Existing time-series of marine
biodiversity and the need for naturetype mapping in Svalbard waters:
Status, financing, and value for developing
management strategies in a changing Arctic
Akvaplan-niva AS Rapport: 6229 - 2
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Akvaplan-niva AS
Rådgivning og forskning innen miljø og akvakultur
Org.nr: NO 937 375 158 MVA
Framsenteret
9296 Tromsø
Tlf: 77 75 03 00, Fax: 77 75 03 01
www.akvaplan.niva.no
Existing time-series of marine biodiversity and the need for nature-type mapping
in Svalbard waters: Status, financing, and value for developing management
strategies in a changing Arctic
Authors
Akvaplan-niva report no
6229 - 2
Paul E. Renaud
Trine Bekkby
Date
30.10.2013
No. of pages
41 pp + 14 pp appendices
Distribution
Public
Client
Client’s reference
Norwegian Environment Agency
12040107 / 03.12.2012
Summary
Nature-type mapping and both physical and biological time-series data are essential tools for
obtaining a good understanding of ecosystem status and how systems respond to natural and
human-based threats. A recent workshop was help to assess which mapping techniques and
time-series data are available for Svalbard's nearshore waters. Despite the isolation and often
harsh conditions of the high Arctic, an impressive collection of oceanographic and biological
time-series exist for the area. Many result from collaborations between Norwegian and
international researchers, and some of the longest are for benthic fauna. In addition,
methodologies developed from more than a decade of work along the Norwegian mainland,
and new physical/geological baseline data, allow the initiation of nature-type mapping efforts
on Svalbard. Continuity of funding and data access present some of the largest challenges for
prolonging existing time-series and new habitat mapping, but these must be overcome to
assure effective environmental management in the region.
Project manager
Quality controller
_________________________
Paul E. Renaud
__________________________
Salve Dahle
© 2013 Akvaplan-niva AS. This report may only be copied as a whole. Copying of part of this
report (sections of text, illustrations, tables, conclusions, etc.) and/or reproduction in other
ways, is only permitted with written consent from Akvaplan-niva AS.
Table of Contents
PREFACE .................................................................................................................................. 6
1 ENVIRONMENTAL MANAGEMENT ON SVALBARD ................................................... 7
2 INTRODUCTION .................................................................................................................. 8
2.1 Nature-type mapping........................................................................................................ 8
2.2 Time series ....................................................................................................................... 9
2.3 National initiatives relevant to this workshop.................................................................. 9
3 NATURE-TYPE MAPPING ................................................................................................ 11
3.1 Mainland techniques and results .................................................................................... 11
3.2 Physical mapping of habitats on Svalbard ..................................................................... 13
3.3 Technology for monitoring and nature-type mapping ................................................... 14
3.4 Habitat sensitivity analysis............................................................................................. 14
3.5 Priority areas/habitats and recommendations ................................................................ 15
4 EXISTING TIME-SERIES ................................................................................................... 18
4.1 Oceanographic time-series ............................................................................................. 18
4.2 Biological time-series .................................................................................................... 20
4.3 Changing biodiversity patterns in Norway and Svalbard .............................................. 28
4.4 Ecological time-series and climate proxies .................................................................... 29
4.5 How can time-series coverage address management needs? ......................................... 30
4.6 Recommendations .......................................................................................................... 31
5 DATABASES ....................................................................................................................... 33
5.1 What exists and where are data currently stored? .......................................................... 33
5.2 Recommendations .......................................................................................................... 34
6 FINANCING STATUS AND POSSIBILITIES FOR FUTURE SUPPORT ....................... 35
7 CONCLUSIONS ................................................................................................................... 37
8 REFERENCES...................................................................................................................... 39
APPENDICES ......................................................................................................................... 42
Appendix 1: Meeting agenda ............................................................................................... 42
Appendix 2: Participants ...................................................................................................... 44
Appendix 3: Change in marine, benthic macro-invertebrates along the Norwegian coast .. 45
Appendix 4: Existing time series around Svalbard .............................................................. 47
Appendix 5: Publications from time-series .......................................................................... 52
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Table of Figures
Figure 1. Map of Svalbard indicating locations of oceanographic observatories and transects.
………………………………………………………………………………………………...18
Figure 2. Map of Svalbard indicating locations of subtidal hardbottom photographic stations.
.................................................................................................................................................. 21
Figure 3. Map of Svalbard indicating location on repeated intertidal community surveys...... 22
Figure 4. Map of Svalbard indicating locations of soft-sediment benthos time-series. ........... 23
Figure 5. Map of Svalbard indicating locations and years of beam trawl and shrimp trawl
sampling or epifaunal communities ......................................................................... 25
Figure 6. Map of Svalbard indicating locations of ice-fauna monitoring locations. ................ 26
Figure 7. Map of Svalbard indicating locations of zooplankton time-series. ........................... 27
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Preface
Successful environmental management requires a strong understanding of habitats, species
present, and the levels of natural variability in the system. Mapping of habitats (more
operationally termed 'nature types') to be managed, and time-series that monitor of key
elements of these habitats, provide valuable tools for integrating scientific knowledge in time
and space. Whereas considerable strides have been made in these areas along the mainland
coast of Norway, a mapping of habitat distribution and sensitivity, and synthesis of relevant
time-series have generally not been conducted Svalbard's coastal waters.
In February 2013, the Directorate for Nature Management (DN) 1 and the University Centre in
Svalbard (UNIS) funded a workshop drawing together governmental agencies and institutes
responsible for producing and using this information in Norway, and those Norwegian and
international research institutions involved in relevant studies on Svalbard. The workshop
took place Longyearbyen, Svalbard and was organized by DN, UNIS, and Akvaplan-niva.
This report summarizes the mapping techniques and time-series data currently available for
managers, and provides recommendations for improving the quality, relevance, and
availability of knowledge of Svalbard waters.
The authors wish to thank Drs. Tove Gabrielsen and Ole Jørgen Lønne for their assistance in
organizing the workshop at UNIS. We are also grateful to Prof. Jørgen Berge for his help with
some of the figures included in the text, and to Hector Andrade for technical assistance.
Paul E. Renaud
Akvaplan-niva
Tromsø
Trine Bekkby
Norwegian Institute for Water Research (NIVA)
Oslo
Cover photos: Hard-bottom time-series photos from Bjørnøya over a period of ten years, showing succession to a
climax community, then re-initiation of community development again after sloughing off of dense cover of
colonial invertebrates. (Photos: Bjørn Gulliksen)
1
In July 2013 the Directorate of Nature Management and the Norwegian Climate and Pollution Agency merged
to form the Norwegian Environment Agency. For the purposes of this report, however, we will retain the
original name and abbreviation, DN.
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1 Environmental management on Svalbard
Environmental management of Svalbard's waters is the responsibility of the Ministry of the
Environment (MD: Miljøverndepartementet), with the Directorate for Nature Management
acting as the managing authority. The Norwegian Polar Institute (NPI) provides strategic and
scientific advice for management through the directorates, while the Governor of Svalbard
(Sysselmannen på Svalbard) is responsible for practical implementation of regulatory policies.
Each of these agencies/institutes funds specific research projects aimed at helping fulfill the
relevant management roles. For this process to be successful, communication among all
parties, including researchers working in Svalbard waters, is critical.
This report summarizes the discussions of a workshop on nature-type mapping and timeseries conducted in Longyearbyen in February 2013. Topics discussed include summaries of
mapping and monitoring efforts underway around Svalbard, recent research findings,
knowledge gaps/needs identified by researchers and managers, funding possibilities for
continuing work, data management strategies, development of management plans for East and
West Svalbard, and priorities for future management-relevant research.
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2 Introduction
2.1 Nature-type mapping
To make well-founded management decisions for the coastal zone, managers and policy
makers need information (i.e. maps) identifying where important habitats and key resourceareas are found, and how valuable they are. Baseline information on the current status of
marine habitats is required in order to detect possible changes in the future (due to e.g. climate
or anthropogenic influences). According to the UN Convention on Biological Diversity
(1992) all countries are obliged to understand and preserve their biological diversity. In
Norway, this was addressed with a White Paper (Report to the Storting, 1996-1997)
describing the policy for achieving sustainable development of the environment, and
instructing all municipalities to collect knowledge on biodiversity. This work was followed by
the establishment of the Norwegian National Program for mapping and monitoring of
biodiversity (initiated in 2003). The marine component of the program (now named the
National Program for mapping of biodiversity - coast) covers a selection of species, habitats
and key areas in the coastal zone, and studies the geological-biological linkages and the
distribution of species and habitats along environmental gradients using spatial analyses and
distribution modeling. The program has been financed by the Ministry of Defense (until 2010
only), the Ministry of the Environment, and the Ministry of Fisheries and Coastal Affairs.
Challenges for the National Program are the quality and resolution of the bathymetric data,
the lack of substrate data in most areas, the few habitats and key areas covered (see chapter
2.3 and Bekkby et al. 2011), and the lack of the in-depth information needed in order to set
value to the occurrences mapped (see Bekkby et al. 2012). Distribution modeling is an
important management tool and is used for sampling design, delineation and distribution
analyses.
In the more offshore areas, the MAREANO project (www.mareano.no) is mapping the
seabed, including bathymetry, terrain, substrate, species, biotopes, habitats and pollution.
MAREANO aims to answer questions about the seascape of the continental shelf, the seabed
sedimentology, biodiversity, biotope and habitat distribution, the relationship between the
physical environment and the biodiversity and amount of contamination stored. MAREANO
is financed by the Ministry of Fisheries and Coastal Affairs, the Ministry of the Environment
and the Ministry of Trade and Industry. Challenges in MAREANO are, for instance, the lack
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of water flow information and the large number of biological samples to analyze (both NIVA
and APN have recently been involved in the MAREANO lab work). There is currently no
extension of MAREANO to Svalbard waters.
2.2 Time series
Whereas time-series from marine systems are rare, the information contained within them is
critical for evaluating change over management-relevant time scales (e.g. Edwards et al.
2010). Remoteness of the Arctic, the relatively low number of researchers, and the limited
research infrastructure in these areas, however, result in there being few time-series of 10 or
more years across the pan-Arctic region. Topics such as climatic change, fisheriesmanagement policy, coastal-zone management, and ecosystem resilience have become highly
relevant for Arctic nations since climatic change is strongly felt in this region, and there is a
need to manage developing industrial activities and expansion of harvesting activities. These
are highly relevant themes for Svalbard in particular, and require decadal or multi-decadal
data series to support management decision-making.
Fortunately, Svalbard has become an important international center for Arctic research in
the past 10 years, and Arctic research has long been an interest in Norway and several other
nations. This has resulted in establishment of monitoring programs (e.g. Monitoring on
Svalbard and Jan Mayen, MOSJ, by the Norwegian Polar Institute), a series of permanent
research stations (in Longyearbyen, Ny-Ålesund, Hornsund), and investment by national and
international governmental agencies in scientific research in the coastal and offshore waters of
the archipelago. Thus, Svalbard can claim some of the longest oceanographic and biological
time-series found in nearshore Arctic systems. Synthesis of these activities, however, has not
yet been performed; and this report serves as a first step in this process. Open communication
of scientific results will allow for development of dynamic management strategies responsive
to new findings, and a more informed scientific community as to needs for managementrelevant time-series data in the region.
2.3 National initiatives relevant to this workshop
In Norway's Monitoring Plan for Marine Biodiversity, four main goals were outlined: 1 Have the ability to identify changes in biodiversity over time, 2 - Establish a scientific basis
for developing management strategies to protect biodiversity, 3 - Evaluate the effect of
species and ecosystem protection strategies, and 4 - Secure data access for national and
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international user-groups (Direktoratet for naturforvaltning 2001). The Plan also suggests
specific types of monitoring that are needed for the Barents Sea/Svalbard area, and includes
programs for monitoring fjord physics, hard and soft bottom benthos, zooplankton, ice fauna,
and epifauna. Time-series are directly called for, with sampling frequencies ranging from
annual to every third year (for long-lived taxa or those with periodic recruitment cycles). The
2013 workshop that is the basis for this report can, therefore, serve as a status report in how
far Norway has come in addressing these goals for the Svalbard region.
The National Program for mapping of biodiversity – coast, covering the mainland coastal
areas of Norway, has been financed since 2003 (field mapping since 2007). These efforts
focus on ecologically and economically valuable or sensitive habitats, including large kelp
forests, ice marginal deposits, soft sediments in the coastal zone, loose calcareous alga,
eelgrass meadows and other seagrass meadows, carbonate sand, oyster areas, large scallop
occurrences, and spawning areas for cod (Bekkby et al. 2011, 2013). Considerable mapping of
the mainland's nature types, from southern regions up to Troms county has been performed,
with the intent on extending this work to Finnmark (the program plan is to finish in 2018).
The 2013 workshop built upon published research and valuation guidelines from the mainland
to address the need and possibilities for habitat mapping on Svalbard. Available baseline data
were identified, and regions and habitats to prioritize were discussed.
There is a new initiative for a program called “coastal MAREANO ” (kyst-MAREANO)
to extend MAREANO-type activities to the inshore waters of mainland Norway. The partners
suggesting such a program is Kartverket (the mapping authorities), the Geological Survey of
Norway (NGU), Institute of Marine Research (IMR) and the Norwegian Institute for Water
Research (NIVA). The plan is to get high quality and high resolution data on bathymetry and
terrain, geology and substrate, water column (such as ocean current, wave exposure and water
transparency), biodiversity, habitats etc. Once in place, it is conceivable that these efforts
could be extended to Svalbard.
In 2004, the Norwegian Research Council published a three-volume series of reports
described the existing time series in climate, marine systems, and terrestrial/limnic systems
(Norges Forskningsrådet 2003, 2004). Few of the physical and biological time-series outlined
extended to the Svalbard area, however. Much of this is due to there being few established
time-series on Svalbard at the time of publication, but even the hard-bottom photographic
series from Svalbard fjords, which had been underway for more than 20 years already, were
not included. In this sense, the present report is a supplement to these three volumes, updating
current activities in Svalbard coastal and shelf waters.
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3 Nature-type mapping
3.1 Mainland techniques and results
Substantial nature-type mapping activity has been carried out along the Norwegian coast, and
considerable baseline data are available. However, before 2003, little information was
provided for management and planning through available portals. In addition, most of the
information available was point data, and managers and planners stated a clear need for more
area-based information in the coastal zone. Finally, there was no guide for which species or
habitats to prioritize and how to valuate these. Consequently, a national guide describing
habitats and key areas in the coastal zone was published (Direktoratet for naturforvaltning
2001), and the national mapping program was initiated. During 2003-2006, the marine
component of the program focused on developing methods for mapping, modelling, and
collecting existing data. The information was integrated in guidelines for municipalities
(Rinde et al. 2007), and resulted in a revision of the handbook for mapping (Direktoratet for
naturforvaltning 2007). The field mapping started in 2007 and is still on-going (will most
likely continue until the end of 2018).
The mapping is limited to coastal areas (i.e. within 1 nautical mile outside the base
line), as these areas are under much pressure, are covered by the EU Water Framework
Directive, and are getting most of the attention from managers and planning authorities. A
selection of habitats and key areas for selected species and populations were mapped from
2007–2011 (see Bekkby et al. 2011 and 2013 for details). These habitats and key areas were
selected because they:
• have high species diversity
• have unique physical or chemical conditions and function as a habitat for characteristic
biological communities
• are habitats for species that require special attention
• are habitats for special populations
• are highly exposed to human activities and impacts.
The coast of Norway is long (> 100 000 km including mainland and islands) and complex,
with large environmental and tidal gradients. Consequently, field mapping is time consuming
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and costly. Alternative methods have therefore been developed to aid in mapping, including
distribution modeling. Table 1 presents the methods used for the different habitats and key
areas.
Table 1. A list of the habitats and key areas mapped as a part of the National programme for mapping
of biodiversity – coast in the period 2007–2011.
Habitats/key areas
Mapping institute
Method used
Habitats
Large kelp forests
NIVA/IMR/APN
Spatial predictive modeling
Ice marginal deposits
NGU
Demarcation from bathymetric data
Soft sediments in the littoral zone
NIVA/APN
Orthophoto verification of digital models
Eelgrass meadows
NIVA/IMR/APN
Model-assisted field mapping
Carbonate sand deposits
NGU/NIVA
Spatial predictive modeling
Oyster areas
IMR
Field mapping
Scallop populations
IMR
Underwater video and bathymetric data
Spawning areas for fish
IMR
Density estimation, egg-drift modeling
Key areas
The main results from the mapping are provided by Bekkby et al. (2011, 2013). The habitats
and key areas mapped in this program are visualized and made available through
www.naturbase.no, a portal developed by the Norwegian Directorate for Nature Management.
Through this web site, information can be downloaded as vector (shape) or SOSI files. These
maps provide managers and planners with information about the location, extent and
biodiversity of valuable marine habitats or key areas for species, information that is critical to
consider when developing strategies for sustainable management in the coastal zone.
Considerable effort has been dedicated to describing the areas and habitats, and the program
has implemented a method for valuation of each of the occurrences of the selected habitats
and key areas based on specified criteria (Bekkby et al. 2012). Providing the occurrences with
a value is important for giving legitimacy to the process of policy development, and results in
more active management practices. The criteria are, as far as possible, objective and
transparent. Where subjective criteria are used, these are thoroughly described. All
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occurrences are classified as either A (nationally very important), B (regionally important) or
C (locally important). Habitat size, density, diversity, age, production rate and intact
ecological function are regarded as the most important criteria but size, density and overlap
with other species/habitats are the most commonly used criteria, since information on the
others is often lacking.
3.2 Physical mapping of habitats on Svalbard
As discussed above, predictive habitat-mapping uses base-maps for relevant physical and
geological parameters in determining where a specific habitat is likely to be found. For
example, kelp bed occurrence has been predicted solely from available hard substrate, bottom
depth, and degree of exposure (wave energy). Along complex shorelines with highly variable
characteristics, it is clear that high-resolution base-maps are required for models to be
accurate. The remoteness and lack of attention mapping has generally had on Svalbard
appears to make it unlikely to find such resolution in key habitat-determining factors here.
Luckily, this is not the case, at least in some fjords. The Norwegian Mapping Authority
(Statens Kartverket) has performed such bathymetric surveys over the past several years in
Kongsfjorden, Isfjorden, and the Smeerenburg area. Output data include water depth (1 m
resolution), sea level, rugosity (roughness), and bottom slope. Backscatter data can provide
sediment and bottom type. In addition, multibeam echo-sounders produce high-resolution, 3D imaging of large areas of the seafloor, which could be very useful in ground-truthing of
habitat-model outputs. Much of these data are, or will soon be, available on-line at a 5m grid
scale. Other parameters may also be available, depending on need, and this is to a large extent
limited by data-storage capacity. One benefit of data from Svalbard over data from mainland
Norway is that nearshore data from Svalbard are not so tightly controlled by the military, and
thus are more freely available for habitat mapping.
The Norwegian Geological Survey (NGU) conducts much of the same types of
geological and physical mapping, most importantly for habitat-mapping purposes as part of
the MAREANO project (www. mareano.no). Geological/ecological interpretations of these
data have led to biotope and marine-landscape maps for some regions of the mainland coast.
For now, however, these surveys have not been conducted in Svalbard coastal waters.
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3.3 Technology for monitoring and nature-type mapping
The challenges inherent to sampling and mapping the Arctic environment are many, and
include long periods of darkness, extreme cold conditions for researchers, sea-ice cover, and
the remoteness of the study area. This has prevented adequate habitat mapping in most Arctic
areas, and this has become an increasing hindrance to knowledge-based environmental
management in the region. Increased economic and scientific interest in the Arctic, including
the recently completed International Polar Year, have recently contributed greatly to
ecosystem understanding, but large expanses of the Arctic, including Svalbard, remain
uninvestigated. Remote sensing from satellites has proven useful for some topics, but is
limited to investigating surface-water parameters during periods without ice cover.
Recently, the Norwegian Research Council has funded a Center of Excellence in
Research (SFF) on in situ remote sensing (Autonomous Marine Operations and Systems,
AMOS) at the Norwegian University of Science and Technology (NTNU). Its fleet of remote
underwater vehicles (ROV), Autonomous underwater vehicles (AUV), and gliders can help
fill many of the missing habitat data on the Svalbard map. These vehicles measure on
different temporal and spatial scales and collect data both passively via installed sensors, or
actively using human-controlled devices. Already, these technologies have contributed to
understanding of biological processes under the ice during the Polar Night (bioluminescence,
vertical migration), but perhaps where it has greatest promise is in contributing to habitat
mapping. Sidescan sonar, synthetic aperture sonar, and hyperspectral imagery can produce 3D habitat maps. Such photomosaics have already been used on the mainland coast for
mapping of kelp-beds, and this activity can be easily transferred to Svalbard to map a variety
of habitats after ground-truthing is performed.
3.4 Habitat sensitivity analysis
It is clear that humans derive economic benefit from exploitation of marine resources, and
these efforts will likely expand in a warmer Arctic with less ice cover. A primary goal of
environmental management is to allow for sustainable use of marine habitats, while
preserving the integrity of those most important or most sensitive habitats. Habitat sensitivity
is a complex process as both the habitat characteristics (species present, rarity of the habitat,
specific services of the habitat) and frequency and intensity of potential threats (pollution,
physical damage by humans, susceptibility to natural processes) must be assessed. This
process is still in its infancy in Svalbard waters, but a newly completed project by Akvaplan-
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niva (APN) provides initial habitat-sensitivity assessment for East Svalbard, for which the
Governor of Svalbard has recently been developing a Management Plan.
Images from more than 1000 photographic and video stations were sampled and
assessed for physical/geological characteristics, biological and ecological parameters (e.g.
unique biodiversity), and economic value/potential. Stations were coded for vulnerability
based on a 1-3 (low to high) scale used in an earlier assessment of oil-spill vulnerability. This
scale is similar to the A, B, C scale used in mainland Norway for valuation of habitats. These
analyses identified several areas of particular value, including southern Edgeøya, the
Hinlopen area, Arctic fjords on Nordaustlandet, and Heleysundet and Freemansundet between
Storfjord and south Hinlopen (Beuchel et al. 2011).
3.5 Priority areas/habitats and recommendations
Whereas considerable amounts of data are available for some parts of Svalbard, information
for many areas is missing, for instance Nordaustlandet and some fjords (e.g.
Wallenburgfjord). For mapping purposes, areas with good coverage of base data should be
given priority, especially if mapping is to include analyses along geophysical gradients. The
eastern part of Svalbard has little base information, while the northwestern part is well
covered with high resolution multibeam data (from the Mapping Authority).
If mapping will take place in an area covered with high-resolution multibeam data,
different terrain variables can be developed and used in spatial analyses. However,
information on physical conditions, such as sediment type, ocean currents, temperature,
salinity and wave exposure is needed, as these variables are relevant for the distribution and
abundance of habitats/species. A discussion on the base data needed is recommended. The
areas selected should include large spatial gradients in geophysical parameters, so that data on
biological variability may be linked to specific geophysical factors.
The western part of Svalbard should be the highest priority region for mapping, as a
management plan is now being developed for this area. Kongsfjorden and Isfjorden are
mentioned as good study areas because of the relevance for population centres, research
facilities and ecological data, and perhaps most importantly, the good coverage of highresolution multibeam data. Furthermore, the western part of Svalbard will most likely be the
first part of Svalbard to experience changes coming from increased influence of Atlantic
waters. Because of the importance of contrasting Atlantic-dominated fjords with Arctic fjords,
Rijpfjorden on Nordaustlandet and Hornsund in southwest Spitzbergen may also be good
target areas. Hornsund has been the subject of considerable research over the past decades due
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to the presence of the Polish research station, and biotic inventory and habitat mapping
projects are underway there.
Svalbard has very dynamic communities, and to what extent mapping habitats on
Svalbard should follow the model from the mainland coast must be discussed. A suggestion
is:
•
Ice scour zone (upper 4m)
•
Kelp forests (and barren grounds)
•
Strong tidal currents (e.g. Heleysundet, Freemansundet)
•
Soft sediments in the littoral zone
•
Corals
•
Chlamys islandica (Icelandic scallop) areas
•
Calcareous algae
•
Glacial deposits
•
Sublittoral soft bottom with "ice rafted" stones
•
Vertical walls under kelp zone
•
Silled fjords (potentially holding O2-depleted bottom waters)Cobble shoreline
Carbonate sand and seagrass meadows are mapped in the National Program, but these habitats
have not been detected in Svalbard. Intertidal areas might also be mapped, as these are
potentially threatened areas. Studies of the intertidal have been conducted in southern
Svalbard, with some indication of changes in distribution and biomass of macroalgal
communities between 1980s and 2000s (see Sec 4.2). Initial mapping may be most efficient if
efforts are conducted at different scales, both using multibeam models and other geophysical
models together with aerial photographs at a large scale, and more detailed information for
smaller scale studies.
In general, the regions and habitats discussed fit well with both data availability and
habitat sensitivity discussed earlier in this section. Having a well-developed sampling
program in place on the mainland provides the necessary expertise for initiating mapping
efforts on Svalbard.
Based on the idealized priorities and the availability of baseline data with which to begin
work immediately, we recommend:
1. Prioritising West Svalbard, as a management plan is being developed for this area
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2. Selecting an area with good coverage of high resolution of multibeam data (e.g.
Kongsfjorden)
3. Developing a plan for collecting/modelling other relevant base data (such as sediment,
ocean current, temperature, salinity and wave exposure)
4. Developing a systematic plan by which habitats or dynamic biological communities
will be mapped
5. Develop a sampling design in order to detect changes along geophysical gradients
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4 Existing time-series
4.1 Oceanographic time-series
Two main types of oceanographic time-series exist for Svalbard waters: stationary moored
oceanographic observatories and CTD (conductivity-temperature-depth) transects repeated at
least once per year. The focus of most oceanographic time-series work has been in fjords of
West Svalbard, and from the continental shelf and shelf-break region between the mainland
coast and northwest Svalbard. In addition, there is one mooring time-series from Rijpfjorden
on Nordaustlandet (Figure 1)
Figure 1. Map of Svalbard indicating locations of oceanographic observatories and transects. 1.
Kongsfjorden (established 2002), 2. Rijpfjorden (established 2006), 3. Billefjorden (established 2007),
4. E-W hydrographic transects between mainland and Svalbard (since 2003). 1-3 are partnerships
between SAMS, UNIS, and UiT, while 4 is sampled by IOPAS.
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Oceanographic moorings are anchored instrument packages sampling the water
column at discrete depths, and on Svalbard these are operated by the Scottish Association of
Marine Science (SAMS), the University Centre on Svalbard (UNIS), and the University of
Tromsø (UiT). Instruments presently on Svalbard moorings measure temperature, salinity,
depth, light, chlorophyll fluorescence, ocean currents, backscatter (a proxy for the amount of
particles in the water), and sedimentation (sinking flux). Moorings are deployed and measure
continuously for a year, after which the instruments are recovered and cleaned, the data
downloaded, and the mooring redeployed. Three mooring time-series are in operation at this
time. In Kongsfjorden the time-series dates back to 2002, in Rijpfjorden back to 2006, and in
Billefjorden data have been collected since 2007.
The moored observatories provide fundamental data on physical oceanography, as
well as serving as a platform for conducting biological experiments that can take advantage of
high-resolution physical measurements (clam growth, zooplankton behavior, ecosystem
functioning). Data produced by these moorings have proven valuable in identifying events
that have strong and lasting impacts on fjord ecology. For example, current meters and
thermal sensors identified intrusions of Atlantic Water from off the shelf into Kongsfjorden in
Winter 2006-2007, leading to a warming of the fjord that prevented sea-ice formation for the
next 3 winters, and affected biological processes in the fjord (Cottier et al. 2007). Such events
have also been observed during winter periods in years following the winter of 2006-7,
effectively preventing persistent sea-ice cover in the fjord in succeeding years.
SAMS and UiT researchers are in the process of linking Svalbard time-series results to
data from moorings across the Arctic to investigate spatial coherence of process and function.
In addition, there are efforts to combine moorings with remotely operated underwater vehicles
at different periods throughout the year to expand the spatial scale of measurement. Finally, it
is hoped that data from at least one moored observatory will soon be fitted with datastreaming capacities, providing scientists, managers, and educators with real-time on-line data
delivery.
CTD transects from the nearshore, across the shelf, and off the continental slope have
been performed between mainland Norway and northwest Svalbard since 1988 by the
Institute of Oceanology, Polish Academy of Sciences (IOPAS). These time-series map the
volume and heat transport of the West Spitzbergen Current toward Svalbard and into the
Arctic Ocean (Piechura and Walczowski 2009). Other instruments on the CTD package
include fluorometers and light sensors. In addition, these transects are often taken
simultaneously with plankton sampling to provide the physical-environment context for
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biological samples. One example of research results from these CTD transects is the
description of interannual variability in dynamics of the West Spitzbergen Current throughout
most of the first decade of the (very warm) 21st century. These results can be extremely
important for biologists investigating, for example, establishment of boreal taxa on Svalbard,
or food quality for breeding seabirds on Svalbard.
4.2 Biological time-series
One of the most encouraging outcomes of the Svalbard workshop was the realization that
Svalbard has the most time-series of any Arctic location, and this is highlighted by the
number and quality of biological time-series of more than 10 years duration. Subtidal hardsubstrate photographic time-series are the longest continuous time-series, spanning more than
30 years. In addition, there are time-series of intertidal, soft-sediment, epifaunal, ice faunal,
and zooplankton communities. In many locations there are corresponding oceanographic dataseries, making those data even more useful. These time-series provide critical baseline data on
biodiversity and community structure, and depending on the duration of the time-series, can
provide information on biological variability or community change over time.
The subtidal hard-bottom photographic time-series, conducted by UiT, cover three
fjord locations around Svalbard, two areas in Hinlopen, and a location on Jan Mayen Island
(Figure 2). In addition, there are several locations in North Norway and Bjørnøya where
similar studies have been conducted. The Kongsfjorden and Smeerenburg series were started
in 1980 and the Isfjorden series was initiated in 2004. The two Hinlopen stations were started
in 2004 and 2007. These stations have been sampled nearly every year since initiation. The
Jan Mayen location was first sampled after a volcanic eruption that produced new habitat in
1970, and then has been sampled four times since, with the last sampling in 1998. Briefly, a
permanent transect location is selected and bolts are driven into the bedrock such that
identical locations can be photographed (non-destructively sampled) during each repeat
sampling. Nearby areas of hard substrate are cleared of organisms by scraping to test
community-recovery trajectories. Organisms occurring in the photographs are identified,
counted, and measured.
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Figure 2. Map of Svalbard indicating locations of subtidal hardbottom photographic stations. 1. Jan
Mayen (begun 1970 and resampled 1972, 1978, 1994, and 1998), 2. Smeerenburg, Kongsfjorden, and
Isfjorden (north to south), with Isfjorden having started in 2004 and the others in 1980 and sampled
annually, 3. Gyldenøyene (begun 2004) and Tommelpynten (begun 2007) in Hinlopen Strait, sampled
annually. All studies in partnership between UiT and UNIS.
Findings from these studies include the observation that scraped substrate at the
Kongsfjorden site took approximately 13 years to recover and resemble natural substrate
(Beuchel and Gulliksen 2008). Additionally, biodiversity in this habitat varies inversely with
the state of the NAO climatic index (a positive NAO, meaning warmer conditions, results in
lower biodiversity; Beuchel et al. 2006). Finally, there was a simultaneous and abrupt
community shift in both Kongsfjorden and Smeerenburg in the early 1990s resulting in a
stable community of increased macroalgae and reduced in ascidian and anemone cover
(Kortsch et al. 2012). This abrupt change took place against a background of gradual (linear)
changes in temperature (increasing) and ice cover (decreasing), suggesting the possibility that
a tipping point may have been reached. At the Jan Mayen site, new substrate was colonized
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by motile organisms, followed by bivalves within a few years. After 15 years, new substrate
at shallow sites had communities similar to those in natural habitats, whereas after nearly 30
years, sites deeper than 15 m had not recovered. These results indicate that community
structure on subtidal hard substrate varies considerably due to local and regional factors.
Recovery from some disturbances takes one or more decades, depending on habitat-specific
characteristics. This suggests that these habitats may be quite sensitive, a finding that is of
significance for management.
Although not requiring complex sampling devices and expensive ship time, intertidal
surveys in Arctic environments are surprisingly rare, and thus variability in biodiversity and
likelihood for change here is poorly understood. IOPAS revisited more than 20 intertidal sites
in southern Svalbard 20 years after initial sampling (Figure 3) and found an increase in
biodiversity of intertidal taxa, higher biomass of macrophytes, and an upward shift in algal
abundance within the intertidal zone. This occurred during a period of increasing temperature
and reduced sea-ice (Węsławski et al. 2010).
Figure 3. Map of Svalbard indicating location on repeated intertidal- community surveys (1987,
2007/2008). This work has been performed by IOPAS.
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Soft-sediment benthic macrofauna are perhaps the most frequently monitored benthic
communities due to their sedentary nature, relatively long life, and ease of quantitative
sampling. Additionally, there is a good understanding of how communities respond to
different types of stress, at least in temperate latitudes. At least four locations in the Svalbard
area have soft-sediment time-series of at least 10 years: transects from the Atlantic Waterdominated Kongsfjorden and van Mijenfjorden, the Arctic Water-dominated Rijpfjorden, and
one across the Polar Front from Storfjorden to the southeast (Figure 4). This work has been
performed by IOPAS and APN. Only minor changes in community structure have been
observed within Kongsfjorden and van Mijenfjorden despite measured changes in fjord
oceanography (Renaud et al. 2006, Kędra et al. 2010), but substantial changes in biomass at
most of the stations of the Polar Front Transect have been noted (M Carroll, APN, unpub).
Figure 4. Map of Svalbard indicating locations of soft-sediment benthos time-series. 1. Polar Front
Transect (1992, 2005, 2007, 2008, 2009, APN), 2. Kongsfjorden (1997, 2006, IOPAS), 3. Van
Mijenfjorden (1980, 2000, 2007, 2013, APN/IOPAS), 4. Rijpfjorden (periodically since 2003, APN).
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Biodiversity and functional complexity have been shown to be significantly different in the
open Barents Sea compared to West Svalbard fjords, as well as between inner and outer fjord
stations (Włodarska-Kowalczuk et al. 2012). The conclusion was that lower species and
functional diversity in fjords make these habitats less resilient than open sea habitats. It may
be, however, that the dynamic Polar Front region leads to lower stability of benthic
communities here compared to more quiescent fjord systems. All four of these time-series are
being continued and perhaps some of these results, as well as potential contrasts due to
predominant water masses, can be better explained in the future.
The Norwegian Institute of Marine Research has performed annual ecosystem survey
cruises for nearly 10 years in conjunction with scientists from the Polar Research Institute of
Marine Fisheries and Oceanography (PINRO) in Russia. Since 2005, a network of stations on
the continental shelf around Svalbard and within some of the fjords has been sampled,
primarily for epifaunal community structure, using beam and shrimp trawls (Figure 5).
Species have been identified and biomass measurements taken. Scientific articles based on
these data are in preparation.
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Figure 5. Map of Svalbard indicating locations and years of beam trawl and shrimp trawl sampling or
epifaunal communities by IMR.
Ice fauna are patchily distributed in first and multi-year ice both within fjords and in
open water, but where they are present, they can be an important food resource for some iceassociated taxa. Furthermore, since ice faunal communities are tied to the sea-ice
environment, climatic warming presents an important threat to this indigenous component of
Arctic biodiversity. Time-series from two general regions have been maintained by UNIS and
UiT scientists since 1982, when UNIS researcher Bjørn Gulliksen conducted the first studies
of ice fauna in Norway (Gulliksen 1984). These data can be compared with a six-year timeseries from Russian drift stations in the central Arctic Ocean (Figure 6). The biomassdominants living in the ice are amphipods that are either predator/scavengers or herbivorous
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(feeding on the algae living attached to the underside of the ice). Biomass north of Svalbard
has been higher in multi-year ice compared with first-year ice (Lønne and Gulliksen 1991a,
b), but the consequences of the severe decline in multi-year ice over the past 2-3 decades have
not been clearly determined. These time series resulted in the hypothesis that export of ice
fauna through Fram Strait and into the Barents Sea represented a loss of ice fauna from the
Arctic (Arndt and Lønne 2002), but recent studies suggest that some taxa may be able to
conduct a wintertime migration at depth back into the Arctic basin using deep ocean currents
(Berge et al. 2012). Much more work is needed to elucidate the life-cycles of ice faunal
species, and more quantitatively describe their ecosystem roles, especially as climate warming
is expected to result in an ice-free Arctic during summer within 30-40 years.
Figure 6. Map of Svalbard indicating locations of ice fauna monitoring locations. 1. Barents Sea
(established 1982, annually until 1986), 2. Multi-year ice north of Svalbard (established 1986, and
sampled irregularly since), 3. Russian drift ice stations in the Arctic Ocean (1975-1980). 1 and 2 run
by UiT/UNIS).
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Zooplankton time-series from Svalbard fjords and shelf waters have been maintained
by scientists from IOPAS, NPI, and UNIS since the late 1990s (Figure 7). As with benthic
sampling, baselines provided by zooplankton time series are vital for identifying 'natural'
levels of variability, and a reference point against which to evaluate ecosystem change.
Arguably, zooplankton communities may be expected to exhibit greater interannual
variability, despite being sampled at approximately the same time each year. Short life cycles
and highly variable patterns of advection into the fjords are just two reasons for significant
differences among years. The ability to collect physical data simultaneously with
zooplankton, however, greatly improves our ability to interpret the biological dynamics. In
some fjords during some years, fatty acid and stable isotope signatures of zooplankton have
also been collected, as have planktivorous-bird feeding and fledging success. These data help
put the zooplankton data into a greater ecological context.
Figure 7. Map of Svalbard indicating locations of zooplankton time series. 1. Kongsfjorden (annually
from 1996, NPI/IOPAS), 2. Hornsund (annually since 2000, IOPAS), 3. Rijpfjorden (annually since
2006, NPI/UNIS), 4. Isfjorden (2006, 2007, 2010, NPI), 5. Svalbard shelf waters (since 1988, IOPAS).
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In most areas covered by these time series, mesozooplankton abundance is higher in
warmer years, with Calanus finmarchicus, a boreal species, varying inversely with the Arctic
C. glacialis. Temperature also appears to affect development rates, and abundances of smaller
taxa (e.g. Oithona spp., Pseudocalanus spp.) (both positive relationships)
(http://mosj.npolar.no). The impact of warmer conditions on zooplankton community
structure results in lower biomass and presumed quality of food brought back to little auk
nests, and increases the amount of energy parents must use to provide for their chicks
(number and duration of foraging trips) (Hovinen et al. in prep). So far, these longer timeseries only exist only for the West Svalbard region, but the Rijpfjorden series, now in its
eighth season, offers promise of an Arctic comparison. Unfortunately, this site is not an active
feeding area for seabirds, so relevance to top predators is not as clear here. These trophic links
between marine and terrestrial systems elucidated by time-series data provide environmental
managers with key information of how ecosystems may change in the future, but also how
these changes may vary within the Svalbard archipelago.
4.3 Changing biodiversity patterns in Norway and Svalbard
Description of biodiversity levels is recognized as critical for management and understanding
of ecosystem process on local, national and international scales. The recently completed
Census of Marine Life projects investigated biodiversity patterns from the genetic to habitat
level for terrestrial, fresh-water, and marine systems. While the intense research efforts led to
the discovery, description, and cataloguing of thousands of taxa, one conclusion was that we
have only started to record what is present, and that this is the most important step for
evaluating ecological change. There are few nations that can claim to have a better
understanding of the marine biodiversity of its coastal and shelf habitats than Norway, a fact
made even more impressive by its vast and varying coastline. This is due to the devoted and
systematic efforts of researchers at the University of Bergen and NTNU, and in particular, the
two dedicated marine biologists. Torleiv Brattegard and Jon Arne Sneli.
These efforts to continuously catalogue the biodiversity of Norwegian marine macroorganisms have been conducted for several decades, with records reaching as far back as the
18th century. Periodic summarizing and publications of this database represent an unique
time-series whereby not only the number and identity of taxa are presented, but also where
along the Norwegian and Svalbard coasts they have been collected. The latest report,
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published in a report for DN in 2011
(http://www.miljodirektoratet.no/no/Publikasjoner/Publikasjoner-fra-DirNat/DNutredninger/Endringer-i-norsk-marin-bunnfauna-1997-2010/), is already updated in this report
(Appendix 3). Since 1997, more than 500 species were added to the total inventory, bringing
the total for the entire Norwegian coast to 3819 species, but today the total is close to 4000
species. In Svalbard waters there are 1853 species recorded, an increase of 313 since a 2004
report. Clearly some of these records are due to increased sampling, but several patterns
emerge indicating real biodiversity change is occurring. First, 75% of new taxa on the
Norwegian coast are from Scotland and the Shetland Islands, and appear on fishing banks
where international fishing fleets and mesoscale oceanographic features are likely transport
vectors. Further, more than 600 of recorded taxa are showing more northward distributions,
with an average shift of 750-1000 km to the north (Narayanaswamy et al. 2010). Few species
are retracting in the southern direction. Combined with the increase in species observed on
Svalbard, these findings suggest climate warming is strongly impacting species diversity and
distribution in Norwegian waters, and on Svalbard. Unfortunately it is rare to have physical
data (temperature in particular) collected with species data, and it is recommended that efforts
are made to do this in the future. Regardless, these data are highly significant for management
of Norwegian (and Svalbard) waters today, and offer clues into future biodiversity patterns in
the region.
4.4 Ecological time-series and climate proxies
Most time-series data come from monitoring of particular species or assemblages, and clearly
this is of great significance for environmental management. In addition, however, time series
of ecological processes can provide an indication of how ecosystem function changes over
time. APN and its national and international partners have been linking time-series on bivalve
growth to climatic fluctuation in attempt to predict how climatic change may affect ecological
processes in the marine ecosystem. Using techniques in the field of sclerochronology (the
science of finding evidence of temporal change in an organism's hard parts– in this case
calcified shells), species occurring in different habitats from Svalbard fjords and surrounding
waters have been assessed. They display different growth patterns that appear to be related to
predominating water masses and local ecological features (glacial runoff, etc.) (e.g. Carroll et
al. 2011). In addition, growth patterns are related to variation in climatic indices e.g. the North
Atlantic Oscillation (NAO). Arctic and boreal bivalve taxa have lifespans ranging from
decades to several centuries (500 years for one species), and thus there is the possibility of
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developing growth time-series spanning the entire industrial age (Carroll et al. 2009). Results
from these studies can help identify the relative (and combined) effects of regional climatic
conditions and local factors so management strategies for maintaining ecosystem function can
be designed and implemented.
4.5 How can time-series coverage address management
needs?
The time series discussed above provide critical information about baseline values and natural
variability in physical and biological systems, without which any assessment of change (due
to climatic variation or other human impacts) would be impossible. Furthermore, the different
data series when viewed together can suggest mechanisms responsible for the observed
patterns, and whether phenomena are local or consistent cross the region. Much of the
sampling of biological time-series is conducted simultaneously with collection of physical
data on the environment (sediment grain size and carbon content, water column temperature
and salinity, ice thickness and age, etc.). These physical/geological data are valuable, but
provide only a snapshot of local conditions. Coupling of biological time series with
observatories where high resolution physical data is collected throughout the year, may be
even more useful in detecting mechanisms of variability and change.
Data alone, however, are meaningless for managers. Management agencies, and those
institutes responsible for providing management advice, must be made aware of what kinds of
time series exist, and should be updated on results. Further, management needs must be
communicated to time-series owners and funding agencies to make sure important knowledge
gaps are filled and relevant data are available in a timely manner. Adequate interpretation of
time-series data, validated by publication in peer-reviewed journals, is needed, but
conclusions must be communicated in non-technical language to appropriate agencies. If this
is done in a timely manner then managers can weigh the scientific evidence along with
economic and social elements, to develop policies, or to identify knowledge gaps.
Researchers, however, rarely perform the necessary communication, even though they are
often funded based on the project's ability to address management needs. Reasons for this are
many, and include time constraints, lack of knowledge of how to present results in a
meaningful way, and ignorance of who the most relevant end-users of the data are. Improved
communication between data producers and end-users is critical for efficient translation of
time-series and nature-type mapping results into management policy. Frequent workshops
sponsored by funding agencies and management bodies can help provide a forum for such
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exchange. One model for this may be the annual research forum for the offshore petroleummonitoring program where the former KLIF, Norway Oil and Gas, and involved consulting
companies meet and discuss methodologies, developing policies, and results from the
previous year's monitoring activities.
The NPI's MOSJ program is one of the primary providers of scientific advice for
environmental management on Svalbard. Time-series/monitoring data are collected and then
assessed via an indicator-based system that describes the ecosystem. This 'scorecard' is then
delivered to Parliament and relevant management agencies. The MOSJ model is valuable
since it includes direct communication of ecosystem monitoring data to the governmental
agencies responsible for developing management strategies. From the biological side, seabird
population size, marine mammals, fish stocks, and zooplankton indices are currently part of
MOSJ, although there are no indicators for benthos, despite the program being operational
since 1999. This is currently in discussion, and hopefully indicators will be established soon.
Only partial funding for carrying out the mandated research is provided by NPI and MD,
however, leaving the independent institutes or project-related sources to support research for
providing MOSJ indicators.
4.6 Recommendations
Time series provide critical monitoring data for both management and research. Ideally, all
major components of the ecosystem should be monitored, each at relevant time intervals.
Surprisingly, most groups are being monitored regularly as part of on-going time series,
although microbes, thought to be particularly sensitive to expected climatic change, have only
recently been monitored (UNIS MicroFun project). Individually or in conjunction with other
time-series, monitoring data should be useful for evaluating impacts of climate change,
fisheries, and pollution. Sampling frequency need not be the same for all components; for
example, annual resolution may be important for short-lived groups subject to variation in
interannual forcing such as zooplankton, whereas sampling every 2-5 years (or longer) may
be sufficient for long-lived benthos. Patterns in community composition are valuable, but it is
also important to monitor ecosystem function. Little regular measurement of ecological
process has been undertaken as part of existing time series.
One weakness that impacts most time-series existing around Svalbard is that of
available taxonomic expertise. For many groups (e.g. sponges, hydrozoans, gelatinous
plankton), expertise is low, or may be lacking completely. Luckily, collaboration with Polish
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and Russian research groups has been able to extend the expertise of Norwegian institutions,
but the situation in Norway is becoming critical. Funding for the few taxonomic experts
present is low, and education of new taxonomists is nearly non-existent. The strong record of
monitoring biodiversity patterns along the mainland and Svalbard coasts (Appendix 3), is thus
in jeopardy. Even simply maintaining this database requires dedicated funding and personnel,
and until now this has been run by one or two individuals in their spare time with limited
funding from DN. If the Svalbard Integrated Arctic Earth Observing System (SIOS) program
is funded, perhaps it can be a resource for continuing this work, but SIOS is neither
guaranteed to be funded, nor is this task explicitly described in the proposal.
Despite many of the challenges mentioned, it is clear that Svalbard is home to perhaps
the best set of marine time-series in the Arctic. For that we should be proud. Changing
ecosystems, funding possibilities, and institutional priorities must not lead to these time-series
being stopped as these are central to national management priorities; the value of a time series
increases with its duration. New monitoring techniques, including cabled (real-time)
observatories, remote sensing, and studies of ecological process and proxies will replace
and/or supplement current practices. Merging time series to address ecosystem-level questions
will likely also become a priority. The use of mooring data to identify novel zooplankton
behaviors (e.g. Berge et al. 2009), and mechanisms for community shifts (e.g. Willis et al.
2008) is one example. Linking multiple biotic data series is also possible and can be
revealing. Large-scale monitoring performed by the Institute of Marine Research (Figure 5)
needs to be better integrated with coastal studies and time-series. Along these lines, a coastalor Svalbard-based extension of the MAREANO project (or the developing coast-MAREANO
project) must include input and participation of the institutions with local expertise and a
history of collecting and publishing data from Svalbard time-series. Although this change in
philosophy, and funding scheme, may be met with some resistance, it is vital to providing
managers with the necessary quality-assured information as quickly as possible.
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5 Databases
5.1 What exists and where are data currently stored?
Storage and archiving of data are critical for maintenance of successful time-series. The longterm nature of the data collection, and need for quality control and cross-platform
compatibility over time are just two reasons for this. There are many options for storing data
collected through time-series studies, but this is both an advantage and a hindrance to the
process. Historically data have been under the control of individual researchers, who may or
may not share the data, provide adequate documentation and metadata, or even pass the data
on to someone else when they retire. Some researchers have provided accessibility over
public and quasi-private web sites. In recent years, institutions have made a stronger effort to
maintain internal databases as well as asserting legal ownership over much of the data
collected by their employees. The Norwegian Marine Data Centre (NMDC) was initiated in
2012 through a grant from the Norwegian Research Council. Climate, oceanographic, and
biological data are primary contributions of the 15 member marine institutions. The goal of
this database to provide seamless access to Norwegian data sets. A central database such as
this, and advanced database products and services that could be available in the future, can
increase efficiency of research efforts. The database is being populated now to different
degrees by the member institutions. Data management policies include the clause that data
from public funds should be open and free to scientists, although the private sector may be
charged for access. Still, many data sets are only available as metadata, with the real data
available internally only.
There is no consistent requirement within or among institutes, however, regarding
archiving of data or metadata. In recent years, large national (Artsdatabanken, Svalbard
Integrated Earth Observing System, NMDC) and international databases (Pangaea, Ocean
Biogeographic Information Service, World Register of Marine Species) have become more
prominent players or are under construction. Some funding agencies, such as the European
Union, require that data collected with their funds be deposited in one or another of these
databases. This fragmented approach of data storage must be resolved for time-series
continuity to be mirrored by data accessibility. Appendix 4 underscores these issues, as data
from most time-series discussed are stored privately or at the institution level, and rarely in
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national databases. No international databases have received Svalbard time-series data to our
knowledge.
5.2 Recommendations
The fragmented and dispersed nature of databases limits their usefulness, and reduces the
investment and incentive to contribute to them. Currently, it is difficult to find metadata for
projects, even if one knows the project exists. For example, the zooplankton time series in
Kongsfjorden may be described in part in the 'Research in Svalbard', NMDC, SIOS, or
Artsdatabanken sites, or may be absent from all sites. A description and some processed data
do exist on the MOSJ web site, but a complete metadata file describing the time series does
not. Raw data are not available anywhere except for internal data archives at NPI. This is just
one example, and the challenges are similar for nearly all the data sets discussed above.
Institutes and individual researchers are hesitant to release data paid for by internal funding,
and if one wishes to provide data, it is not clear how to decide where to send it. This is not a
problem unique to Norway, although there seems to be an unnecessarily large variety of
'national' databases for such a small community.
Several possible solutions exist, but it seems clear that the problem must be addressed
in a two-step process. First, a single national database for metadata should exist, and all other
databases should not accept data, but instead refer researchers to this central database. This
will simplify both providing metadata and searching for relevant research data. Metadata files
should then include a clear reference to where the raw (or processed) data can be located.
Ideally, this should be an international database, but we recognize this is a strong political
issue whereby different agencies feel their database is necessary. Regardless, these databases
should be open to all, and not just select member institutions. Requirements by funding
agencies for submission of data into open databases must be expanded, but should follow a
clear data management policy developed in collaboration with many potential data providers.
Finally, a system for petitioning institutions for data not currently available should be
established and administered by the Norwegian Research Council, and relevant ministries.
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6 Financing status and possibilities for future
support
Adequate long-term financing is one of the most important challenges for establishing and
maintaining time-series. Most granting agencies have funding cycles of 2-4 years for research
projects, which can help maintain time-series in the short term, but are not sufficient for the
providing support for the multiple decades that are usually necessary for identifying natural
cycles, variability, and real change. Most existing time series on Svalbard originated from
initiatives of individuals or research groups, and have been funded from mixed sources, but
predominantly through internal funding from home institutions (Appendix 4). Even within
MOSJ, where a state agency has been tasked with providing expert advice to management,
dedicated funding to support required activities has been difficult to secure. NPI has finances
to pay for monitoring for some of the MOSJ indices, but a more secure funding base for all
indicators is needed. If state agencies are not provided with money to secure funding of
management-relevant time-series, and standard research-funding sources have funding
windows that are too short to guarantee continuity of monitoring efforts, the burden of
supporting this work will continue to fall on research institutes. The result of such a 'solution'
is that time series providing important management advice may be interrupted periodically, or
stopped completely, depending on institute priorities. This will clearly have negative impacts
on the development of Svalbard environmental policy.
One alternative, however, is the possibility for building upon the infrastructure of the
Fram Centre for Climate and the Environment. The Fram Centre in Tromsø has more than 20
member institutions engaged in multi-disciplinary research and consulting with high
relevance to management. It is funded by many of Norway's governmental departments, but
primarily the Environmental Protection Department (MD), and provides funding for research
and outreach relevant to northern Norway and Svalbard, particularly in reference to one of the
most pressing issues in the region: climate change. Research is structured around 5 flagships
with habitat or topical focus, but it may be possible to establish a new flagship responsible for
maintaining key time-series. Fram institutes already lead some of the longest and most
extensive time-series in the Arctic, including many of those Svalbard data-series discussed in
this report. Additionally, climate change is happening now, and management and mitigation
efforts have never been more in need of high quality knowledge to support their activities than
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
35
now. Finally, Departments and Ministries should have the flexibility to commit the resources
necessary to support time-series over many years. This possibility must be pursued as it has
the promise to resolve some of the financing challenges presently facing time-series research.
On mainland Norway, marine habitat mapping efforts have received dedicated funding,
primarily from the Directorate for Nature Management and the former Ministry of Fisheries
and Coastal Affairs 2. The National Program for Mapping of Marine Habitats was initiated in
2007, and nearly two-thirds of the mainland coast has been mapped. The Ministry of
Environment and the Ministry of Industry and Fisheries intend to complete the project within
the next several years. For now, however, there is no commitment to have a program for
Svalbard, and thus, no funding currently exists. Whereas some basic data have been collected
(see Sec. 3.2 above), and many of the tools used on the mainland can be employed on
Svalbard, a plan for this work is also lacking. One of the goals of this workshop was to
identify whether there is need for this work on Svalbard, and determine the institutes, existing
data, and available resources that can contribute to this work. Since the Governor of Svalbard
needs to make decisions about how to implement policies to reduce threat to valuable and
vulnerable nature types, the need is clearly pressing. Hopefully, financing for a planning stage
is soon in place, to be quickly followed by project funding.
2
Beginning January 2014, relevant section of the Ministry of Fisheries and Coastal Affairs will fall under the
new Ministry of Industry and Fisheries.
36
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
7 Conclusions
Despite clear logistical and financial challenges, there is a surprisingly extensive set of ongoing time series collecting physical and biological data in Svalbard's nearshore waters. These
time series range in length from 6 to over 30 years, and have produced a strong foundation for
management decisions today, and a baseline for detecting system change in the future (as we
have already seen changing biodiversity patterns). Increased use of developing technology
can be a cost effective way of continuing or expanding some data series, and collaboration of
researchers with the newly-established Autonomous Marine Operations and Systems (AMOS)
Centre of Excellence is encouraged.
Combining results from different time-series may provide a broader ecosystem-based
basis for interpretation of observed patterns. Similarly, ecological time-series can be
increasingly valuable for assessing ecosystem status over time, and potential human and
climatic impacts. This more integrated use of data, however, highlights the need for better
communication between researchers and managers so results are provided in a timely and
easy-to-use manner. Integration of efforts among researchers is also critical as there are
multiple national and international institutions responsible for these time series.
Nature-type mapping offers a highly valuable management tool that has thus far been
missing in Svalbard waters. Initial efforts to optimize mainland models for use on Svalbard
should focus on west Svalbard fjords where high-resolution multi-beam data are available.
Sensitive habitats have been identified and classified for some areas, but the basis for this is
relatively crude and more directed mapping efforts should augment these initial studies. Any
expansion of MAREANO-type mapping programs in Svalbard waters must not be undertaken
without regard for the vast experience and expertise of those who have worked in the area for
the past several decades.
Greater centralization of regional and national database initiatives would encourage
use of databases and greater public access to data. This is increasingly important as data series
become longer and extent beyond the careers of researchers who initiated them. Perhaps the
greatest challenge for maintaining time series and performing spatial mapping of nature types
is the lack of dedicated funding sources for such activities. This works lies outside the typical
time-frames for research projects and the budgetary lines of management agencies. The Fram
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
37
Centre for Climate and the Environment in Tromsø is funded by a number Ministries, and is
one potential source of long-term support as there is a direct connection between time-series
studies and the mission of the Fram Centre. Nature-type mapping has historically fallen under
the Ministries of Environment and Fisheries/Coastal Affairs, and it can hopefully be extended
to Svalbard under these funding sources. The knowledge gained from this work, however, has
value for many other ministries, and co-funding on this work seems appropriate.
38
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
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Appendices
Appendix 1: Meeting agenda
Workshop on habitat mapping and time series in coastal areas of Svalbard
05-07 February 2013
Svalbard Research Park
Longyearbyen
Day 0 (05 Feb 2013):
~1405 arrive. Bus from airport, settle into rooms in UNIS 'researcher hotel'
1900 informal DINNER (Kroa)
Day 1 (06 Feb 2013):
0900 (moderator: Paul Renaud)
Time series, biodiversity, and nature-type mapping
Welcome: Ole Arve Misund (UNIS) 5 min
Welcome: Ingrid Bysveen (DN) 10 min
Welcome: Svein Kristensen (UiT) 5 min
Welcome/orientation: Paul Renaud (APN) 5 min
Anne Britt Storeng (DN) 15 min coastal mapping
Bjørn Gulliksen (UiT) 30 min UW photographic time series
Torleiv Brattegard (UiB) 20 min biodiversity/mapping
1025 ----Coffee 15 min----OJ Lønne (UNIS) 20 min Ice fauna time series
Frank Beuchel (APN) 20 min East Svalbard habitat sensitivity
Trine Bekkby (NIVA/UiO) 20 min Habitat mapping
1240 LUNCH
1330 PM (moderator: Jørgen Berge)
Other marine time-series (including needs/plans for future)
Finlo Cottier/Jørgen Berge 20 min SAMS/UNIS/NPI Svalbard fjord moorings
Anette Wold 20 min NPI/IOPAS zooplankton time series
Jan Marcin Weslawski 20 min IOPAS/APN fjord benthos
Michael Carroll 20 min APN Polar Front time series
1450 ---Coffee 15 min ---Geir Johnson (NTNU) 20 min Remote sampling for time series and mapping
42
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
Boele Kuipers (Kartverket) 15 min Svalbard bathymetry and seafloor
characteristics
Svein Kristensen (UiT) -15 min Norwegian Marine Data Centre
Ingrid Bertinussen (NPI) -15 min MOSJ
Paul Renaud (APN) 15 min A time-series flagship for FRAM
Reidulf Bøe (NGU) 15 min NGU data mapping
2000 DINNER (Rica Funktionærmessen)
Informal talk: Salve Dahle
Day 2 (07 February 2013)
0900 Management needs and resources (moderator: Ingrid Bysveen)
Ingrid Bysveen (Welcome/orientation) -5 min
Elin Lien (Sysselmannen) (15 min) – SM needs for Svalbard management
Discussion #1: Mapping (45 min): What benthic communities/nature types
should have first priority for mapping
1005 --- Coffee 15 min--Discussion #2: Monitoring (45 min) What existing time series are most
important for management purposes and what time series do we need to get a
more complete marine monitoring??
Discussion #3: Possibilities for financing new mapping and monitoring activity
(20 min)
Discussion #4: Central databases and coordination and how do we proceed?
(20 min)
Paul Renaud (Summing up of discussions) (10 min)
Ole Arve Misund and Ingrid Bysveen: Final remarks and closing of the
workshop (5 min)
1200 LUNCH
Flight to Tromsø/Oslo: 1445
Time-series and nature-type mapping in Svalbard waters
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43
Appendix 2: Participants
Name
Institute
Carl Ballantine
Trine Bekkby
Jørgen Berge
Ingrid Berthinussen
Frank Beuchel
Torleiv Brattegard
Ingrid Bysveen
Reidulv Bøe
Michael Carroll
Salve Dahle
Bjørn Gulliksen
Geir Johnsen
Lis Lindal Jørgensen
Svein Kristiansen
Boele Kuipers
Elin Lien
Harald Lura
Ole Jørgen Lønne
Ole Arve Misund
Paul Renaud
Jon Arne Snelli
Anne-Britt Storeng
Øystein Varpe
Cecilie von Quillfeldt
Jürgen Weissenberger
Jan Marcin Węsławski
Anette Wold
University of Tromsø
Norwegian Institute for Water Research, Univ. of Oslo
University of Tromsø
Norwegian Polar Institute
Akvaplan-niva
University of Bergen
Directorate for Nature Management1
Norwegian Geological Survey
Akvaplan-niva
Akvaplan-niva
University of Tromsø
NTNU
Institute of Marine Research
University of Tromsø
Statens Kartverk
Governor of Svalbard
ConocoPhillips
University Centre in Svalbard
University Centre in Svalbard
Akvaplan-niva
NTNU
Directorate for Nature Management1
Akvaplan-niva
Norwegian Polar Institute
Statoil
Institute of Oceanology, PAS
Norwegian Polar Institute
1
now the Norwegian Environment Agency
44
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
Appendix 3: Change in marine, benthic macro-invertebrates along the Norwegian coast
Torleiv Brattegard © updated 22.08.2013
Gruppe
Notes (see below):
From distribution tables from the years -
Southern
distribution
1)
1996
2)
2001
3)
2006
4)
2012
1)
1996
PanNorwegian
distribution
2)
3)
2001 2006
Northern
distribution
Unknown
distribution
Sum spp.
4)
2012
1)
1996
2)
2001
3)
2006
4)
2012
1)
1996
2)
2001
3)
2006
4)
2012
1)
1996
2)
2001
3)
2006
4)
2012
Porifera
Svamper
78
71
74
101
109
100
112
120
51
48
50
50
37
44
45
22
275
263
281
293
Octocorallia
Hexacorallia
Staurozoa
Hydrozoa
Åttetallskoraller
Sekstallskoraller
Stilkmaneter
Hydrozoer
18
42
1
48
16
45
2
57
17
44
1
54
18
43
1
48
6
15
3
73
6
14
2
74
6
17
3
74
7
20
3
77
10
15
2
21
8
14
1
22
9
15
1
20
10
19
2
20
2
7
0
12
1
10
0
19
1
5
0
13
1
4
0
6
36
79
6
154
31
83
5
172
33
81
5
161
36
86
6
151
Nemertea
Slimormer
20
19
17
22
14
13
15
16
4
4
1
7
21
18
19
16
59
54
58
61
Caudofoveata
Solenogastres
Polyplacophora
Prosobranchia
Heterobranchia
Cephalopoda *
Bivalvia
Scaphopoda
Ormebløtdyr
Ormebløtdyr
Leddsnegler
Forgjellesnegler
Heterobranker
Blekkspruter
Skjell
Sjøtenner
5
8
5
107
122
7
115
7
5
6
5
108
129
6
126
7
5
8
6
102
117
7
116
5
3
10
4
93
113
7
106
2
1
0
7
54
36
3
42
2
1
0
7
52
33
3
35
2
1
1
7
67
50
3
46
4
3
1
8
84
59
3
74
8
1
4
1
66
14
1
40
1
1
4
1
68
14
1
43
1
6
1
1
10
9
1
42
1
1
6
1
65
12
1
43
2
0
8
0
12
16
0
2
0
0
5
0
0
8
0
3
0
0
10
0
4
11
0
5
0
0
10
0
12
18
0
2
0
7
20
13
239
188
11
199
10
7
15
13
228
184
10
207
10
7
25
14
240
187
11
209
10
7
27
13
254
202
11
225
12
Sipuncula
Stjerneormer
8
8
8
7
7
7
7
8
1
1
1
1
0
0
0
0
16
16
16
16
Entoprocta
Begerormer
13
13
13
13
7
6
6
7
0
0
0
0
1
1
1
2
21
20
20
22
Echiura
Skjeormer
2
2
2
3
1
1
1
1
1
1
1
1
1
1
1
0
5
5
5
5
Polychaeta
Oligochaeta
Hirudinea
Flerbørstemark
Fåbørstemark
Igler
284
9
3
289
9
3
256
12
3
299
12
3
229
10
4
200
16
4
304
13
4
380
13
4
55
2
3
54
2
3
56
0
3
95
0
3
50
4
1
60
4
1
26
0
2
29
0
1
618
25
11
603
31
11
642
25
12
803
25
11
Pycnogonida
Ostracoda
Copepoda (Calanoida) #
Tantulocarida (parasitic)
Cirripedia (freeliving)
Cirripedia (parasitic)
Leptostraca
Havedderkopper
Muslingkreps
Hoppekreps
Bagatellkreps
Rankeføtter
Rankeføtter
Leptostraker
12
80
23
0
3
16
2
15
83
21
0
3
14
2
10
76
21
0
3
16
2
11
68
24
0
4
13
1
14
46
2
0
5
3
0
13
46
2
0
6
3
1
16
52
2
0
6
3
0
18
64
2
0
6
3
1
13
13
3
0
2
0
1
13
8
3
0
2
0
1
12
9
3
0
2
0
1
12
10
3
0
2
0
1
0
7
2
1
0
2
1
0
7
2
1
0
2
0
0
7
4
1
0
2
1
0
4
4
1
0
2
1
39
146
30
1
10
21
4
41
144
28
1
11
19
4
38
144
30
1
11
21
4
41
146
33
1
12
18
4
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
45
Appendix 3: Continuation…
Decapoda
Lophogastrida
Mysida
Amphipoda
Cumacea
Tanaidacea
Isopoda (freeliving)
Isopoda (parasitic)
Tifotkreps
Lofogastrider
Pungreker
Amfipoder
Halekreps
Tanaider
Isopoder
Isopoder
Priapulida
Priapulider
Phoronida
Hesteskoormer
Bryozoa/Ectoprocta
Mosdyr
Brachiopoda
Chaetognatha (benthic)
69
1
29
189
19
14
35
16
73
1
29
184
18
15
36
15
77
1
26
167
18
14
35
17
0
0
0
73
1
25
151
19
15
29
17
21
0
6
141
31
12
43
4
18
0
7
136
31
10
43
4
0
2
2
2
2
3
3
0
0
51
65
61
57
155
145
Armfotinger
3
3
3
2
3
4
Pilormer
1
1
1
1
0
0
Hemichordata
Hemikordater
5
4
4
4
0
Xenoturbellida
Xenoturbellider
1
1
1
1
0
Crinoida
Asteroida
Ophiuroida
Echinoida
Holothuroida
Sjøliljer
Sjøstjerner
Slangestjerner
Sjøpiggsvin
Sjøpølser
3
10
22
9
17
3
10
22
9
18
3
10
19
9
17
3
9
15
9
14
Ascidiacea
Sekkdyr
31
30
29
Cephalochordata
Lansettfisker
1
1
1
1566
1604
1511
18
0
10
162
32
12
43
4
2
0
24
0
11
196
36
12
50
4
7
0
9
70
6
4
4
1
8
0
8
70
7
4
5
1
8
0
8
72
6
4
4
1
8
0
8
83
8
4
4
1
0
0
0
8
0
0
3
4
0
0
0
9
0
1
3
4
0
0
0
22
0
0
3
3
0
0
0
6
0
0
3
3
97
1
44
408
56
30
85
25
99
1
44
399
56
30
87
24
103
1
44
423
56
30
85
25
105
1
44
436
63
31
86
25
2
1
1
1
1
0
0
0
0
3
3
3
3
0
0
0
0
0
0
0
0
0
2
2
3
3
159
42
39
45
53
12
16
8
3
260
265
271
272
4
5
2
2
2
2
0
0
0
0
8
9
9
9
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
5
4
4
5
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
17
7
6
7
1
17
6
6
6
1
17
11
6
10
1
18
15
7
12
2
16
7
1
7
2
17
6
1
7
2
17
7
1
7
2
18
7
1
7
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
0
0
0
6
43
36
16
31
6
45
34
16
31
6
44
37
16
31
6
47
37
17
33
31
22
22
26
29
21
21
21
22
1
1
0
0
75
74
76
82
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1509
1171
1105
1571
525
517
461
596
215
222
194
153
3477
3448
3558
3829
157
1335
Sum of change in number of accepted species
1996-2001
1996-2006
38
-66
-55
1996-2012
2001-2006
-8
164
-57
-93
400
230
-29
-21
71
-56
81
-62
-28
352
110
2001-2012
-95
466
79
-69
381
2006-2012
-2
236
135
-41
271
Note 1. Corrected figures from Brattegard & Holthe (eds)(1997) in Research Report for DN Nr. 1997-1
Note 2. Corrected figures from the updated version of Brattegard & Holthe (1997) in DN Research report 2001-3
Note 3. Figures from the original tables used for making DN's database for marine, benthic macro-organisms.
Note 4. From updated tables for year 2012 - by Torleiv Brattegard.
Note *. Only benthic and hyperbenthic species.
Note #. Only hyperbenthic species.
46
7
-64
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
Appendix 4: Existing time series around Svalbard
Descriptive name of time
series
Institute
PHYSICAL TIME SERIES
isostatic rising
Kartverket
sea level measurements
Kartverket
bathymetry
Kartverket
detailed bathymetry
Svalbard
Kartverket
bathymetry derived
products (rugosity, slope,
geomorphology)
backscatter
water column reflections
CTD
Ferrybox
Other
Institutes
involved
Location
Time period covered
Financing sources
Data availability
Ny Ålesund
Svalbard, (Ny
Ålesund
permanent)
Norge, Svalbard
Svalbard
(Kongsfjorden,
Isfjorden,
Amsterdamøya)
1992 ->
Kartverket
Free (IGPS)
1992 ->
Kartverket
Free (seHavnvå, API)
Kartverket
Restrictions
1998 (shallow 2007)
Kartverket
Kartverket
Svalbard
1999 (shallow 2007)
Kartverket
Kartverket
Kartverket
Kartverket
Svalbard
Svalbard
Svalbard
Along the Ferry
route. Every other
trip goes to Ny
Ålesund
2000 (shallow 2007)
2001 (shallow 2007)
2002 (shallow 2007)
Kartverket
Kartverket
Kartverket
NIVA
APN
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
From 2008, 19-20
rounds/yr, measures
every min at 4 m depth
47
NIVA, Klif, Fram
Temp, salinity,
oxygen, particles,
chl-a fluorescence,
Appendix 4: Continuation…
PHYSICAL TIME SERIES
APN IMR, NP
(Klif and
Fram
programs)
APN IMR, NP
(Klif and
Fram
programs)
Ferrybox
NIVA
Ferrybox
NIVA
Ferrybox
NIVA
APN
Satellite data
NIVA
None
Kongsfjord oceanographic
observatory
Rijpfjord oceanographic
observatory
Billefjord oceanographic
observatory
Hydrography on shelf
1988- 2012
Hydrography in fjords KGF
and H. 1996- 2012
48
SAMS
SAMS
SAMS
IOPAS
IOPAS
UNIS, NPI,
UiT
UNIS, NPI,
UiT
UNIS, NPI,
UiT
Along the Ferry
route. Every other
trip goes to Ny
Ålesund
Along the Ferry
route. Every other
trip goes to Ny
Ålesund
Along the Ferry
route. Every other
trip goes to Ny
Ålesund
Svalbard and
Barents Sea
2010-2016
NIVA, Klif, Fram
(since 2010)
Water samples for
nutrinet, acidification
(ALK, TIC)
From 2013
NIVA, Fram (since
2010)
Autom. measures of
pH and pCO2 in 2013.
Only few dataset
available
ESA
Deck sensors of light
and ocean colour for
satellite validation
2002-2012
Norwegian Space
Centre
Satellite ocean colour
Kongsfjord near
Kvadehuken
2002-2015
Rijpfjord
2003, 2006-2015
Billefjord
2006-2013 (most years) UNIS, SAMS
Bjornoya to NW
Spitsbergen
Hornsund,
Kongsfjorden
NERC, SAMS, NPI,
UNIS, UiT, NFR
NPI, UNIS, SAMS,
NFR
1988- 2013
IOPAN/ national
1996- 2013
IOPAN/ national
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
On-line public
(sams.uk)
On-line public
(sams.uk)
On-line public
(sams.uk)
Metadata,
publications
Metadata,
publications
Appendix 4: Continuation…
BIOLOGICAL TIME SERIES
Microplankton in fjords
IOPAN
Kongsfjorden, Hornsund
Microplankton in fjords
IOPAN
Adventfjorden
NP.
Hornsund,
Kongsfjorden
2009 - 2013
IOPAN/NP.
UNIS
Adventfjorden
2012- 2014
UNIS
1996-2013
NPI, Research
projects
IOPAS, UNIS,
Kongsfjorden
AWI
Zooplankton timeseries
NPI
Zooplankton timeseries
NPI & UNIS IOPAS
Rjipfjorden
2006-2013
NPI, Research
projects
Zooplankton timeseries
NPI & UNIS IOPAS
Isfjorden
2006, 2007,2010
Research projects
Zooplankton timeseries
IOPAS
Hornsund
2001-2013
Zooplankton in West
Spitzbergen Current
Zooplankton - Little auks
food base
IOPAS
IOPAS
Loff / Georg
Shorebird abundance and
APN / UNIS Bangjord
phenology (DELTA)
and others
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
Bjornoya to NW
1987-1989, 2001-2013
Spitsbergen
Hornsund,
2001- 2013
Magdalenefjorden
Adventfjorden
1996-2013 (high
resolution in 2008,
2009, 2011, 2013)
49
IOPAN/ national
IOPAN/ national/
NFM
Svalbards
Miljøvernfond,
Framsenteret,
UNIS
Metadata,
publications
Metadata,
publications
Database NPI,
http://olav.npolar.no
:3020/
2004 & 2010-2013
Database NPI,
http://olav.npolar.no
:3020/
2006-07 & 2010
Database NPI,
http://olav.npolar.no
:3020/
Metadata available at
IOPAS,
Metadata,
publications
Metadata,
publications
Appendix 4: Continuation…
BIOLOGICAL TIME SERIES
Marine molluscs of the
Svalbard area
All Taxa Biodiversity
Inventory in Hornsund
UiT and UNIS benthos and
diving stations database
Ecosystem Survey Helmer Hansen
Algal vegetation from
outer part of Isfjorden
Algal vegetation
Hansneset, Kongsfjorden
Algal vegetation in
Isfjorden
Littoral flora and fauna –
decadal survey – West
Coast
Kongsfjorden permanent
photostation time series
Smeerenburg permanent
photostation time series
Bjørnøya permanent
photostation time series
Sagaskjæret permanent
photostation time series
HinlopenTommelpynten
time series depth transect
50
NTNU
Svalbard
1850 - 2005
DN, UNIS
File with all available
data valuated
IOPAS
Hornsund
2000- 2013
IOPAN/ national
Open data source
1002 stations all
around Svalbard
1976-present
APN, UiT, UNIS,
DN
IMR
Around Svalbard
2009-2012 and ongoing
FKD
UiO/BI
Isfjorden
1956/57 and 2007
AFG/SSF
Species lists
APN
UNIS, UiT
UiO/BI
AWI
Kongsfjorden
1996/97 and 2012
AWI + AFG/SSF
Species lists
UNIS
UiO/BI
Isfjorden
2010-2012
UNIS + AFG/SSF
Species lists
from Sorkapp to
Isfjorden
1988, 2008, 2010
IOPAN/ national
Metadata,
publications
1980-present
UNIS, NFR, UiT
1980-present
UNIS, UiT
1980-1998; 2009
UNIS, UiT
2006- present
UNIS, UiT
2002-2008 (?)
UNIS, SMVF, UiT
IOPAN
UiT
APN, UNIS
UiT
APN, UNIS
UiT
APN, UNIS
UiT
APN, UNIS
UiT
APN, UNIS
78.58,63N
11.29,89E
79.41,32N
11.04,02E
74.29N 18.45E
78.12,47
13.56,91E
79.34,16N
18.39,95E
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
Appendix 4: Continuation…
BIOLOGICAL TIME SERIES
Hinlopen Gyldenøya time
series depth transect
Artificial substrate
colonization
Meiofauna in Hornsund
and Kongsfjord soft
bottom
Meiofauna in Hausgarten
area
Depth gradient in soft
bottom fauna - decadal
change
Soft bottom zoobenthos
in Van Mijenfjorden decadal change
Soft bottom zoobenthos
in KGF and Hornsund
UiT
APN, UNIS
79.40N 19.45E
2003-2009
UNIS, SMVF
IOPAS
Adventfjorden
2007- 2013
IOPAN/ national
Metadata,
publications
IOPAS
Hornsund,
Kongsfjorden
2009-2013
IOPAN/ national
Metadata,
publications
IOPAS
AWI, Univ.
Ghent
Hausgarten, off
Kongsfjorden
2001-2009
IOPAN/ AWI
Metadata,
publications
IOPAS
AWI
KongsfjordenHausgarten
2000, 2010
IOPAN/ AWI
Metadata,
publications
APN
IOPAN
Van Mijenfjorden
1980, 2000, 2007, 2013
IOPAN/AKVAPLAN
Metadata,
publications
IOPAS
APN
1996-2013
IOPAN/ national
Metadata,
publications
Polar Front Transect
APN
NIVA
Rijpfjorden Transect
APN
NPI, UNIS
Bivalve Chronologies
APN
NPI, UNIS,
UiT
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
Hornsund,
Kongsfjorden
Barents Sea Polar
Front
Rijpfjorden
Multiple (at least
15 sites spanning
diverse habitats
and water masses)
1992-2009 (extends to
1980/82 some stations)
2003-2010
1960's-present
51
Internal
Internal
NFR
Internal, publications
Appendix 5: Publications from time-series
Ambrose Jr. W.G., Carroll, M.L., Greenacre, M., Thorrold, S.R., McMahon, K.W. (2006)
Variation in Serripes groenlandicus (Bivalvia) growth in a Norwegian high-Arctic
fjord: Evidence for local- and large-scale climatic forcing. Global Change Biology 12:
1595-1607.
Ambrose, W.G. Jr., Renaud, P.E., Locke, W.L., Cottier, F., Berge, J., Carroll, M.L., Levin, B.,
Ryan, S. (2012) Growth line deposition and variability in growth of two circumpolar
bivalves (Serripes groenlandicus and Clinocardium ciliatum). Polar Biology 35: 345354.
Berge J, Cottier F,. et al. 2009. Diel vertical migration of Arctic zooplankton during the polar
night. Biol Lett. doi:10.1098/rsbl.2008.0484.
Beszczynska-Möller A., Węsławski J.M., Walczowski W., Zajaczkowski M. 1997 Estimation
of glacial meltwater discharge into Svalbard coastal water. Oceanologia 39(3):289297.
Beuchel, F., Gulliksen, B. (2003) Monitoring of rocky-bottom macrobenthic communities on
locations at Svalbard and Jan Mayen using digital image analysis. Conference paper
CM 2003/J:07, ICES, ASC in Tallin/ Estonia, 23-26. September 2003.
Beuchel F, Gulliksen B, Carroll ML 2006 Long-term patterns of rocky-bottom macrobenthic
community structure in an Arctic fjord (Kongsfjorden, Svalbard) in relation to climate
variability (1980-2003). Journal of Marine Systems 63:35-48.
Beuchel F., B. Gulliksen 2008 Temporal patterns of benthic community development in an
Arctic fjord (Kongsfjorden, Svalbard): results of a 24-year manipulation study. Polar
Biology 31(8):913-924.
Carroll, M.L., Ambrose Jr. W.G., Levin B.S., Ratner A.R., Ryan S.K., Henkes G.A. (2011)
Climatic regulation of Clinocardium ciliatum (bivalvia) growth in the northwestern
Barents Sea. Palaeogeography, Palaeoclimatology, Palaeoecology 302:10-20.
52
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
Carroll, M.L., Ambrose, W.G., Levin, B.S., Locke, W.E., Henkes, G.A., Hop, H., Renaud,
P.E. (2011) Pan-Svalbard growth rate variability and environmental regulation in the
Arctic bivalve Serripes groenlandicus. Journal of Marine Systems 88: 239–251.
Carroll, M.L., Johnson, L.B., Henkes, G.A., McMahon, K.W., Voronkov, A., Ambrose Jr.,
W.G., Denisenko, S.G. (2009) Bivalves as indicators of environmental variation and
potential anthropogenic impacts in the southern Barents Sea. Marine Pollution Bulletin
59: 193-206.
Carstensen J, Weydmann A, Olszewska A, Kwaśniewski S. 2012. Effects of environmental
conditions on the biomass of Calanus spp. in the Nordic Seas. J Plankton Res JPR
34:951- 966.
Cottier FR, Nilsen, F, Inall ME, Gerland S, Tverberg V, Svendsen H 2007. Wintertime
warming of Arctic shelves in response to large-scale atmospheric circulation.
Geophysical Research Letters. L10607.
Cottier FR, Tverberg V, Inall ME, Svendsen H, Nilsen, F, Griffiths C 2005. Water mass
modification in an Arctic fjord through cross-shelf exchange: The seasonal
hydrography of Kongsfjorden, Svalbard. Journal of Geophysical Research. 110,
C12005.
Daase M, et al. (submitted) Timing of reproductive events in Calanus glacialis: a Pan-Arctic
perspective.
Grzelak K., Kotwicki L. 2012. Meiofaunal distribution in Hornsund fjord, Spitsbergen. Polar
Biology 35:269-280.
Hop H, et al. 2006. Physical and biological characteristics of the pelagic system across Fram
Strait to Kongsfjorden. Prog Oceanogr 71:182-231.
Kędra M., Gromisz S., Jaskula R., Legezynska J., Maciejewska B., Malec E., Opanowski A.,
Ostrowska K., Włodarska-Kowalczuk M., Węsławski JM., 2010, Soft bottom fauna of
an All Taxa Biodiversity Site - Hornsund (77ºN, Svalbard). Polish Polar Research, 31,
309-326.
Kędra M, Włodarska-Kowalczuk M, Węsławski JM. 2010. Decadal change in macrobenthic
soft-bottom community structure in a high Arctic fjord (Kongsfjorden, Svalbard) Polar
Biology 33: 1-13.
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
53
Kortsch S., R. Primicerio, F. Beuchel, P. Renaud, J Rodrigues, OJ Lønne, B Gulliksen 2012
Climate-driven regime shifts in Arctic marine benthos. Proceedings National Academy
of Sciences 109:14052-14057.
Kuklinski P, Berge J, McFaddon L, Dmoch K, Zajaczkowski M, Nygård H, Piwosz K,
Tatarek A. 2013. Seasonality of occurrence and recruitment of Arctic marine benthic
invertebrate larvae in relation to environmental variables. Polar Biology 36: 549-560.
Kwaśniewski S, et al. (2003) Distribution of Calanus species in Kongsfjorden, a glacial fjord
in Svalbard. J Plankt Res 25:1-20
Kwaśniewski S. et al. (2010) The impact of different hydrographic conditions and
zooplankton communities on provisioning Little Auks along the West coast of
Spitsbergen. Progr Oceanogr 87:72-82.
Kwaśniewski S., Gluchowska M., Walkusz W., et al. 2012. Interannual changes in
zooplankton on the West Spitsbergen Shelf in relation to hydrography and their
consequences for the diet of planktivorous seabirds. ICES J. Mar. Sci. 69: 890-901.
Kwaśniewski et al. (in prep.) Long-term (1996-2008) changes in the mesozooplankton
community in Kongsfjorden with relations to environmental forcing.
Leu, E., et al. 2011. Consequences of changing sea ice cover for primary and secondary
producers in the European Arctic shelf seas. Prog Oceanogr 90:18-32.
Luukkonen A (2009) The use of space and food resources by purple sandpipers (Calidris
maritima) in a high Arctic estuary in relation to tidal dynamics. University of Turku
and UNIS.
Nygård, H, Berge J, Søreide J, Vihtakari M, Falk-Petersen S. 2012. The amphipod scavenging
guild in two Arctic fjords: seasonal variations, abundance and trophic interactions.
Aquatic Biology 14:247-264.
Omang, O. C. D., H. P. Kierulf (2011), Past and present-day ice mass variation on Svalbard
revealed by superconducting gravimeter and GPS measurements. Geophys Res Lett
38: L22304, doi:10.1029/2011GL049266.
Piwosz K, Walkusz W, Hapter R, Wieczorek P, Hop H, Wiktor J. 2009. Comparison of
productivity and phytoplankton in a warm (Kongsfjorden) and a cold (Hornsund)
Spitsbergen fjord in mid-summer. Polar Biology 32: 549 - 559.
54
Akvaplan-niva AS, 9296 Tromsø
www.akvaplan.niva.no
Regelin B (2011) Purple sandpipers (Calidris maritima) feeding in an Arctic estuary: tidal
cycle and seasonal dynamics in abundance. University of Uppsala and UNIS.
Renaud, P.E., Wlodarska-Kowalczuk M., Trannum H., Holte B., Weslawski J.M., Cochrane
S., Dahle S., Gulliksen B., 2007. Multidecadal stability of benthic community
structure in a high-Arctic glacial fjord (van Mijenfjord, Spitsbergen),Polar Biology,
30, 295-305.
Søreide J, Leu E, Berge J, Greave M, Falk-Petersen S. 2010. Timing of omega-3 fatty acid
production: key factor in a changing Arctic. Global Change Biology doi:
10.1111/j.1365-2486.2010.02175.x.
Walczowski W., Piechura J. 2011.Influence of the West Spitsbergen Current on the local
climate, International Journal of Climatology 31: doi: 10.1002/joc.2338.
Walkusz, W., et al. (2009) Seasonal and spatial changes in the zooplankton community of
Kongsfjorden, Svalbard. Polar Res 28:254-281
Wallace MI, Cottier FR, Berge J, Tarling, GA, Griffiths C, Brierley AS. 2010. Comparison of
zooplankton vertical migration in an ice-free and a seasonally ice-covered Arctic fjord:
An insight into the influence of sea ice cover on zooplankton behavior Limnology and
Oceanography, 55, 831-845.
Weslawski JM, Wiktor J.jr., Kotwicki L. 2010 Increase in biodiversity in the Arctic rocky
littoral, Sorkappland, Svalbard after 20 years of climate warming. Marine Biodiversity
40, 123- 130.
Weydmann A, Kwaśniewski S. 2008. Distribution of Calanus populations in a glaciated fjord
in the Arctic (Hornsund, Spitsbergen). Polar Biol 31:1023-1035.
Weydmann A, Kwaśniewski S. in prep.
Willis KJ, Cottier FR, Kwasniewski S, Wold A, Falk-Petersen S 2006. The influence of
advection on zooplankton community composition in an Arctic fjord (Kongsfjorden,
Svalbard). Journal of Marine Systems. 61, 39-54.
Wlodarska-Kowalczuk, M., Kendall, M.A., Weslawski, J.M., Klages, M., Soltwedel, T.,
2004, Depth gradients of benthic standing stock and diversity on the continental
margin at a high latitude ice-free site (off West Spitsbergen, 79 N). Deep-Sea
Research I, 51, 1903-1914.
Time-series and nature-type mapping in Svalbard waters
Akvaplan-niva AS Rapport 6229 - 2
55