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Record W6939997322 · doi:10.6084/m9.figshare.c.5433567

A relative wave exposure index for the coastal zone of the Scotian Shelf-Bay of Fundy Bioregion

2022· other· en· W6939997322 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsShoreBioregionKelp forestCoastal hazardsCoastal erosionHabitatMarine spatial planningClimate changeBiomass (ecology)Beach morphodynamics

Abstract

fetched live from OpenAlex

Exposure to wind-driven waves forms a key physical gradient in coastal areas that influences both ecological communities and human activities in the nearshore. For example, gradients in wave exposure are associated with patterns of diversity, abundance, and distribution of invertebrate communities along rocky shores (Norderhaug et al. 2012, Arribas et al. 2014). Exposure also influences important vegetated biogenic habitats such as seagrass and kelp beds through effects on primary productivity (Krumhansl &amp; Scheibling 2011a, Krumhansl et al. 2021), resilience (Krumhansl et al. 2021), distribution and landscape patterns (Fonseca &amp; Bell 1998, Bekkby et al. 2008), detrital export (Krumhansl &amp; Scheibling 2011a), and rates of herbivory (Krumhansl &amp; Scheibling 2011b, Frey &amp; Gagnon 2015). Spatial variation and changes in the wave environment also impact human use of the coastal zone. For example, exposure factors into siting of ocean-based aquaculture operations (Lader et al. 2017) and decisions related to the development and adaptation of coastal infrastructure in the face of a changing climate (Hatcher &amp; Forbes 2015). Therefore, the availability of wave exposure indices with regional coverage at a relatively high spatial resolution is required to support ecological modelling as well as marine spatial planning that guide the conservation and use of coastal ocean resources. <br>We developed a spatial layer (35-m resolution) that provides a relative exposure index (REI) to wind-driven waves covering the entire coastal zone of the Canadian Scotian Shelf-Bay of Fundy Bioregion within 5 km from shore and the 50-m depth contour. REI is a fetch-derived index based on methods described in Keddy (1982) and Fonseca &amp; Bell (1998). Our index combines calculations of fetch from 32 compass headings with modelled wind data (ERA5 reanalysis product) from the Copernicus Climate Data Store (Hersbach et al. 2018). Fetch is the unimpeded distance over which wind-driven waves can build (Shore Protection Manual 1975), and measured here as the distance (m) from a point in the ocean to land along a given heading. The resulting index is scaled between 0 (most protected) and 1 (most exposed). <br>Here we provide the REI layer in raster format and a link to the source R and Python code developed to calculate fetch, download, summarize, and interpolate the modelled wind data, compute REI for input point features in an evenly spaced fishnet grid, and convert points to raster.<br><b>References</b><b><br></b>Arribas LP, Donnarumma L, Palomo MG, Scrosati RA (2014) Intertidal mussels as ecosystem engineers: Their associated invertebrate biodiversity under contrasting wave exposures. Mar Biodivers 44:203–211.<br>Bekkby T, Rinde E, Erikstad L, Bakkestuen V, Longva O, Christensen O, Isæus M, Isachsen PE (2008) Spatial probability modelling of eelgrass (Zostera marina) distribution on the west coast of Norway. ICES J Mar Sci 65:1093–1101.<br>Fonseca MS, Bell SS (1998) Influence of physical setting on seagrass landscapes. Mar Ecol Prog Ser 171:109–121.<br>Frey DL, Gagnon P (2015) Thermal and Hydrodynamic Environments Mediate Individual and Aggregative Feeding of a Functionally Important Omnivore in Reef Communities. PLoS One 10:1–28.<br>Hatcher S V., Forbes DL (2015) Exposure to coastal hazards in a rapidly expanding northern urban centre, Iqaluit, Nunavut. Arctic 68:453–471.<br>Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz Sabater J, Nicolas J, Peubey C, Radu R, Rozum I, Schepers D, Simmons A, Soci C, Dee D, Thépaut J-N (2018): ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 23-07-2021), 10.24381/cds.bd0915c6<br>Keddy PA (1982) Quantifying within-lake gradients of wave energy: interrelationships of wave energy, substrate particle size and shoreline plants in Axe Lake, Ontario. Aquat Biol 14:41–58.<br>Krumhansl KA, Dowd M, Wong MC (2021) Multiple Metrics of Temperature, Light, and Water Motion Drive Gradients in Eelgrass Productivity and Resilience. Front Mar Sci 8:1–20.<br>Krumhansl KA, Scheibling RE (2011a) Detrital production in Nova Scotian kelp beds: patterns and processes. Mar Ecol Prog Ser 421:67–82.<br>Krumhansl KA, Scheibling RE (2011b) Spatial and temporal variation in grazing damage by the gastropod Lacuna vincta in Nova Scotian kelp beds. Aquat Biol 13:163–173.<br>Lader P, Kristiansen D, Alver M, Bjelland HV, Myrhaug D (2017) Classification of aquaculture locations in Norway with respect to wind wave exposure. In Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, Trondheim, Norway.<br>Norderhaug KM, Christie H, Andersen GS, Bekkby T (2012) Does the diversity of kelp forest macrofauna increase with wave exposure? J Sea Res 69:36–42.<br>Shore Protection Manual vol. 1. 1975. Fort Belvoir: US Army Coastal Engineering Research Center.<br>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.700
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.7000.000

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 head0.029
GPT teacher head0.212
Teacher spread0.183 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations3
Published2022
Admission routes1
Has abstractyes

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