Bio


My work focuses on the use of machine learning approaches to understand the drivers of the movements of sharks and tuna, then predictively map these species to inform conservation management. I have developed software that automates and facilitates the use of Boosted Regression Tree techniques to ecological data, advancing the use of this approach among the shark community. Areas of study have been sharks off the coast of southern England, rays in the Irish Sea, lemon sharks in the Bahamas, anchovy off California, tuna in the North Atlantic, and sawfish off Florida.

Honors & Awards


  • Travel award, American Elasmobranch Society (2017)
  • Travel award, Marine Institute (2016)
  • Travel award (second), Marine Institute (2016)
  • Travel award, Shark Trust (2015)
  • Travel award, Marine Institute (2015)

Boards, Advisory Committees, Professional Organizations


  • Equity and Diversity Committee, American Elasmobranch Society, USA (2021 - Present)
  • Science Committee, Saving The Blue research NGO, USA/Bahamas (2019 - Present)

Professional Education


  • Doctor of Philosophy, Galway-Mayo Institute of Technology (2017)
  • Master of Science, University Of Aberdeen (2006)
  • Bachelor of Science, University Of Southampton (2004)
  • PhD, Galway-Mayo Institute of Technology, Ireland, Fisheries Science (2017)
  • MRes, Aberdeen University, UK, Marine & Fisheries Science (2006)
  • Bcs (Hons), Southampton University, UK, Oceanography w/ Marine Biology (2004)

Stanford Advisors


Research Interests


  • Data Sciences

Current Research and Scholarly Interests


My work focuses on the use of machine learning approaches to understand the drivers of the movements of sharks and tuna, then predictively map these species to inform conservation management. I have developed software that automates and facilitates the use of Boosted Regression Tree techniques to ecological data, advancing the use of this approach among the shark community. Areas of study have been sharks off the coast of southern England, rays in the Irish Sea, lemon sharks in the Bahamas, anchovy off California, tuna in the North Atlantic, and sawfish off Florida.

Lab Affiliations


All Publications


  • Bright spots as climate-smart marine spatial planning tools for conservation and blue growth. Global change biology Queiros, A. M., Talbot, E., Beaumont, N. J., Somerfield, P. J., Kay, S., Pascoe, C., Dedman, S., Fernandes, J. A., Jueterbock, A., Miller, P. I., Sailley, S. F., Sara, G., Carr, L. M., Austen, M. C., Widdicombe, S., Rilov, G., Levin, L. A., Hull, S. C., Walmsley, S. F., Nic Aonghusa, C. 2021

    Abstract

    Marine spatial planning that addresses ocean climate-driven change ('climate-smart MSP') is a global aspiration to support economic growth, food security and ecosystem sustainability. Ocean climate change ('CC') modelling may become a key decision-support tool for MSP, but traditional modelling analysis and communication challenges prevent their broad uptake. We employed MSP-specific ocean climate modelling analyses to inform a real-life MSP process; addressing how nature conservation and fisheries could be adapted to CC. We found that the currently planned distribution of these activities may become unsustainable during the policy's implementation due to CC, leading to a shortfall in its sustainability and blue growth targets. Significant, climate-driven ecosystem-level shifts in ocean components underpinning designated sites and fishing activity were estimated, reflecting different magnitudes of shifts in benthic versus pelagic, and inshore versus offshore habitats. Supporting adaptation, we then identified: CC refugia (areas where the ecosystem remains within the boundaries of its present state); CC hotspots (where climate drives the ecosystem towards a new state, inconsistent with each sectors' present use distribution); and for the first time, identified bright spots (areas where oceanographic processes drive range expansion opportunities that may support sustainable growth in the medium term). We thus create the means to: identify where sector-relevant ecosystem change is attributable to CC; incorporate resilient delivery of conservation and sustainable ecosystem management aims into MSP; and to harness opportunities for blue growth where they exist. Capturing CC bright spots alongside refugia within protected areas may present important opportunities to meet sustainability targets while helping support the fishing sector in a changing climate. By capitalizing on the natural distribution of climate resilience within ocean ecosystems, such climate-adaptive spatial management strategies could be seen as nature-based solutions to limit the impact of CC on ocean ecosystems and dependent blue economy sectors, paving the way for climate-smart MSP.

    View details for DOI 10.1111/gcb.15827

    View details for PubMedID 34486773

  • Delineation and mapping of coastal shark habitat within a shallow lagoonal estuary. PloS one Bangley, C. W., Paramore, L. n., Dedman, S. n., Rulifson, R. A. 2018; 13 (4): e0195221

    Abstract

    Estuaries function as important nursery and foraging habitats for many coastal species, including highly migratory sharks. Pamlico Sound, North Carolina, is one of the largest estuaries in the continental United States and provides a variety of potential habitats for sharks. In order to identify and spatially delineate shark habitats within Pamlico Sound, shark catch and environmental data were analyzed from the 2007-2014 North Carolina Division of Marine Fisheries (NCDMF) gillnet and longline surveys conducted within the estuary. Principal species were identified and environmental data recorded at survey sites (depth, temperature, salinity, dissolved oxygen, submerged aquatic vegetation (SAV) distance, and inlet distance) were interpolated across Pamlico Sound to create seasonal environmental grids with a 90-m2 cell size. Boosted Regression Tree (BRT) analysis was used to identify the most important environmental factors and ranges associated with presence of each principal species, and the resulting models were used to predict shark capture probability based on the environmental values within the grid cells. The Atlantic Sharpnose Shark (Rhizoprionodon terraenovae), Blacktip Shark (Carcharhinus limbatus), Bull Shark (Carcharhinus leucas), Sandbar Shark (Carcharhinus plumbeus), Smooth Dogfish (Mustelus canis), and Spiny Dogfish (Squalus acanthias) were the principal species in Pamlico Sound. Most species were associated with proximity to the inlet and/or high salinity, and warm temperatures, but the Bull Shark preferred greater inlet distances and the Spiny Dogfish preferred lower temperatures than the other species. Extensive Smooth Dogfish habitat overlap with seagrass beds suggests that seagrass may be a critical part of nursery habitat for this species. Spatial delineation of shark habitat within the estuary will allow for better protection of essential habitat and assessment of potential interactions with other species.

    View details for DOI 10.1371/journal.pone.0195221

    View details for PubMedID 29649261

    View details for PubMedCentralID PMC5896943

  • Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning. PloS one Dedman, S. n., Officer, R. n., Clarke, M. n., Reid, D. G., Brophy, D. n. 2017; 12 (12): e0188955

    Abstract

    Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage.We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement.By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios.

    View details for DOI 10.1371/journal.pone.0188955

    View details for PubMedID 29216310

    View details for PubMedCentralID PMC5720763