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Diversification insulates fisher catch and revenue in heavily exploited tropical fisheries

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Science Advances  21 Feb 2020:
Vol. 6, no. 8, eaaz0587
DOI: 10.1126/sciadv.aaz0587
  • Fig. 1 Catch-per-unit-effort (kilograms per day) per fishing trip from 1990 to 2016.

    (A) Total catches, where line is predicted generalized additive model (GAM) temporal smooth excluding seasonal, oceanographic, and vessel effects (±2 SEM), overlaid with points showing the partial effect of all other model covariates. (B) Catches by species group, where species are ordered by the relative contribution to overall catches. Lines are GAM temporal smooth predictions excluding seasonal, oceanographic and vessel effects, colored according to the percent change in CPUE relative to its maximum value (see fig. S1 for SEM of species-level trends).

  • Fig. 2 Catch diversification and vessel capacity effects on mean CPUE from 2006 to 2016.

    (A to D) Selected fishing capacity covariates with moderate or strong effects on CPUE. Lines are the median effect from 1000 posterior samples excluding the effect of all other model covariates (fixed and random), with 50% (dark) and 95% (light) credible interval (CI), overlaid with partial effect points accounting for all other model covariates. Insets are histograms of the observed data for each covariate. HP, horsepowers. (E) Fixed covariate effect sizes, showing posterior median and 95% (thin) and 50% (thick) CI.

  • Fig. 3 Diversification effects on CPUE.

    (A) Effect of catch diversity for the focal fleet over 2006–2015. (B) Effect of catch diversity on temporal catch trends for the full fleet over 1990–2016, predicted for high (90% quantile of observed diversity values; purple) and low (10% quantile; green) catch diversity. Catch diversity is the Simpson index of each fishing trip averaged across each year. Lines are the median effect from 1000 posterior samples excluding the effect of all other model covariates (fixed and random), with 50% (dark) and 95% (light) CI, overlaid with partial effect points accounting for all other model covariates. Standardized effect sizes (±95% CI) for the full fleet model are inset in (B).

  • Fig. 4 Effect of catch diversity on fishing revenue.

    (A) Change in daily fishing revenue (USD per fisher per day) across the range of observed catch diversity values, with fitted line (median) shaded with 50% (dark) and 95% (light) CI. (B) Standardized parameter coefficients indicating catch diversification effect on revenue per fisher and per kilogram (posterior median, 50 and 95% CI). (C) Change in daily fishing revenue corrected for catch weight (USD per kilogram per fisher per day) in each year, with posterior median (±50 and 95% CI) of slope coefficients. Diversification relationships with slopes that do not overlap 0 are colored red (see fig. S7 for year effects on daily fishing revenue). (D) Market value of each target group for 2008–2016 (USD per kilogram). Years with strong effects in (B) are colored red on x-axis labels (see fig. S8 for intra-annual variation in values).

  • Table 1 Covariates for predictive models fitted to focal fleet datasets (2006–2015).

    CAS, catch assessment surveys; SFA, Seychelles Fishing Authority vessel database collected in 2017; VMS, vessel monitoring data. Scale indicates the resolution of data collection. CAS covariates were averaged across surveys to give annual estimates.

    CovariateDefinitionScaleSourceVessel trait
    Fishing strategy
    Catch diversitySimpson’s index of catch
    diversity
    Individual fishing tripCASAbility to target multiple
    species buffers catches to
    declines in single species
    groups (within fishing trips)
    Catch compositionBray-Curtis index of catch
    dissimilarity
    AnnualCASAbility to switch target
    species buffers catches to
    declines in single species
    groups (among fishing trips)
    Vessel capacity
    LongitudeLongitude of fishing activityAnnualVMSLocation of fishing grounds
    LatitudeLatitude of fishing activityAnnualVMSLocation of fishing grounds
    Fishing ground areaArea of fishing activity
    (square kilometers)
    AnnualVMSAbility to explore large and/
    or distant fishing grounds
    where fish populations may
    be less exploited or access
    areas of seasonal abundance
    (e.g., fish aggregations)
    buffers catches to local
    declines
    Boat sizeLength of boat (meters)SFAInfluences fishing trip
    duration, fishing ground
    area, capacity to store fish,
    size of crew, impact of sea
    condition, amount of gear;
    enables vessels to target
    distant fisheries
    Engine powerEngine size (horsepower)SFADetermines time spent
    fishing versus traveling to
    fishing grounds
    FuelFuel consumed (metric tons
    and USD)
    Individual fishing tripCASFinancial capacity for distant
    and/or multiday fishing trips
    that may provide larger
    catches
    Fishing trip durationDays spent at seaIndividual fishing tripCASLonger fishing trips may
    enable fishers to reach
    distant and less-exploited
    fishing grounds, increasing
    catch rates

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/8/eaaz0587/DC1

    Fig. S1. Change in CPUE from 1990 to 2016 for eight species groups.

    Fig. S2. Total landed weight from offshore fisheries over 2000–2016.

    Fig. S3. Fishing grounds of the focal fleet (2006–2015) and effort of the full fleet (1992–2016).

    Fig. S4. Vessel catch trends for the 41 vessels in the focal fleet over 2005–2016.

    Fig. S5. Catch diversification from 1990–2016 for full and focal fleets.

    Fig. S6. Catch diversification according to boat length.

    Fig. S7. Effect of catch diversity on daily fishing revenue (USD per fisher per day) across years.

    Fig. S8. Market value of each target group for 2008–2016 (USD per kilogram).

    Fig. S9. Spatial movement and fishing grounds of focal fleet from 2006 to 2015.

    Fig. S10. Correlation between CPUE metrics.

    Table S1. Species caught in Seychelles’ offshore small-scale fisheries, categorized into groups of closely related species or species that share similar habitats (i.e., the fisheries analyzed).

    Table S2. Prior distributions for Bayesian models.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Change in CPUE from 1990 to 2016 for eight species groups.
    • Fig. S2. Total landed weight from offshore fisheries over 2000–2016.
    • Fig. S3. Fishing grounds of the focal fleet (2006–2015) and effort of the full fleet (1992–2016).
    • Fig. S4. Vessel catch trends for the 41 vessels in the focal fleet over 2005–2016.
    • Fig. S5.Catch diversification from 1990–2016 for full and focal fleets.
    • Fig. S6. Catch diversification according to boat length.
    • Fig. S7. Effect of catch diversity on daily fishing revenue (USD per fisher per day) across years.
    • Fig. S8. Market value of each target group for 2008–2016 (USD per kilogram).
    • Fig. S9. Spatial movement and fishing grounds of focal fleet from 2006 to 2015.
    • Fig. S10. Correlation between CPUE metrics.
    • Table S1. Species caught in Seychelles’ offshore small-scale fisheries, categorized into groups of closely related species or species that share similar habitats (i.e., the fisheries analyzed).
    • Table S2. Prior distributions for Bayesian models.

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