get PDFThe work in this thesis presents three manuscripts, described in Chapters 2 to 4.
Chapter 2 presents an evaluation of the popular N-mixture model in a Bayesian framework to corroborate and extend issues concerning N-mixture models previously discussed in a classical framework. Specifically, the chapter focuses on prior specification, when no prior information is available, as well as on model selection. For prior specification, a novel objective prior that is proper is implemented and tested, and its performance is compared to approximations of the Jeffreys prior. Model selection of an extensive class of N-mixture models is performed using the Watanable-Akaike information criterion (WAIC) in a wide range of scenarios.
Chapter 3 presents a Bayesian hierarchical modelling framework for count data on species that exhibit temporary emigration (TE) at a site with temporally replicated sampling. This modelling framework accounts for observation error and models TE parametrically and non-parametrically to provide estimates of temporal population size. Temporal models and Dirichlet process mixture models are introduced to model TE parametrically and non-parametrically, respectively. Both of these approaches give rise to interesting ecological interpretations of TE. Additionally, using an efficient Bayesian variable selection algorithm, this modelling framework is further extended to identify important predictors of observation error.
Chapter 4 presents a Bayesian spatial model that simultaneously models disease dynamics and population dynamics using spatial capture-recapture data and imperfect diagnostic tests. Accounting for observation error in both detection and diagnostic tests, this framework enables a better understanding of how disease dynamics relate to population demographics in spatiotemporal contexts at an individual level. Specifically, disease transmission is modelled as a function of population density.
The supplementary material for each paper is presented in the appendix
Statistical models for data on recreational fishing
Get PDFRecreational fishing is a globally significant activity, often involving angling but also encompassing various other methods in both marine and freshwater environments. While it provides notable health benefits, economic contributions, and conservation support, it can also exert substantial pressures on fish populations and ecosystems. To effectively manage and sustain this activity, it is crucial to quantify the scale, benefits, and impacts of recreational fishing. Traditional methods for monitoring, such as onsite surveys and recall surveys, face challenges in terms of cost, time, and data reliability, especially in accounting for the growing sector of angling tourism. Recent advancements in technology have introduced alternative data collection methods, particularly through smartphone applications. These apps, like Fishbrain, allow anglers to record their catches, offering a vast and continuous stream of data that could significantly enhance recreational fisheries management. However, to harness these data effectively, it is essential to develop robust statistical models to understand their strengths and limitations.
This thesis aims to advance the understanding and management of recreational fishing through the development and application of statistical models using data from traditional face-to-face surveys and app-based records.
Chapter 2 focuses on modelling marine recreational fishing data from a 2012-2013 UK survey, employing zero-inflated Poisson models with shrinkage methods to identify key predictors for catch rates. This chapter introduces a grid-based search algorithm for determining shrinkage penalties, and considers data on a number of key UK species.
Chapter 3 considers Fishbrain data from 2018 to 2021 to study the spatiotemporal patterns of recorded catches for four key marine species in the UK and Ireland. This analysis uses integrated Laplace approximation methods to develop models that provide a framework for visualising large-scale catch data, comparing different error structures, and producing predictive maps for each species.
Chapter 4 extends the analysis to a global scale, examining angling tourism patterns using Fishbrain data. This chapter utilises network models and clustering methods to analyse over 100,000 catches, focusing on international travel and its changes in response to the COVID-19 pandemic. It identifies communities of countries with strong angling connections and explores the implications of these patterns for fisheries management.
Finally, Chapter 5 discusses the broader implications of the research and suggests future directions. The findings underscore the potential of app-based data to complement traditional survey methods, offering a rich resource for sustainable recreational fisheries management. This work contributes to the evidence base needed for informed marine policy and highlights the importance of integrating diverse data sources to support productive and sustainable fisheries
Bayesian methods for interpretable and scalable modelling of population dynamics
Get PDFThe work in this thesis presents novel Bayesian methods for enhancing wildlife monitoring, a critical component in addressing climate change due to the regulatory role of species and their habitats in the climate system. Given the consistent decline in biodiversity, marked by shifts in life history events, the importance of effective wildlife monitoring is underscored. The thesis highlights the significance of understanding species behavior for better management, utilizing time-series monitoring and speciesborne devices like GPS and acoustic recorders to gather detailed behavioral data. These studies focus on latent behavioral states with Markovian dependence, for which Hidden Markov Models (HMMs) are frequently employed. Monitoring population dynamics is also emphasized, as population size is a key measure for assessing biodiversity loss and informing conservation policies. Depending on survey duration and species characteristics, populations can be closed or open. Common models like Jolly-Seber (JS) and Cormack-Jolly-Seber (CJS) are used, with temporary emigration (TE) models accommodating species with seasonal patterns. The thesis addresses the critical role of sampling schemes in population dynamics studies. Capture-Recapture (CR) is extensively used but when infeasible, Batch-Mark (BM) sampling serves as an alternative, providing accurate inference in open population models. Employing parametric and non-parametric hierarchical Bayesian models, the thesis uses Bayes’ Theorem to update prior beliefs based on observed data. The non-parametric approach, particularly using the P´olya Tree prior, offers flexibility by allowing data to shape the distribution. Markov Chain Monte Carlo (MCMC) methods are used to sample from posterior distributions for behavioral states and population parameters, demonstrating the robust application of these Bayesian methods in ecological datasets.
Chapter 2 develops a parametric Bayesian model to infer species behavior over time using Bayesian HMMs. The model addresses the challenge of selecting latent states by employing a Reversible Jump MCMC algorithm and repulsive priors to prevent overfitting. Extensive simulations and real case studies on muskox and Cape gannets demonstrate the model’s utility. Chapter 3 introduces a non-parametric Bayesian model for population dynamics using JS models for BM data, leveraging the P´olya Tree Prior. The model is computationally efficient and exact, accommodating various survey designs and incorporating capture losses. Robustness is demonstrated through simulations and case studies on weather loaches and golden mantella frogs. Chapter 4 addresses the complexity of TE models with Approximate Bayesian Computation (ABC), using Sequential Monte Carlo (SMC) ABC for efficient posterior sampling. The method’s f lexibility is shown through simulations and a case study on alpine common toad. Overall, this thesis advances Bayesian methods for wildlife monitoring, providing robust tools for understanding species behavior and population dynamics, ultimately contributing to biodiversity conservation and climate change mitigation
Capture-recapture models with heterogeneous temporary emigration
Get PDFWe propose a novel approach for modeling capture-recapture (CR) data on open populations that exhibit temporary emigration, while also accounting for individual heterogeneity to allow for differences in visit patterns and capture probabilities between individuals. Our modeling approach combines changepoint processes-fitted using an adaptive approach-for inferring individual visits, with Bayesian mixture modeling-fitted using a nonparametric approach-for identifying dusters of individuals with similar visit patterns or capture probabilities. The proposed method is extremely flexible as it can be applied to any CR dataset and is not reliant upon specialized sampling schemes, such as Pollock's robust design. We fit the new model to motivating data on salmon anglers collected annually at the Gaula river in Norway. Our results when analyzing data from the 2017, 2018, and 2019 seasons reveal two clusters of anglers-consistent across years-with substantially different visit patterns. Most anglers are allocated to the "occasional visitors" cluster, making infrequent and shorter visits with mean total length of stay at the river of around seven days, whereas there also exists a small cluster of "super visitors," with regular and longer visits, with mean total length of stay of around 30 days in a season. Our estimate of the probability of catching salmon whilst at the river is more than three times higher than that obtained when using a model that does not account for temporary emigration, giving us a better understanding of the impact of fishing at the river. Finally, we discuss the effect of the COVID-19 pandemic on the angling population by modeling data from the 2020 season. Supplementary materials for this article are available online
Statistical Development of Ecological Removal Models
Get PDFRemoval sampling is commonly used to estimate abundance of populations in which captured individuals are permanently removed from a study area. The classic removal model (Moran, 1951) assumes a constant capture probability and all animals are available for detection throughout the study, which results in a simple geometric decline of counts of removed individuals over time. However, the real data collected from some species exhibit unexpected fluctuations in the number of captured animals. The work in this thesis is driven by real data on common lizards, Zootoca vivipara and great crested newts, Triturus cristatus, where existing approaches may give rise to misleading conclusions.
When modelling removal data it is crucial to account for imperfect availability in the population, as individuals could sometimes temporarily become undetectable at
study area, or emerge from an area outside the study. This thesis deals with three
aspects of removal modelling: (i) We develop a robust design multievent removal
modelling (RMER framework) which allows considerable flexibility in estimating temporary emigration as well as capture probability and the size of populations. We
also consider the effect of sparse data and investigate the use of modelling different
sources of data in conjunction with the removal data (Besbeas et al, 2002). (ii) The
estimation of temporary emigration or population renewal for removal data relies on
the use of the robust design (Zhou et al. 2018). However, there are many removal
data which lack the robust design structure. Motivated by the analysis of a data
set of common lizards collected under standard sampling protocol, we develop and
evaluate the use of penalised maximum likelihood estimation to allow populations to be open to new individuals via birth/arrival for data sets without the robust design. (iii) We use four criteria to explore study design aspects of removal data with the robust design, including the trade-off in survey effort allocation between primary periods and secondary periods for a fixed level of total sampling effort. The models we propose can account for temporary emigration or new arrivals of individuals during removal sampling and represent a step forward with respect to current modelling approaches and will guide wildlife management
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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