ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-193-2017KRILLBASE: a circumpolar database of Antarctic krill and salp
numerical densities, 1926–2016AtkinsonAngusaat@pml.ac.ukHillSimeon L.PakhomovEvgeny A.SiegelVolkerAnadonRicardoChibaSanaeDalyKendra L.DownieRodFieldingSophieFretwellPeterGerrishLauraHosieGraham W.JessoppMark J.https://orcid.org/0000-0002-2692-3730KawaguchiSoKrafftBjørn A.LoebValerieNishikawaJunhttps://orcid.org/0000-0003-1044-0600PeatHelen J.ReissChristian S.RossRobin M.QuetinLangdon B.SchmidtKatrinSteinbergDeborah K.SubramaniamRoshni C.TarlingGeraint A.WardPeterPlymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, Pl13DH,
UKBritish Antarctic Survey, High Cross, Madingley Road, Cambridge,
CB3 OET, UKDepartment of Earth, Ocean and Atmospheric Sciences, University of
British Columbia, 2207-2020 Main Mall, Vancouver, BC, V6T 1Z4, CanadaInstitute for the Oceans and Fisheries (IOF), AERL, 231-2202 Main
Mall, University of British Columbia, Vancouver, BC, V6T 1Z4, CanadaThünen Institute of Sea Fisheries, Palmaille 9, 22767 Hamburg,
GermanyDepartment Biology of Organisms and Systems,
University of Oviedo, C/ Catedrático Rodrigo Uría s/n, 33071
Oviedo, Asturias, SpainJAMSTEC, 3173-25 Showamachi, Kanazawaku, Yokohama 2360001, JapanCollege of Marine Science, University of South Florida, St.
Petersburg, FL 33701, USAWorld Wildlife Fund, The Living Planet Centre, Rufford House, Brewery
Road, Woking, Surrey, GU214LL, UKSir Alister Hardy Foundation for Ocean Sciences, The Laboratory,
Citadel Hill, Plymouth, PL1 2PB, UKMaREI Centre, Environmental Research Institute, University College
Cork, Haulbowline Rd, Cork, IrelandAustralian Antarctic Division, Channel Highway, Kingston, TAS 7050,
AustraliaInstitute of Marine Research, P.O. Box 1870, 5817 Nordnes, Bergen,
NorwayMoss Landing Marine Laboratories, 8272 Moss Landing Road, Moss
Landing, CA 95039, USATokai University, 3-20-1, Orido, Shimizu, Shizuoka 424-8610, JapanNOAA Fisheries, Southwest Fisheries Science Center, Antarctic
Ecosystem Research Division, 8901 La Jolla Shores Dr, La Jolla, CA 92037, USAMarine Science Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106-6150, USAVirginia Institute of Marine Science, College of William & Mary,
Gloucester Pt., VA 23062, USAAntarctic Climate and Ecosystems Cooperative Research Centre (ACE
CRC), University of Tasmania, Private Bag 80, Hobart, TAS 7001, AustraliaAngus Atkinson (aat@pml.ac.uk)16March20179119321020October201615November201610February201714February2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://essd.copernicus.org/articles/9/193/2017/essd-9-193-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/193/2017/essd-9-193-2017.pdf
Antarctic krill (Euphausia superba) and salps are major
macroplankton contributors to Southern Ocean food webs and krill are also
fished commercially. Managing this fishery sustainably, against a backdrop
of rapid regional climate change, requires information on distribution and
time trends. Many data on the abundance of both taxa have been obtained from
net sampling surveys since 1926, but much of this is stored in national
archives, sometimes only in notebooks. In order to make these important data
accessible we have collated available abundance data (numerical density, no. m-2)
of postlarval E. superba and salp individual (multiple
species, and whether singly or in chains). These were combined into a
central database, KRILLBASE, together with environmental information,
standardisation and metadata. The aim is to provide a temporal-spatial data
resource to support a variety of research such as biogeochemistry,
autecology, higher predator foraging and food web modelling in addition to
fisheries management and conservation. Previous versions of KRILLBASE have
led to a series of papers since 2004 which illustrate some of the potential
uses of this database. With increasing numbers of requests for these data we
here provide an updated version of KRILLBASE that contains data from 15 194
net hauls, including 12 758 with krill abundance data and 9726 with salp
abundance data. These data were collected by 10 nations and span 56 seasons
in two epochs (1926–1939 and 1976–2016). Here, we illustrate the seasonal,
inter-annual, regional and depth coverage of sampling, and provide both
circumpolar- and regional-scale distribution maps. Krill abundance data have
been standardised to accommodate variation in sampling methods, and we have
presented these as well as the raw data. Information is provided on how to
screen, interpret and use KRILLBASE to reduce artefacts in interpretation,
with contact points for the main data providers.
The DOI for the published data set is 10.5285/8b00a915-94e3-4a04-a903-dd4956346439.
Introduction
The crustacean euphausiid species Euphausia superba (hereafter
“krill”) and the tunicate family Salpidae (hereafter “salps”) are key
large zooplankton taxa of the Southern Ocean. Both taxa are important in
biogeochemical cycling and nutrient export (Pakhomov et al., 2002; Phillips
et al., 2009; Gleiber et al., 2012; Schmidt et al., 2016). They
have broadly similar size, but have fundamentally different life cycles,
habitat preferences, and nutritional composition and thus have contrasting
roles in the food web. Krill is a major food item for a suite of vertebrate
and invertebrate predator species (Murphy et al., 2007; Trathan and Hill,
2016). Salps appear in the diets of various invertebrates, fish and birds
but do not seem to be as important as krill to most of the air-breathing
predator group (Pakhomov et al., 2002). Also, compared to krill, salps seem
to prefer warmer, deeper water habitats with moderate food concentrations
and less sea ice (Pakhomov et al., 2002; Loeb and Santora, 2012).
Over the past 100 years the Southern Ocean has experienced regional warming
(Gille, 2002; Meredith and King, 2005; Whitehouse et al., 2008) and
regionally variable changes in sea ice cover (de la Mare, 1997; Murphy et
al., 2014; Stammerjohn et al., 2012). Whether there has been a consequent
reorganisation of plankton distributions is a topic of much interest and
debate (Pakhomov et al., 2002; Atkinson et al., 2004; Ward et al., 2012;
Loeb et al., 1997, 2015). Climate model ensembles predict that current positive
trends in atmospheric Southern Annular Mode (SAM) anomalies will continue
this century (Gillett and Fyfe, 2013). Since the population dynamics of key
euphausiid and salp species relate to these climatic drivers (Saba et al.,
2014; Ross et al., 2014; Steinberg et al., 2015; Loeb and Santora, 2015), we
need to understand the spatial and temporal dynamics of both krill and
salps.
In addition to their ecological role, krill are also the dominant fished
species in the Southern Ocean in terms of catch weight, with a potential
sustainable yield equivalent to 11 % of current global fishery landings
(Grant et al., 2013). The Antarctic krill fishery is managed by the
Commission for the Conservation of Antarctic Marine Living Resources
(CCAMLR) which is committed to precautionary, ecosystem-based management.
This means that CCAMLR is responsible for managing the impacts of the
fishery on the health, resilience and integrity of the wider ecosystem.
However, there is little information about many relevant aspects of krill
ecology and population dynamics (Siegel and Watkins 2016), including genetic
stock identity (Jarman and Deagle, 2016), and predator–prey relationships
(Trathan and Hill, 2016). Reducing these uncertainties might be necessary
for CCAMLR to achieve its conservation objectives (Constable, 2011).
Fishery managers and stakeholder groups aim to improve more finely resolved
temporal and spatial management approaches, but more information is needed
to achieve this (Hill and Cannon, 2013). Thus, understanding krill
distribution and dynamics is also important for the development of
sustainable fishery management and conservation policy (e.g. identifying
suitable Marine Protected Areas and assessing the dynamics of fished
stocks). Consequently, a cross-sector group representing the fishing
industry, scientists and conservation NGOs has recently called for
improvements in the availability of information to improve understanding of
the state of the krill-based ecosystem and management of the fishery (Hill
et al., 2014).
Spatial-temporal information on krill and salps can come from scientific
surveys using acoustics or nets, predator studies or data from the fishery.
Each has its strengths and weaknesses, and these are expanded on elsewhere
(Atkinson et al., 2012b). For net sampling surveys, data are available from a
variety of expeditions since the 1920s. These individual surveys provide
important snapshots of the ecosystem but in isolation they cannot provide a
broader context. Annual monitoring programmes collecting net and acoustics
data over standardised survey grids were initiated in the late 1980s and
early 1990s (Reiss et al., 2008; Fielding et al., 2014; Steinberg et al.,
2015; Kinzey et al., 2015; Krafft et al., 2016). However, despite the
technology used, these multi-year time series surveys only cover a tiny
fraction of the Southern Ocean area. A larger-scale and longer-term
perspective is thus useful to provide context for the standardised
monitoring data sets.
Distribution of sampling stations in KRILLBASE, showing
generally elevated sampling effort in and around designated areas of
protection and management. These stations may have krill or salp data or
both; Fig. S1 in the Supplement provides the distribution of just the krill
sampling stations.
The KRILLBASE project was started at the end of the 1990s to bring together
the data necessary for this broader context. It was initiated by Angus
Atkinson, Evgeny Pakhomov and Volker Siegel and is one of many examples of
international collaboration in Antarctic research. Over the last 15 years we
have documented and collated over 200 data sets, some of which are 90 years
old and previously only available on paper log-sheets, distributed across
library archives. KRILLBASE thus pre-dates many other data rescue and
compilation initiatives. Only by combining data in this way can we provide
coverage on a scale commensurate with that of large marine ecosystems or
with management and conservation areas (Fig. 1). The most recent update to
KRILLBASE was completed in 2016, and making these data more accessible
improves the capacity of a broader community to investigate the dynamics and
distribution of ecologically important krill and salps, and to enhance the
responsible management of krill fisheries and the conservation of Southern
Ocean ecosystems.
The objectives of publishing the revised KRILLBASE are (a) to provide a link
to key data and metadata for those wishing access to the krill and salp data
sets, (b) to illustrate the scope and coverage, with examples of potential
uses of these data, (c) to explain in detail its structure, with caveats and
guidelines on how the data can be used, and (d) to provide a single, citable
reference for these combined data sets.
Sources of data for KRILLBASE, according to nation and
major sampling programme. Sources are listed in descending order of number of
hauls provided. More information on the actual data sources (including the
references used where data were transcribed from publications) is provided
in the SOURCE field of the database. Coverage is not necessarily evenly
spread within the longitudinal boundaries, which are presented in nearest
integer degrees. For haul type – H: normal haul; SH: stratified haul that has
been pooled into an equivalent “stratified pooled haul”. SM: survey mean
haul, where density estimates are only available as a mean from multiple
stations comprising a survey (see Sect. 2.3).
NationalHaulSamplingRange of longi-MonthsDepthdata sourceNumber of net hauls typeyearstude coveredcoveredNet types(m)*Source of dataTotalkrill datasalp dataUS AMLR programme386431641440H, SM1990–201163–44∘ WJan–MarIsaacs–Kidd midwater trawl170Sent by Loeb, Hewitt, Reiss, data via US AMLR ReportsDiscovery (UK) data315616372723H, SH1926–1939, 1951CircumpolarJan–Mar, Nov–DecN70V, N100b,N200BArchived data from original net sampling logsheets checked against a euphausiid Discovery era database by AtkinsonGerman GAMLR data235223521694H, SH, SM1976, 1978,1980–1986, 1988–1990, 1994, 1995,1997, 2001,2004122∘ W–14∘ EJan–June, Oct–DecMainly RMT8,also 0.6 m bongos and Isaacs–Kidd midwater trawl185Sent by Siegel, plus a small amount of data transcribed from publicationsSoviet data157915571577H, SH1983–1990, 1992CircumpolarJan–Apr, DecBongo, Isaacs–Kidd trawl, Melnikov's net, Modified Juday net100Sent by PakhomovUS Palmer LTER Program124712470H1993–201678–64∘ WJan–Feb2 × 2 m fixed frame with 700 µm mesh.120From Palmer LTER data holdings http://pal.lternet.edu/ (last access July 2016)British Antarctic Survey data923923810H11982, 1985,1996–1999, 2001–2005, 2007–200966–26∘ WJan–Apr, Oct–DecRMT1, RMT 8,0.62 cm bongo, LHPR with 38 cm nosecone205Sent by Ward, also data accessed from BAS Polar Data Centre and including SIBEX data holdingsOther US National Programs593550219H, SM1981, 1983,1984, 1986,199462–36∘ WJan–Mar, Nov–Dec0.6 m bongo,Plummet net,Tucker trawl200Data mainly transcribed from various publications, with AMERIEZ cruise data sent by DalyAustralian data508508316H, SH1981,1983–1987, 1991–1993, 1996, 1999,2001, 200630–150∘ EJan–Mar, Aug,Oct–DecSquare 0.5 m net,0.5 and 1 m bongos, ORI net, RMT 8200Data sent by Hosie and Kawaguchi, Some data transcribed from Anare Research Notes and from publications.South African data413343413H1980, 1981,1983,1994–1998, 2001, 200386∘ W–179∘ EJan–May, Oct, Decbongo, Mocness, RMT8300Sent by PakhomovJapanese data16381163H, SH1984,1988–199663∘ W–158∘ EJan–Mar, DecNorpac net, Square 0.5 m net, ORI net, Large Isaacs–Kidd trawl, Kaiyo Maru trawl150JARE data from Chiba, SIBEX data from Nishikawa, also transcribed from publicationsPolish data159159159H, SH1981, 198466–43∘ WJan–Mar, Dec0.5 and 0.6 mbongos175Transcribed from publicationsCCAMLR data (international)117117117H200069–23∘ WJan–FebRMT8200International data from CCAMLR Synoptic survey data obtained via CCAMLRSpanish data999999H199666–59∘ WDec–JanModified WP2 net200FRUELA Cruise data sent by AnadonNorwegian data21210H200837∘ W–15∘ EJan–MarMacroplankton trawl750AKES data sent by Krafft
* Median bottom sampling depth.
Data and methodsKRILLBASE overview: summary
The data introduced here were compiled as part of a long-term project to
rescue and compile data on a range of krill and salp variables, derived from
net sampling surveys. This paper introduces the most recent version of the
krill and salp abundance data. More specifically, the main fields indicate
numerical density (i.e. the number of individual postlarval krill or salps
under 1 m2 of sea-surface area), which we refer to as abundance for
brevity. The version of the data that we present here
(10.5285/8b00a915-94e3-4a04-a903-dd4956346439,
which can be accessed via
https://www.bas.ac.uk/project/krillbase) amalgamates existing time series
and other surveys of numerical density of postlarval krill,
Euphausia superba, and salps. These data span 1926–1939 (plus 1951)
and 1976–2016, albeit with variable spatial and temporal coverage. It is
important to emphasise that this is a multi-national composite database not
a synoptic snapshot or a true time series, so care is needed when using and
interpreting these data due to the different sampling methods used. Table
1 provides a summary of its composite structure. In this paper
phrases referring to KRILLBASE column headings are in uppercase italics
(e.g. BOTTOM_SAMPLING_DEPTH_M) whereas searchable terms within the data (e.g.
stratified haul) are italicised.
The basic data set is in a single table with an accompanying table of column
descriptions. These are available either in their entirety as two
downloadable CSV files, or as a resource that can be queried online. Both of
these versions can be accessed via the 10.5285/8b00a915-94e3-4a04-a903-dd4956346439.
Metadata are available via (a) this paper, which forms a reference that needs
to be cited for the data source, and (b) detailed descriptions of data sources
for each row of the data. These data are held at the Polar Data Centre at
British Antarctic Survey to allow traceability, continuity of access and
future updating.
Relationships to other databases
Antarctic zooplankton data are well represented in a series of databases and
metabases, and the inter-relationships among these can be confusing.
KRILLBASE and other data collections and time series form a global network
entitled IGMETS (International Group of Marine Time Series,
http://igmets.net/), linked to the COPEPOD project
http://www.st.nmfs.noaa.gov/copepod/. IGMETS is a metabase that
provides a valuable catalogue of marine biological time series.
Other initiatives emphasise the spatial and taxonomic component of data
records. For example a previous version of the KRILLBASE data is stored as
presence/absence data at SCAR-MarBIN http://www.scarmarbin.be/ (De
Broyer et al., 2014). SCAR-MarBIN from the Antarctic node of global-scale
initiatives including the Ocean Biogeographical Information System (OBIS,
http://www.iobis.org/) and the Global Biodiversity Information Facility
(GBIF, http://www.gbif.org/). Previous versions of KRILLBASE are also
available from CCAMLR (https://www.ccamlr.org/) and as part of a
gridded global data set of macroplankton biomass (Moriarty et al., 2013). The
present version augments this with 50 % more data. If necessary the
abundance values can be converted to an approximation of biomass
(mg C m-3) using, for example, the procedure of Moriarty et al. (2013),
who first calculated the number of individuals per m3 by dividing
density by sampling depth
(BOTTOM_SAMPLING_DEPTH_M-TOP_SAMPLING_DEPTH_M), and
then applied fixed conversion factors of 63 and 24 mg C ind-1 for
krill and salps respectively.
Two of the data sets used in KRILLBASE are available from their respective
data websites (http://pal.lternet.edu/ and
https://swfsc.noaa.gov/aerd/). Although these do not include the
standardised krill abundances available in KRILLBASE, we refer the user to
these two websites to obtain the most up-to-date source data from the
Palmer-LTER and US-AMLR time series data. A separate data holding external to
KRILLBASE, for example including winter krill data from US SO-GLOBEC, is at
BCO-DMO http://www.bco-dmo.org/. The purpose of KRILLBASE is not to
duplicate all of these efforts but to bring the krill and salp data together
within a single file linked to metadata, in order hopefully to make it more
user friendly.
Structure of KRILLBASE
It is important to differentiate “records” (i.e. rows of the data in
KRILLBASE) from “net hauls” and from “sampling stations”. The most common
situation is for each record to represent a single net haul at a single
station. There is one indexing column (labelled “STATION” and 28 further
columns (i.e. fields) describing searchable and filterable date, time,
position, sampling and environmental information as well as krill and salp
abundance. The detailed description of each of these columns is provided in
Table 2, while more detail on the nets used for sampling is in Table 3).
While most of the 14 543 records pertain to a single haul made at a station,
there are actually four types of record. These are differentiated in the
“RECORD_TYPE” column. The most common record, where a single net haul was
taken at the station, is simply labelled “haul”. The second
category is labelled “stratified haul”, (2243 records), and these
hauls form part of a depth-resolved stratified series made at a station
(e.g. 0–50, 50–100, 100–200). The third category is “stratified pooled haul” (567 records) and these pool the abovementioned
stratified hauls into a single combined “virtual haul”, in this
example from 0–200 m. The fourth category (48 records) is labelled
“survey mean”. In these the record provides the arithmetic mean
abundance from multiple stations within a survey. While less than optimal,
this aggregated information was the only data recoverable from the relevant
surveys, which provided data from a valuable 1290 stations during the 1980s.
Detailed description of the columns in KRILLBASE.
Column headingDescriptionSTATIONUnique identifier for each record (row). The first three letters identify the source of the data (starting letters of the name of the individual, national programme, or country which provided the data). The next four numbers identify the season of sampling (e.g. 1926 spans October 1925 to September 1926). The next three letters provide additional sample information, often referring either to the net type used or the name of the sampling survey. Additional characters at the end list the station numbers etc. These are, as far as possible, the same as used in the original sources, with British Antarctic Survey and Palmer LTER cruise station numbers being replaced by cruise-unique “event numbers”. Records are typically resolved to station but see RECORD_TYPE for more information on resolution.RECORD_ TYPEThis is an important field that will need screening before any use of the database. Records labelled “haul” are the usual situation meaning that the record refers to a single net haul. “Survey mean” represents a record where the krill or salp density represents an arithmetic mean of a group of stations whose central position and sampling point are thus provided in the database with less accuracy then the other records. Survey means are given only when it was not possible to obtain station-specific data. “Stratified haul” represents a haul, usually within the top 200 m, which forms part of a stratified series (e.g. 0–50, 50–100, 100–200 m). “Stratified pooled haul” represents a record that integrates these respective stratified hauls, whereby the krill or salp densities from the component nets have been summed (in this example into an equivalent 0–200 m haul). Thus to avoid double counting, any use of the data should sift out either stratified hauls or stratified pooled hauls.NUMBER_ OF_STATIONSFor Survey mean data (see RECORD_TYPE) this refers to the number of stations that have been averaged to provide the krill or salp density values.NUMBER_ OF_NETSThis refers to the number of sequentially fished nets included in the estimate (e.g. the value would be 3 for a stratified pooled haul consisting of a stratified series sampling 0–50, 50–100 and 100–200 m, and it would be 32 for a survey mean which averages 32 hauls). A LHPR haul counts as one net despite multiple gauzes being cut. This value is also 1 for a paired bongo haul (two nets fished concurrently).LATITUDESouth is negative. Units are decimal degrees.LONGITUDEWest is negative. Units are decimal degrees.SEASONThis is the austral “summer” season of sampling. For example the 1926 season spans all data from 1 October 1925 through to 30 September 1926.DAYS_FROM_ 1ST_OCTThis is the day of sampling during the austral season. Therefore 1 October is DAYS_FROM_1ST_OCT = 1. The value for dates after 28 February vary depending on whether they occur during a leap year.DATEThe date of sampling, based on the dates provided to us (see “DATE ACCURACY” column).DATE_ ACCURACY“D” means the exact day of sampling is known. “M” means that we have been provided only with the month in which samples were taken, so the record's DATE value is entered as the middle of the month. “Y” means only the year of sampling was provided, so the date is recorded here simply as 1 January (this affects one record only).NET_TIMEThis is the time of the haul: either the start, midpoint or end times of hauls were used, as provided to us. Absent data means no net time information was available, or it was not entered into the database because the station was already classified as either day or night (Discovery data net times are recorded in their published “Station Lists” but not entered in KRILLBASE). Net times for Stratified pooled hauls represent that of the shallowest net of the series.GMT_OR_ LOCALInformation on whether the time in the previous column is GMT (labelled “GMT”). Data which were provided as local times with a stated offset to GMT have been converted to GMT. Data which were provided as local times with no offset have not been converted and are labelled “local”. Absent data means there was no net time information.DAY_NIGHTThis field indicates whether the net was hauled in daylight (labelled “day”) or night time (labelled “night”) and was used in the calculation of standardised krill densities. See DAY_NIGHT_METHOD for information on the source of these data.DAY_NIGHT_ METHODMethod used to determine whether the net was hauled in daylight or at night time, which depends on the time information available: 1 – DAY_NIGHT is based on calculated solar elevation determined using NET_TIME, 2 – DAY_NIGHT is as recorded in the ship's log, 3 – no DAY_NIGHT information was available, and standardised krill densities were adjusted for the probability that the haul was conducted in daylight.
Continued.
Column headingDescriptionNET_TYPEThis is a brief name for the sampling net used. See Table 3 for more detailed descriptions of each net.MOUTH_AREA_ OF_NET_M2This is a nominal mouth area of the net calculated from the net dimensions. It is typically the simple linear area of the mouth, but for RMT8 and 1 it is assigned as value of 8 and 1 respectively. Bongo nets are assigned as an area of both openings combined and LHPR is given as maximum net diameter – both of these are used to crudely compensate for the lack of towing bridles and wire/release gear directly in front of the net, as compared to the standard ring nets often of similar net dimensions.TOP_SAMPLING_ DEPTH_MShallowest sampling depth (m).BOTTOM_ SAMPLING_ DEPTH_MDeepest sampling depth (m). Note that whilst most hauls were oblique, double oblique or vertical, a small minority were nearly horizontal, as shown by similar top and bottom depths. These would need to be screened out of nearly all analyses as they provide little information on numerical densities (no. m-2).VOLUME_ FILTERED_M3Volume of water (m3) filtered by the net. This value is provided only when the value is provided with the density data.N_OR_S_ POLAR_FRONTPosition (North or South) relative to the Antarctic Polar Front as published by Orsi et al. (1995).WATER_DEPTH_ MEAN_ WITHIN_10KMMean water depth within a 10 km radius. In South Polar Stereographic projection, the stations were superimposed on the Gebco 2014 Grid bathymetry (http://www.gebco.net) and all pixels within a 10 km radius of the station were extracted. After removing data above sea level, the remaining pixel value for water depth was averaged.WATER_DEPTH_ RANGE_ WITHIN_10KMDepth range within a 10 km radius. In the procedure above, having removed pixels above sea level, the range in water depth was calculated as the difference between the shallowest and the deepest pixel. This will provide an index of even-ness of bathymetry (e.g. proximity to seamounts, canyons, continental slope).CLIMATOLO- GICAL_ TEMPERATURELong-term average February sea-surface temperature for the sampling location. This is not the actual sea temperature at the time of sampling but a climatological mean sea-surface value for February, averaged over the years 1979 to 2014, based on data downloaded July 2016 from http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc/. Data were provided on a 0.75∘ by 0.75∘ grid and we extracted mean values using the same 10 km buffer method used for the bathymetry. These values may indicate a relative thermal regime as a basis for station characterisation.SD_OF_SURVEY_ MEAN_KRILLThe standard deviation of the krill densities extracted from the publications where the survey mean value of krill density is provided (see column RECORD_TYPE).NUMBER_OF_ KRILL_UNDER_ 1M2Numerical density, N, of numbers of postlarval krill under 1 m2 (or, where based on a length frequency distribution as in the Discovery Investigations, it is krill > 19 mm in length). Where the numbers of krill n were provided per m3 filtered, the density of krill was calculated based on top-sampling depth t and bottom-sampling depth b in metres as N=n×(b-t).STANDARDISED_ KRILL_UNDER_ 1M2Standardised numerical density of postlarval krill. To reduce possible artefacts arising from differences in sampling method in KRILLBASE, this column presents krill density according to a single sampling method. This method is a 0–200 m night-time RMT8 haul on 1 January, following the standardisation method in Atkinson et al. (2008). See main text for more details.CAVEATSAny issues which might require particular caution when using the data (e.g. potential inaccuracies in estimated date or day/night or sampling depths outside of the normal range) are listed here. Default is blank.NUMBER_OF_ SALPS_UNDER_ 1M2The numerical density of salps, calculated as for krill. All individuals are counted, irrespective of which salp species or whether they are solitaries or components of aggregate chains. Standardised salp densities have not been calculated.SOURCEInformation about the source of the data, including a citable reference where available.
Nets used in KRILLBASE. The nets are listed in
alphabetical order.
Name given inNominal mouthNumber ofDescription of netKRILLBASEareahauls0.5 m bongo0.39230.5 m diameter bongo from ABDEX cruises (nominal mouth area is that of both nets)0.6 m bongo0.5710400.6 m diameter bongo net (nominal mouth area is of both nets)0.62 m bongo0.6452BAS bongo: 62 cm diameter (nominal mouth area is of both nets), 0.1 and 0.2 mm mesh0.71 m bongo0.792610.71 cm bongo net (Nominal mouth area is of both nets)1 m ringnet0.79111Modern 1 m diameter ring net2 m fixed frame net412472 m square sided, fixed frame net, 700 µm main mesh, 500 µm cod end (Palmer LTER grid)IKS net148IKS 1 mm mesh net, 1 m2, 1 mm meshIsaacs–Kidd3.084217Isaac Kidd midwater trawl, 4.5 mm meshJuday net0.11150.37 m diameter Juday net, 0.15 mm meshKaiyu Maru trawl850Kaiyo Maru midwater trawl (KYMT: 9 and 7 m2 mouth area), 3.4 mm mesh (Nishikawa et al., 1995)Large Isaacs–Kidd6300Large Isaacs–Kidd trawl including 10′ one used for Japanese SIBEX and the 6 m2 (4.5 mm mesh) one for Russian/Ukrainian samplingLarge Melnikov net0.5170.5 m2 Melnikov trawl, 0.63 mm meshLHPR0.4528Longhurst Hardy Plankton Recorder with 38 cm diameter nosecone used by BAS (0.2 mm mesh)MOCNESS16MOCNESS netModified Juday net0.5694Modified Juday net, 0.5 m2 mouth area, 0.178 mm meshN100B0.791835Discovery's N100B net (1 m diam. ring net)N200B3.1418N200B net used briefly in 1926 (2 m diameter ring net: soon abandoned as hard to handle)N70V net0.391396Discovery's closing N70V net, also Polish N70V netNorpac net0.16440.45 m diameter NORPAC net of JARE expeditions (330 µm net with flowmeter)ORI net2.0135Japanese ORI net, 1.6 m diameter mouth, 2 mm meshPlummet net1261 m2 plummet net used on AMERIEZ (US) cruises in 1980sRMT1194RMT 1 net, 0.33 mm meshRMT882753RMT 8 net, 5 mm meshMacroplankton trawl3821“Macroplankton trawl” of research vessel G.O. Sars (AKES data), 3 mm mesh size measured from knot to knot/7 mm stretched mesh. The trawl has the same mesh in all panels from mouth to cod end. Towing speed was 2.5–3 kn. Data and trawl gear is described in Krafft et al. (2010).Small Melnikov net0.221780.22 m2 Melnikov trawl, 0.63 mm meshORI-VMPS0.2585Square net, 0.5 m across from Australian ANARE and Japanese (Nishikawa and Tsuda, 2001) samplingTucker trawl998Tucker trawl, 4 mm main mesh to a 1 mm cod end, towed at 2 kn. Described in Lancraft et al. (1989)WP20.2699WP2 net from Spanish FRUELA cruises
The krill data are presented as both the observed abundance
(NUMBER_OF_KRILL_UNDER_1M2, no. m-2) and the abundance
standardised relative to a benchmark
(STANDARDISED_KRILL_UNDER_1M2, no. m-2), which is explained
in Sect. 2.7. The salp data are presented as observed abundance for all
species combined, where an individual can be either a solitary oozoid or an
individual within an aggregate chain (NUMBER_OF_SALPS_UNDER_1M2,
no. m-2).
Overall there are 15 191 hauls in the database, from 13 542 stations. Of
these hauls, 7295 have abundance information on both krill and salps. Others
have absent data for either salps or krill, and these are flagged as “not a
number” (NaN). This distinguishes it clearly from zero, which
indicates that either no krill or no salps were caught. Absent data should
therefore not be confused with zeros.
In stratified pooled haul records the
NUMBER_OF_KRILL_UNDER_1M2 and
NUMBER_OF_SALPS_UNDER_1M2 values are the sums of the component
stratified hauls, but are not given (NaN) if data were
missing from one or more of the stratified hauls. Location
information is generally taken from the deepest component stratified haul. Time information is taken from the shallowest component
stratified haul as krill densities are most sensitive to light
levels in the surface layers.
Data processing and error checking
Stations were plotted one survey at a time to identify errors in station
positions, stations plotting on land, or with latitude and longitudes
transposed or with the wrong sign. Implausibly large distances between
consecutive sampling points were identified and corrected. Suspiciously low
densities were identified, based on known or estimated volumes filtered by
the various nets and the assumption that no fewer than one krill could have
been caught. This procedure identified and led to the correction of a major
error made on one portion of the data when converting numbers of krill per
1000 m3 to numbers of krill per m-2. Tests of date, time and
position coincidence led to the removal of several portions of data that had
been entered twice with different station numbers.
The veracity of high krill abundances are hard to check, since densities in
swarms have been estimated in the thousands per m3 of water. The highest
density values for krill and salps were 9384 and 5886 inds. m-3,
respectively. These form a natural tail to the frequency distribution of
catch densities (Fig. 2) and are not isolated outliers. They are also well
within expected values (Hamner and Hamner, 2000). The highly patchy spatial
distribution of each taxon results in right-skewed frequency distributions,
with modes at zero, i.e. no krill caught (Fig. 2). This distribution type is
an important consideration in analyses.
Water depths for every net sample were obtained by superimposing the stations
on a GEBCO_2014 grid, version 20150318, www.gebco.net bathymetry using
Arc GIS 10.4.1 and extracting the minimum, mean and maximum water depth
within 10 km of each station. The bathymetric information derived from this
provides an additional check of the veracity of position information. We
identified 32 records in which the BOTTOM_SAMPLING_DEPTH_M was
implausibly deeper than the maximum depth in the vicinity of the haul. For 10
of these, the longitude or latitude was reported as an integer. Integer
coordinates and shallow bathymetry may indicate inaccuracies in position
information. Users should be aware that inaccuracies in latitude can also
affect the assessment of DAY_NIGHT information used in the
calculation of standardised krill abundances. A couple of reported krill
catches were from warmer waters north of the Antarctic Polar Front, giving
grounds for suspicion, for example of identification. We kept these records
since expatriated individuals are a possibility and we did not want to judge
the data provided. Data caveat issues are indicated and described in the
fields DATE_ACCURACY and CAVEATS respectively.
Frequency distribution of krill and salp abundances in the
database. The data were filtered to remove stratified hauls before
plotting the frequency of remaining hauls in relation to logarithmic bins.
Data are presented for (a) krill raw (unstandardised) abundance,
(b) krill standardised abundance and (c) salp (unstandardised)
abundance.
Variation in sampling coverage and method
Figure 1 shows that KRILLBASE sampling is highly uneven, focusing on areas
of fishing or historical interest to nations in the Atlantic sector
(USA, Germany, UK, Poland, South Africa, Spain) or Indian sectors (Soviet
Union, Japan, Australia). While Fig. 1 plots the stations with either krill
or salp data or both, Fig. S1 in the Supplement plots only those stations
with krill data. Data compilation was mainly focused on the Antarctic zone;
765 records are north of the Antarctic Polar Front. “Discovery” sampling
(i.e. those data obtained as part of the Discovery Investigations in the
1920s and 1930s) started nearer South Georgia and became increasingly
circumpolar but, despite this, major gaps in sample coverage exist in
important areas such as the Ross Sea, Weddell Sea and in large parts of the
Pacific sector.
The composite nature of KRILLBASE means that the sampling methods vary.
Figure 3 illustrates this with a circumpolar comparison of the seasonal
timing of sampling (Fig. 3a), bottom depth of sampling (Fig. 3b) and mouth
area of the net (Fig. 3c). Time of year of sampling has a potentially strong
influence on the abundance of zooplankton, due to life cycle and behavioural
traits such as seasonal vertical migration (Foxton, 1966; Atkinson et al.,
2012a; Cleary et al., 2016). While samples were obtained during most months
of the year, 89 % of the hauls were conducted in the period December to
March (Fig. 4), with no longitudinal bias in timing (Fig. 3a). However, in
sparsely sampled areas, particularly north of the Antarctic Polar Front,
sample timing varied greatly, underlining the caution needed in interpreting
these samples. The original objectives for using KRILLBASE did not require
winter samples but some winter data are available from several key surveys
(e.g. http://www.bco-dmo.org/) and could be included in subsequent
updates of KRILLBASE.
Circumpolar variation in sampling method. This
plot is based on all data in KRILLBASE, whether for krill or salps or both.
(a) Time of year of sampling (mean day from 1 October).
(b) Bottom depth of sampling. The data set plotted includes the stratified pooled
hauls and thus excludes their component stratified hauls (see Sect. 2.3).
(c) Mean mouth area of the net, based on the nominal values presented
for each net type in Table 3. Antarctic Polar Front position is from Orsi et
al. (1995).
Most hauls in KRILLBASE were made between the surface and 100–200 m depth,
but vertical coverage varied greatly between the component surveys, as
indicated by the chequered colours of Fig. 3b. Some screening by the user is
necessary to remove stations where an unrepresentative portion of the depth
distribution was covered. Figure 5 summarises the vertical distribution of
krill and salps where stratified series of net hauls were undertaken (269
krill stations and 563 salp stations). This shows the highest densities of
krill in the top 200 m, with declining densities below this. KRILLBASE is
suitable for exploring the horizontal distribution of krill in the important
epipelagic zone, but is unsuitable to map horizontal distribution below
200 m. These deeper and near- seabed zones are being increasingly recognised
as important habitats for krill (Gutt and Siegel, 1994; Clarke and Tyler,
2008; Schmidt et al., 2011; Cleary et al., 2016).
Relative frequency of stations sampled within each month
of the year.
Vertical distribution of krill and salps based on
793 stratified krill hauls and 2130 stratified salp hauls. Given the
non-standard depth horizons between the various surveys sampling in this
manner, the data were first subdivided into a nominal seven categories of mean
sampling depths, namely 0–50, 50–100, 100–150, 150–200, 200–300,
300–500 and > 500 m. Mean krill or salp densities are presented
in each of these mean depth groups, plotted against mean sampling depth
within each depth band.
Salps have a deeper distribution than krill (Fig. 3) as a result of greater
diel and seasonal vertical migrations (Foxton, 1966; Loeb and Santora, 2012). Care is therefore needed to avoid negative bias due
to shallow net sampling. A standardisation method similar to that applied to
krill may reduce these inconsistencies and provide a better picture of the
spatial distribution of salps.
Inter-annual coverage
Figure 6 divides the Southern Ocean into broad sectors to illustrate the
inter-annual coverage of sampling. The coverage for salps broadly follows
that for krill, with good coverage in the Atlantic sector from 1926 to 1938 and
after 1976. In the Indian Ocean sector some data exist from the late 1930s
when “Discovery” sampling became circumpolar, reasonable coverage occurred
from 1981 to the mid-1990s, but few data have been collected there since.
While coverage in the Pacific sector is too sporadic to document time trends,
data for the other two sectors are sufficient to examine sectorial patterns
of inter-annual and decadal-scale variability of both krill and salps.
Inter-annual sampling coverage. Number of
stations sampled south of the Antarctic Polar Front in each austral season
(October to following September). These are presented for (a) the
Atlantic sector (nominally defined as 90∘ W–10∘ E),
(b) the Indian sector (10–120∘ E) and (c) the Pacific
sector (120∘ E–90∘ W).
The survey mean data are included in Fig. 6, and they provide
important information for the period before coordinated monitoring
programmes. These data can be included in regional scale analyses (e.g.
time series analyses), but since the data pertain only to the whole survey
and not the component stations, care is needed when interpreting the data at
finer scales than the 3∘ latitude by 9∘ longitude grids
illustrated.
Change in day length with time of year at various
latitudes, indicating the effect of date inaccuracies on time of day
adjustments made during standardisation of krill abundance.
Standardisation: methods
The compiled data represent a range of sampling methods with different net
types, sampling depths, times of day and times of year (Fig. 3). Such
differences in sampling strategy could potentially bias the outcome of
analyses. For example, differences in net mouth size will lead to variable
avoidance and the mesh size will affect retention. Differences in net
geometry, towing speed and trajectory will further affect catches, as will
light levels and swarm packing density (Hamner and Hamner, 2000; Everson and
Bone, 1986; Krag et al., 2014). For example, catchability decreases as light
levels increase, meaning that there can be a latitudinal effect because
summer days are much longer at high latitudes (Fig. 7). These issues were
recognised by Marr (1962) and Mackintosh (1973), who adjusted the densities
accordingly when producing circumpolar distribution maps.
Summary of standardisation process.
Standardise forStandard haul characteristicsConversion factorDefinitionsConversion factor applied when:Sampling depth0–200 m0.11B / (1 + 0.105B)B = BOTTOM_SAMPLING_ DEPTH_MBOTTOM_SAMPLING_ DEPTH_M < 200Time of dayNight-time2.255DAY_NIGHT = 0 (day time)2.255XX= NEW_DAYLENGTH (specified as a proportion)DAY_NIGHT =blank (no information)Net mouth area and time of year of samplingNet mouth area = 8 m2
Time of year = 1 JanuaryLpredopt/KpredKpred=P×Knon-zeroP=exp(L)/[1+exp(L)]
Log10(Knon-zero)=0.474-0.1912Log10M+0.00416J-0.00002898J2L=-0.6478+2.335×Log10M+0.0204J-0.0001086J2M= MOUTH_AREA_OF_NET_M3 J= JULIAN_DAY_FROM_OCT Lpredopt=Popt×Knon-zerooptPopt=0.92Knon-zeroopt=2.74MOUTH_AREA_OF_NET_M3 <> 8 or DAYS_FROM_1st_OCT <> 93
To minimise the influence of sampling differences, our database includes both
the raw numerical abundances of krill and values standardised to a single
sampling method. We calculated the standardised krill abundances using the
process and conversion factors described in the supplementary appendix of
Atkinson et al. (2008). The standardised abundance
(STANDARDISED_KRILL_UNDER_1M2) is an estimate of the krill
abundance that would have been observed if the haul had conformed with a
sampling method consisting of a night-time haul on 1 January, fishing to a
depth of 200 m with a mouth area of 8 m2. This strategy achieves
near-maximum krill catch that is possible with scientific nets.
Standardisation was implemented by multiplying the raw abundances
(NUMBER_OF_KRILL_UNDER_1M2, N) by conditional conversion
factors as follows:
N′=N0.11B1+105B2.255X2.5208Kpred,
where N′ is the standardised krill abundance, B is the bottom
sampling depth, X is a scalar to adjust the day-to-night conversion
factor (2.255) and Kpred is the expected krill abundance based
on a general linear model in which mouth area and time of year are the
independent variables (see Table 4 and Atkinson et al., 2008, for further
details). X=1 when the net was hauled in daylight and X=1/2.255 when
it was hauled at night. We also calculated standardised krill densities for
nets where there was insufficient information to determine whether hauling
occurred in daylight or at night. In these cases the value of X is the
probability that the net was hauled in daylight (i.e. day length in
hours / 24).
Derivation of Day or night information.
Information availableInformation used to standardise time of dayValid Net time (GMT, or Local with specified offset)Calculate solar elevation and use to determine Day or nightNo valid Net time but valid day or night information from ship's log (values 0 or 1)Use ship's log information to indicate Day or nightNo valid Net time or ship's log information (e.g. when a Local time is specified but no offset is given, and the ship's log does not specify day or night or indicates twilight)Calculate Day-length and use to adjust conversion factor
The revision of KRILLBASE included reassessment of the DAY_NIGHT field
(indicating whether the net was hauled in the daylight or at night; see
Table 5). Where valid sampling time information was available (consisting of
a GMT NET_TIME or a local NET_TIME and sufficient information to adjust to
GMT), we used the Twilight Excel workbook available from
http://www.ecy.wa.gov/programs/eap/models.html to determine whether the
haul was conducted in daylight (defined by a solar elevation
>-0.833∘). Where no valid sampling time information was
available, but there was an indication of day or night in the original data,
we used this information. Where it was not possible to make this assessment
because of insufficient information, we used the Twilight Excel
workbook to calculate day length for the sampling date and location, which
was then used to adjust the standardised krill density as described above. As
this type of standardised krill abundance (indicated by a value of 3 in the
DAY_NIGHT_METHOD field) uses a different time of day adjustment from other
standardised krill abundances it is good practice to assess its influence on
results.
Standardisation: caveats on the use of standardised
krill densities
KRILLBASE includes standardised krill abundance information for every haul,
stratified pooled haul and survey mean except those with
TOP_SAMPLING_DEPTH_M deeper than 50 m (because hauls which exclude the
surface layers are not comparable with those that include these layers).
These standardised densities will be most reliable when the information
underlying the standardisation is accurate. Thus where dates or times have
been estimated (for example for survey mean data) the database
provides information on the accuracy of date information (DATE_ACCURACY) and
the type of time information (DAY_NIGHT_METHOD) available in each record.
Although the ideal method for depth standardisation is to make all hauls
equivalent to a haul sampling from 0 to 200 m depth, the standardisation
described in Atkinson et al. (2008) and used here, is a partial solution
which standardises bottom-sampling depth to 200 m when the actual value is
less than 200 m. It does not exclude krill caught deeper than 200 m, where
krill densities are generally lower (Schmidt et al., 2011), nor does it
adjust for nets that did not sample to the surface (TOP_SAMPLING_DEPTH
greater than 0 m). Users are advised to screen the data to ensure that top-sampling depths are consistent with their requirements, noting that there are
691 hauls in the current version of KRILLBASE have top-sampling
depths deeper than 5 m and Atkinson et al. (2008) excluded such hauls before
calculating the conversion factors.
Date information affects the standardisation through the adjustments for time
of year and time of day. Atkinson et al. (2008) derived the conversion
factors from a data set where the latest sampling date was 26 April. Recent
KRILLBASE updates include hauls taken as late as 30 August, but we have not
provided standardised krill densities for sampling dates after 30 April
because the standardisation is extremely sensitive to dates after this point
(e.g. the time-of-year adjustment for 30 August increases krill density by a
factor of 3834, compared to a factor of 10 for 26 April, and a factor of 1.16
for 31 January). This strong effect of time of year of sampling on abundance
likely reflects both mortality and seasonal vertical migration of krill out
of the surface layer late in the season (Cleary et al., 2016)
Inaccuracies in the date will also affect the time-of-year adjustment applied
in standardisation. In the single record where the date is given only to the
year, the assigned date was 1 January, meaning that there is no time-of-year
adjustment and standardised density is conservative. When the date is given
for month as well as year, the assigned full date is the middle of the month,
meaning that true dates further away from 1 January will be treated more
conservatively as a consequence and true dates closer to 1 January will be
treated less conservatively. The effect of any date inaccuracies increases
with time from 1 January. The DATA_CAVEATS field in the database
clearly indicates for each row which, if any, of the above caveats applies.
Circumpolar distribution maps of krill based on
(a) unstandardised krill densities (no. m-2),
(b) standardised krill densities and (c) unstandardised
salp densities, showing the stations sampled for these. All maps are South
Polar Stereographic projection with grid size of 3∘ latitude by
9∘ longitude. Positions of krill stations are in Fig. S1 in the
Supplement. The legend values and colour codings of cells refer to the
arithmetic mean krill densities recorded within the cell.
Results and discussionEffects of heterogeneous data sources and
standardisation: spatial effects
Figure 8 compares the circumpolar distribution of krill and salps, allowing a
comparison between the standardised and unstandardised krill values obtained
from KRILLBASE. While hauls with zero krill
remained as such, median standardised krill abundance of positive hauls was
2.2 times greater than that of unstandardised values. The overall
circumpolar pattern of relative abundance is similar whether based on raw or
standardised abundances but the detail in some areas does differ. This is
likely due to longer summer days at higher latitudes (requiring upwards
adjustment of most catches to night values) or the localised use of poor
sampling combinations (e.g. smaller nets and/or early or late season
sampling).
The patchy distributions of krill and salps and spatial differences in
sampling density influence the spatial patterns shown in the maps. A few
grid cells suggest extremely high krill or salp abundance, but some of these
cells only include a few stations. Conversely, cells suggesting absence
frequently have too few stations for a reliable picture. Users need to allow
for variable sampling coverage, and while our standardisation attempts to
reduce net sampling inconsistencies, it does not adjust for variable
precision.
Effects of heterogeneous data sources and standardisation: temporal
effects
The South Georgia area exemplifies the krill-based ecosystem and this has
been sampled for many years (Murphy et al., 2007). We have therefore selected
a subset of KRILLBASE in this area to show how sampling method can vary from
year to year and how this could affect time trends (Fig. 9). This area has
been sampled with a wide variety of methods since the 1920s, and the mean
krill abundance varies greatly from year to year due to recruitment
variability (Fig. 9a; see also Murphy et al., 2007; Fielding et al., 2014).
While the standardised annual mean krill abundances are typically greater
than the unstandardised values, the offset varies substantially. This is for
a number of reasons, including variable mouth areas and sampling depths of
the net (Fig. 9b) and variable time of year and time of day of sampling
(Fig. 9c). For example, net mouth area is generally larger (albeit more
variable) in the modern post-1970s era, concomitant with an increase in
bottom-sampling depth of the nets. Likewise, during the modern era, the
proportions of hauls in mid-summer and at night have increased.
Inter-annual variability in sampling. Year-to-year variation in net
sampling, and its effect on the difference between standardised and
unstandardised krill density. Austral season is plotted on the x axis of
all panels with a vertical line demarcating the Discovery sampling era from
the post-1975 sampling era. (a) inter-annual variation in arithmetic
mean krill densities in the greater South Georgia area (30–40∘ W,
50–60∘ S, based on hauls from October to April with a top-sampling
depth < 20 m and bottom-sampling depth > 50 m following Atkinson et
al., 2008). While we have not plotted data with fewer than 10 hauls in any
year, the symbols are in three sizes to illustrate the variability in
sampling effort – smallest: 10–20; medium: 20–50; and largest > 50 hauls
per season. (b) Inter-annual variability in mean mouth area of the
net and mean bottom-sampling depth of the net from the hauls in
panel (a). (c) Inter-annual variability in Julian day of
sampling (days from 1 October) and the percentage of night-time hauls.
(d) Percentage of hauls over continental shelves of the sampling
area, defined as water depth < 1000 m.
The above factors are included in the standardisation process, but other
issues may be important when deciding how to screen data and interpret time
trends from a heterogeneous data set such as KRILLBASE. One factor is the
density of sampling coverage within any given year. We have not plotted years
when there are very few stations sampled (< 10 stations) because a patchy
swarming species like krill is likely to be missed altogether by such limited
sampling. However, the number of stations sampled varies greatly from year to
year (Fig. 6) so we have scaled the size of the symbols according to numbers
of stations to illustrate the variable confidence in the annual means.
A second important feature may be the geographical coverage of sampling
(Fig. 9d). Even within a defined area such as South Georgia, the emphasis of
sampling campaigns may change. For example 1926 and 1927 were local krill
surveys aimed for management of the whaling industry then based at South
Georgia, but throughout the 1930s “Discovery” sampling became increasingly
circumpolar. The 1980s were characterised by large-scale surveys, for
instance coordinated by the international Biological Investigations of Marine
Antarctic Systems and Stocks (BIOMASS) programme, while monitoring in the
1990s and 2000s was more shelf-orientated.
Basin-scale krill (a, b) and salp
distribution (c, d) within two well-studied sectors of the Southern
Ocean, plotted on a finer, 1∘ latitude by 2∘ longitude grid
to highlight habitat differences between the two taxa.
The comprehensive data descriptions in this paper allow potential
users to understand the breadth of the database and the main caveats that
need to be considered to ensure that interpretations are realistic and valid.
Two of the components of KRILLBASE, the Palmer Antarctica Long-Term
Ecological Research (Palmer LTER) and Antarctic Marine Living Resources
(AMLR) projects, are live, ongoing monitoring programmes. See
http://pal.lternet.edu/ and https://swfsc.noaa.gov/aerd/,
respectively, for the most up-to-date versions of these two time series. For
the Palmer LTER time series, we have presented only the standardised versions
of the krill data, and not the raw krill or salp data. These are instead
available direct from http://pal.lternet.edu/. For the KRILLBASE data
set described in this paper, please use the
10.5285/8b00a915-94e3-4a04-a903-dd4956346439 to obtain data and consult
the relevant data sources (Table 1) regarding queries. This data paper in
addition to the data doi should be cited as the metadata and the source of
the data, to allow traceability in the use of this database. This will
hopefully provide leverage for obtaining future funding to continue rescuing
and updating valuable historical data sets from the Southern Ocean. As a
final word we urge users to take a few minutes to consult the metadata, in
particular Table 2, since almost every use of KRILLBASE will require an
initial screening of some of the records.
Conclusions and recommendationsUses and limitations of KRILLBASE
The first version of KRILLBASE was used by Atkinson et al. (2004) to quantify
the circumpolar distribution of krill and salps, examine regional trends in
their densities and determine inter-annual relationships between krill
density and winter sea ice cover. Inter-annual changes in mean krill
abundance were subsequently related to temperature by Whitehouse et
al. (2008), to whale dynamics by Braithwaite et al. (2015) and to the
dynamics of other so-called wasp-waist species by Atkinson et al. (2014). The
fact that krill and salp abundances vary so much between years is an
advantage for this inter-annual scale of analysis, because the signal is
stronger than the noise.
The spatial component of KRILLBASE has been used more widely. Circumpolar
distributions have been used as a context and validation for various models
and analyses including biogeochemical carbon cycling (Moriarty, 2009), krill
and climate change (Flores et al., 2012; Hill et al., 2013; Piňones and
Federov, 2016), population connectivity (Thorpe et al., 2007; Siegel and
Watkins, 2016), predator foraging (Pangerc, 2010) and vertical and horizontal
krill habitat analyses (Atkinson et al., 2008; Schmidt et al., 2011). These
studies have tended to focus on large scales, but smaller-scale analyses of
well-sampled areas (as shown in Fig. 10) are amenable to KRILLBASE, for
example to interpret predator foraging areas. The caveat here is that these
maps are not synoptic, but instead are more akin to probability maps of where
krill or salps occur, providing a context for more synoptic snapshots from
surveys (Siegel et al., 2004; Kawaguchi et al., 2004).
In parallel to expansion of the abundance component of KRILLBASE, we are
generating a large database on krill length frequency, sex, and maturity
stage from scientific and fisheries data, a work still in progress.
Combining the length frequency and abundance components provides insights
into biomass and production at large scales, allowing a degree of scaling-up
of acoustics-derived biomass surveys (Atkinson et al., 2009). The sex/length
frequency component has since been used, for example, to relate circumpolar
trends in body length to feeding conditions (Schmidt et al., 2014), and to
examine sex-related changes in seasonal growth and shrinkage (Tarling et
al., 2016).
In comparison to krill, fewer studies have used the salp component of
KRILLBASE. Lee et al. (2010) examined inter-annual variability in krill and
salps simultaneously, emphasising the opposite nature of the trends observed
in the two taxa. Given the fact that about half of the current KRILLBASE net
hauls have both krill and salps recorded, a simultaneous evaluation of the
two taxa would be valuable. In any of these analyses, however, we emphasise
that great care is needed when interpreting time trends, in order to prevent
aliasing of real patterns with differences in sampling methods. This applies
equally to salps and to krill, for example, the seasonal and diel vertical
migrations of salps mean they are prone to under-sampling by shallow nets
(Fig. 4).
An additional caveat concerns the issues of net sampling efficiency for
mobile species such as krill. RMT8 catches during night-time were set as our
benchmark for standardisation because they were the most efficient means of
capturing krill, but even these catches were likely to have underestimated
absolute abundance. This is due to both net avoidance and escapement of the
smallest juveniles through the meshes. Nevertheless, the overall circumpolar
biomass of krill based on averaged KRILLBASE data is 379 Mt, so
it is unlikely that this sampling method is yielding order of magnitude
underestimates (Atkinson et al., 2009). KRILLBASE may provide insights on the
relative distribution and temporal variation in krill density, but modern
acoustic methods calibrated with nets are the accepted method for determining
krill biomass (Fielding et al., 2014). Integrating the assessments from these
two fundamentally different types of sampling represents the most robust
practice to achieve large-scale and long-term estimates of krill biomass.
The Supplement related to this article is available online at doi:10.5194/essd-9-193-2017-supplement.
A. Atkinson, S. L. Hill, E. A. Pakhomov and V. Siegel
are the instigators of KRILLBASE or this project to produce the data paper,
and are listed in alphabetical order. The remaining authors are contributors
to the database and the current paper, also listed in alphabetical order.
Original concept and initial database: A. Atkinson, V. Siegel, and
E. A. Pakhomov. Additional data sets: V. Loeb, C. S. Reiss, D. K. Steinberg,
L. B. Quetin, R. M. Ross, P. Ward, S. Kawaguchi, G. W. Hosie, S. Fielding,
S. Chiba, J. Nishikawa, R. Anadon, and B. A. Krafft; Data checking,
manipulation, spatial analysis, standardisation and editing: A. Atkinson,
S. L. Hill, R. C. Subramaniam, H. J. Peat, L. Gerrish, P. Fretwell,
M. J. Jessopp, K. Schmidt, V. Siegel, and E. A. Pakhomov. Final maps:
L. Gerrish. Final data-basing: H. J. Peat; Drafting manuscript: S. L. Hill,
and A. Atkinson. Input to manuscript: all.
The authors declare that they have no conflict of
interest.
Acknowledgements
We are greatly indebted to the crews and scientists who have collected
thousands of net samples over the last 90 years, analysed the catches, and
then provided data in a format that is useable. Boris Trotsenko was a major
facilitator in rescuing old Soviet Union data. Marie-Fanny Racault accessed
satellite temperature climatology data and Janet Silk helped with spatial
data checks. We are grateful to Peter Rothery for the original
standardisation of the krill density data. D. K. Steinberg acknowledges the
US National Science Foundation (grant PLR-1440435). B. A. Krafft was
supported by the Royal Norwegian Ministry of Fisheries and Coastal affairs,
the Institute of Marine Research, the University of Bergen, the Norwegian
Antarctic Research Expeditions (NARE), the Norwegian Research Council,
Statoil Hydro and the Norwegian Petroleum Directorate. In the last 5 years
the funding to update the database was via the Antarctic Climate and
Ecosystem Cooperative Research Centre (for R. C. Subramaniam) and the UK
Natural Environment Research Council and Department for Environment, Food and
Rural Affairs grant NE/L003279/1, Marine Ecosystems Research Program (for
Angus Atkinson). After this the final production of the database and creation
of this data paper was funded by the World Wildlife Fund. Edited by: F. Huettmann Reviewed by: T.
O'Brien and two anonymous referees
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