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Record W2802684549

Ecological importance of nearshore habitats to sustain small-scale fisheries

2018· dissertation· en· W2802684549 on OpenAlexaboutno aff
Mauricio Carrasquilla

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryHabitatEcologyScale (ratio)Fisheries scienceGeographyEnvironmental scienceOceanographyFisheries managementFishingBiologyGeologyCartography
DOInot available

Abstract

fetched live from OpenAlex

In the marine realm, there has been considerable habitat degradation caused by multiple human disturbances that often act synergistically, strongly affecting fish and invertebrate populations and, consequently, one of the major stakeholders of these resources, fishers. However, the mechanisms underlying how marine habitats support fisheries remain understudied. In this dissertation I examined the importance of fish habitat at global, regional and local scales in two distinct systems (mangrove habitats in the tropics and rockfish habitats in inshore waters of Vancouver Island) combining a suite of different approaches. First, I explored the mangrove-fishery linkage relationship by conducting a global meta-analysis. I found strong evidence supporting the importance of mangrove area to enhance fisheries. This relationship, however, varied across countries, likely based on regional geomorphological settings and fishery management policies. Subsequently, I determined the use of mangrove and adjacent habitats by fish in a tropical lagoon system in the continental Caribbean (Colombia), systems often overlooked in the Caribbean when analyzing mangroves as fish habitat. I collected fish with gillnets at different distances from mangroves and at different sites within the same lagoon system. While fish used mangroves, fish abundance was not higher in these habitats compared to adjacent ones, as predicted. However, diversity tended to be higher in mangroves. Nevertheless, the major driver affecting abundance, diversity and biomass was salinity. That is, diversity and abundance decreased as salinity increased. Next, I used a Local Ecological Knowledge approach to explore the mangrove-fishery linkage relationship because fishers are seldom incorporated into such relationships. By conducting semi-structured interviews I found that fishers fish close to their village and to mangroves, that in addition to fishing they use mangroves for firewood and as construction material. Fishers also agreed that mangroves are important for their fishing activity, as these habitats are critical for fish and crustaceans caught in the system. Finally, I examined the importance of derived benthic parameters for rockfish abundance and distribution at large spatial scales (100s km) in inshore waters of Vancouver Island. I established that higher complexity better explains presence and higher abundance of rockfish. Furthermore, the results provided valuable information for fishery and spatial management and habitat conservation to help recover rockfish populations. All together, these findings highlight the urgency to preserve coastal marine habitats for both juvenile and adult marine organisms to sustain small-scale fisheries as a food source and for traditional purposes. While conserving habitats is a key component of a broader and more complex ecosystem approach that includes overfishing and other anthropogenic pressures, in the absence of a holistic approach the chances of success are minimal.

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)0.002
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.261
Teacher spread0.240 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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