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Record W2982165667 · doi:10.4095/305854

Modeling distributions of cold-water corals in the Newfoundland and Labrador region using MaxEnt

2017· report· en· W2982165667 on OpenAlexaboutno aff
Lauren Gullage, Evan Edinger, Rodolphe Devillers

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

Species distribution models (SDMs) are increasingly used in ecology and conservation to predict species distributions based on benthic habitat requirements. SDMs help when designing conservation approaches for regions lacking detailed data on species requiring protection; however, such approaches are limited by the quality of the training data, and by the modeling approaches used. In order to support the design of marine protected areas that can protect cold-water corals and sponges in the Newfoundland and Labrador region, Eastern Canada, we used MaxEnt to model the distributions of 19 coral species belonging to four functional groups: large gorgonians, small gorgonians, sea pens, and soft corals. Coral distribution data from 2004-2011, obtained from the Department of Fisheries and Oceans Canada (DFO) multi-species trawl surveys, were used to generate SDMs. These data encompass the south coast of Newfoundland, the Grand Banks, the Flemish Pass, Northeast Newfoundland Shelf, and Labrador shelf to 60N. Environmental datasets used to generate models included: GEBCO bathymetry, six terrain attributes derived from bathymetry, bottom temperature and salinity, and surface chlorophyll A concentrations. Modeled distributions predicted greatest coral abundance along the continental shelf break and upper slope throughout the region. When analyzed at the functional group level, large gorgonians were predicted to be most abundant off northern Labrador, off the central Labrador shelf, and in parts of the SW Grand Banks. Small gorgonian distributions were most concentrated along the Labrador Shelf break, the NE Newfoundland Shelf break, as well as along the shelf break of the SW Grand Banks. Sea pen distributions were greatest along the SW Grand Banks and the south coast, particularly within the Laurentian Channel. In contrast, soft corals were most abundant on the bank tops. Agreement between observed and modeled distributions was assessed using area under the curve (AUC), true skill statistics (TSS), and comparisons with independent datasets. Findings consistently illustrated high model accuracy (e.g. AUC >0.9). Analysis of predicted distributions at the species level found large differences among species within functional groups, particularly among the large gorgonians. Results indicate that coral functional groups based on taxonomy do not group species sharing similar environmental preferences, and that SDMs should instead be performed on individual species of interest. Furthermore, previous models for the region based upon Random Forest modeling have predicted sponge and coral habitats extend into deep water areas of the continental rise. Our study does not confirm those predictions, and limited knowledge of the bathyal and abyssal depths in these regions do not suggest appropriate bottom types for these species. In contrast, our results predict coral distributions are concentrated along the continental slope and shelf break, having a high level of agreement with the training dataset, and with predicted global distributions of the same taxa. Our models suggest that conservation efforts for corals should be concentrated at the shelf-break and upper continental slope, where they are known to occur, rather than in deep-water areas where they might occur, and where there are fewer competing human uses than on the shelf break and upper slope.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.341
Threshold uncertainty score0.686

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.139
GPT teacher head0.325
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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