Modelling abundance hotspots for data-poor Irish Sea rays

Implications for the conservation of deep-water corals in the face of multiple stressors: A case study from the New Zealand region

The waters around New Zealand are a global hotspot of biodiversity for deep-water corals; approximately one sixth of the known deep-water coral species of the world have been recorded in the region. Deep-water corals are vulnerable to climate-related stressors and from the damaging effects of commercial fisheries. Current protection measures do not account for the vulnerability of deep-water corals to future climatic conditions, which are predicted to alter the distribution of suitable habitat for them. Using recently developed habitat suitability models for 12 taxa of deep-water corals fitted to current and future seafloor environmental conditions (under different future climatic conditions: SSP2 - 4.5 and SSP3 - 7.0) we explore possible levels of spatial protection using the decision-support tool Zonation. Specifically, we assess the impact of bottom trawling on predictions of current distributions of deep-water corals, and then assess the effectiveness of possible protection for deep-water corals, while accounting for habitat refugia under future climatic conditions. The cumulative impact of bottom trawling was predicted to impact all taxa, but particularly the reef-forming corals. Core areas of suitable habitat were predicted to decrease under future climatic conditions for many taxa. We found that designing protection using current day predictions alone, having accounted for the impacts of historic fishing impacts, was unlikely to provide adequate conservation for deep water-corals under future climate change. Accounting for future distributions in spatial planning identified areas which may provide climate refugia whilst still providing efficient protection for current distributions. These gains in conservation value may be particularly important given the predicted reduction in suitable habitat for deep-water corals due to bottom fishing and climate change. Finally, the possible impact that protection measures may have on deep-water fisheries was assessed using a measure of current fishing value (kg km-2 fish) and future fishing value (predicted under future climate change scenarios).

Delineation and mapping of coastal shark habitat within a shallow lagoonal estuary

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.

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

Boosted Regression Trees. Excellent for data-poor spatial management but hard to use

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.

BRTs automated and simplified for accessible general use with rich feature set

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.

Gbm.auto for management in various settings

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.