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Record W2761037219 · doi:10.29215/pecen.v1i1.175

Physical, biological and human-induced effects on the reef fishes of Fernando de Noronha archipelago, Brazil

2017· article· pt· W2761037219 on OpenAlexaff
Paulo Roberto de Medeiros, Ana Maria Alves Medeiros

Bibliographic record

VenuePesquisa e Ensino em Ciências Exatas e da Natureza · 2017
Typearticle
Languagept
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsImpact
FundersInstituto Chico Mendes de Conservação da BiodiversidadeConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsBiologyFish <Actinopterygii>EcologyZoologyFishery

Abstract

fetched live from OpenAlex

Vários fatores têm o potencial de influenciar a distribuição e a composição das comunidades de peixes recifais. Entre os mais importantes estão a rugosidade, a exposição às ondas, a cobertura do substrato e as atividades humanas. O presente estudo avaliou a influência desses fatores nos peixes recifais de áreas com um gradiente de restrição a atividades humanas no arquipélago Fernando de Noronha, nordeste do Brasil e determinou suas importâncias relativas para a ecologia de peixes recifais. A rugosidade não influenciou a riqueza de peixes, mas influenciou o número de indivíduos, de jovens e de espécies residentes, enquanto a cobertura bêntica não pareceu ser um determinante importante para quaisquer das variáveis de peixes avaliadas. Esses resultados sugerem que a disponibilidade de abrigo (proteção física) é mais limitante que a disponibilidade de alimento (i.e. cobertura bêntica). Além disso, hidrodinamismo apresentou valores relativamente baixos, porém, influenciando negativamente os peixes. As atividades recreativas, apesar de aparentemente não-impactantes, tiveram um efeito negativo na abundância de peixes, com a área parcialmente protegida (Atalaia) mostrando uma estrutura semelhante à área não-protegida (Porto). Esses resultados sugerem que a presença humana, mesmo em áreas fiscalizadas, pode interferir na estrutura dos peixes recifais. É necessário uma proposta de gestão e de práticas de turismo de baixo impacto especialmente nas áreas parcialmente protegidas e não-protegidas. Palavras chave: Oceano Atlântico, cobertura bêntica, peixes recifais, hidrodinamismo, turismo. Abstract: Several factors have the potential to influence the distribution and composition of reef fish communities. Amongst the most important are rugosity, wave exposure, substrate cover and human activities. The present study evaluated the influence of these factors on reef fishes from sites following a gradient of human-induced effects on the Fernando de Noronha archipelago, northeast Brazil and determined their relative importance to reef fish ecology. Rugosity did not influence fish richness, but had a positive influence on number of individuals, juveniles and endemic species, whereas benthic cover did not seem to be an important determinant for any fish variable evaluated. These results suggest that availability of shelter (physical protection) is more limiting than availability of food (i.e. benthic cover). Furthermore, water flow showed somewhat low values, but even so, had negative effects on fish numbers. Recreational activities, albeit seemingly non-impacting, had a negative effect on fish abundance with the partially protected site (Atalaia) showing a similar community structure to the unrestricted site (Porto). These results suggest that human presence, even when supervised, may interfere on reef fish structure. Low-impact tourism practices are required especially in partially protected and unprotected areas. Key words: Atlantic Ocean, benthic cover, reef fishes, hydrodynamics, tourism.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.288
Teacher spread0.256 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2017
Admission routes1
Has abstractyes

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