The Development of a Spatially Dynamic Model to Evaluate Management Scenarios in a Scallop Fishery

Fisher behaviour remains a key source of uncertainty in fisheries management. Failing to account for the behavioural response of fishers can lead to unexpected or unintended consequences of management; our understanding of fisher behaviour, as well as our ability to translate this understanding into predictive management models, is underdeveloped. This thesis aimed to develop an individual-based model (IBM) that could be used by fishers and managers to evaluate the impacts of management scenarios in the Isle of Man scallop fishery. Questionnaire interview data and a conjoint analysis were used to understand fishing behaviour and to generate realistic parameters to input to an IBM of fishing activity. Vessel monitoring system (VMS) and logbook data were also analysed to inform the model development, and to provide the data against which the model could be validated. There is increasing interest in using automatic identification system (AIS) as an alternative to VMS when investigating fishing activity, so a comparison of AIS and VMS data was presented, highlighting substantial gaps in the coverage of AIS data. By using simple foraging decision rules, parameterised by questionnaire data, it was possible to build an IBM that could reproduce patterns seen in the Isle of Man scallop fishery with reasonable similarity. Comparing multiple submodels of fishing behaviour provided insights into predicting fishing activity, and identified the most structurally realistic models. It illustrated the importance of incorporating random behaviour in a model design, potentially to account for social aspects of fishing decisions that are more difficult to quantify. It also demonstrated that predicting responses to management by modelling fishers as optimal foragers that act in an economically rational manner may overestimate the capacity of the fleet to compensate for restrictions such as closed areas, and underestimate the fishing footprint. Fishery systems may be too complex to distil to a single simple and ‘accurate’ model, but having a suite of models that together give a reasonable representation of the fishery could allow the range of likely impacts of management to be better considered. This thesis demonstrates the value of individual-based modelling for both understanding fisher behaviour and predicting the outcomes of management. It has also provided strong evidence to support the use of questionnaire interview data in modelling fishing activity. Comprehensively documenting the stages of model development provided a transparent model validation which would enable managers to make informed decisions about how to apply such a model. Using an IBM to predict the response of fishers to management could facilitate more informed compromises between management objectives, and reduce uncertainty in fisheries management

Near Disappearance of the Angelshark Squatina Squatina Over Half A Century of Observations

Marine extinctions are particularly difficult to detect and almost all have been discovered after the fact. Retrospective analyses are essential to avoid concluding no-extinction when one has occurred. We reconstruct the Angelshark population trajectory in a former hotspot (Wales), using interviews and opportunistic records. After correcting for observation effort and recall bias, we estimate a 70% (1.5% yr-1) decline in abundance over 46 years. While formerly widespread, Angelshark distribution contracted to a central core of Cardigan Bay. Angelshark declined almost unnoticed in one of the best-monitored and most intensively managed seas in the world. Bycatch may be minimised by limiting netting on shingle reefs in Cardigan Bay. We provide the first quantitative time series to reveal the timing and trajectory of decline of Angelshark in the coastal waters of Wales and uncover historical centres of abundance and remnant populations that provide the first opportunity for the focus of conservation.&nbsp

Popularising semiotics.

Semiotics, deconstructionism, structuralism and postmodernism are words which lurk on boundaries of the consciousness of most of us. But they remain shadowy presences except on the rare occasions when we need to wrestle out of them an explanation of just what they are all about. In this issue of Trends we grapple with one of them, semiotics. C. S. Peirce, the American, pragmatist philosopher who coined the term, saw semiotics as a 'method of methods', useful in many disciplines to clarify their own theory and practice. Everyone uses signs and symbols. Everyone thinks they know the meanings of the signs and symbols they use. But why do they have meanings? Where do the meanings come from? Why are the signs and symbols used by one person or group so frequently misinterpreted by others? Semiotics may seem esoteric, but its interests are central to all communication. Consequently all communicators should be concerned with at least some of the problems dealt with semioticians. To guide us on our exploration of semiotics the publishers of Trends, the Centre for the Study of Communication and Culture, have enlisted the aid of Professor Keyan Tomaselli and his colleagues at the Centre for Cultural and Media Studies of the University of Natal, who for some years have been studying the cultural side of semiotics. So eager has their response been that we have devoted two issues of Trends to their reports. The contents of these two issues manifest the views of the authors more than is usual for Trends, and they are not necessarily those of the editors; but the CSCC feels that the perspective of the CCMS deserves both expression and discussion

The Development of a Spatially Dynamic Model to Evaluate Management Scenarios in a Scallop Fishery

Fisher behaviour remains a key source of uncertainty in fisheries management. Failing to account for the behavioural response of fishers can lead to unexpected or unintended consequences of management; our understanding of fisher behaviour, as well as our ability to translate this understanding into predictive management models, is underdeveloped. This thesis aimed to develop an individual-based model (IBM) that could be used by fishers and managers to evaluate the impacts of management scenarios in the Isle of Man scallop fishery. Questionnaire interview data and a conjoint analysis were used to understand fishing behaviour and to generate realistic parameters to input to an IBM of fishing activity. Vessel monitoring system (VMS) and logbook data were also analysed to inform the model development, and to provide the data against which the model could be validated. There is increasing interest in using automatic identification system (AIS) as an alternative to VMS when investigating fishing activity, so a comparison of AIS and VMS data was presented, highlighting substantial gaps in the coverage of AIS data. By using simple foraging decision rules, parameterised by questionnaire data, it was possible to build an IBM that could reproduce patterns seen in the Isle of Man scallop fishery with reasonable similarity. Comparing multiple submodels of fishing behaviour provided insights into predicting fishing activity, and identified the most structurally realistic models. It illustrated the importance of incorporating random behaviour in a model design, potentially to account for social aspects of fishing decisions that are more difficult to quantify. It also demonstrated that predicting responses to management by modelling fishers as optimal foragers that act in an economically rational manner may overestimate the capacity of the fleet to compensate for restrictions such as closed areas, and underestimate the fishing footprint. Fishery systems may be too complex to distil to a single simple and ‘accurate’ model, but having a suite of models that together give a reasonable representation of the fishery could allow the range of likely impacts of management to be better considered. This thesis demonstrates the value of individual-based modelling for both understanding fisher behaviour and predicting the outcomes of management. It has also provided strong evidence to support the use of questionnaire interview data in modelling fishing activity. Comprehensively documenting the stages of model development provided a transparent model validation which would enable managers to make informed decisions about how to apply such a model. Using an IBM to predict the response of fishers to management could facilitate more informed compromises between management objectives, and reduce uncertainty in fisheries management

Reorganisation following disturbance: multi trait-based methods in R

Trait-based approaches in ecology are now commonplace. Originating in terrestrial plant ecology, multi trait-based methods are increasingly applied across ecological disciplines to quantify community structure beyond taxonomic descriptions, to understand mechanistic rules for community assembly, and predict changes following disturbance (Zakharova et al. 2019 Ecol Model). Using morphological and ecological traits as a proxy for the ecological roles of species, these methods can translate multivariate species data into synthetic, complementary, and responsive indicators of ecosystem state (Mouillot et al. 2013 TREE; Richardson et al. 2018 Glob Chang Biol). The analytical tools to do so are increasingly refined and mathematically demanding and so are typically applied via accessible packages in R (e.g. Magneville et al. 2021 Ecography). Nonetheless, the use of these packages requires a degree of computational literacy. Computational literacy (informatics competencies including data and coding literacy) is deemed a critical skill STEM students must acquire for effective science education or careers to meet the demands of the 21st century, but its integrated teaching alongside natural science subjects is lacking (Braun and Huwe 2022 Front Educ). Our Data Set teaching module addresses this gap by providing teaching materials aimed at third-year undergraduate students (junior level bachelor’s degree in the United States) in the form of a 16-hour practical (total class time) divided into six separate sessions: 1 x introductory lecture; 1 x 6-hr computer practical; 3 x 2-hr computer practicals; 1 x 3-hr in-person poster presentation conference. The Data Set is designed to teach students to use the statistical programming tool R to examine how coral reef fish communities were impacted by a severe marine heatwave which resulted in mass coral bleaching on the Great Barrier Reef, Australia (Richardson et al. 2018 Glob Chang Biol). Through student-active approaches including guided enquiry, problem-based learning, critical thinking, and ‘authentic’ assessment (poster presentation), students are offered knowledge of trait-based ecology, and taught skills in data manipulation, analysis, and visualization in R; hypothesis testing; and communicating science

Om några Sommelius-porträtt

Understanding the distribution of fishing activity is fundamental to quantifying its impact on the seabed. Vessel monitoring system (VMS) data provides a means to understand the footprint (extent and intensity) of fishing activity. Automatic Identification System (AIS) data could offer a higher resolution alternative to VMS data, but differences in coverage and interpretation need to be better understood. VMS and AIS data were compared for individual scallop fishing vessels. There were substantial gaps in the AIS data coverage; AIS data only captured 26% of the time spent fishing compared to VMS data. The amount of missing data varied substantially between vessels (45-99% of each individuals' AIS data were missing). A cubic Hermite spline interpolation of VMS data provided the greatest similarity between VMS and AIS data. But the scale at which the data were analysed (size of the grid cells) had the greatest influence on estimates of fishing footprints. The present gaps in coverage of AIS may make it inappropriate for absolute estimates of fishing activity. VMS already provides a means of collecting more complete fishing position data, shielded from public view. Hence, there is an incentive to increase the VMS poll frequency to calculate more accurate fishing footprints.</p