Simon Dedman

Galway-Mayo Institute of Technology, Marine and Freshwater Research Centre, Alumnus

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I am an elasmobranch scientist specialising in spatial modelling using machine learning approaches (Boosted Regression Trees), applied to fisheries management through Maximum Sustainable Yield, manually coded into the open-source Decision Support Tool 'gbm.auto' in R. I acquired my PhD in 2017 from Galway-Mayo Institute of Technology, Ireland, and now live in San Carlos, California, USA. I am a member of the American Elasmobranch Society, European Elasmobranch Association, UK Shark Trust, WWF, and Mensa, and regularly attend AES, EEA and ICES annual conferences.
Supervisors: Rick Officer, Deirdre Brophy, Maurice Clarke, and David G Reid
Address: San Carlos, California, USA

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Aristotle University of Thessaloniki

Jayjit Majumdar

University of Kalyani

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UNL - New University of Lisbon

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National Institute of Oceanography and Fisheries

Giampaolo Orlandoni

Universidad de Los Andes (Venezuela)

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Universidad de Cartagena

Bogdan Gabrys

University of Technology Sydney

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Universidade Federal de Santa Catarina - UFSC (Federal University of Santa Catarina)

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Mugla University

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Papers by Simon Dedman

gbm.auto: Automated Boosted Regression Tree Modelling and Mapping Suite

A day in the life: quantifying nursery habitat use in a coastal shark species

Animal behaviour, May 1, 2024

movegroup: Visualizing and Quantifying Space Use Data for Groups of Animals

Correction: Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

PLOS ONE, Feb 2, 2018

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Towards a flexible Decision Support Tool for MSY-based Marine Protected Area design for skates and rays

Ices Journal of Marine Science, Aug 26, 2016

It is recommended that demersal elasmobranchs be managed using spatial proxies for Maximum Sustai... more It is recommended that demersal elasmobranchs be managed using spatial proxies for Maximum Sustainable Yield. Here we combine escapement biomass-the percentage of the stock which must be retained each year to conserve it-with maps of predicted CPUE of four ray species (cuckoo (Leucoraja naevus), thornback (Raja clavata), blonde (R. brachyura), and spotted (R. montagui)), created using Boosted Regression Tree modelling. We then use a Decision Support Tool to generate location and size options for Marine Protected Areas to protect these stocks, based on the priorities of the various stakeholders, notably the minimisation of fishing effort displacement. Variations of conservation/fishing priorities are simulated, as well as differential priorities for individual species, with a focus on protecting nursery grounds and spawning areas. Prioritising high CPUE cells results in a smaller closed area that displaces the most fishing effort, whereas prioritising low fishing effort results in a larger closed area that displaces the least fishing effort. The final result is a complete software package that produces maps of predicted species CPUE from limited survey data, allowing disparate stakeholders and policymakers to discuss management options within a mapping interface.

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Advanced Spatial Modeling to Inform Management of Data-Poor Juvenile and Adult Female Rays

Fishes, Aug 4, 2017

Chronic overfishing has depleted numerous elasmobranch stocks in the North East Atlantic, but add... more Chronic overfishing has depleted numerous elasmobranch stocks in the North East Atlantic, but addressing this issue has been hampered by management complications and lacking data. Spatial management approaches have thus been advocated. This work presents a novel application and further development of an advanced spatial modeling technique to identify candidate nursery grounds and spawning areas for conservation, by subsetting already limited data. Boosted Regression Tree models are used to predict abundance of juvenile and mature female cuckoo (Leucoraja naevus), thornback (Raja clavata), blonde (Raja brachyura), and spotted (Raja montagui) rays in the Irish Sea using fish survey data and data describing fishing pressure, predation and environmental variables. Model-predicted spatial abundance maps of these subsets reveal distinct nuances in species distributions with greater predictive power than maps of the whole stock. These resulting maps are then integrated into a single easily understood map using a novel approach, standardizing and facilitating the spatial management of data-limited fish stocks.

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Assignment of tracks from tagged Atlantic bluefin tuna Thunnus thynnus to potential stocks using behavioural differences and habitat partitioning

Frontiers in Marine Science

IntroductionAtlantic bluefin tuna (ABT) are large, migratory pelagic predators of high economic i... more IntroductionAtlantic bluefin tuna (ABT) are large, migratory pelagic predators of high economic importance. ABT are currently managed as two independent stocks assigned to discrete spawning areas (Gulf of Mexico, and Mediterranean); however, stock overlap outside spawning areas makes accurate assignment of catch to stock-of-origin difficult.MethodsWithin this two-stock paradigm, we characterised stock-specific spatial distributions and behaviours by comparing habitat usage and vertical movement behaviours of 118 electronically tagged adult ABT spatially assigned to the GOM and Med spawning grounds. These spatial and behavioural differences were used in tests to probabilistically assign unknown individuals (which did not visit the GOM/Med spawning areas) to a stock.ResultsThis new methodological approach using existing tag data, enables increased assignment of a track to a potential stock, to be achieved before genetic assignments. We identified certain markedly different movement pa...

Evidence of bluefin tuna (Thunnus thynnus) spawning in the Slope Sea region of the Northwest Atlantic from electronic tags

ICES Journal of Marine Science

Atlantic bluefin tuna (ABT) are large, wide-ranging pelagic predators, which typically migrate be... more Atlantic bluefin tuna (ABT) are large, wide-ranging pelagic predators, which typically migrate between foraging regions in the North Atlantic and two principal spawning regions, the Gulf of Mexico and the Mediterranean Sea. A new spawning area has been described in the Slope Sea (SS) region off New England; however, the relationship between ABT that spawn in the SS and ABT using the principal spawning regions remains poorly understood. We used electronic tags to examine the location, temperature, and diving behaviour of ABT in the SS, and identified 24 individuals that were present during the spawning season (June–August) with tag data showing temperatures and behaviour consistent with spawning ABT. In general, the SS spawners had similar spatial ranges to Mediterranean-spawning ABT; however, some individuals displayed distinct behaviours that were identified first in the Gulf of Mexico spawners. Using monthly spatial distributions, we estimated that the SS spawners have high exposu...

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$Research paper thumbnail of Parameters (cross-validation score (CV), learning rate (lf), bag fraction(bf)) and contribution (% of tree splits) for binary BRT models predicting capture likelihood of six principal shark species in Pamlico Sound$

Parameters (cross-validation score (CV), learning rate (lf), bag fraction(bf)) and contribution (% of tree splits) for binary BRT models predicting capture likelihood of six principal shark species in Pamlico Sound

Bold text denotes the three environmental factors contributing the most tree splits for ... more

Percent of individuals of principal shark species classified as each species based on environmental factors by linear discriminant function analysis

Correct classifications are in bold.</p

Total numbers of sharks of each principal species captured in Pamlico Sound by year, season, and month

A.) Smooth and Spiny Dogfish and B.) Atlantic Sharpnose, Blacktip, Bull, and Sandbar Sha... more

Predicted capture likelihood maps for the six principal shark species within Pamlico Sound

Predicted capture likelihood is based on binary presence/absence BRT results.</p

Cumulative area closure maps derived under the biomass (top left), effort (top right), combination (bottom left) and conservation (bottom right) sorting techniques, from gbm.valuemap

Cumulative area closure maps derived under the biomass (top left), effort (top right), c... more

Marginal effects of environmental factors contributing to predicting shark species capture likelihood

Marginal effects are of the three environmental factors that contributed the three highe... more Marginal effects are of the three environmental factors that contributed the three highest proportions of tree splits to the binary BRT model predicting capture likelihood of each of the six principal shark species within Pamlico Sound. Percentages in parentheses represent the proportion of tree splits contributed by the variable.</p

Linear correlations between environmental factors recorded during 2007–2014 North Carolina Division of Marine Fisheries gillnet and longline surveys in Pamlico Sound

Linear correlations between environmental factors recorded during 2007–2014 North Caroli... more

Automated Boosted Regression Tree Modelling and Mapping Suite [R package gbm.auto version 1.4.1]

Bright spots as climate‐smart marine spatial planning tools for conservation and blue growth

Global Change Biology, 2021

This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmer... more

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Total number captured (number of sets occurring for principal species in parentheses) and mean ± standard deviation total length (TL) and environmental factor measurements (minimum and maximum in parentheses) for each shark species captured during North Carolina Division of Marine Fisheries gilln...

Species denoted with an asterisk were designated principal species.</p

Population dynamics and relative abundance of and habitat suitability for adult red drum (Sciaenops ocellatus) vulnerable to exploitation in nearshore waters of the north-central Gulf of Mexico

Fishery Bulletin, May 19, 2022

North Carolina Division of Marine Fisheries gillnet and longline survey stations within Pamlico Sound from 2007–2014

Gillnet sets are marked with red triangles and longline sets are marked with blue circle... more

gbm.auto: Automated Boosted Regression Tree Modelling and Mapping Suite

A day in the life: quantifying nursery habitat use in a coastal shark species

Animal behaviour, May 1, 2024

movegroup: Visualizing and Quantifying Space Use Data for Groups of Animals

Correction: Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

PLOS ONE, Feb 2, 2018

Download

Towards a flexible Decision Support Tool for MSY-based Marine Protected Area design for skates and rays

Ices Journal of Marine Science, Aug 26, 2016

Download

Advanced Spatial Modeling to Inform Management of Data-Poor Juvenile and Adult Female Rays

Fishes, Aug 4, 2017

Download

Assignment of tracks from tagged Atlantic bluefin tuna Thunnus thynnus to potential stocks using behavioural differences and habitat partitioning

Frontiers in Marine Science

Evidence of bluefin tuna (Thunnus thynnus) spawning in the Slope Sea region of the Northwest Atlantic from electronic tags

ICES Journal of Marine Science

Download

Bold text denotes the three environmental factors contributing the most tree splits for ... more

Percent of individuals of principal shark species classified as each species based on environmental factors by linear discriminant function analysis

Correct classifications are in bold.</p

Total numbers of sharks of each principal species captured in Pamlico Sound by year, season, and month

A.) Smooth and Spiny Dogfish and B.) Atlantic Sharpnose, Blacktip, Bull, and Sandbar Sha... more

Predicted capture likelihood maps for the six principal shark species within Pamlico Sound

Predicted capture likelihood is based on binary presence/absence BRT results.</p

Cumulative area closure maps derived under the biomass (top left), effort (top right), combination (bottom left) and conservation (bottom right) sorting techniques, from gbm.valuemap

Cumulative area closure maps derived under the biomass (top left), effort (top right), c... more

Marginal effects of environmental factors contributing to predicting shark species capture likelihood

Linear correlations between environmental factors recorded during 2007–2014 North Carolina Division of Marine Fisheries gillnet and longline surveys in Pamlico Sound

Linear correlations between environmental factors recorded during 2007–2014 North Caroli... more

Automated Boosted Regression Tree Modelling and Mapping Suite [R package gbm.auto version 1.4.1]

Bright spots as climate‐smart marine spatial planning tools for conservation and blue growth

Global Change Biology, 2021

This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmer... more

Download

Species denoted with an asterisk were designated principal species.</p

Population dynamics and relative abundance of and habitat suitability for adult red drum (Sciaenops ocellatus) vulnerable to exploitation in nearshore waters of the north-central Gulf of Mexico

Fishery Bulletin, May 19, 2022

North Carolina Division of Marine Fisheries gillnet and longline survey stations within Pamlico Sound from 2007–2014

Gillnet sets are marked with red triangles and longline sets are marked with blue circle... more

Simon Dedman

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