Research ArticleOCEANOGRAPHY

A strategy for the conservation of biodiversity on mid-ocean ridges from deep-sea mining

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Science Advances  04 Jul 2018:
Vol. 4, no. 7, eaar4313
DOI: 10.1126/sciadv.aar4313
  • Fig. 1 Study area and management context.

    The case study area is centered on the ridge axis from the southern boundary of the Portuguese ECS claim to the northern boundary of the UK ECS claim at Ascension Island and extends 500 km to either side of the axis. Two management subunits are proposed here: nMAR and the RTF. Existing French, Polish, and Russian Federation exploration contracts for SMS are from the ISA database (www.isa.org.jm).

  • Fig. 2 Biogeographic context, important areas, and APEI scenarios.

    APEI scenarios were anchored by important areas identified by expert opinion before scenario development began. Important areas include (A) critical transform faults (that is, Vema and Romanche), biogeographic transition zones (that is, the bathyal transition zone in the region of the RTF), and genetic hybrid zones (that is, Broken Spur). Three APEI network scenarios were developed for the nMAR subunit, with core lengths along the ridge axis of (B) 100 km, (C) 200 km, and (D) 300 km; each APEI also has a 50-km buffer on the northern and southern sides of the core zone.

  • Fig. 3 APEI network performance assessment (nMAR management subunit).

    Bottom: Scores for 17 metrics derived to capture performance (5 being the best) of scenarios against the five CBD network criteria (see legend for color code; light shading, 100-km scenario; medium shading, 200-km scenario; dark shading, 300-km model). Table 1 defines the metrics and metric equations. Table S2 shows the raw values and commentary. Dotted line, conservation targets for each score; CC, climate change. Top: Summary scores for each network criterion (calculated by taking the average scenario score of the metrics for a criterion). Scenario core lengths are provided on the x axis.

  • Table 1 Network criteria, conservation targets, and metrics.

    CBD network criteria (bold) including definitions quoted from CBD (29), metrics (italics), conservation targets, and metric equations used in this study, with relevant comments.

    Network criteria
    Metrics
    Definitions and metric equations
    (normalized to 0 to 5 range)
    Conservation targets and comments
    Important areas“[Important Areas are] geographically or oceanographically discrete areas that provide important services to one or more
    species/populations of an ecosystem or to the ecosystem as a whole, compared to other surrounding areas…”
    Major transform faultsAPEI percent coverage/100% × 5.The objective is to protect 100% of important areas. Scores are based on
    percent area conserved (for transition zones), percent by number of
    features conserved (for hybrid zones), and percent of length conserved
    (for transform faults).
    Biogeographic transition zones
    Genetic hybrid zones
    Representativity“Representativity is captured in a network when it consists of areas representing the different biogeographical subdivisions of
    the global oceans and regional seas that reasonably reflect the full range of ecosystems, including the biotic and habitat
    diversity of those marine ecosystems.”
    Discrete habitat variables:
    Spreading ridge
    Active vents
    Inactive vents
    Fracture zones
    Seamounts
    APEI percent coverage/50% × 5,
    where any score greater
    than 5 was set to 5.
    The objective is to protect a representative amount (30 to 50%) of key
    habitat within the study region. Scores are based on percent area
    conserved (for spreading ridges), percent by number of features
    conserved (for active and inactive vents, and seamounts), and by
    percent of length conserved (for transform faults).
    Note: Active hydrothermal vents and other vulnerable marine ecosystems
    are at risk of serious harm from SMS mining activities. We expect 100%
    of active hydrothermal vent ecosystems and other habitats at risk of
    serious harm to be protected through conservation measures,
    including, but not limited, to APEIs.
    Continuous variables that
    describe the regional
    seascape:
    Slopes
    Depth
    Seafloor POC flux
    5 − (RMSE × 5)The objective is to mimic the distribution of variables determined to be
    key drivers of biodiversity in proportion to their occurrence in the
    management subunit. Root mean square error (RMSE) was calculated
    as the difference between cumulative frequency distributions within
    the APEI scenario and the study region. All variables were classified
    into 10 to 15 bins to remove the effect of the number of bins on RMSE.
    Connectivity“Connectivity in the design of a network allows for linkages whereby protected sites benefit from larval and/or species
    exchanges, and functional linkages from other network sites. In a connected network individual sites benefit one another.”
    Regional connectivity6 − (max distance between cores/75th percentile
    median dispersal distance), where any score
    greater than 5 was set to 5.
    The objective is to ensure that there is no major disruption to dispersal
    across the network of APEIs. The maximum distance between APEIs
    compared to median faunal dispersal distances is an indicator of the
    potential for disrupting dispersal within the entire management
    subunit.
    Network population
    persistence
    6 − mean gap ratio (that is, the mean distance
    between cores/mean core length),
    where any score greater than 5 was set to 5.
    The objective is to promote the viability of populations by self-seeding
    within APEIs and/or dispersal between APEIs. By minimizing the
    difference in length of APEI core areas versus distance between core
    areas, species that on average disperse beyond the APEI have a good
    chance of being able to disperse to adjacent APEIs. Minimizing this
    “gap ratio” should enhance persistence of species across the network,
    as well as within individual APEIs, and increase resilience across the
    network to localized disturbances.
    Replication“Replication of ecological features means that more than one site shall contain examples of a given feature in the given
    biogeographic area. The term “features” means “species, habitats and ecological processes” that naturally occur in the given
    biogeographic area.”
    ReplicationNumber of APEIs where any score greater
    than 5 was set to 5.
    The objective is to have three to five replicate APEIs within a
    management unit, to decrease the likelihood of local catastrophes
    causing systemic biodiversity loss.
    Viability and adequacy“Adequate and viable sites indicate that all sites within a network should have size and protection sufficient to ensure the
    ecological viability and integrity of the feature(s) for which they were selected.”
    Total area(APEI percent coverage/50%) × 5, where
    any score greater than 5 was set to 5.
    The objective is to conserve an adequate portion (30 to 50%) of the
    management unit to ensure the viability of populations within it. Total
    area conserved is a proxy for overall adequacy of a network. The total
    area metric was calculated similarly to the habitat representativity
    metrics above.
    Within APEI persistence5 × (APEI core length/200 km),
    where any score greater than
    5 was set to 5.
    The objective is to ensure that APEIs are large enough to maintain
    minimum viable populations, and metapopulations, within a single
    APEI. The larger the APEI, the greater the probability self-recruitment
    within the APEI, and the lower the percentage of larval export from
    the APEI, which should enhance the persistence of populations,
    metapopulations, and communities within an APEI. 200 km was used
    as the minimum scale required to encompass two times the median
    dispersal distance of 75% of deep-sea fauna with known dispersal
    scales (53).
    Climate Change:
    Absolute similarity
    5 − (RMSE × 5)The objective is to conserve areas where climate impacts would be
    minimized. The more close distributions of key climate variables (pH,
    temperature, dissolved O2 concentrations, and seafloor POC flux) in the
    future (that is, 2100) APEI cores mimic the current (that is, 2013)
    distribution in the management unit, the less impact is expected. RMSE
    was calculated as the difference between cumulative frequency
    distributions within the APEI scenario and the study region. All
    variables were classified into 10 to 15 bins to remove the effect of the
    number of bins on RMSE.
    Climate change:
    Relative local change
    (APEI percent coverage/50%) × 5,
    where any score greater than
    5 was set to 5.
    The objective is to conserve 30 to 50% of the areas projected to be least
    affected by climate change. Least affected cells were defined as the
    10% of cells with the lowest percent change between current (2013)
    and predicted (2100) values of the four key climate variables (pH,
    temperature, dissolved O2 concentrations, and POC flux to the
    seafloor). The percent of those cells falling in APEI cores for each
    scenario was calculated following the approach used for
    representativity metrics (continuously distributed variables).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/7/eaar4313/DC1

    Table S1. Surrogate parameters related to biodiversity and deep-sea ecosystem structure and function and examples.

    Table S2. Raw values and performance metric scores.

    Table S3. Climate change metric results.

    References (97127)

  • Supplementary Materials

  • This PDF file includes:
    • Table S1. Surrogate parameters related to biodiversity and deep-sea ecosystem structure and function and examples.
    • Table S2. Raw values and performance metric scores.
    • Table S3. Climate change metric results.
    • References ( 97127)

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