Red mullet in northern Spain (GSA 6) and the Adriatic (GSAs 17-18)

A spatio-temporal species distribution model: biomass, habitat suitability, trawling and marine heatwaves

climate-env-ais working group

21 October 2026

How we worked

1 · Working group
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2 · Google Drive
Shared analyses
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3 · Curation
Read, clean, organise
Théophile and Claude review the Drive, fix paths and code, reproduce results
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4 · GitHub
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5 · Quarto report
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Methods, results and discussion points; archives all the analyses
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6 · Quarto slides
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7 · Online
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Discussion points go back to the group for the next round of analyses
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Aims and data

Aims

  • Describe red mullet (Mullus barbatus) biomass in space and time in two areas
  • Express habitat suitability on a 0-1 scale
  • Then relate it to trawling effort (GFW AIS) and marine heatwaves

Data decisions

  • Response: biomass (kg/km²) per MEDITS haul
  • 2014-2021: no Croatian MEDITS survey in 2012-2013 (also avoids the early AIS ramp-up)
  • Two covariates only: bottom temperature (Copernicus, matched to each haul) and depth

617 valid hauls, GSA 6 (northern Spain)

1,631 valid hauls, GSAs 17-18 (Adriatic)

Red mullet absent from 17% of GSA 6 hauls and 11% of Adriatic hauls.

Discussion point: two thin years: GSA 6 in 2020 (39 hauls, COVID-19) and the Adriatic in 2017 (61 hauls).

The model: sdmTMB

log(biomass) = bottom temperature + depth + ω (spatial) + ε (spatio-temporal, AR(1) by year)

  • Tweedie observation model: zeros and skewed catches in one distribution
  • ω: persistent hotspots not explained by the covariates
  • ε: year-to-year departures from that pattern, correlated between years
  • Fitted separately in each area on an SPDE mesh (184 and 414 vertices)
GSA 6 GSAs 17-18
Per +100 m depth −94% −68%
Per +1 °C bottom temp. +18% +17%
Spatial range 86 km 151 km
Largest |ε| 0.7 2.9

Change in predicted biomass per unit of each covariate, on the response scale.

Goodness of fit

  • Both models converge (all sanity() checks passed)
  • Residuals on the normal line: Tweedie fits zeros and large catches
  • Observed vs fitted (log): r = 0.90 (GSA 6), 0.88 (Adriatic)
  • In-sample fit; cross-validation would test new hauls

Bottom temperature: two areas compared

+17% biomass per +1 °C in the Adriatic (95% CI +14% to +21%)

+18% per +1 °C in GSA 6 (95% CI −4% to +45%): not significant

Discussion point: GSA 6 hauls span only 12.9-17.6 °C (Adriatic 11.2-28.2 °C), so the temperature effect is poorly constrained there and depth dominates.

Predicted biomass: GSA 6

Highest on the inner shelf along the whole coast, dropping quickly offshore.

Predicted biomass: Adriatic

Highest in the shallow northern basin and along the Croatian coast; a gentler depth gradient over a much wider shelf.

Spatial and spatio-temporal random effects

ω (first column): persistent hotspots not explained by depth and temperature

ε by year: small in GSA 6, large in the Adriatic (up to ±2.9, a factor of ~18 in biomass)

Biomass index

Sum of predicted biomass × cell area, per year

−3% GSA 6, 2015 → 2021 (Tweedie GAM of Section 2b: −12% in density)

77% to 146% The Adriatic index fluctuates more, between 77% and 146% of its 2014-2021 mean

Discussion point: each area peaks in its least-sampled year. Index standard errors (get_index()) would show how uncertain these years are.

Habitat suitability index (HSI)

Fixed effects (depth + bottom temperature) rescaled to 0-1 across all years in each area; reproduces the group’s maps. Highest on the shallow inner shelf; in the Adriatic, the northern basin scores high almost everywhere.

Change in habitat suitability, 2014 to 2021

  • GSA 6: median +0.1%, almost no change
  • Adriatic: median −1.8%; 63% of cells lose suitability, mainly the northern basin

Discussion point: the % change of a 0-1 index explodes where HSI ≈ 0 (up to +217% in one GSA 6 cell): use the difference in HSI and a 2014-2021 trend rather than two years.

Overlap between red mullet and trawling

Local correlation between red mullet and GFW trawling effort, by year

  • Red: trawling concentrated where red mullet is abundant
  • GSA 6: mostly negative, positive patches on the outer shelf
  • Adriatic: a positive band through the north-western and central basin every year; coastal edge negative

Discussion point: in small windows, correlations near ±1 are common by chance: add a significance test or a whole-area overlap measure.

Marine heatwaves in the two areas

Summer heatwaves (Hobday et al. 2016) computed on the prediction grid of each area

  • GSA 6 strongest summers: 2017, 2018, 2020
  • Adriatic strongest summers: 2015, 2018, 2019

Discussion point: the two areas do not share their heatwave years (2020: strong in GSA 6, almost none in the Adriatic), so comparisons need area-specific years.

Trawling in strong and weak heatwave summers

  • Daily GFW trawler effort, June-August, 3 strong vs 3 weak summers, from 2015
  • Total hours: +1.9% in GSA 6, -1.3% in the Adriatic, within year-to-year noise

Do the fleets fish the same grounds?

Core grounds (cells with 80% of hours): blue-grey in both types of summer, red strong only, dark blue weak only.

GSA 6 Adriatic
Overlap D, strong vs weak 0.72 0.72
Overlap D, same type 0.71, 0.7 0.71, 0.72
Centre shift 1.4 km 3.9 km
Effort on red mullet hotspots 13% / 14% 23% / 20%

Same grounds in strong and weak heatwave summers: overlap as high as between two summers of the same type (D: 1 = identical effort distribution).

Heatwaves and red mullet biomass across years

Adriatic: biomass drops in the two strongest heatwave summers (2015, 2018), recovers the year after; overall ρ = -0.14

GSA 6: the opposite, its strongest heatwave summers are among its higher-biomass years (ρ = 0.53)

Discussion point: eight years per area, two areas in opposite directions: a hypothesis to test, not a result.

Heatwaves and red mullet biomass within years

Year-specific deviation of predicted biomass (ε) in each cell, against heatwaves and trawling, with depth, latitude and year in the model

  • GSA 6: small effects
  • Adriatic: not monotonic, a dip at moderate heat, higher biomass in the hottest cells
  • No trawling effect in either area
  • Does not reproduce the annual dips

Next steps

  • Index standard errors with get_index(), especially for the thinly sampled years
  • Cross-validation to test predictive skill
  • Add an intercept or year effects to the model formula
  • Habitat change as an HSI difference or 2014-2021 trend, rather than % change between two years
  • Overlap with trawling: significance and a single summary per area and year
  • Heatwaves: compare effort on heatwave days vs other days of the same summers; add heatwave intensity or a bottom temperature anomaly as a covariate of the model

Slides, report and code

MEDITS from JRC; environment from Copernicus Marine; trawling effort from Global Fishing Watch. Figures and numbers from the report rendered 22 Oct 2026, 11:34.