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Record W4385225370 · doi:10.1101/2023.07.20.549877

Model-based evaluation of critical nursery habitats for juvenile blue crabs through ontogeny: abundance and survival in seagrass, salt marsh, and unstructured bottom

2023· preprint· en· W4385225370 on OpenAlexaff
A. Challen Hyman, Grace S. Chiu, Michael S. Seebo, Alison Smith, Gabrielle G. Saluta, Kathleen E. Knick, Romuald N. Lipcius

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNursery habitatSeagrassSalt marshHabitatBiologyAbundance (ecology)EcologyPopulationJuvenileMarshBayFisheryWetlandGeography

Abstract

fetched live from OpenAlex

Nursery habitats confer higher food availability and reduced predation risk, thereby contributing disproportionately more individuals per unit area to the population compared with other habitats. Nursery status is inferred through evaluation of four metrics: density, growth, survival, and linkage between juveniles and adults. Moreover, organisms commonly use multiple habitats as nurseries throughout ontogeny to satisfy shifting resource requirements. To this end, we conducted manipulative and mensurative field experiments to evaluate two nursery metrics, abundance and survival, for juvenile blue crabs across multiple size classes and habitats, including structurally complex habitats — seagrass meadows and salt marshes — and unstructured habitat (sand flats) in the York River, Chesapeake Bay. We also considered effects of site-specific spatial orientation within the York River, seasonality, physicochemical variables, and postlarval influx. Our results showed that abundance was higher in both seagrass meadows and salt marshes relative to unstructured sand, and positively associated with turbidity and post-larval abundance. Notably, seagrass habitats harbored the highest abundances of small ( ≤ 15 mm carapace width) juveniles, whereas salt marsh edge harbored the highest abundance of medium (16–30 mm carapace width) and large (31– 60 mm carapace width) juveniles. Moreover, survival was positively associated with juvenile size and structurally complex habitats relative to unvegetated controls. Seasonally, survival peaked in April, reached a seasonal minimum in August, and increased throughout fall. Finally, habitat-specific survival was dependent on spatial position: survival was elevated at upriver salt marsh and unstructured sand habitats compared to downriver counterparts. In nursery habitats of marine species, evaluation of survival, growth, abundance and ontogenetic habitat shifts has typically focused on relatively broad size ranges through the juvenile phase. Yet, ontogenetic shifts in habitat use may occur within much narrower size ranges, which has not been well studied and which is critical to the conservation and restoration of nursery habitats. We found that habitat-specific utilization rates differed by juvenile size class over a surprisingly narrow range of size, and were related to (1) the structural and biological characteristics of the nominal nursery habitats, (2) spatial gradients of environmental variables within the tributary, and (3) the likely trade-offs between growth and survival through ontogeny. Taken together, abundance and survival results indicate that seagrass meadows are key nurseries primarily for early-stage juveniles, whereas salt marshes are an intermediate nursery habitat for larger individuals to maximize growth-to-mortality ratios. Our results underscore the need to consider both habitats as critical nurseries for juvenile blue crabs throughout ontogeny.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.041
GPT teacher head0.304
Teacher spread0.263 · 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
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
Published2023
Admission routes1
Has abstractyes

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