Laurenz Thomsen Jacobs University Bremen GmbH

1. 28.04.22
1
9 international partners
30 international partners
RECOMMENDATIONS FOR ENVIRONMENTAL MONITORING AROUND DEEP-SEA MINING SITES
Discussion on international regulations for monitoring of Deep-Sea Mining industry
Understanding the combination of laboratory experiments with numerical modelling for monitoring aspects
Laurenz Thomsen, Jacobs University Bremen
With Input from:
credit: Diva Am on and Craig Sm ith, Uni Haw aii
1
Ecosystems associated with polymetallic nodules are thought
to recover extremely slowly (decades to centuries) or
potentially not at all, shifting into an alternate regime based on
benthic impact experiments that have simulated some aspects
of mining activities.
Since deep-sea mining has not yet begun, most information on
plume generation is theoretical or based on fine-scale field
experiments
Early papers that modelled the spread of plumes suggested
they may have an impact 100 km away from the mine site in
nodule areas, and this figure was used in the design of buffer
zones around Areas of Particular Environmental Interest in the
Clarion Clipperton Zone (CCZ), to prevent impact in the core
area.
Discussions on international regulations
2
ISA calls for use of the Best Available Scientific Evidence (BASE), Best Available Techniques (BAT) and the
Best Environmental Practices (BEP).
“Best Available Scientific Evidence” means the best scientific information and data accessible and
attainable
“Best Available Techniques” means the latest stage of development, and state-of- the-art processes, of
facilities or of methods of operation.
“Best Environmental Practices” means the application of the most appropriate combination of
environmental control measures and strategies, that will change with time in the light of improved
knowledge.
Discussions on international regulations
3
Manganese nodule claim area max 75,000 km2
Area affected by plume
Area affected by plume
Mined area
Mined area
Mined
area
100
50
0
Km
Manganese nodule claim area max 75,000 km2
Mined area
Mined area
Area affected by plume
Area affected by plume
100
50
0
Km
Mined
area
Area affected by plume
Hypothetical contract area with three areas of mined
deposits. Blue areas represents pristine seabed
unaffected by mining; blue diagonal pattern
represents mined areas; pale orange represents
areas affected by the plume. Upper panel shows an
area that could be impacted by plumes that spread
tens of kilometres from the mined area. Lower panel
shows areas that could be impacted if plume spread
is controlled to just a few kilometres away from the
mining operation.
Weaver et al., 2022
Discussions on international regulations
4

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What plume properties are the most important to
measure?
Are indicator taxa suitable for measuring plume impact?
How can biological tolerances to plumes be determined?
Over what timescales do measurements need to be
taken?
Weaver et al., 2022
Discussions on international regulations
5
6
PERSPECTIVE
https://doi.org/10.1038/s41559-019-1091-z
1
Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy. 2
Stazione Zoologica Anton Dohrn, Naples, Italy.
3
Instituto de Ciencias del Mar (ICM-CSIC), Paseo Marítimo de la Barceloneta, Barcelona, Spain. 4
National Oceanography Centre, Southampton, UK.
5
Department of Sciences and Engineering of Materials, Environment and Urban Planning (SIMAU), Polytechnic University of Marche, Ancona, Italy.
6
IUCN Global Marine and Polar Programme, Gland, Switzerland. 7
School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne,
UK. 8
The Biodiversity Research Group, The School of Biological Sciences, Centre for Biodiversity and Conservation Science, The University of Queensland,
Brisbane, Queensland, Australia. 9
Louisiana Universities Marine Consortium, Chauvin, LA, USA. 10
Center for Marine Biodiversity and Conservation and
Integrative Oceanography Division, Scripps Institution of Oceanography, University of California, La Jolla, CA, USA. 11
Department of Geography, The
Hebrew University of Jerusalem, Jerusalem, Israel. 12
Norwegian Institute for Water Research, Oslo, Norway. 13
Monterey Bay Aquarium Research Institute,
Moss Landing, CA, USA. 14
Department of Oceanography, University of Hawaii at Mano’a, Honolulu, HI, USA. 15
Departments of Ocean Sciences and
Biology, Memorial University of Newfoundland. St, John’s, Newfoundland, Canada. 16
Jacobs University, Bremen, Germany. 17
Division of Marine Science
and Conservation, Nicholas School of the Environment, Duke University, Durham, NC, USA. 18
School of Biological Sciences and Swire Institute of Marine
Science, The University of Hong Kong, Hong Kong SAR, China. *e-mail: r.danovaro@univpm.it
I
ndustrial activities spanning from fisheries to oil and gas extraction
are accelerating anthropogenic pressures on the deep sea1–3
, lead-
ing to the degradation of benthic and pelagic environments, where
biological diversity remains largely unknown (Box 1). However,
global impacts have not spared deep-sea ecosystems4–7
and species
loss and habitat destruction severely alter an increasing portion of
deep-sea ecosystems2,8,9
. Cumulative anthropogenic impacts act
synergistically with climate-induced changes on properties and pro-
cesses of the deep ocean, thus degrading environmental quality10–12
.
Deep-sea biodiversity plays a central role in provisioning ser-
vices (for example, food, biochemical compounds for human health
and wellbeing), and species loss can greatly reduce ecosystem func-
tions that support these services8
. Furthermore, high biodiversity
levels increase ecosystem resilience to perturbations13
, elevating the
importance of maintaining biodiversity as a key management objec-
tive in the pursuit of sustainable use of resources14
.
Sustaining healthy and productive deep oceans requires knowl-
edge of baseline conditions and rates of change in marine ecosys-
tems. The environmental status and resources of the coastal zones
link to deep-sea ecosystems6,15
through bi-directional exchanges of
materials, nutrients, contaminants and organisms16–18
; changes in
one system may therefore impact others. Several ongoing initiatives
consider the need for monitoring baseline conditions in marine
shallow and deep-sea ecosystems and their changes (Box 2).
The Group on Earth Observations Biodiversity Observation
Network (GEO BON) has proposed some Essential Biodiversity
Variables (EBVs), to set up future monitoring programs19
. These
variables are organized into six classes and are general enough to be
applied to terrestrial, freshwater and marine realms20
.
The Global Ocean Observing System (GOOS) has started the
identification of the Essential Ocean Variables (EOVs21
) and has
promoted the Deep Ocean Observation Strategy (DOOS), which
enhances the need for identification of EOVs relevant to the deep-
sea environment22
. However, GOOS EOVs do not include the anal-
ysis of stressors (for example, habitat integrity, pollutants, plastics
and so on), which are clearly needed to assess deep-sea ecosystem
health. Moreover, the monitoring of deep-sea ecosystems for bio-
diversity conservation requires specific variables and technological
Ecological variables for developing a global
deep-ocean monitoring and conservation strategy
Roberto Danovaro! !1,2
*, Emanuela Fanelli1
, Jacopo Aguzzi3
, David Billett4
, Laura Carugati1
,
Cinzia Corinaldesi5
, Antonio Dell’Anno1
, Kristina Gjerde6
, Alan J. Jamieson7
, Salit Kark8
,
Craig McClain9
, Lisa Levin! !10
, Noam Levin11
, Eva Ramirez-Llodra12
, Henry Ruhl4,13
, Craig R. Smith14
,
Paul V. R. Snelgrove15
, Laurenz Thomsen16
, Cindy L. Van Dover17
and Moriaki Yasuhara! !18
The deep sea (>200 m depth) encompasses >95% of the world’s ocean volume and represents the largest and least explored
biome on Earth (<0.0001% of ocean surface), yet is increasingly under threat from multiple direct and indirect anthropogenic
pressures. Our ability to preserve both benthic and pelagic deep-sea ecosystems depends upon effective ecosystem-based
management strategies and monitoring based on widely agreed deep-sea ecological variables. Here, we identify a set of deep-
sea essential ecological variables among five scientific areas of the deep ocean: (1) biodiversity; (2) ecosystem functions;
(3) impacts and risk assessment; (4) climate change, adaptation and evolution; and (5) ecosystem conservation. Conducting
an expert elicitation (1,155 deep-sea scientists consulted and 112 respondents), our analysis indicates a wide consensus
amongst deep-sea experts that monitoring should prioritize large organisms (that is, macro- and megafauna) living in deep
waters and in benthic habitats, whereas monitoring of ecosystem functioning should focus on trophic structure and biomass
production. Habitat degradation and recovery rates are identified as crucial features for monitoring deep-sea ecosystem
health, while global climate change will likely shift bathymetric distributions and cause local extinction in deep-sea species.
Finally, deep-sea conservation efforts should focus primarily on vulnerable marine ecosystems and habitat-forming species.
Deep-sea observation efforts that prioritize these variables will help to support the implementation of effective management
strategies on a global scale.
NATURE ECOLOGY & EVOLUTION | VOL 4 | FEBRUARY 2020 | 181–192 | www.nature.com/natecolevol 181
452 3 FEBRUARY 2017 • VOL 355 ISSUE 6324 sciencemag.org SCIENCE
PHOTO:
MAGE
COURTESY
OF
CHUCK
FISHER,
PENN.
STATE
UNIVERSITY,
AND
TIM
SHANK,
WHOI.
DEEP
SEA
TIME
LAPSE
CAMERA
SYSTEM
BY
WHOI
MISO
By R. Danovaro,1,2
* J. Aguzzi,3
* E. Fanelli,4
*
D. Billett,5
K. Gjerde,6,7
A. Jamieson,8
E. Ramirez-Llodra,9
C. R. Smith,10
P. V. R.
Snelgrove,11
L. Thomsen,12
C. L. Van Dover13
I
ncreasing exploration and industrial ex-
ploitation of the vast and fragile deep-
ocean environment for a wide range of
resources (e.g., oil, gas, fisheries, new
molecules, and soon, minerals) raises
global concerns about potential ecologi-
cal impacts (1–3). Multiple impacts on deep-
sea ecosystems (>200 m below sea level;
~65% of the Earth's surface is covered by
deep ocean) caused by human activities may
act synergistically and span extensive areas.
Cumulative impacts could eventually cause
regime shifts and alter deep-ocean life-sup-
port services, such as the biological pump
or nutrient recycling (2, 4, 5). Although
international law and national legislation
largely ignore the deep sea’s critical role in
the functioning and buffering of planetary
systems, there are promising developments
in support of deep-sea protection at the
United Nations and the International Sea-
bed Authority (ISA). We propose a strategy
that builds from existing infrastructures to
address research and monitoring needs to
inform governments and regulators.
Growing demands for ocean space and
seabed resources have generated a need for
international laws and policies (6) to enable
OCEAN GOVERNANCE
An ecosystem-based deep-ocean strategy
Monitoring and assessment must underpin development of a new international agreement
POLICY FORUM
INSIGHTS
DA_0203PolicyForum.indd 452 2/1/17 10:21 AM
Published by AAAS
Corrected 17 February 2017. See full text.
on
April
6,
2020
http://science.sciencemag.org/
Downloaded
from
Contents lists available at ScienceDirect
Progress in Oceanography
journal homepage: www.elsevier.com/locate/pocean
Visual monitoring of key deep-sea megafauna with an Internet Operated
crawler as a tool for ecological status assessment
Damianos Chatzievangeloua,⁎
, Jacopo Aguzzib
, Andrea Ogstonc
, Alejandro Suárezb
,
Laurenz Thomsena
a
Jacobs University, Bremen 28759, Germany
b
Instituto de Ciencias del Mar (ICM-CSIC), Barcelona 08003, Spain
c
University of Washington, Seattle, WA 98195, USA
A R T I C L E I N F O
Keywords:
Crawler
Barkley Canyon hydrates
Poison modeling
Deep-sea monitoring
Food pulses
Seafloor disturbance
A B S T R A C T
The spatio-temporal distribution of seven abundant morphospecies (i.e. taxa identified based on morphological
traits) and the diversity of the benthic megafaunal community at the Barkley Canyon hydrates site (870 m,
Vancouver Island, BC, Canada) were assessed in 18 linear imaging transects (each transect ~30 m long), con-
ducted with an Internet Operated Deep-sea Crawler in November 2016. Faunal counts (as proxy for local
abundances) were treated as Poisson-behaving variables and were modeled and correlated to habitat quality (i.e.
level of physical seafloor disturbance, as previously induced by the crawler), fluctuating oceanographic condi-
tions (i.e. tides and currents, seasonal transitions and episodic particle fluxes) and temporally and spatially
varying food availability (i.e. elevated chlorophyll levels, proximity to the hydrate mounds as additional energy
sources). The model outcome was used to establish a potential trigger-notification procedure for substrate dis-
turbance monitoring when ratios of faunal abundances exceed the expected range for any morphospecies.
Finally, a quality assessment of the crawler’s performance as a multidisciplinary monitoring platform was
performed using individual and morphospecies accumulation curves, which aids in optimizing and standardizing
data collection protocols. We showed that the abundances and diversity of hydrates megafaunal community
differed in relation to physical seafloor disturbance, proximity to the hydrate mounds and episodic food input, at
varying degrees. Our work sets a solid base for the future real-time, long-term monitoring of ecosystem func-
tioning and health status in deep-sea areas exposed to industrial substrate alterations (i.e. mining, fisheries, etc.).
1. Introduction
The deep sea covers more than half of the planet’s surface and in-
cludes many diverse and extreme environments (Ramírez-Llodra et al.,
2010, 2011; Sutton et al., 2017). In this scenario, science is competing
with industry to explore areas where resource extraction has been
planned in advance (Danovaro et al., 2017; Boetius and Haeckel, 2018).
The ever-altering effects of human activities on the biodiversity of both
well-studied and yet-to-be-explored deep-sea ecosystems (Barnes et al.,
2013) point out the need for the rapid improvement of our knowledge
regarding species presence, abundance, life traits and resulting biodi-
versity in deep benthic areas (Ruhl et al., 2011; Snelgrove et al., 2014;
Van Dover et al., 2017).
Among different deep-sea habitats, the study of submarine canyons
has gained special attention on a scientific, economic and societal level
(Fernández-Arcaya et al., 2017, Matos et al., 2018). By enhancing cross
shelf-break exchange, canyons can act as conduits for sediment, organic
matter, nutrients, energy and fish larvae, while their complex mor-
phology leads to high local and regional spatial heterogeneity of ha-
bitats (Allen and Durrieu de Madron, 2009; De Leo et al., 2010, 2012;
Levin and Sibuet, 2012; Robert et al., 2014; Domke et al., 2017). Pulses
of phytodetritus outside the main spring and summer surface plankton
blooms can be directed towards deeper waters through canyons, con-
tributing to the function of the oceanic biological pump (Thomsen
et al., 2017). These fluxes can provide an additional energy source for
benthic invertebrates and thus modify the local abundances and be-
havior of benthic megafauna (De Leo et al., 2012; Thomsen et al.,
2017), especially in depths within oxygen minimum zones (OMZs) for
heterotrophs tolerant to low oxygen (Domke et al., 2017).
The complexity and extreme nature of canyon ecosystems, along
with the ever increasing societal concern for their management and
protection, directs deep-sea research into a new era of ‘smarter’
https://doi.org/10.1016/j.pocean.2020.102321
Received 3 May 2019; Received in revised form 6 November 2019; Accepted 1 April 2020
⁎
Corresponding author.
E-mail address: d.hatzievangelou@jacobs-university.de (D. Chatzievangelou).
Environmental monitoring
6
Gillard (2020)
Image credit: A. Purser
1. Particle size
2. Aggregation rate
3. Settling velocities
4. Erosion at sediment-water interface
Laboratory
Controlled environment
in situ conditions (PSU, °C)
Understand physical processes
In-situ
Equipment development
Crucial parameters for model
reliability and accuracy
Plume dispersion
7
Hydrodynamic behavior of sediment plume
• A complete hydrodynamic particle dataset from couples of 10 mg/l up 20 g/L sediment
plume was created.
• Simulation in 1000 L water column simulator.
Time 0 Time 20h
8
3 Phases:
• Main aggregation (≈ 20 minutes)
• Export
• Late aggregation
Gillard et al., 2019
• from 20 to 2000 µm
in 20 minutes at 10 g/l
Fast settling Slow settling in fluid mud
8

3. 28.04.22
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• Different nodules types were sampled from the German licence area
• A 16 m length seawater racetrack flume was used
• Topography of nodule’s field before and after simulating settling mining
sediment plume.
• Current velocity field change.
M ain hall at the OceanLab
Quantification of sediment blanketing layer
9
Quantification of sediment blanketing layer
Original sample Blanketed sample Blanketing thickness
• Blanketing thickness depends on:
• Plume concentration and aggregation
• The nodule’s structure (size, roughness, penetration into the BBL)
The blanketing tends to smooth out the bottom roughness created by the nodule’s field
10
10
Numerical simulation of the re-deposition pattern for (A) a flat
seabed and (B) higher release height above the sea floor
Purkiani et al., 2021
Gillard et al: Physical and hydrodynamic properties of deep sea mining-generated, abyssal
sediment plumes in the Clarion Clipperton Fracture Zone (eastern-central Pacific)
Art. 5, page 8 of 14
ing a reliable dataset for the behavior of deep-sea particles
during the discharge of mining plumes.
Analysis of sediment particle size distribution
Attention should be paid to the location site of the mining
activity and the sediments that predominate there. Sedi-
mentological grain sizes in the German nodule license
area in the CCZ showed a multimodal size distribution
pattern. Sediments from the vast abyssal plain environ-
ment generally exhibit a finer grain size (d50
= 20 µm), in
agreement with the particle size spectrum obtained from
several other deep-sea locations (McCave, 1984; Bianchi
and McCave, 2000; Thomsen and Gust, 2000). Coarser
sediments (d50
= 32 µm) were found near seamounts and
ridges. Sediment size distribution in the area is controlled
by the hydrodynamic regime of the respective environ-
ment following the sortable silt theory (McCave et al.,
1995). In a higher current velocity field, such as at the
base of a seamount or ridge (White et al., 2008), sediment
particle selection and sorting result in coarser particle size
distribution over time.
Sampled at the side of a large seamount, the sediment
core 106 MUC depicted the highest median particle size
(d50
= 52 µm; Figure 2). This site was dominated by larger
biogenic deposition (particles > 63 µm) throughout the
entire depth of the sediment core, suggesting that bio-
logically derived material predominantly accumulates at
this site. To date, no record of size-dependent, particulate
organic carbon (POC) content for this study site is availa-
ble. According to Mewes et al. (2014) and Volz et al. (2018),
the total organic carbon (TOC) concentration varies
between 0.2 and 0.6 [wt.%] in the German licensed area in
the CCZ. Taking into account the generally low sedimenta-
tion rate of ca. 5 mm 10–3
year–1
in the CCZ (Khripounoff
et al., 2006), this seamount which rises to a water depth
of 2300 m is unlikely to cause elevated biological produc-
tion at the sea surface but interferes with the deep-sea
currents, producing a wake effect (Roden, 1991; Comeau
et al., 1995). As seamounts and ridges are considered a
hotspot for biodiversity (Samadi et al., 2006; Sautya et al.,
2011), they should remain out of the dispersion path of a
mining plume.
Figure 5: Sediment deposition of plume particles and blanketing of the seafloor. The thickness (mm, color bar
scale) of sediment deposition is shown for three particle size classes (d25
, d50
, and d75
) after 4 days of continuous sedi-
ment discharge (60 t h–1
) 5 m above the seafloor (position 0;0), followed by 1 day of settling at an initial discharge
concentration of 500 mg dw L–1
. The mean current velocity is shown as arrows that correspond to 3.8 cm s–1
in A–C
(low flow; G = 0 s–1
) and 10 cm s–1
in D–F (eddy flow; G = 2.4 s–1
). DOI: https://doi.org/10.1525/elementa.343.f5
Sediment deposition of plume particles and blanketing
of the seafloor. Gillard et al., 2019
Modelling
11 12

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Exemplary workflow for the prediction of extent and direction of plume dispersion for defining the ‘best possible’ deployment
position for autonomous sensor platforms Haalboom et al., 2022
Preparation of mining activities
13
Copyright GSR
14
15 16

5. 28.04.22
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What we learned so far:
As experimentally derived, modelled and measured: the plume remains mostly withing the first
few kilometres around the exploration site
A fine fraction (> 0.1 mg/l) is exported
AI based data analyses from AUV deployments and sensors greatly enhance sustainable
management
Natural restoration of surrounding habitats will be crucial, possibly including carbon enrichment
> 30 manuscripts in prep./review in 2022
18
Lowering OPEX
New technologies
Full ocean depth
3D data acquisition
Automatic species detection
Carbon mineralization rates
Docking at fuel cell
19
References of this talk
Haalboom, S., Schoening, Urban, Gazis, de Stigter, Gillard, Baeye, Hollstein, Purkiani, Reichart, Thomsen, Haeckel, Vink, and Greinert (in press) Monitoring of
anthropogenic sediment plumes in the Clarion-Clipperton Zone, NE equatorial Pacific Ocean. Front. Mar. Sci.
Weaver, P. P. E., Aguzzi, J., Boschen-Rose, R. E., Colaço, A., de Stigter, H., Gollner, S., ... & Thomsen, L. (2022). Assessing plume impacts caused by
polymetallic nodule mining vehicles. Marine Policy, 139, 105011.
Aguzzi, J., Albiez, J., Flögel, S., Godø, O.R., Grimsbø, E., Marini, S., Pfannkuche, O., Rodriguez, E., Thomsen, L., Torkelsen, T. and Valencia, J., (2020). A Flexible
Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring. Sensors, 20(6), p.1614.
Gillard B, Purkiani, Chatzievangelou, Vink, Iversen, Thomsen; Physical and hydrodynamic properties of deep sea mining-generated, abyssal sediment plumes
in the Clarion Clipperton Fracture Zone (eastern-central Pacific). Elementa: Science of the Anthropocene 1 January 2019; 7 5. doi:
https://doi.org/10.1525/elementa.343
Purkiani K, Gillard B, Paul A, Haeckel M, Haalboom S, Greinert J, de Stigter H, Hollstein M, Baeye M, Vink A, Thomsen L and Schulz M (2021) Numerical
Simulation of Deep-Sea Sediment Transport Induced by a Dredge Experiment in the Northeastern Pacific Ocean. Front. Mar. Sci. 8:719463. doi:
10.3389/fmars.2021.719463
Danovaro R., Fanelli E., Aguzzi J3, Carugati L., Corinaldesi C., Dell’Anno A., Gjerde K, Jamieson A.J., Kark S., McClain C., Levin L., Levin N., Ramirez-Llodra E.,
Ruhl H., Smith C.R., Snelgrove P.V.R. Thomsen L., Van Dover C., Yasuhara M. (2020). Ecological variables for developing a global deep-ocean monitoring
and conservation strategy. Nature Ecology & Evolution 04, 181-192
Danovaro, R., Aguzzi, J., Fanelli, E., Billett, D., Gjerde, K., Jamieson, A., Ramirez-Llodra, E., Smith, C.R., Snelgrove, P.V.R., Thomsen, L. and Van Dover, C.L.,
2017. An ecosystem-based deep-ocean strategy. Science, 355(6324), pp.452-454.
Contact: l.thomsen@jacobs-university.de
20