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Oceanography and
Marine Biology
An Annual Review
Volume 58
Edited by
S. J. Hawkins, A. L. Allcock, A. E. Bates, A. J. Evans, L. B. Firth,
C. D. McQuaid, B. D. Russell, I. P. Smith, S. E. Swearer, P. A. Todd
First edition published 2021
ISBN: 978-0-367-36794-7 (hbk)
ISBN: 978-0-429-35149-5 (ebk)
Chapter 2
Towards an Optimal Design for Ecosystem-Level Ocean
Observatories
Rodney A. Rountree, Jacopo Aguzzi, Simone Marini, Emanuela Fanelli,
Fabio C. De Leo, Joaquin Del Rio & Francis Juanes
(CC BY-NC-ND 4.0)
79
Oceanography and Marine Biology: An Annual Review, 2020, 58, 79 –106
© S. J. Hawkins, A. L. Allcock, A. E. Bates, A. J. Evans, L. B. Firth, C. D. McQuaid, B. D. Russell,
I. P. Smith, S. E. Swearer, P.A. Todd, Editors
Taylor & Francis
TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-
LEVEL OCEAN OBSERVATORIES
RODN EY A. ROU NTREE1,2, JACOPO AGUZZ I3, SIMONE MARINI4, EMANUELA
FANE LLI 5, FABIO C. DE LEO6,2 , JOAQUIN DEL RIO7 & FRANCIS JUANES2
1The Fish Listener, 23 Joshua Lane, Waquoit, Massachusetts, USA
2Department of Biology, University of Victoria, Victoria, Canada
3Instituto de Ciencias del Mar (ICM-CSIC), Barcelona, Spain
4National Research Council of Italy (CNR), Institute of Marine Sciences, La Spezia, Italy
5Department of Life and Environmental Sciences,
PolytechnicUniversity of Marche, Ancona, Italy
6Ocean Networks Canada, University of Victoria, Victoria, Canada
7OBSEA, SARTI, Universitat Politecnica de Catalunya (UPC), Barcelona, Spain
Abstract Four operational factors, together with high development cost, currently limit the use of
ocean observatories in ecological and sheries applications: 1) limited spatial coverage, 2) limited
integration of multiple types of technologies, 3) limitations in the experimental design for in situ
studies, and 4) potential unpredicted bias in monitoring outcomes due to the infrastructure’s presence
and functioning footprint. To address these limitations, we propose a novel concept of a standardised
‘ecosystem observatory module’ structure composed of a central node and three tethered satellite
pods together with permanent mobile platforms. The module would be designed with a rigid spatial
conguration to optimise overlap among multiple observation technologies, each providing 360°
coverage of a cylindrical or hemi-spherical volume around the module, including permanent stereo
video cameras, acoustic imaging sonar cameras, horizontal multibeam echosounders, and a passive
acoustic array. The incorporation of multiple integrated observation technologies would enable
unprecedented quantication of macrofaunal composition, abundance, and density surrounding the
module, as well as the ability to track the movements of individual shes and macroinvertebrates. Such
a standardised modular design would allow for the hierarchical spatial connection of observatory
modules into local module clusters and larger geographic module networks providing synoptic
data within and across linked ecosystems suitable for sheries and ecosystem-level monitoring on
multiple scales.
Keywords: Ocean observatories, Ocean technology, Ecological monitoring, Networks, Coenoclines,
Deep-sea, Behaviour, Optoacoustic technologies, Passive acoustic, Fish sounds, Cyber interfaces
Introduction
Ocean observatories have become important resources for oceanographic observations around the
world and consist of networks of instruments primarily designed to collect data on oceanographic
and geophysical conditions in real time over long durations (Tunnicliffe etal. 2003, Schoeld
& Glenn 2004, Aguzzi etal. 2012, Gould etal. 2013). However, increasingly, observatories are
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RODNEY A. ROUNTREE ET AL.
becoming useful tools for biologists interested in animal behaviour, ecosystem ecology, and sheries
applications (ACT 2007, Aguzzi etal. 2011b, 2015a, Barns etal. 2013). Four operational factors,
besides development costs, limit applicability of existing ocean observations systems for use as
tools in sheries and ecosystem level applications: 1) limited spatial coverage, 2) limited integration
of multiple types of technologies (i.e. multiple modalities of observation), 3) limitations in the
experimental design for in situ studies, and 4) potential unpredicted bias in monitoring outcomes
due to the infrastructure’s presence and functioning footprint. These limitations have slowed the
spread of ocean observatory use for sheries and other ecological applications (e.g. benthopelagic
connectivity), highlighting the need for efforts to improve observatory design (e.g. Handegard etal.
2013, Locascio etal. 2018).
The objective of this review is to show how ocean observatories, combined with other
observational sampling technologies, can be better designed from sheries and ecology perspectives
for the monitoring of marine ecosystems and their connectivity through coenoclines (i.e. gradients
of communities) formed along depth, latitude, and geographic gradients. What is unique about
the suggested approach is that systems would be designed from the beginning for ecosystem-level
observations on large spatial and temporal scales and would be replicated in many locations for
global coverage. In order to meet these objectives, observatories need to be highly standardised
and produce quantitative observations that are comparable among locations and over time. The
present review presents a concept of standardised modular platform design that is intended to
stimulate discussion and renement within the scientic community. The ecosystem observatory
module (EOM) concept (hereafter simply referred to as the ‘module’) consists of a central node
and three tethered satellite pods (hereafter referred to as ‘satellites’). A modular design means that
the platforms should be designed so that they can be prefabricated, and therefore produced at lower
cost, but be exible enough to allow customisation and implementation in different habitats. Such
a design serves two purposes: rst, it provides directly comparable data among different locations,
and second, it will encourage wider implementation of observatories around the globe. Most of
the instrumentation proposed for each module has already been developed and implemented in
some existing cabled observatories, though signicant improvements in capabilities and reduction
in cost are needed (see review in Aguzzi etal. 2019). In addition, much of the software needed to
realise large-scale observatory networks that are useful to sheries scientists, resource managers, and
ecologists are still in the early stages of development (Allken etal. 2018, Juanes 2018, Marini etal.
2018a,b). Therefore, the development of data delivery systems that are accessible to a wide range of
stakeholders from different disciplines and backgrounds is of vital importance for the effective use
of ocean observatories for sheries and ecological applications (Pearlman etal. 2019). It is therefore
important to design data packaging and delivery systems in concert with the observatory structural
and instrumentation design, rather than as an afterthought.
The implementation of permanent monitoring systems should deliver data on animal movement
across habitat gradients (Aguzzi etal. 2015a) and energy ux interchange (Thomsen etal. 2017),
providing measures of ecosystem functioning (Aguzzi etal. 2019). Time series of visual counts
for different species by different monitoring modules and their satellites of a network may provide
spatially meaningful representations of a population’s abundance uctuations when data are summed
(i.e. scaled) together (Aguzzi et al. 2019). Spatiotemporal variations in population abundances
could then be used to track the status of ecosystem services such as sheries resources. Major
requirements for ecosystem-level ocean observatory networks include 1) spatial quantication of
organism abundance, density, and biomass through cross-referencing of data obtained from multiple
observation technologies; 2) quantication of the impact of the observatory structure and operation
of its instruments on the local biota; 3) a design for use of observatories as in situ laboratories; 4)
spatial clustering of observatories/devices to optimise observation on multiple spatial scales over
appropriate coenoclines; 5) integration of ocean observatory data with observational data collected
through other sampling methodologies (e.g. ship, satellite, drifter, and buoy-based surveys and
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TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
animal-borne devices); 6) automatic data processing, such as detection of sh images or sounds to
enhance data analysis by end-users; and 7) seamless presentation of multiple data streams to end-
users that are synchronised in time across all instruments within a module and ultimately across all
module locations.
This review starts with a brief summary of ocean habitat connectivity to provide context, followed
by a description of a proposed ecosystem observatory system, including modules, components, and
ways of combining modules into clusters and networks to allow monitoring along habitat gradients
and coenoclines. A description of monitoring modelling and forecasting of observatory data is
followed by an explanation of how observatories could be integrated with animal-borne technologies
and the cyber developments needed to support monitoring networks accessible to users of different
backgrounds. The nal section is a rationale for the incorporation of observatory systems into
commercial developments such as windfarms and oil platforms (e.g. Fujii & Jamieson 2016) to serve
as partial mitigation for potential ecosystem impacts by extending marine ecosystem monitoring
capability beyond what would otherwise be nancially feasible.
Background: Ecosystem connectivity
Current ocean observatories have limited applicability to sheries and ecosystem monitoring, in
part because marine habitats exhibit complex linkages that operate on many different scales. A
brief summary of ocean ecosystem connectivity helps to provide context for the rationale for highly
standardised observatories that are organised in hierarchical spatial congurations to enhance
quantication of ecosystem attributes along habitat gradients or coenoclines.
Researchers have long known that marine ecosystems are intricately linked through passive and
active mechanisms for matter and energy transference. For example, estuaries serve as an important
direct and indirect source of nutrients for coastal marine waters and thereby help to sustain coastal
and deep-water sheries (e.g. Teal 1962, Haines 1979, Nixon 1980, Odum 1980, Pomeroy & Wiegert
1981, Dame etal. 1986). Passive processes involve bi-directional uxes of nutrients, pollutants,
and plankton carried by water movements such as runoff, river ow, tides, up- and down-welling,
storm events, and dense shelf-water cascading, all acting along a habitat gradient from freshwater to
coastal areas and to the deep sea (Figure 1; Canals etal. 2006, Afonso etal. 2014, Puig etal. 2014,
Rogers 2015, Thomsen etal. 2017). Active processes also contribute to energy/matter transference
in the form of rhythmic and arrhythmic population movements across seabed and water column
depth gradients, such as diel vertical migrations (DVMs), which represent the largest natural daily
movement of biomass on the planet (e.g. Graeme etal. 2010, Doya etal. 2014, Aguzzi etal. 2015b,
De Leo etal. 2018; Figures 1 and 2).
Mechanisms that regulate nekton distribution and movements along bathymetric and latitudinal
coenoclines are similar and involve interactions between environmental (e.g. temperature gradients
and cyclic uctuations) and biological conditions (e.g. food and shelter availability and predation
risk) (see reviews in Rountree 1992, Deegan etal. 2000, Rountree & Able 2007, Aguzzi & Company
2010, Aguzzi et al. 2011a). Horizontal linkages have been referred to as the ‘chain-of-migration’
(Rountree 1992, Deegan etal. 2000, Rountree & Able 2007), while vertical migrations have been
referred to as the ‘ladder-of-migration’ (Vinogradov 1953, 1955, 1971). Mechanisms for linkages
along a depth coenocline from the photic to dysphotic pelagic zones and the deep-sea benthos include
‘organic rain’ (Vinogradov 1971, McCave 1975, Honjo 1980, Alldredge & Silver 1988, Thomsen
etal. 2017), ontogenetic (i.e. with size or life-stage) vertical migration of organisms (e.g. Merrett
1978, Wakeeld & Smith 1990, Kobari etal. 2008, De Leo etal. 2018), and cyclic vertical migrations
such as observed in the deep scattering layers (DSLs, Vinogradov 1953, Marshall 1971, Longhurst
1976, Mauchline 1980, Aguzzi & Company 2010, Naylor 2010, Aguzzi etal. 2017). In particular,
rhythmic depth strata movements similar to DVMs also occur within the benthic boundary layer
across shelves and slopes by endobenthic burrowing organisms (Aguzzi & Company 2010). Indirect
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RODNEY A. ROUNTREE ET AL.
Figure 1 Exa mple of some major linkages among habitats dist ributed along horizonta l and vertical coenoclines
connecting terrestrial to deep-sea ecosystems along a large river system. Major linkages are provided by a
chain-of-migration connecting habitats horizontally, while a ladder-of-migration connects vertical habitats
through ontogenetic and cyclical movements of organisms (see Figure 2). Other mechanisms of linkage include:
(A) run-off from land to sea; (B) nutrient, detritus, and organism ‘outwelling’ and corresponding ‘inwelling’;
and (C) upwelling/downwelling occur largely due to water movements such as tides and storms; (D) deposition
occurs where water velocity slows to allow precipitation of suspended materials and entrapment and mortality
of organisms, as well as faecal deposition of migrating organism; and, nally, (E) organic and inorganic rain.
Figure 2 Example of mechanisms of energetic linkages among adjacent habitats or ecosystems through the
distribution and movements of organisms. Major mechanisms include diffusion, ontogenetic migration, and
chain-of-migration. Diffusion results from trophic transfer of energy among overlapping assemblages and
is poorly understood. Ontogenetic migration results from movements of organisms among habitats as they
grow and can be size, environmental condition (such as temperature), or seasonally mediated. The chain-of-
migration (and analogous ladder-of-migration) results from rhythmic movements of organisms among habitats
on seasonal, lunar, diel, or tidal cycles. The smallest links in the chain are between adjacent habitats, but
links from direct movements of organisms can occur on any spatial scale among habitats located along the
same coenocline. Major mechanisms of energy transfer include predator–prey interactions, spawning, faecal
deposition, and local mortality.
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TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
day-night synchronisation of biological activity in deep-sea aphotic realms may also occur due
to the movements of deep-scattering layer organisms (e.g. Irigoien etal. 2014). These rhythmic
movements may also be accompanied by changes in background illumination at the seabed, when
species making up the scattering layers are bioluminescent (i.e. bioluminescence panoramas; Aguzzi
etal. 2017).
All these types of ontogenetic and rhythmic (e.g. diel and seasonal) movements produce energy
uxes that affect the functioning of ecosystems connected through a coenocline (Rountree & Able
2007, Aguzzi etal. 2011a; Figures 1 and 2), which are difcult to quantify with isolated ocean
observatories. Accordingly, any technological development dedicated to ecosystem exploration,
monitoring, and ultimately management (sensu Danovaro etal. 2017) should be planned by combining
Lagrangian sampling strategies (i.e. capable of tracking individuals and population movements)
as well as Eulerian approaches (i.e. a ‘snapshot’ capable of characterising locally the community
changes produced by species displacements). For the former strategy, large-scale movements of
animals are being studied through telemetry via satellite (Hussey etal. 2015). Nevertheless, only a
few environmental parameters (e.g. depth and salinity) and no other ecological features (e.g. species
interactions) are measured as explanatory factors of behaviour. For the latter strategy, a virtually
holistic environmental monitoring approach is possible, but typically at a ne scale, which can be
difcult to scale up to larger systems (Aguzzi etal. 2019). Accordingly, a merger of both strategies
would be possible by the establishment of networks of monitoring stations that allow animal and
population tracking at a high rate in a simultaneous fashion across large geographic scales and across
latitudinal and depth gradients.
In this context, sheries scientists have recognised the need to move from single species to
ecosystem-based management approaches, but progress has been slow due to the complexity of
coenoclines and the difculty of obtaining synoptic data on appropriate scales (e.g. Marshall etal.
2018). Fishery management agencies can simultaneously advocate tracking and quantifying stocks
as a monitoring action required to inform management measures and implement no-take zones
(Maxwell etal. 2015). This point is crucial, as many essential sh habitats (EFHs, e.g. spawning or
nursery areas) are not permanent; thus, the establishment of shery restricted areas (FAO 2018) or
other spatial management measures for sh and habitat protection could follow an adaptive approach
(Walters 2007). Such a spatially dynamic approach will require different pathways for technological
development in species and ecosystem monitoring. Such an approach is currently being pursued in
the development of a cross-communication capability between cabled observatories and animal-
borne technologies (e.g. hydrophones for acoustic tag recognition; Hussey etal. 2015).
Marine strategic areas are dened as ecologically iconic zones where multiannual surveying, as
carried out by vessel-oriented technologies, is strongly recommended for scientic or management
purposes (Aguzzi etal. 2019). Data on species demographic indicators (e.g. density, size and biomass),
community composition (i.e. richness), and the effects of environmental controls on biodiversity
obtained in this way for one iconic zone could be scaled to other areas with similar geomorphologic
and oceanographic features as similar seascapes (Danovaro etal. 2017). Relevant areas have been and
continue to be instrumented with different types of pelagic and benthic multiparametric platforms
as part of observational networks (Tunnicliffe etal. 2003, Barnes etal. 2013), providing different
levels of monitoring capability and manipulative interventions (e.g. ONC 2019, OOI 2019). However,
such large networks could be improved by the development of the ecosystem observatory module
design, with its increased focus on obtaining temporally and spatially overlapping data from multiple
observation technologies.
Observatories are invasive technologies that produce noise, lighting, and motions that can be
foreign to the habitat under study. In addition, it is important that observatories be designed to better
understand their invasive impact to comply with international legislation (e.g. underwater noise
as an ecological descriptor; Audoly etal. 2016, 2017). Therefore, there is a need for observatories
to have built-in capabilities to monitor their own effect on the surrounding habitat and biota. The
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EOM design seeks to provide self-monitoring capabilities for two reasons: 1) measurement bias and
2) degree of impact by the structures’ presence and functioning on the local environment (typical
sizes of the main components of observatory systems are likely to be around 3–5 m on each side and
2–4 m in height). Since any observatory will function as an articial reef and thereby modify the
local habitat characteristics that are being measured (Vardaro etal. 2007, Blanco etal. 2013), more
attention is needed to understand the attraction, repulsion, and residency effects of the structures
and their operations (e.g. pan-tilt camera motor noise, mobile platform noise, and illumination at
imaging) on sessile and motile species and their interactions with each other (e.g. the establishment
of fouling communities on the structure could inuence the local trophic structure). Over time, such
developments can result in enough changes that the observatory data will no longer reect the habitat
that it was designed to observe.
The ecosystem observatory module
A conceptual schematic of a proposed ecosystem observatory module and its components is provided
in Figure 3, and the function of each sensor and component device is outlined in Table 1. Standard
components of each module would include: 1) central node and associated instruments, 2) mobile
platforms, 3) three satellite pods, 4) a passive acoustic array, 5) a spatial conguration and software
to optimise cross-referencing among observational data, and 6) autonomous instruments. Optionally,
some modules would be enhanced with the addition of a pelagic satellite to collect data on sea-
surface and water-column organisms and conditions.
The central node and its instruments
The central node serves as the primary instrumentation platform, power supply, and data link for
the module. It also houses dockage, data transfer links, and power supply for three types of mobile
Figure 3 Schematic illustration of the proposed standard ecosystem observatory module consisting of
a central node, three satellites, AUV, ROV and crawler mobile platforms and their dockage, and various
autonomous devices. Hydrophones on the central node and each satellite form a 3-dimensional passive acoustic
array. Crawlers would operate on predetermined tracks lines to reduce their impact on the substrate.
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TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
Table 1 Optoacoustic-image and passive acoustic sensors installed on the standard cabled
ecosystem observatory module and its associated mobile docked platforms
Components Instruments Purpose
Central node Multiple Module power supply, data deposition and transmission, instrument
platform, and mobile dockage platform.
Hydrophones Passive acoustic monitoring, including recording of environmental noise,
system noise, and biological sounds over the biologically relevant
frequency range of 1 Hz to 150 kHz. Component of the module’s
3-dimensional passive acoustic array for sound source location;
cross-reference with video, acoustic imaging sonar, and echosounder for
species identication, tracking, and target strength quantication.
Stereo-video cameras Video recording of conditions and organisms over 360° around the central
node to determine the size and spatial location of individual organisms.
Use for identifying or conrming the source of sounds, targets in the
acoustic image, and bioacoustic echosounder targets.
Pan-tilt HD cameras User-controlled video cameras with pan-tilt control, zoom capability, and
lighting control, for use in investigating selected eld of view areas,
infrastructure elements, and to zoom in on selected passive acoustic,
acoustic image, and echosounder targets for identication and
behavioural observations.
Acoustic imaging
sonar cameras
Recording the presence and movements of animals in a 360° cylindrical
volume surrounding the central node during all visibility conditions;
cross-reference with passive acoustic array source location, stereo camera
location, pan-tilt cameras, and echosounder targets for species
identication, tracking, and target strength quantication.
Rotary horizontal
multibeam
echosounder
Bioacoustic echosounder to quantify distribution of organisms in the water
column within a 360° zone surrounding the central node and extending
outward for a radius of 100–800 m. Cross-reference with passive acoustic
array source location, stereo camera localisation, and pan-tilt cameras,
for species identication, tracking, and target strength quantication.
Environmental sensor
package
Continuous recording of habitat variables, for example, pressure,
temperature, salinity, current speed and direction, methane, oxygen,
nitrates, pH, chlorophyll, and turbidity.
Acoustic and optic
receivers and
transponders
Acoustic receivers for animal- and instrument-borne telemetry signals.
Also including receivers for acoustic modem-based or optical
communication and data transmission. In some cases, transponders can
be used for two-way communication with animal- and instrument-borne
devices.
Crawler and dockage Placing and servicing autonomous devices and satellite experimental
payloads; conduct physical and biological sampling in the area
surrounding central node along xed and predetermined tracks.
ROV and dockage Central node servicing; place and service autonomous devices and satellite
experimental payloads, conduct physical and biological sampling in area
surrounding node, conduct video transect surveys, document fouling
organism and species associations with infrastructure, investigate
unknown targets detected by observation technologies.
AUV and dockage Conduct benthic habitat and biota distribution mapping transects around
the central node and throughout area between modules within an
observatory cluster. Investigate unknown echosounder targets beyond the
range of the crawler and ROV and of video and acoustic imaging sonar
ranges.
(Continued)
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RODNEY A. ROUNTREE ET AL.
platforms (Figure 3, Table 1). Standard observation instruments on the central node would include
stereo video cameras, acoustic imaging sonar cameras (e.g. dual-frequency identication sonar:
DIDSON), and bioacoustic echosounders, as well as a passive acoustic system capable of recording
sounds over a biologically relevant bandwidth (1 Hz to 150 kHz). In order for these systems to
provide observations useful for ecosystem-level monitoring, they must provide spatially and
temporally quantiable data. For example, pan-tilt high-denition cameras that are often standard
on observatories are not conducive to the collection of occurrence data on even a presence/absence
level because the direction, depth, and angle of the eld of view are constantly changing and hence
the absence of organisms cannot be determined.
To achieve the desired quantication, the module should be designed so that each technology
provides 3-dimensional data over 360° around the module and overlaps with others to the
maximum degree possible (Figure 4). However, each device will have different ranges, beam
angles, and time resolutions which must be integrated to provide seamless views to the end-
user (see section on cyber developments subsequently). Comparison of data from the overlapping
3-dimensional views provides the ability to cross-reference data to improve identication and
measurement accuracy (Figure 4). Stereo video cameras should be used to obtain the 360° view
around the central node because they also provide 3-dimensional location and organism size
data (Bosch etal. 2019). Although we are not aware of previous stereo video camera applications
on existing observatories, they have been widely used in sheries and ecological applications,
including deep-sea applications (e.g. Harvey & Shortis 1998, Shortis etal. 2008, Williams etal.
2010, 2018, Bonin etal. 2011, Merritt etal. 2011, Shortis & Abdo 2016). It is important that these
devices not be under user control, because they must provide the maximum stability of views
over time (i.e. constant eld of view within the device’s limits). However, it is advisable for each
module’s central node to contain at least one pan-tilt video camera under user control to allow
the examination of specic phenomena (e.g. burrow emergence of different individuals or rate
of access to carrion) and to help validate the identication of organisms observed with the xed
video or other observation instrument.
Table 1 (Continued) Optoacoustic-image and passive acoustic sensors installed on the standard
cabled ecosystem observatory module and its associated mobile docked platforms
Components Instruments Purpose
Satellite pods Hydrophones Passive acoustic recording of ambient sounds (see previously).
Components of the module’s passive acoustic array for sound
localisation.
Stereo-video cameras 360° calibrated visual recording of organisms (see previously) around the
satellite and cross-reference with observational data from the central
node.
Pan-tilt video
cameras
User-controlled video cameras (see previously). Also, to supplement and
cross-reference observational data from the central node instruments.
Environmental
sensors
Record microdistribution of physical parameters (see previously) expected
to vary within the module area.
Experiment or
observation payload
Exchangeable ‘plug-and-play’ payload containing instruments for
user-designed data collection or experimentation, such as settlement trays
with different substrates (e.g. carbon, wood, or bones and even litter),
experimentation on light effect on species, tagging, and so on.
Autonomous
devices
Mission dependent Stand-alone sound recorders, cameras, cages, mesocosms, and other
devices to be placed by ROV or crawler to monitor short- and long-term
conditions at a specic location such as monitoring a sh nest or sessile
invertebrates. Other possible devices include animal collection traps and
stand-alone small-scale experimental packages.
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TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
Mobile platforms
Three types of mobile platforms would be docked at the central node of each module, including a
seaoor ‘crawler’, neutrally buoyant remotely operated vehicle (ROV), and autonomous underwater
vehicle (AUV). Although there is some redundancy among ROV, AUV, and crawler platforms, each
provides unique capabilities and disadvantages. All three types of platforms are useful for surveying
habitat and organism distribution surrounding the module, and each can be used to investigate
Figure 4 Schematic 3-dimensional illustration (not to scale) of the spatial conguration of the central node and
satellites of a module and the overlap among video, acoustic imaging sonar, and echosounder spatial coverage
areas (inset provides a bird’s-eye-view of the spatial conguration). Integration of multiple 3-dimensional
observation modalities in a module provides cross-referencing data to identify and track organisms. (1–6) a
single individual of species A is tracked as it moves through the module area. Changes in echosounder target
strength due to orientation changes can be quantied by comparison of different observations at each location,
enhancing our ability to determine sh identication from target strength data. (1) Silent individual detected
by echosounder and acoustic imaging sonar, provides target strength, orientation, location, and size estimates.
(2) Same individual produces sound loud enough to be localised by the passive acoustic array, actual location
and identication conrmed by stereo-video, acoustic image, and echosounder. Sound received level can then
be corrected for exact location to obtain sound source level and detection range. (3) Now silent, it is detected
by stereo video, acoustic imaging sonar, and echosounder from the central node, as well as stereo-video from
a satellite. (4) The individual moves out of camera range but continues to be tracked by acoustic imaging sonar
and echosounder. (5) As the individual leaves the module area, it continues to be tracked by echosounder. (6)
Separate individual of species A is detected on echosounder only, but target strength consistency of species A
at other locations permits accurate attribution to species within the wider area covered by the echosounder. (7)
A second unknown species is detected by acoustic imaging sonar and echosounder and is identied based on
localisation on a known sound. (8) The reaction of multiple sh of species C to light is quantied by acoustic
imaging sonar and echosounder.
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RODNEY A. ROUNTREE ET AL.
specic phenomena observed around the module and can aid in the identication of unknown targets
detected by the video, acoustic imaging sonar, echosounder, and passive acoustic array.
ROVs and AUVs are both mobile assets; the AUV has a much greater range and is not limited
by its tether. In contrast, although hampered in some ways by a tether, the ROV has manipulative
capabilities (i.e. by robotic arms), can carry larger payloads, and can be directly controlled by a user
in real time. The AUV provides the best mechanism for mapping and monitoring habitat and biota
(benthic and pelagic) of the area surrounding a module and the larger area encompassed by the
module’s satellites. In addition to providing habitat-mapping capabilities of the area immediately
surrounding the central node, the ROV can also be used to place autonomous instruments, exchange
satellite payload packages, and service all infrastructure components of the module (Sivčev etal.
2018). The ROV can also be equipped with push-corers in order to sample sediments. The ROV’s
high mobility also allows important functions such as the monitoring of the fouling community,
interactions of organisms with the infrastructure and its instruments, and faunal residence (e.g.
sheltering).
A drawback of both AUVs and ROVs is that thrusters must be in operation even when hovering
at a station, thus creating high levels of noise and turbulence that limit their ability to conduct
unbiased sampling and observations at a specic location for any period of time (Rountree & Juanes
2010; Durden etal. 2016a). An important, but often overlooked, noise problem with ROVs is that
their acoustic tracking and guidance systems produce intense broadband noise that may inuence
animal behaviour and can also bias measurements of the acoustic properties of biological sounds
(Rountree & Juanes 2010). In addition, the intense tracking pings make it harder for a human user
to process soundscape data (R.A. Rountree pers. obs.). Another drawback of ROVs is the need for
lights for operations (Rountree & Juanes 2010). The main limitations of AUVs are related to the
development of suitable docking infrastructures that can provide for data downloading and fast
inductive recharging of batteries to increase AUV operating time.
A crawler can more effectively conduct point-census surveys that can provide data at specic
locations for extended time periods (minutes to hours), during which noise production and turbulence
can be substantially reduced compared with the other mobile platforms. A drawback to the crawler is
its physical disturbance of the benthic habitat and impact on benthic organisms along its movement
track, but this can be reduced to a narrow strip of seabed by limiting the crawler to a constant
corridor for displacement (Chatzievangelou etal. 2016). Since such potential impacts would be
magnied in the area around a permanent observatory, we recommend that crawlers be operated on
predetermined and constant tracks to minimise habitat disturbance (Figure 3).
Tethered satellite pods
The three standardised satellites of each module would have several functions: 1) provide
observational redundancy and spatial overlap of observations with the central node observations to
assist in organism detection, identication, and development of 3-dimensional distribution maps in
the area surrounding the module (Figure 4), 2) provide observation of biotic responses to the central
node and its mobile platform presence and operations; and 3) serve as platforms for changeable
instrument packages designed to address specic research hypotheses.
A central premise of the proposed ecosystem observatory module design is that it includes
multiple modalities of observation that are synchronised in time and provide the maximum spatial
overlap. Therefore, the spatial conguration is dependent on optimising the overlap among the systems
under local conditions, as well as limitations of tethering with regard to ROV and crawler access to
the satellites. In many locations, satellites placed at 120° intervals and at distances on the order of
10 m from the central node would be most suitable (Figure 4). Minimally, each satellite would be
equipped with stereo video cameras capable of capturing a 360° cylindrical or hemispherical view
around the satellite. Ideally, they would also include the same acoustic imaging sonar and bioacoustic
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echosounder instruments as those on the central node, but at the present time, these systems are
prohibitively costly to achieve the ideal redundancy and overlap within the module area. As these
technologies advance sufciently to allow cost-effective 360° coverage, they should be added to the
satellites to improve spatial overlap over a larger area surrounding the central node.
Observational data obtained by the satellites of the area surrounding the central node and by the
central node of the area surrounding each satellite would provide a powerful means of determining
faunal interactions with the structures, including behavioural reactions to instrument operations (e.g.
lights and sounds, Figure 4).
Ocean observatories should be thought of as permanently instrumented areas where scientists of
different backgrounds have an opportunity to perform manipulative experiments, favouring iconic
environments such as the deep sea, for example, and resulting in a transition from a still largely
descriptive science toward a more experimental, hypothesis-driven, approach. In order to better serve
as platforms for hypothesis-driven research objectives, the satellites need to be designed with an
infrastructure that allows for ‘slide-in slide-out’ exchange of experimental payloads for hook-up to
power and data transfer. Examples of potential payloads might include settlement trays, experiments
on the response of biota to articial light regimes (useful for behaviour studies but also to examine
the impact of observatory lights), observation of biota response to bioluminescent light, response
to various baits, response to sound playback experiments (useful to understand behaviour and also
the impact of observatory generated noise on the biota), experimental attempts to mark or tag biota
through ingestion of tags or automatic capture, tag and release mechanisms (having the dual purpose
of studying sh movements and residency and using the observatory structure as habitat), the effects
of new colonised substrates on species and succession experiments, habitat manipulation experiments
such as predator exclusions, microcosm and mesocosm experiments, and many other possibilities.
Passive acoustic array
Passive acoustic monitoring of shes and invertebrates has become an important tool in sheries and
ecosystem studies (Rountree etal. 2006, Luczkovich etal. 2008); however, inherent problems have
slowed its more widespread application, including lack of catalogues of sh sound data (Rountree
etal. 2002), lack of information on source levels and detection ranges, and lack of sufciently
developed autodetection software (Rountree etal. 2006, Luczkovich etal. 2008). The use of multiple
observation technologies to aid in the in situ validation of sound source identity, source level, and
detection ranges is in its infancy (Rountree etal. 2003, Rountree 2008, Rountree & Juanes 2010), but
a combination of using a passive acoustic array with video for the in situ identication of unknown
sh sounds has recently been demonstrated (Mouy etal. 2018). The application of passive acoustic
arrays for localisation and cross-reference with other forms of observation on ocean observatories
are particularly promising, especially in the deep sea where many shes possess sonic muscles
that are presumably used for sound production (Rountree et al. 2012, Wall et al. 2013). Calls for
the increased use of passive acoustics for shes and invertebrates to be incorporated into ocean
observing systems have been made at workshops for decades (Rountree etal. 2003, ACT 2007,
R.A. Rountree pers. obs.), but implementation has been slow (Locascio etal. 2018). It should be
emphasised that incidental sounds produced by shes and invertebrates as by-products of movement,
feeding, or physiological processes can be important markers of species identity and useful for
monitoring temporal and spatial patterns in the associated behaviour (Rountree etal. 2006, 2018).
Thus, passive acoustics can be a useful tool for monitoring both vocal and non-vocal organisms and
their behaviours at observatories.
Because of the potential importance of passive acoustic monitoring as an important tool in ocean
observatories, it is essential to include a hydrophone array in the EOM design. At the minimum,
hydrophones should be placed on the central node and each satellite to create a four-element
3-dimensional array that can localise sounds originating near the central node. However, a greatly
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improved ability to localise the low-amplitude sounds created by many shes and invertebrates
could be achieved by placing compact arrays of six hydrophones on each element (sensu Mouy etal.
2018) or by placing additional hydrophones at intervals along the tethers from the central node to
each satellite (Figure 3).
Stand-alone sensors and other devices
Autonomous instruments and recording devices (e.g. Corgnati et al. 2016, Marini et al. 2018a)
deployed and serviced by the mobile platforms would be incorporated into the area surrounding the
module to provide unique data on biota in the surrounding habitat and additional opportunities for
in situ experimentation (Figure 3). For example, autonomous video recorders could be placed close
enough to individual sh nest sites, or individual sessile invertebrates, to use short-range infra-red
lighting to make long-term observations on microhabitat use, behaviour, and species associations.
Autonomous instruments could also be used to measure gradients in conditions at increasing
distances from the central node or specic satellites in an effort to quantify the effects of habitat
heterogeneity on animal presence and habitat use and the observatory’s inuence on environmental
conditions, habitat structure, and organism distribution (i.e. to distinguish between natural variation
and artefacts resulting from effects of the module). Many other types of autonomous devices can be
envisioned to carry out hypothesis-driven experiments, such as small mesocosms, settlement trays,
exclusion cages, and benthic animal traps.
Importance of observation data overlap
Time synchronisation and spatial overlap of all observation data, within the resolution limits of each
type of instrument, within a standardised spatial conguration, is one of the most important attributes
of the proposed ecosystem observatory module design, as it allows for the cross-referencing needed
for species detection, identication, and tracking (Figure 4). Consideration of how best to optimise
the spatial coverage and overlap of observation data and how it can be packaged for users should be
part of the design process for implementation of the EOM concept.
Ideal spacing between the central node and satellites is determined by optimising overlap
among spatial coverage of all instruments for local conditions. Stereo video cameras provide the
highest accuracy of species identication, size, and location in the area surrounding the module
but are limited to periods of natural or articial lighting during hours of darkness or in the aphotic
zone. Acoustic imaging sonar provides accurate location of targets but poorer species identication
(Aguzzi etal. 2019). However, it is not limited by lighting. Horizontal multibeam echosounders
provide highly accurate 3-dimensional location over a large spatial area surrounding the module, but
identication is limited by the accuracy of back-scatter target strength data, which are inuenced by
sh size and orientation to the acoustic beam, creating uncertainty in multispecies scenarios (Juanes
2018). Sounds detected by the passive acoustic array can be used to identify species when sounds
are well known, but until detailed catalogues of sh and invertebrate sounds become available, most
sounds detected and localised will be from unknown sources.
Cross-referencing of echosounder data with acoustic imaging sonar, video, and passive acoustic
data can provide valuable validation of target strength data for organisms and thereby enhance
biomass estimations around the observatory, as well as providing target strength data for other
independent conventional bioacoustics surveys (e.g. traditional sheries pelagic surveys that rely on
accurate target strength data for bioacoustic assessment of sh stocks). Similarly, cross-referencing of
acoustic imaging sonar with echosounder, video, and passive acoustic data can provide identication
validation of acoustic image targets in the near eld (ranges of up to the limit of visibility) and
echosounder targets in the far eld (ranges up to 1000 m). Finally, cross-references of unknown
sounds localised by the passive acoustic array with video, acoustic imaging sonar, and echosounder
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data can provide sound source identication and quantication of source level and detection range
(Rountree 2008, Mouy etal. 2018).
Simultaneous observations from all technologies would make it possible to track organisms
continuously as they move around the module (Figure 4). Therefore, at each location, data on changes
in sh orientation and location can be used to quantify their inuence on echosounder target strength.
As more and more data are compiled, accuracy of identication and tracking and estimates of
sh size, abundance, density, sound source level, and sound detection range can be improved. A
360° view around the observatory by multiple observation technologies allows users to estimate
the abundance of biota per unit volume while correcting for movements of individual sh and
other organisms. A sh swimming in circles around the structure can be counted accurately as one
individual, rather than multiple individuals moving in and out of a video eld of view. To obtain
these type of data, modules must be congured with satellites in close enough proximity to provide
adequate coverage of mobile biota (Figure 4).
Optional pelagic satellite
The ecological monitoring of modules can be signicantly enhanced by the addition of surface
and water-column assets that can combine benthic observations with water-column and surface
observations to monitor both surface-associated organisms and conditions as well as those of the
water column (Figure 5). Besides providing a monitoring capability of the pelagic ecosystems,
ecosystem observatory modules enhanced with a pelagic satellite can provide unprecedented
information on pelagic-benthic ecosystem connectivity. This can be accomplished by placing a
buoyed surface platform in contact with a module via an instrumented mooring line. Surface buoys
and mooring lines have the potential capacity for numerous instruments to be distributed throughout
the water column to synoptically monitor ne-scale hydrographic and biogeochemical parameters as,
for example, corrosive (i.e. low pH, high pCO2) oxygen minimum zone waters that intrude seasonally
onto continental shelf-edge zones (Juniper etal. 2016). Instruments can be either xed (e.g. Bahamon
etal. 2011) or movable as yo-yo systems for sh monitoring from decommissioned platforms (Fujii
& Jamieson 2016). They can also serve as access platforms to allow some types of maintenance of
observatories placed on the seabed (depending on depth and conditions).
Each buoy would be tted with a weather station, microphone, and video camera to monitor
surface conditions and shipping activity (e.g. Aguzzi etal. 2011b, OBSEA 2019). Recordings of aerial
noises associated with weather, sea state, and shipping can be validated by the video and compared
with simultaneous acoustic recordings from hydrophones to provide important insight into the source
of underwater sounds and help to quantify noise impacts on the aquatic soundscape. The surface
buoy would also support downward-projecting video, acoustic imaging sonar, and echosounder
instruments to provide similar capabilities to those of the bottom mounted instruments and hence
valuable data on pelagic components of the ecosystem. All instruments would be connected to the
cabled observatory for data transmission and power supply, with no need for satellite communication.
The development of a new cargo elevator technology (Figure 5) would allow the rapid delivery
and retrieval of instruments and materials to and from the module. For example, in combination
with the module’s ROV or crawler, scientists could deliver a new experimental payload to one of the
satellites and remove the old unit. Another example would be to deliver fresh bait to a baited camera
system or to retrieve organisms captured by instruments at the module. An elevator system could
dramatically increase our ability to deploy and retrieve materials to the module because it would no
longer depend solely on the use of expensive ship-based submersibles or ROV bottom time.
Pelagic satellites can also be used as docking and communication stations for specially adapted
aerial drones (Figure 5). One of the most important applications of drones would be to map spatial
and temporal distributions of marine birds, mammals, turtles, and large pelagic shes (e.g. Toonen
& Bush 2018). Pleustonic and neustonic components of the ecosystem could also be mapped,
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including distributions of jellysh, Sargassum, and other surface organisms and the development of
windrows. In addition, they can map the distribution of organic matter subsidies, including kelp and
marine mammal carcasses, and also track pollution, such as oating plastics, oil slicks, and other
buoyant pollutants. This can also be crucial to monitor alien species and forecast potential areas of
invasion, as plastic debris and other oating materials contribute to the transfer of non-native species
(Vetger etal. 2014). Drone systems are already being successfully developed to conduct passive
acoustic surveys (Lloyd etal. 2017). A communication tower on the buoy would enable researchers
to communicate with the drones through a relay from the cabled observatory and also provide short-
range communication with research ships and aircraft.
Satellite remote sensing has become an important tool in oceanography and sheries monitoring
(e.g. Santos 2000, Blondeau-Patissier etal. 2014), but ground-truthing of data is critical for accurate
Figure 5 Schematic of an ecosystem observatory module enhanced with a pelagic satellite composed of a
surface buoy and associated instruments to monitor vertical distribution of organisms and physical properties.
The surface buoy would be equipped with downward-looking video ca mera, acoustic imaging sonar system, and
echosounder similar to those deployed on the benthic module components. It would also include a microphone
and 360° video to capture above-water audio and video data of weather and shipping conditions for correlation
with underwater recordings. The mooring line would be variously equipped with monitoring instruments at
different depths and a cargo elevator system to transport materials, such as new scientic payloads for satellite
nodes between benthic and surface systems. The pelagic satellite includes a drone system to map aerial (e.g.
birds) and aquatic megafauna (mammals, sh, turtles), as well as neustonic and pleustonic organisms and
pollutants. Drones can also be used to carry an instrument payload such as a hydrophone or uorometer and
other instrumentation for spatial mapping.
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interpretation and modelling (Congalton 1991). The Southeast Atlantic Coastal Ocean Observing
System (SEACOOS) included a pilot study of the potential for integration of satellite remote sensing
and ocean observation systems (Nelson & Weisberg 2008), which found that coordination among
data providers, management, modellers, and users was a critical bottleneck. Field validation efforts
are important but expensive and difcult to coordinate. Observatory-based drone sampling can also
be used to enhance satellite remote sensing programs by conducting some types of coordinated eld
validation sampling. Ecosystem observatory-based drones could provide a more cost-effective tool
for obtaining oceanographic data for a wide range of measurements from sea surface temperature to
primary production in order to tune satellite data interpretation and modelling. Some drones could
be equipped with a payload of specialised equipment for specic projects, such as a chlorophyll
uorometer, or for deployment of sonobuoys, drifters, and expandable vertical prolers. Thus,
integration of ocean observatories with remote sensing satellite systems can improve the accuracy
of spatial mapping of large-scale environmental conditions.
Ecosystem observatory module clusters and networks
To be able to provide meaningful ecological data at different spatial scales (i.e. from local conditions
to geographic areas) accounting for key factors such as habitat heterogeneity along a coenocline
(e.g. Rex & Etter 2010, Lecours etal. 2015, Zeppilli etal. 2016), local modules should be associated
into a spatial hierarchy of clusters and networks, called ecosystem observatory module clusters
and ecosystem observatory module networks. Adopting a highly reproducible module design for
observatories should reduce costs and allow for replication of data at different locations.
The spatial conguration of modules within clusters and clusters within networks is critical to
providing spatial and temporal overlap among the various observation technologies required for
cross-referencing and validation. Experiments are needed to determine the optimal conguration
under local conditions. In these experiments, a minimum of three modules within clusters and three
clusters within networks are needed to ensure at least minimal coverage and overlap. A cluster design
of three modules separated on the order of hundreds of metres would be an effective way to scale
up data collection from individual sites to habitat (Figure 6). At distances of hundreds of metres,
bioacoustics coverage among the modules in a cluster would overlap to provide the ability to estimate
water-column biota density in a homogenous fashion over a large area (0.5–1 km2 or more; Figure6)
and to quantify the effect of module structure and operations on biota occurrence and behaviour.
Cross-reference data from each module would greatly improve the accuracy of the identication and
density estimation of biota within the cluster area, but well outside of individual modules, and allow
for detailed benthic habitat mapping over the larger area encompassed by the cluster. Such coverage
would facilitate accurate faunal abundance and density estimates necessary for sheries and other
applications and reduce observatory bias on measurements due to attraction and avoidance responses
of organisms to the observatory structures.
Finally, advanced AUV capabilities would enable the AUV to be used to map habitat and benthic
biota distributions between and among modules within the cluster. In some scenarios, all modules
within a cluster might share one AUV that patrols among them and can dock at any module. In
other scenarios, AUVs provided by each module would provide the cluster with multiple AUVs for
more rapid and detailed mapping. Observational data obtained from the AUV tracks can further
increase our ability to validate the identity of bioacoustic and passive acoustic targets outside of
the modules but within the cluster area. In some cases, AUVs might be programmed to investigate
passive acoustic or echosounder targets beyond the range of the other observational instruments
within a cluster area to improve identication and density estimates. Where feasible, an observatory
cluster would include one module equipped with a pelagic satellite that could provide drone support
for the entire cluster to enhance studies of vertical connectivity from the surface to the benthos at
the cluster location.
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Sentinel system
Observatory systems combining multiple EOM clusters along a coenocline form a ‘sentinel system’
observatory network (Figure 7). A minimum of three EOM clusters (i.e. nine modules arranged
in a spatial hierarchy) would be needed to elevate the monitoring network from examination of
local habitats to ecosystems and large geographic regions (Figure 7). It should be clear that such
a sentinel system would ideally be one component of a larger monitoring effort that coordinates
data from conventional ship, satellite, buoy-based, and animal-borne survey programmes. For
example, establishing a sentinel system composed of clusters (each of which provides high-resolution
monitoring on a scale of 0.5–1 km2) in the upper and lower sections of a major estuary (e.g. the
Chesapeake Bay) and another on the continental shelf just offshore would be effective at monitoring
movements of coastal shes that utilise the estuary as seasonal feeding or nursery grounds. Similarly,
deployment along coastlines can provide information on the timing of seasonal movements of shes
and habitat connectivity along migration corridors. Sentinel systems would be useful to monitor
migration patterns of shes and invertebrates by documenting rst detection, last detection, and
residence period at different points along the gradient. Such a system would also be useful for
monitoring the invasion of organisms into new territories (Juanes 2018) by placing clusters along
the predicted invasion pathway.
Figure 6 Schematic illustration of an ecosystem observatory module cluster designed to provide synoptic
data on differing spatial scales within the cluster area. Three or more modules should be arranged in geometric
clusters to allow detailed spatial comparisons within a larger spatial array. Spacing between modules is
dependent on local conditions, AUV range, and optimal echosounder coverage. Clusters with module spacing
allowing for overlap among bioacoustics echosounders, with greatest overlap in the centre of the cluster, enable
highly accurate identication of water column organisms over a large spatial area. One or more AUVs would
be designed to navigate among modules in the cluster to map habitat and organism distributions within the
cluster area and provide additional ground-truth data for organism identication based on their target strength.
Demersal and benthic organism and habitat mapping resolution is greatest around the modules but is also high
within the wider area encompassed by the observatory module cluster.
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Ecosystem surveillance, modelling, and forecasting
Fixed and mobile platforms allow for an experimental approach to the study and monitoring
of ecosystem functioning at different spatiotemporal scales (over kilometres and years). The
combination of stereo video, acoustic imaging, and echosounder imaging provides the ability to
quantify abundance, size, and biomass of organisms over a wide size range, as well as to identify
multiple types of behavioural reactions to natural or articial stimuli. In addition, the simultaneous
acquisition of biochemical and oceanographic data can inform researchers of potential causative
factors for observed behaviour and abundance patterns. However, automatic processing of the
high volumes of data generated by the observatories would be essential. Automated detection and
classication methodologies based on the various observation technologies are rapidly advancing
(e.g. Allken etal. 2018, Juanes 2018, Marini etal. 2018b). However, we suggest that the concept of
an ecosystem observatory user data interface would greatly enhance the application, testing, and
quality control of detection algorithms by providing a simple computer interface for user-aided
system learning (see ‘Cyber developments in support of monitoring networks’ section subsequently;
Figure 8).
Ecosystem observatory networks can be used to estimate local species abundances derived from
the image-based identication and counting of individuals, made possible through integration of
multiple observation technologies (see Figures 4 and 8). In addition, the methodology provides an
ability to develop size-class frequency data and species biomass estimation based on the estimated
size and counts of individuals (Durden etal. 2016b). Cross-referencing of data from ROV, crawler,
AUV, and echosounder data with validation data from each module provides the ability to obtain
standardised abundance and biomass data for the entire observatory network area (Figures 4, 6
and 7). Simultaneous monitoring of a large suite of environmental factors such as temperature,
turbidity, chlorophyll concentration, and other biochemical factors, together with ne-scale temporal
and spatial distribution patterns of organisms, would provide important data on environmental
regulators of species population structure and behavioural patterns. Temporal patterns in species
Figure 7 Sentinel ecosystem observatory networks (not to scale) composed of multiple module clusters
distributed across a habitat gradient or coenocline occurring from estuarine/riverine areas to coastal zones and
the shelf, down to the deep-continental margin of the slope and abyssal plain.
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richness, abundance, biomass, size-class structure, and role of environmental regulators within an
observatory network, supplemented with data from other monitoring programmes, could provide the
raw data needed to develop ecosystem modelling and forecasting programs for the habitat or region
surrounding the network.
Data from multiple observatory networks could then be linked to make comparisons among
areas, populations, and environmental regulatory factors to develop regional and ultimately global
monitoring programmes. Spatially representative and long-term monitoring provides the ability
to distinguish between population/community regulation by repetitive phenomena (e.g. rhythmic
abundance variations due to seasonal environmental changes and ontogenetic migrations, spawning
Figure 8 Hypothetical user interface of data from an ecosystem observatory module, composed of the central
node plus the three satellites. All data windows (A–E) play in time simultaneously, as indicated by the time
cursor in the scientic data and passive acoustic sound windows (D–E). Video, acoustic image, and echosounder
displays show only the portion of the 360° area surrounding the module which has been selected by the user,
with the angular view selection bar common to all three (below C). However, when the playback is paused,
the user can simultaneously scroll through all 360° surrounding the module in the video, acoustic image,
and echosounder windows (A–C). The overlapping observation modalities and integrated visual displays are
a powerful tool for examining correspondences among environmental conditions, observatory operations,
and animal behaviour. When autodetection is available for one or more of the observation technologies, the
user can validate detections in other windows, for example, targets ‘a–d’ are detected in video (A), acoustic
image (B), and echosounder data (C). Data from each instrument can then be compiled to provide the most
accurate information on species identication, 3-dimensional location, size, and target strength together with
environmental conditions at the time of detection. In addition, sound source targets localised by the passive
acoustic array and shown in the sound window (sound labelled ‘d’ in E) can be identied by its corresponding
location in the other windows (A–C).
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migrations; Aguzzi & Company 2010, Aguzzi etal. 2011a) from long-term (decadal and longer)
processes such as shifts in species distributions due to climate change and changes in resource
exploitation. If the information obtained from the observatory network system and associated
modelling and forecasting programs is automated, it may be possible to develop ecosystem alarm
protocols that detect anomalies in ecosystem parameters that might signal undesired environmental
states such as impending population collapse of keystone species.
Integration of benthopelagic networks
with animal-borne technologies
Cross-connection of ecosystem observatory module networks with free-moving animal-borne sensor
(ABS) technologies can also be envisaged. Inclusion of technology into the module design that
allows communication with independent ABS (Figure 3) is particularly promising for obtaining
data on animal behaviour as well as data from animal-borne environmental monitoring programmes
(see, for example, the Animal Telemetry Network Implementation Plan 2016–2021, NOC 2016).
Presently, data loggers connected to animals are getting ever more miniaturised (e.g. Nassar etal.
2018) and still primarily store oceanographic information about travelled seascapes (Wilmer etal.
2015, Fehlmann & King 2016) but only limited ecological information on intra- and interspecic
interactions experienced by the traveller. This weakness is being corrected in part by the development
of animal-borne cameras. Animal-borne video collection directly allows the derivation of ecological
information based on what is seen by individuals during their displacements (Moll etal. 2007).
Moreover, the progressive miniaturisation of implant components will eventually allow camera
installation on animals of very different sizes (although lming may be constrained at night or in
deep water).
If both the observatory module and animal-borne technologies are capable of two-way
communication, then data-intensive video-sampling by animal-borne technologies can be enhanced
by dumping data to the observatory, thereby freeing up data storage and increasing their useful
lifespan. Similarly, modules can be tuned to receive telemetric data from tagged animals freely
moving across depths and basins (Hussey etal. 2015). This cross-communication can complement
the monitoring capability of already existing pelagic and coastal-shallow networks (e.g. OTN 2019).
Presently, for the development of technological tracking of epibenthic animals carrying an acoustic
emitter, displacements can be measured into a network of moored receiving hydrophone stations
(Rotllant etal. 2014, Tuck etal. 2015). Such development is necessarily limited by the range of
hydrophone detection capabilities and could be potentially expanded when animal tracking is
assisted by moving platforms, delivering real-time data on their positioning. Tracking expansion
is presently pursued by using wave-gliders and AUVs (e.g. Lin etal. 2016, Masmitja etal. 2017).
Cyber developments in support of monitoring networks
Networks of xed and mobile units for coordinated ecological monitoring require not only hardware
development but a concomitant suitable cyber architecture for data communication, processing,
storage, and visualisation of interrelated multidisciplinary data of different types (Florea & Buiu
2017). Moreover, cyber infrastructures should provide proper ‘virtual research environments’ (VREs),
which can be described as online collaborative environments that allow open access and program
development for best science practices (Martin etal. 2019, Morris etal. 2019, Pearlman etal. 2019).
These VREs should be built on top of interrelated multiparametric data access platforms similar to
those developed for the Ocean Networks Canada Web services application program interface (API)
and Sandbox tool set (Rempel & Cabrera 2018). It is critical that such VREs serve as libraries of
multiparametric data (e.g. imaging, acoustics, physical, biochemical) derived from the observatories,
as well as open-source automated classication and statistical analysis programs.
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As ecology researchers increasingly deploy embedded sensor networks, they are being
confronted with an array of challenges in capturing, organising, and managing large amounts of
data (Borgman etal. 2007). User navigation into network data banks and analysis capability requires
the design of efcient interfaces between people and computers. Such a design should include all
steps of information ow, from data collection at each sensor and platform to its global elaboration.
This type of information ow framework is well described by ecoinformatics (Michener & Jones
2012), which arose from the need to integrate environmental and information sciences to provide
the language tools and standardisation practices necessary to access and analyse massive amounts
of heterogeneous data (e.g. by developing data banking).
Data integration would include several disciplines related to information technology that allow control
of data collection, processing, integration, and use in VRE systems by multiple sensor technologies. The
sensor web enablement (SWE) approach dened by the Open Geospatial Consortium (OGC) standards
(Del Río etal. 2018, Chaturvedi & Kolbe 2019) is a low-level specication of functionalities that allow
any kind of compliant sensor to interact with other sensors, with human users, or with properly dened
intelligent services. Networks of SWE-compliant sensors allow for a remote interaction by simply
triggering them on and off or by changing their acquisition conguration in order to adapt the monitoring
activities for specic purposes. The intelligent services capable of interacting with the SWE-compliant
sensors are generally dened according to the Internet of Things (IoT) technology paradigm (Qin etal.
2016, Čolaković & Hadžialić 2018), which refers to the capability of making content and services
understandable by devices without human involvement. To achieve this goal within the marine science
and technology community, data science methodologies (Skiena 2017) based on articial intelligence
should be capable of extracting the relevant content from the acquired data, then using this content
for interacting with the SWE-compliant observatory or for populating appropriate data repositories
(e.g. the Copernicus or the SeaDataNet initiatives). For example, data acquired by SWE sensors and
managed by intelligent services could be of the biophony (sounds of known shes, cetaceans, birds,
unknown biological sounds, etc.), the geophony (natural sounds like wind, rain, thunder, waves, etc.),
and the anthropophony (noise from ships, seismic surveys, and the observatory itself), which would then
be utilised by sound type classication software to document spatial and temporal patterns in sound
occurrence and correlations between biophony and anthrophony to assess noise impacts. SWE sensors
could similarly be used for biogeochemical data or visual data acquired by stand-alone devices capable
of communicating the relevant acquired information (Marini etal. 2018a).
Since all marine monitoring networks are increasingly service- and end-user oriented, their
data management cyber infrastructures are also being upgraded to retrieve, store, and process data
in real time, acting as a cognitive system for data interpretation for humankind (Shenoi etal. 2015).
Systems should enable any end-user worldwide to investigate ecological processes via interactive
web interfaces, allowing navigation into banks of multiparametric ‘big’ biological and environmental
data (Figure 8). Responses should be visualised in the form of synthetic graphic outputs, highlighting
signicant global trends and cause–effect relationships. Such visualisation would be based on high-
level data science activities performed within VRE capable of allowing non-expert users to compose
complex workows based on tools with high technological and scientic content (Buck etal. 2019).
Data output could be based on automated time series analysis (Aguzzi etal. 2012, Skiena 2017,
Recknagel & Michener 2018) as well as on multivariate statistics, which would then allow modelling
of biological responses to key environmental variables. The use of such powerful software tools
on big biological and environmental data will transition ocean observatory systems from a largely
observational to a more quantitative monitoring platform for ecological and sheries applications.
Data ow management from multiple observation technologies
It is critical that data streams from all the observation instruments and sensors be synchronised and
maintained as relationally integrated data that are interoperable with other observation networks
99
TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
(e.g. the ONC’s Oceans 2.0 program; ONC 2019). Data should be enriched with the appropriate
semantic information that allows their retrieval by semantic-based search engines (Aguzzi et al.
2015a). A user annotating events in one dataset should be able to seamlessly populate the same
annotation in all other data streams (Figure 8). For example, a user marking the location of a sound
in the hydrophone recording should be able to locate the corresponding data position automatically
in video, acoustic image, echosounder, and environmental datasets (e.g. ‘d’ in Figure 8). Although
observatories currently provide metadata containing information on observatory instrumentation
functioning performance, maintenance status and functioning history, data quality assurance and
control, calibration, and other aspects (e.g. ONC 2019), this may not be sufcient for end-users
who are not capable of cross-referencing all this information automatically, because it must rst be
downloaded and integrated by the users themselves.
A user interface that provides all module data integrated together in an interactive visual
display would be a powerful tool for researchers (Figure 8). For example, a user viewing a video
would immediately see not only environmental and other observational data but also the activity
state of all instrumentation (e.g. lights on, rotary motor active, ADCP active, ROV thrusters on or
off). Comparison of data from the overlapping 3-dimensional views in video, acoustic image, and
echosounder windows provides the ability to cross-reference data to improve identication and
measurement accuracy. For example, if a video detector identies targets ‘a’ to ‘d’, its ‘ghost’ target
can be displayed in the acoustic image and echosounder windows to look for matches or to compare
with automatic detections in those datasets. That will help a user determine if some detections are
valid or to identify unknown detection targets. The user could then download a dataset containing
all the attributes of the target based on the different observation types as well as corresponding
environmental and operational state data. Such information can provide valuable clues to understand
species response to the observatory and potential biases in behavioural observations, in addition
to providing data on biotic responses to environmental conditions and the raw data necessary to
compile species abundance and volume density maps. The ability to download data seamlessly in
these kinds of relational datasets is of the utmost importance to encouraging widespread utilisation
of observatory data among scientists, resource managers, and educators.
Observatory integration within
commercial development projects
Scientists around the world struggle to obtain funding for even small observatory systems. The cost
of observatory infrastructure, such as the platform and dedicated data/power transmission cables
to shore, often constitutes the largest expense and greatly limits observatory capabilities. Offshore
development projects (e.g. telecommunication cables, wind farms, tidal/current turbines, and oil/
gas platforms) provide a unique opportunity for advancement of ocean science if government
and industry leaders have the foresight to integrate ocean observatory systems into offshore
development design (e.g. Danovaro etal. 2017). It is hoped that current large scientic actions
are being conceived at higher institutional levels to combine the two visions and design offshore
energy systems that can provide both much-needed renewable energy and also much-needed ocean
observatory systems (e.g. see DELOS and LoVe initiatives as reviewed by Aguzzi etal. 2019).
Offshore energy development can provide platforms for many EOMs at a location and thus the
ability to construct large EOM cluster networks capable of delivering an unprecedented view of
underwater life to scientists, shers, and the public. Further, because power and data cables are a
necessary part of the energy delivery system, scientists could have a fully functional data transfer
network to shore already in place. If commercial industries incorporated observatory systems into
project design from the beginning, rather than post-construction, and consider the advantages of
an improved public image, the cost of required environmental monitoring and mitigation would
likely be more palatable.
100
RODNEY A. ROUNTREE ET AL.
Acknowledgements
This work was developed within the framework of the Tecnoterra (ICM-CSIC/UPC) and the
following project activities: ARIM (Autonomous Robotic sea-oor Infrastructure for benthopelagic
Monitoring; MartTERA ERA-Net Cofound) and RESBIO (TEC2017-87861-R; Ministerio de
Ciencia, Innovación y Universidades). Inspiration for our modular observatory design and some
of its components resulted from discussions of participants at an international workshop on ocean
observation technology hosted by Oceans Network Canada, in Barcelona, Spain on October 4–5,
2018. A special thanks is also devoted to Dr T. Fujii, OceanLab University of Aberdeen for his
helpful comments during the manuscript preparation.
References
ACT (Alliance for Coastal Technologies). 2007. Underwater Passive Acoustic Monitoring for Remote Regions.
A Workshop of Research Scientists, Technology Developers, and Resource Managers. Hawaii Institute
of Marine Biology, Coconut Island, Hawaii, Feb. 7–9, 2007. Alliance for Coastal Technologies Ref. No.
ACT-07-02. Solomons, Maryland: Alliance for Coastal Technologies.
Afonso, P., McGinty, N., Graça, G., Fontes, J., Inácio, M., Totland, A. & Menezes, G. 2014. Vertical migrations
of a deep-sea sh and its prey. PLOS ONE 9(5), e9 788 4.
Aguzzi, J., Chatzievangelou, D., Marini, S. et al. 2019. New high-tech interactive and exible networks
for the future monitoring of deep-sea ecosystems. Environmental Science and Technology 53,
6616 –6631.
Aguzzi, J. & Company, J.B. 2010. Chronobiology of deep-water decapod crustaceans on continental margins.
Advances in Marine Biology 58, 155 –225.
Aguzzi, J., Company, J.B., Costa, C. etal. 2012. Challenges to assessment of benthic populat ions and biodiversity
as a result of rhythmic behaviour: Video solutions from cabled observatories. Oceanography and Marine
Biology: An Annual Review 50, 235–286.
Aguzzi, J., Company, J.B., Costa, C., Menesatti, P., Garcia, J.A., Bahamon, N., Puig, P. & Sardà, F. 2011a.
Activity rhythms in the deep-sea: A chronobiological approach. Frontiers in Bioscience 16, 131–150 .
Aguzzi, J., Doya, C., Tecchio, S. etal. 2015a. Coastal observatories for monitoring of sh behaviour and their
responses to environmental changes. Reviews in Fish Biology and Fisheries 25, 463– 483.
Aguzzi, J., Fanelli, E., Ciuffardi, T., Schirone, A., Craig, G. & the NEMO Consortium. 2017. Inertial
bioluminescence rhythms at the Central Mediterranean KM3NeT deep-sea neutrino telescope. Scientic
Reports 7, 44938.
Aguzzi, J., Manuél, A., Condal, F. etal. 2011b. The new SEAoor OBservatory (OBSEA) for remote and long-
term coastal ecosystem monitoring. Sensors 11, 5850 –5872.
Aguzzi, J., Sbragaglia, V., Tecchio, S., Navarro, J. & Company, J.B. 2015b. Rhythmic behaviour of marine
benthopelagic species and the synchronous dynamics of benthic communities. Deep-Sea Research I
95, 1–11.
Alldredge, A.L. & Silver, M.W. 1988. Characteristics, dynamics and signicance of marine snow. Progress in
Oceanography 20, 41–82.
Allken, V., Handegard, N.O., Rosen, S., Schreyeck, T., Mahiout, T. & Malde, K. 2018. Fish species identication
using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science 76(1),
342–349.
Audoly, C., Gaggero, T., Baudin, E., Folegot, T., Rizzuto, E., Mullor, R.S., André, M., Rousset, C. & Kellett, P.
2017. Mitigation of underwater radiated noise related to shipping and its impact on marine life: A practical
approach developed in the scope of AQUO Project. Journal of Oceanic Engineering IEEE 42(2), 373–387.
Audoly, C., Rousset, C., Baudin, E. & Folegot, T. 2016. AQUO Project. Research on solutions for the mitigation
of shipping noise and its impact on marine fauna: Synthesis of guidelines. In Proceedings of the 23rd
International Congress on Sound Vibrations, 10–14 July, 2016. Athens, Greece: ICSV23, pp. 1–8.
Bahamon, N., Aguzzi, J., Bernardello, R., Ahumada-Sempoal, M.A., Puigdefabregas, J., Cateura, J., Muñoz,
E., Velásquez, Z. & Cruzado, A. 2011. The new pelagic Operational Observatory of the Catalan Sea
(OOCS) for the multisensor coordinated measurement of atmospheric and oceanographic conditions.
Sensors 11(12), 11251–11272.
101
TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
Barnes, C., Best, M.M.R., Johnson, F.R., Pautet, L. & Pirenne, B. 2013. Challenges, benets, and opportunities
in installing and operating cabled ocean observatories: Perspectives from NEPTUNE Canada. IEEE
Journal of Oceanic Engineering 38(1), 144 –157.
Blanco, R., Shields, M.A. & Jamieson, A.J. 2013. Macrofouling of deep-sea instrumentation after three years
at 3690 m depth in the Charlie Gibbs fracture zone, mid-Atlantic ridge, with emphasis on hydroids
(Cnidaria: Hydrozoa). Deep Sea Research Part II: Topical Studies in Oceanography 98, 370–373.
Blondeau-Patissier, D., Gower, J.F., Dekker, A.G., Phinn, S.R. & Brando, V.E. 2014. A review of ocean
color remote sensing methods and statistical techniques for the detection, mapping and analysis of
phytoplankton blooms in coastal and open oceans. Progress in Oceanography 123, 123 –14 4.
Bonin, F., Burguera, A. & Oliver, G. 2011. Imaging systems for advanced underwater vehicles. Journal of
Maritime Research 8(1), 65–86.
Borgman, C.L., Wallis, J.C. & Enyedy, N. 2007. Little science confronts the data deluge: Habitat ecology,
embedded sensor networks, and digital libraries. International Journal on Digital Libraries 7, 17–30.
Bosch, J., Istenič, K., Gracias, N., Garcia, R. & Ridao, P. 2019. Omnidirectional multicamera video stitching
using depth maps. IEEE Journal of Oceanic Engineering. doi: 10.1109/JOE.2019.2924276
Buck, J.J., Bainbridge, S.J., Burger, E.F. etal. 2019. Ocean data product integration through innovation – The
next level of data interoperability. Frontiers in Marine Science 6, 1–19.
Canals, M., Puig, P., de Madron, X.D., Heussner, S., Palanques, A. & Fabres, J. 2006. Flushing submarine
canyons. Nature 444( 7117), 3 54.
Chaturvedi, K. & Kolbe, T.H. 2019. Towards establishing cross-platform interoperability for sensors in smart
cities. Sensors 19, 562. doi:10.3390/s19030562
Chatzievangelou, D., Doya, C., Mihály, S., Sastri, A.R., Thomsen, L. & Aguzzi, J. 2016. High-frequency
patterns in the abundance of benthic species near a cold-seep: An Internet Operated Vehicle application.
PLOS ONE 11(10), e0163808.
Čolaković, A. & Hadžialić, M. 2018. Internet of Things (IoT): A review of enabling technologies, challenges,
and open research issues. Computer Networks 144, 17–39.
Congalton, R.G. 1991. A review of assessing the accuracy of classications of remotely sensed data. Remote
Sensing of Environment 37, 35–46.
Corgnati, L., Marini, S., Mazzei, L., Ottaviani, E., Aliani, S., Conversi, A. & Griffa, A. 2016. Looking inside the
ocean: Toward an autonomous imaging system for monitoring gelatinous zooplankton. Sensors 16, 2124.
Dame, R.F., Chrzanowski, T.H., Bildstein, K. etal. 1986. The outwelling hypothesis and North Inlet, South
Carolina. Marine Ecology Progress Series 33, 217–2 29.
Danovaro, R., Aguzzi, J., Fanelli, E. etal. 2017. A new international ecosystem-based strategy for the global
deep ocean. Science 355, 452–454.
Deegan, L.A., Hughes, J.E. & Rountree, R.A. 2000. Salt marsh suppor t of marine transient species. In Concepts
and Controversies in Tidal Marsh Ecology, M. Weinstein & D. Kreeger (eds). Boston, Massachusetts:
Kluwer Aca demic Publishers, 333–365.
De Leo, F., Ogata, B., Sastri, A., Heeseman n, M., Mi hály, S., Galbraith, M. & Morley, M.G. 2018. High-frequency
observations from a deep-sea cabled observatory reveal seasonal overwintering of Neocalanus spp. in
Barkley Canyon, NE Pacic: Insights into particulate organic carbon ux. Progress in Oceanography
169, 120–13 7.
Del Río, J., Toma, D.M., Martínez, E., O’Reilly, T.C., Delory, E., Pearlman, J.S., Waldmann, C. & Jirka, S.
2018. A Sensor Web Architecture for integrating smart oceanographic sensors into the semantic sensor
Web. IEEE Journal of Oceanic Engineering 43(4), 830–842.
Doya, C., Aguzzi, J., Pardo, M., Matabos, M., Company, J.B., Costa, C. & Milhaly, S. 2014. Diel behavioral
rhythms in the sablesh (Anoplopoma mbria) and other benthic species, as recorded by deep-sea cabled
observatories in Barkley canyon (NEPTUNE-Canada). Journal of Marine Systems 130, 69–78.
Durden, J.M., Bett, B.J., Horton, T., Serpell-Stevens, A., Morris, K.J., Billet, D.S.M. & Ruhl, H.A. 2016b.
Improving the estimation of deep-sea megabenthos biomass: Dimension to wet weight conversions for
abyssal invertebrates. Marine Ecology Progress Series 552, 71–79.
Durden, J.M., Schoening, T., Althaus, F. etal. 2016a. Perspectives in visual imaging for marine biology and
ecology: From acquisition to understanding. Oceanography and Marine Biology Annual Review 54, 1–72.
FAO (Food and Agriculture Organization of the United Nations). 2018. The State of Mediterranean and
Black Sea Fisheries. General Fisheries Commission for the Mediterranean. Rome: Food and Agriculture
Organization.
102
RODNEY A. ROUNTREE ET AL.
Fehlmann, G. & King, A.J. 2016. Bio-logging. Current Biology 26, 830– 831.
Florea, A.G. & Buiu, C. (eds) 2017. Membrane Computing for the Distributed Control of Robotic Swarms.
Advances in Computational Intelligence and Robotics (ACIR) Book Series. IGI Global. doi:
10.4018/978-1-5225 -2280-5.
Fujii, T. & Jamieson, A.J. 2016. Fine-scale monitoring of sh movements and multiple environmental
parameters around a decommissioned offshore oil platform: A pilot study in the North Sea. Ocean
Engineering 126, 481–487.
Gould, J., Sloyan, B., Visbeck, M. 2013. In situ ocean observations: A brief history, present status, and future
directions. International Geophysics 103, 59–81.
Graeme, C.H., Ferreira, L.C., Sequeira, A.M.M. etal. 2010. Key questions in marine megafauna movement
ecology. Trends in Ecology and Evolution 31, 4 63 –475.
Haines, E.B. 1979. Interactions between Georgia salt marshes and coastal waters: A changing paradigm. In
Ecological Processes in Coastal and Marine Systems, R.J. Livingston (ed.). New York: Plenum, 35– 46.
Handegard, N.O., Du Buisson, L., Brehmer, P. etal. 2013. Towards an acoustic-based coupled observation
and modelling system for monitoring and predicting ecosystem dynamics of the open ocean. Fish and
Fisheries 14(4), 605–615.
Harvey, E.S. & Shortis, M.R. 1998. Calibration stability of an underwater stereo-video system: Implications
for measurement accuracy and precision. Marine Technology Society Journal 32(2) , 3 –17.
Honjo, S. 1980. Material uxes and modes of sedimentation in the mesopelagic and bathypelagic zones.
Journal of Marine Research 38, 53 –97.
Hussey, N.E., Kessel, S.T., Aarestrup, K. etal. 2015. Aquatic animal telemetry: A panoramic window into the
underwater world. Science 348, 1221–1231.
Irigoien, X., Klevjer, T.A., Røstad, A. etal. 2014. Large mesopelagic shes’ biomass and trophic efciency in
the open ocean. Nature Communications 5, 3271.
Juanes, F. 2018. Visual and acoustic sensor for early detection of biological invasions: Current uses and future
potential. Journal for Nature Conservation 42, 7–11.
Juniper, S.K., Sastri, A., Mihály, S., Whitehead, J., Brent, E., Helmuth, T. & Miller, L. 2016. Continuous pCO2
time series from Ocean Networks Canada cabled observatories on the northeast Pacic shelf-edge/upper
slope and in the sub-tidal Arctic. European Geophysical Union, Geophysical Research Abstracts 18,
EGU2016-9426.
Kobari, T., Steinberg, D.K., Ueda, A., Tsuda, A., Silver, M.W. & Kitamura, M. 2008. Impacts of ontogenetically
migrating copepods on downward carbon ux in the western subarctic Pacic Ocean. Deep-Sea Research
II 55, 1648 –1660.
Lecours, V., Devillers, R., Schneider, D.C., Lucieer, V.L., Brown, C.J. & Edinger, E.N. 2015. Spatial scale and
geographic context in benthic habitat mapping: Review and future directions. Marine Ecology Progress
Series 535, 259–284.
Lin, Y., Hsiung, S.-C., Piersal, R., Whitem, C., Lowe, C.G. & Clark, C.M. 2016. A multi-autonomous underwater
vehicle system for autonomous tracking of marine life. Journal of Field Robotics 34, 757–774.
Lloyd, S., Lepper, P. & Pomeroy, S. 2017. Evaluation of UAVs as an underwater acoustics sensor deployment
platform. International Journal of Remote Sensing 38, 2808–2817.
Locascio, J., Mann, D., Wilcox, K. & Luther, M. 2018. Incorporation of acoustic sensors on a coastal ocean
monitoring platform for measurements of biological activity. Marine Technology Society Journal 52(3),
64 –70.
Longhurst, A.R. 1976. Vertical migration. In The Ecology of the Seas, D.H. Cushing & J.J. Walsh (eds). Oxford:
Blackwell Scientic Publications, 116–137.
Luczkovich, J.J., Mann, D.A. & Rountree, R.A. 2008. Passive acoustics as a tool in sheries science.
Transactions of the American Fisheries Society 137, 533–541.
Marini, S., Corgnati, L., Manotovani, C., Bastianini, M., Ottaviani, E., Fanelli, E., Aguzzi, J., Griffa, A. &
Poulain, P.M. 2018a. Automated estimate of sh abundance through the autonomous imaging device
GUA RD1. Measurement 126, 72 –75.
Marini, S., Fanelli, E., Sbragaglia, V., Azzurro, E., Del Rio, J. & Aguzzi, J. 2018b. Tracking sh abundance by
underwater image recognition. Scientic Reports 8, 13748.
Marshall, K.N., Levin, P.S., Essington, T.E. etal. 2018. Ecosystem-based sheries management for social–
ecological systems: Renewing the focus in the United States with next generation shery ecosystem
plans. Conservation Letters 11(1), e12 367.
103
TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
Marshall, N.B. 1971. Explorations in the Life of Fishes. Cambridge, Massachusetts: Harvard University Press.
Martin, P., Remy, L., Theodoridou, M., Jeffery, K. & Zhao, Z. 2019. Mapping heterogeneous research
infrastructure metadata into a unied catalogue for use in a generic virtual research environment. Future
Generation Computer Systems 101, 327–340.
Masmitja, I., Bouvet, P.J., Gomarítz, S., Aguzzi, J. & Del Río, J. 2017. Accuracy and precision studies for range-
only underwater target tracking in shallow waters. In Supporting World Development Through Electrical
and Electronic Measurements, 22nd IMEKO TC4 International Symposium and 20th International
Workshop on ADC Modelling and Testing, 14–15 September 2017. Iasi, Romania. Budapest, Hungary:
International Measurement Confederation (IMEKO), 94–99.
Mauchline, J. 1980. The biology of mysids and euphausiids. Part 2. The biology of euphausiids. Advances in
Marine Biology 18, 372– 623.
Maxwell, S.M., Hazen, E.L., Lewison, R.L. et al. 2015. Dynamic ocean management: Dening and
conceptualizing real-time management of the ocean. Marine Policy 58, 42–50.
McCave, I.N. 1975. Vertical ux of carbon in the ocean. Deep-Sea Research 22, 491–502.
Merrett, N.R. 1978. On the identity and pelagic occurrence of larval and juvenile stages of rattail shes (family
Macrouridae) from 60°N, 20°W and 53°N, 20°W. Deep-Sea Research I 25, 147–160.
Merritt, D., Donovan, M.K., Kelley, C., Waterhouse, L., Parke, M., Wong, K. & Drazen, J.C. 2011. BotCam: A
baited camera system for nonextractive monitoring of bottomsh species. Fishery Bulletin 109(1), 56 –67.
Michener, W.K. & Jones, M.B. 2012. Ecoinformatics: Supporting ecology as a data-intensive science. Tren ds
in Ecology and Evolution 27, 85–93.
Moll, R.J., Millspaugh, J.J., Beringer, J., Sartwell, J. & He, Z. 2007. A new ‘view’ of ecology and conservation
through animal-borne video systems. Trends in Ecology and Evolution 22, 660–668.
Morris, C., Andreetto, P., Banci, L. etal. 2019. West-Life: A virtual research environment for structural biology.
Journal of Structural Biology: X 1 (January–March 2019), 100006. doi: 10.1016/j.yjsbx.2019.100006.
Mouy, X., Rountree, R., Juanes, F. & Dosso, S.E. 2018. Cataloguing sh sounds in the wild using combined
acoustic and video recordings. The Journal of the Acoustical Society of America 143 (5), 333–339.
Nassar, J.M., Khan, S.M., Velling, S.J., Diaz-Gaxiola, A., Shaikh, S.F., Geraldi, N.R., Sevilla, G.A.T., Duarte,
C.M. & Hussain, M.M. 2018. Compliant lightweight non-invasive standalone ‘Marine Skin’ tagging
system. npj Flexible Electronics 2(1), 13, doi: 10.1038/s41528-018-0025-1
Naylor, E. 2010. Chronobiology of Marine Organisms. Cambridge: Cambridge University Press.
Nelson, J.R. & Weisberg, R.H. 2008. In situ observations and satellite remote sensing in SEACOOS: Program
development and lessons learned. Marine Technology Society Journal 42(3) , 41–54 .
Nixon, S.W. 1980. Between coastal marshes and coastal waters: A review of twenty years of speculation and
research on the role of salt marshes in estuarine productivity and water chemistry. In Estuarine and
Wetland Processes, P. Hamilton & K.B. MacDonald (eds). New York: Plenum, 437–525.
NOC (National Ocean Council). 2016. Animal Telemetry Network Implementation Plan 2016–2021.
Washington, DC: National Ocean Council.
OBSEA. 2019. OBSEA Expandable Seaoor Observatory. Barcelona: SARTI Technological Development
Center of Remote Acquisition and Data Processing Systems. Online. https://obsea.es/ (accessed 18 April
2019).
Odum, E.P. 1980. The status of three ecosystem-level hypotheses regarding salt marsh estuaries: Tidal subsidy,
outwelling, and detritus-based food chains. In Estuarine Perspectives, V.S. Kennedy (ed.). New York:
Academic Press, 485– 495.
ONC (Ocean Networks Canada). 2019. Discover the Ocean. Understand the Planet. Victoria, British Columbia:
Oean Networks Canada. Online. http://www.oceannetworks.ca/sights-sounds/images/maps (accessed
18 April 2019).
OOI (Ocean Observatories Initiative). 2019. The Ocean Observatories Initiative. Woods Hole, Massachusetts:
Ocean Observatories Initiative. Online. https://oceanobservatories.org/ (accessed 18 April 2019).
OTN (Ocean Tracking Network). 2019. Ocean Tracking Network. Halifax, Nova Scotia: Ocean Tracking
Network. Online. http://oceantrackingnetwork.org/ (accessed 18 April 2019).
Pearlman, J.S., Bushnell, M., Coppola, L. etal. 2019. Evolving and sustaining ocean best practices and
standards for the next decade. Frontiers in Marine Science 6, 277.
Pomeroy, L.R. & Wiegert, R.G. (eds) 1981. The Ecology of a Salt Marsh. New York: Springer.
Puig, P., Palanques, A. & Martín, J. 2014. Contemporary sediment-transport processes in submarine canyons.
Annual Review of Marine Science 6, 53–77.
104
RODNEY A. ROUNTREE ET AL.
Qin, Y., Sheng, Q.Z., Falkner, N.J.G., Dustdar, S., Wang, H. & Vasilakos, A.V. 2016. When things matter: A
survey on data-centric internet of things. Journal of Network and Computer Applications 64, 137–153.
Recknagel, F. & Michener, W.K. (eds) 2018. Ecological Informatics: Data Management and Knowledge
Discovery. Cham, Switzerland: Springer Nature, 3rd edition.
Rempel, A. & Cabrera, D.A. 2018. The Oceans 2.0 Sandbox. Victoria, British Columbia: Ocean Networks Canada.
Online. https://wiki.oceannetworks.ca/display/O2A/The+Oceans + 2.0+Sandbox (accessed 18 April 2019).
Rex, M.A. & Etter, R.J. (eds) 2010. Deep-Sea Biodiversity: Pattern and Scale. Cambridge, Massachusetts:
Har vard University Press.
Rogers, A.D. 2015. Environmental change in the deep ocean. Annual Review of Environment and Resources
40, 1–38.
Rotllant, G., Aguzzi, J., Sarria, D. etal. 2014. Pilot acoustic tracking study on adult spiny lobsters (Palinurus
mauritanicus) and spider crabs (Maja squinado) within an articial reef. Hydrobiologia 742, 27–38.
Rountree, R.A. 1992. Fish and macroinvertebrate community structure and habitat use patterns in salt marsh
creeks of southern New Jersey, with a discussion of marsh carbon export. Ph.D. Dissertation, Rutgers,
The State University of New Jersey, United States of America.
Rountree, R.A. 2008. Do you hear what I hear? Future technological development and needs in passive
acoustics underwater observation. Marine Technology Reporter 51(9), 40– 46.
Rountree, R.A. & Able, K.W. 2007. Spatial and temporal habitat use patterns for salt marsh nekton: Implications
for functions. Aquatic Ecology 41, 25– 45.
Rountree, R.A., Gilmore, R.G., Goudey, C.A., Hawkins, A.D., Luczkovich, J. & Mann, D.A. 2006. Listening
to sh: applications of passive acoustics to sheries science. Fisheries 31(9), 433– 446.
Rountree, R.A., Goudey, C. & Hawkins, T. (eds) 2003. Listening to Fish. Proceedings of the International
Workshop on the Applications of Passive Acoustics to Fisheries. April 8–10, 2002, Dedham,
Massachusetts. MIT Sea Grant Technical Report MITSG 03-2. Online. http://web.mit.edu/seagrant/
aqua/cfer/acoustics/PAprocBrFINAL.pdf (accessed 3 October 2019).
Rountree, R.A. & Juanes, F. 2010. First attempt to use a remotely operated vehicle to observe soniferous sh
behaviour in the Gulf of Maine, Western Atlantic Ocean. Current Zoology 56(1), 90 –99.
Rountree, R.A., Juanes, F. & Bolgan, M. 2018. Air movement sound production by alewife, white sucker, and
four salmonid shes suggests the phenomenon is widespread among freshwater shes. PLOS ONE 13(9),
e02 0 42 47.
Rountree, R.A., Juanes, F., Goudey, C.A. & Ekstrom, K.E. 2012. Is biological sound production important
in the deep sea? In The Effects of Noise on Aquatic Life, A.N. Popper & A. Hawkins (eds). New York:
Springer Science+Business Media, LLC, 118–183. doi: 10.1007/978-1-4419-7311-5_41
Rountree, R.A., Perkins, P.J., Kenney, R.D. & Hinga, K.R. 2002. Sounds of western North Atlantic shes: Data
rescue. Bioacoustics 12, 242–244.
Santos, A.M.P. 2000. Fisheries oceanography using satellite and airborne remote sensing methods: A review.
Fisheries Research 49(1), 1–20.
Schoeld, O. & Glenn, S. 2004. Introduction to special section: Coastal ocean observatories. Journal of
Geophysical Research: Oceans, 109(C12 S01). doi: 10.1029/200 4JC 00 2577
Shenoi, R.A., Bowker, J.A., Dzielendziak, A.S. etal. 2015. Global marine technology trends 2030. Southampton:
Lloyd’s Register, Farnborough: QinetiQ, Southampton: University of Southampton. Online. https://issuu.
com/lr_marine/docs/55046_lr2030_web-lr_25mb/1 (accessed 18 April 2019).
Shortis, M. & Abdo, E.H.D. 2016. A review of underwater stereo-image measurement for marine biology and
ecology applications. Oceanography and Marine Biology: An Annual Review 47, 257–292.
Shortis, M.R., Seager, J.W., Williams, A., Barker, B.A. & Sherlock, M. 2008. Using stereo-video for deep water
benthic habitat surveys. Marine Technology Society Journal 42(4), 28 –37.
Sivčev, S., Coleman, J., Omerdić, E., Dooly, G. & Toal, D. 2018. Underwater manipulators: A review. Ocean
Engineering 163, 431– 450.
Skiena, S.S. 2017. The Data Science Design Manual. Cham, Switzerland: Springer Nature.
Teal, J.M. 1962. Energy ow in the salt marsh ecosystem of Georgia. Ecology 43, 614–6 24 .
Thomsen, L., Aguzzi, J., Costa, C., De Leo, F., Ogston, A. & Purser, A. 2017. The oceanic biological pump:
Rapid carbon transfer to depth at continental margins during winter. Scientic Reports 7(1), 10763.
Toonen, H.M. & Bush, S.R. 2018. The digital frontiers of sheries governance: Fish attraction devices,
drones and satellites. Journal of Environmental Policy and Planning 22(1), 125–137. doi:
10.108 0/1523908X.2018.1461084
105
TOWARDS AN OPTIMAL DESIGN FOR ECOSYSTEM-LEVEL OCEAN OBSERVATORIES
Tuck, I.D., Parsons, D.M., Hartill, B.W. & Chiswell, S.M. 2015. Scampi (Metanephrops challengeri) emergence
patterns and catchability. ICES Journal of Marine Science 72, 199–210 .
Tunnicliffe, V., Dewey, R. & Smith, D. 2003. Research plans for a mid-depth cabled seaoor observatory in
Western Canada. Oceanography 16(4), 53 –59.
Vardaro, M.F., Parmley, D. & Smith, K.L. Jr. 2007. A study of possible “reef effects” caused by a long-term
time-lapse camera in the deep North Pacic. Deep Sea Research Part I: Oceanographic Research Papers
54(8), 1231–1240.
Vetger, A.C., Barletta, M., Beck, C. etal. 2014. Global research priorities to mitigate plastic pollution impacts
on marine wildlife. Endangered Species Research 25, 225 –24 7.
Vinogradov, M.E. 1953. The role of vertical migration of the zooplankton in the feeding of deep sea animals.
Priroda 6, 95–96.
Vinogradov, M.E. 1955. Vertical migrations of zooplankton and their importance for the nutrition of abyssal
pelagic faunas. Transactions of the Institute of Oceanology (USSR) 13, 71–76.
Vinogradov, M.E. (ed.) 1971. Life Activit y of Pelagic Communities in the Ocean Tropics (Based on Data of the
44th Cruise of the R/V Vityaz). Institute of Oceanography, Academy of Science of the USSR. Izdatel’stvo
“Nauka.” Moskva. (Kaner, N., Transl., 1973. Israel Program for Scientic Translations, Jerusalem).
Wakeeld, W.W. & Smith, K.L. Jr. 1990. Ontogenetic vertical migration in Sebastolobus altivelis as a
mechanism for transport of particulate organic matter at continental slope depths. Limnology and
Oceanography 35(6), 1314–1328.
Wall, C.C., Rountree, R.A., Pomerleau, C. & Juanes, F. 2013. An exploration for deep-sea sh sounds off
Vancouver Island from the NEPTUNE Canada ocean observing system. Deep-Sea Research I 83, 57–64.
Walters, C.C. 2007. Is adaptive management helping to solve sheries problems? AMBIO: A Journal of the
Human Environment 36(4), 304–308.
Williams, K., Rooper, C.N., De Robertis, A., Levine, M. & Towler, R. 2018. A method for computing volumetr ic
sh density using stereo cameras. Journal of Experimental Marine Biology and Ecology 508, 21–26.
Williams, K., Rooper, C.N. & Towler, R. 2010. Use of stereo camera systems for assessment of rocksh
abundance in untrawlable areas and for recording pollock behavior during midwater trawls. Fishery
Bulletin 108(3), 352 –362.
Wilmer, C.C., Nickel, B., Bryce, C.M., Smith, J.A., Wheat, R.E. & Yovovich, V. 2015. The golden age of
bio-logging: How animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753.
Zeppilli, D., Pusceddu, A., Trincardi, F. & Danovaro, R. 2016. Seaoor heterogeneity inuences the
biodiversity–ecosystem functioning relationships in the deep sea. Scientic Reports 6, 26352.
