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. 2015 Jun 1;10(6):e0127395.
doi: 10.1371/journal.pone.0127395. eCollection 2015.

Species distribution models of tropical deep-sea snappers

Céline Gomez  1 , Ashley J Williams  1 , Simon J Nicol  1 , Camille Mellin  2 , Kim L Loeun  1 , Corey J A Bradshaw  3
Affiliations

Species distribution models of tropical deep-sea snappers

Céline Gomez et al. PLoS One. .

Abstract

Deep-sea fisheries provide an important source of protein to Pacific Island countries and territories that are highly dependent on fish for food security. However, spatial management of these deep-sea habitats is hindered by insufficient data. We developed species distribution models using spatially limited presence data for the main harvested species in the Western Central Pacific Ocean. We used bathymetric and water temperature data to develop presence-only species distribution models for the commercially exploited deep-sea snappers Etelis Cuvier 1828, Pristipomoides Valenciennes 1830, and Aphareus Cuvier 1830. We evaluated the performance of four different algorithms (CTA, GLM, MARS, and MAXENT) within the BIOMOD framework to obtain an ensemble of predicted distributions. We projected these predictions across the Western Central Pacific Ocean to produce maps of potential deep-sea snapper distributions in 32 countries and territories. Depth was consistently the best predictor of presence for all species groups across all models. Bathymetric slope was consistently the poorest predictor. Temperature at depth was a good predictor of presence for GLM only. Model precision was highest for MAXENT and CTA. There were strong regional patterns in predicted distribution of suitable habitat, with the largest areas of suitable habitat (> 35% of the Exclusive Economic Zone) predicted in seven South Pacific countries and territories (Fiji, Matthew & Hunter, Nauru, New Caledonia, Tonga, Vanuatu and Wallis & Futuna). Predicted habitat also varied among species, with the proportion of predicted habitat highest for Aphareus and lowest for Etelis. Despite data paucity, the relationship between deep-sea snapper presence and their environments was sufficiently strong to predict their distribution across a large area of the Pacific Ocean. Our results therefore provide a strong baseline for designing monitoring programs that balance resource exploitation and conservation planning, and for predicting future distributions of deep-sea snappers.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Location of presence data for deep-sea snapper in New Caledonia and Tonga.
Fig 2
Fig 2. Location of presence data for deep-sea snapper across the Western Central Pacific Ocean.
The locations for all Pristipomoides data are identical to those for Aphareus or Etelis and are indicated by all species.
Fig 3
Fig 3. Relative contribution of oceanographic variables to model predictions calculated by resampling (n = 100) and averaged over pseudo-absence datasets (with corresponding standard deviation error bars) for each algorithm for Etelis Cuvier 1828, PristipomoidesValenciennes 1830, and Aphareus Cuvier 1830.
Month effect has been removed for temperature at depth (T°C). CTA: classification tree analysis; MARS: multiple adaptive regression spline; GLM: generalized linear model; MAXENT: maximum of entropy.
Fig 4
Fig 4. Projection of ensemble models in the Western Central Pacific region showing mean of probabilities of presence for Etelis Cuvier 1828, (upper panel), Pristipomoides Valenciennes 1830 (middle panel) and Aphareus Cuvier 1830 (lower panel), evaluated with true skill statistics and receiver operating characteristics (see Table 4).
Fig 5
Fig 5. Relationship between estimated unexploited biomass (source: Dalzell & Preston, 1992) and predicted suitable habitat of deep-sea snappers.
Data are shown for predicted habitat when all three species groups (Etelis, Pristipomoides, Aphareus) and at least one species group were predicted to be present.
See this image and copyright information in PMC

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Grants and funding

This research was funded by the Australian Agency for International Development (AusAID) through the Pacific Fisheries for Food Security Program (PFFSP), the French Pacific Fund, and the Zone Économique de Nouvelle-Calédonie (ZoNéCo) program. CM was funded by the Marine Biodiversity Hub (www.nerpmarine.edu.au).

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