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Record W2073423327 · doi:10.1371/journal.pone.0011842

The Biodiversity of the Mediterranean Sea: Estimates, Patterns, and Threats

2010· review· en· W2073423327 on OpenAlexafffund
Marta Coll, Chiara Piroddi, Jeroen Steenbeek, Kristin Kaschner, Frida Ben Rais Lasram, Jacopo Aguzzi, Enric Ballesteros, Carlo Nike Bianchi, Jordi Corbera, Thanos Dailianis, Roberto Danovaro, Marta Estrada, Carlo Froglia, Josep M. Gasol, Ruthy Gertwagen, João Gil, François Guilhaumon, Kathleen Kesner‐Reyes, Miltiadis‐Spyridon Kitsos, Athanasios Koukouras, Nikolaos Lampadariou, Elijah Laxamana, Carlos M. López-Fé de la Cuadra, Heike K. Lotze, Daniel Martín, David Mouillot, Daniel Oró, Saša Raicevich, Josephine Rius-Barile, José Ignacio Sáiz-Salinas, Carles San Vicente, Samuel Somot, José Templado, Xavier Turón, Dimitris Vafidis, Roger Villanueva, Eleni Voultsiadou

Bibliographic record

VenuePLoS ONE · 2010
Typereview
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans CanadaDalhousie University
FundersTotal FoundationMinisterio de Ciencia e InnovaciónDalhousie UniversityFP7 People: Marie-Curie ActionsGeneralitat de CatalunyaEuropean CommissionAlfred P. Sloan Foundation
KeywordsBiodiversityOverexploitationMediterranean climateMediterranean seaGeographyMarine ecosystemHabitat destructionEcologyClimate changeBiotaEcosystemHabitatGlobal biodiversityMediterranean BasinBiology

Abstract

fetched live from OpenAlex

The Mediterranean Sea is a marine biodiversity hot spot. Here we combined an extensive literature analysis with expert opinions to update publicly available estimates of major taxa in this marine ecosystem and to revise and update several species lists. We also assessed overall spatial and temporal patterns of species diversity and identified major changes and threats. Our results listed approximately 17,000 marine species occurring in the Mediterranean Sea. However, our estimates of marine diversity are still incomplete as yet-undescribed species will be added in the future. Diversity for microbes is substantially underestimated, and the deep-sea areas and portions of the southern and eastern region are still poorly known. In addition, the invasion of alien species is a crucial factor that will continue to change the biodiversity of the Mediterranean, mainly in its eastern basin that can spread rapidly northwards and westwards due to the warming of the Mediterranean Sea. Spatial patterns showed a general decrease in biodiversity from northwestern to southeastern regions following a gradient of production, with some exceptions and caution due to gaps in our knowledge of the biota along the southern and eastern rims. Biodiversity was also generally higher in coastal areas and continental shelves, and decreases with depth. Temporal trends indicated that overexploitation and habitat loss have been the main human drivers of historical changes in biodiversity. At present, habitat loss and degradation, followed by fishing impacts, pollution, climate change, eutrophication, and the establishment of alien species are the most important threats and affect the greatest number of taxonomic groups. All these impacts are expected to grow in importance in the future, especially climate change and habitat degradation. The spatial identification of hot spots highlighted the ecological importance of most of the western Mediterranean shelves (and in particular, the Strait of Gibraltar and the adjacent Alboran Sea), western African coast, the Adriatic, and the Aegean Sea, which show high concentrations of endangered, threatened, or vulnerable species. The Levantine Basin, severely impacted by the invasion of species, is endangered as well. This abstract has been translated to other languages (File S1).

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
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 head0.076
GPT teacher head0.248
Teacher spread0.172 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2,082
Published2010
Admission routes2
Has abstractyes

Explore more

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