tian Möllmann & Ole Vestergaard EMI: Jonne Kotta FGFRI: Antti … · 2008. 4. 30. · for...
Transcript of tian Möllmann & Ole Vestergaard EMI: Jonne Kotta FGFRI: Antti … · 2008. 4. 30. · for...
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Title Guidelines for harmonisation of marine data
BALANCE Report No. 32 Date December 2007
Authors Lena Bergström (Ed.), Ulf Bergström, Martin Isæus, Jonne Kotta, Christian Möllmann, Alfred Sandström, Claus R Sparrevohn, Jonna Tomkiewicz & Ole Vester-gaard Method descriptions provided by: DIFRES: Claus R Sparrevohn, Jonna Tomkiewicz, Chris-tian Möllmann & Ole Vestergaard EMI: Jonne Kotta FGFRI: Antti Lappalainen IAE: Juris Aigars IFM-GEOMAR: Gerd Kraus, Hans-Harald Hinrichsen & Rüdiger Voss Metria: Sandra Wennberg Metsä: Minna Tallqvist & Martin Snickars NERI: Karsten Dahl & Jürgen Hansen NIVA: Martin Isæus SBF: Lena Bergström, Ulf Bergström, Alfred Sandström & Göran Sundblad SFI: Wlodimirz Grygiel
Approved by Johnny Reker
Revision Description By Checked Approved Date
Key words Classification
Open
Internal
Proprietary
Distribution No of copies
BALANCE Secretariat BALANCE Partnership
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CONTENTS
1 INTRODUCTION ............................................................................................................ 1 1.1 Purpose of the report ...................................................................................................... 1 1.2 Relationship to current international recommendations.................................................. 1 1.3 Data sets and methods................................................................................................... 1
2 GENERAL GUIDELINES FOR HABITAT MAPPING...................................................... 2 2.1 Analyses of distributions ................................................................................................. 2 2.1.1 Spatial interpolation ........................................................................................................ 2 2.1.2 Criteria analysis .............................................................................................................. 3 2.1.3 Statistical modelling ........................................................................................................ 3 2.2 Variables in habitat mapping .......................................................................................... 5 2.2.1 Response variables ........................................................................................................ 5 2.2.2 Environmental predictor variables .................................................................................. 5
3 GENERAL GUIDELINES FOR DATA INTERCALIBRATION......................................... 7 3.1 Data types....................................................................................................................... 7 3.2 Method efficiency............................................................................................................ 7 3.3 Definitions of terms related to marine habitat mapping .................................................. 8 3.4 Data sets used in BALANCE: Compilation of method descriptions ................................ 9
4 HARMONISATION OF FISH DATA.............................................................................. 10 4.1 Biological aspects ......................................................................................................... 10 4.2 Description of methods for data collection.................................................................... 10 4.3 Data Intercalibration...................................................................................................... 15 4.3.1 Nursery areas ............................................................................................................... 15 4.3.2 Foraging and spawning areas (Pilot area 1, study area 2) ........................................... 17 4.4 Data applicability for habitat mapping........................................................................... 17 4.5 Suggestions for forthcoming data collection ................................................................. 18
5 HARMONISATION OF MACROFAUNA DATA ............................................................ 20 5.1 Biological aspects ......................................................................................................... 20 5.2 Description of methods for data collection.................................................................... 23 5.3 Data intercalibration...................................................................................................... 24 5.4 Data applicability for habitat mapping........................................................................... 25 5.5 Suggestions for forthcoming data collection ................................................................. 27
6 HARMONISATION OF ZOOPLANKTON DATA........................................................... 28 6.1 Biological aspects ......................................................................................................... 28 6.2 Description of methods for data collection.................................................................... 28 6.3 Recommendations for data intercalibration .................................................................. 29 6.4 Data applicability for habitat mapping........................................................................... 29 6.5 Suggestions for forthcoming data collection ................................................................. 29
7 HARMONISATION OF PHYTOBENTHIC DATA.......................................................... 30 7.1 Biological aspects ......................................................................................................... 30 7.2 Description of methods for data collection.................................................................... 30
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7.3 Recommendations for data intercalibration .................................................................. 31 7.4 Data applicability for habitat mapping........................................................................... 31 7.5 Suggestions for forthcoming data collection ................................................................. 32
8 SUMMARY AND CONCLUSIONS ............................................................................... 33 8.1 Fish ............................................................................................................................... 33 8.1.1 Suggestions for future data collection........................................................................... 33 8.2 Macrofauna................................................................................................................... 34 8.2.1 Suggestions for future data collection........................................................................... 34 8.3 Zooplankton .................................................................................................................. 35 8.3.1 Suggestions for future data collection........................................................................... 35 8.4 Benthic vegetation ........................................................................................................ 35 8.4.1 Suggestions for future data collection........................................................................... 36
9 REFERENCES ............................................................................................................. 37
10 APPENDICES............................................................................................................... 43 10.1 Appendix 1. Methods for sampling fish data................................................................. 43 10.2 Appendix 2. Methods for sampling macrofauna data ................................................... 49 10.3 Appendix 3. Methods for sampling zooplankton data ................................................... 51 10.4 Appendix 4. Methods for sampling phytobenthos data................................................. 53 10.5 Appendix 5. Methods for sampling phytoplankton, hydrography & hydrochem. data ... 56
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1 INTRODUCTION
Comprehensive information on the distribution of marine habitats is currently scattered and limited, particularly in comparison to terrestrial habitats. One main aim of the BALANCE project is to produce transnational maps of marine species, habitats and landscapes in the Baltic Sea Region (BSR) by collating existing biological and geologi-cal data sets and by spatial modelling. As data sets are commonly produced by differ-ent standards in different nations, the harmonisation of data sets from different sources is an essential part of the progress.
1.1 Purpose of the report
This report provides general guidelines for biological data collection and intercalibration of data sets from the perspective of their usefulness in habitat mapping and modelling. Specific recommendations are given for the harmonisation of biological data sets used within BALANCE. Data sets that are openly available may be retrieved upon request to each data owner. At the end of the project GIS shapefiles of key products will also be accessed through the HELCOM website (www.helcom.fi). Hydrographic data are not specifically included in this report, but as they are often an essential part of marine habitat mapping and spatial modelling, and are routinely sam-pled together with biological data in many cases, some methods used for hydrographic data sampling are also described. This also applies for phytoplankton data, which is a potentially useful environmental variable in modelling, although not explicitly mapped within BALANCE.
1.2 Relationship to current international recommendations
International recommendations for data collection and quantification developed within HELCOM, ICES and EU are of high significance for research cooperation within the BSR. As discussed and decided at a workshop held in Uppsala, November 8-10, 2005 and at a workshop arranged by the Swedish EPA in Stockholm February 3, 2006, the approach of BALANCE in this context should be to encompass available international recommendations to the extent that they fulfil requirements for large-scale habitat mapping, and the demands of the stakeholders. Thus, the focus of this report will be to describe and discuss the available systems for data collection and view their useful-ness from a habitat mapping and modelling perspective.
1.3 Data sets and methods
Method descriptions in the report refer to data sets in use or planned for use within BALANCE by June 30, 2006, as defined by a questionnaire that was sent out to and responded on by all involved partners (See section 4). The data sets confine to either of the following two categories:
1. Spatial raw data: Data sets are used in mapping or modelling and are openly available. The project partners or data owners enter Metadata for the biological data into the BALANCE data portal, and data may be retrieved upon request. This is in order to ensure their standard and that relevant updates are included.
2. Spatial modelling data: The data sets are used in modelling but may not be openly available due to restrictions of the data owners. However, the maps of species and habitat distributions produced by BALANCE partners are available.
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2 GENERAL GUIDELINES FOR HABITAT MAPPING
All data sets that are georeferenced can be plotted spatially, and the maximum resolu-tion of the obtained map is identical to the sample size. However, the accuracy and coverage of the obtained map will depend on which methods are used for data sam-pling and mapping.
2.1 Analyses of distributions
Maps based on direct field sampling typically have high biological accuracy but provide data sets with only partial coverage. Direct mapping with total coverage is economically unfeasible at larger scales, as an extensive amount of data collection and analysis is required. Instead, different modelling approaches are used to predict the distribution of species beyond that of the observed data. Three basic approaches for increasing cov-erage are defined as:
1. spatial interpolation 2. criteria analysis 3. statistical modelling
Their basic assumptions and applicability are reviewed in the sections below. Additionally, distributional maps may be provided by the analysis of remotely sensed data, based on satellite images, aerial photography or aerial laser scanning. Satellite images may be used to obtain biological maps over large areas of shallow or surface waters, and importantly, also maps of some relevant abiotic factors such as water tur-bidity, water depth and temperature. The reliability of the results is highly dependent on access to ground-truth data on which the image reclassification is based, and the po-tential output is limited in terms of depth range, particularly in the BSR where waters are commonly turbid. However, the maps that can be provided have a potentially good resolution, with a grain size down to 10m or less. Preliminary results (presented in BALANCE draft report “Evaluation of remote sensing methods as a tool to characterize shallow marine habitats” (BALANCE WP2 MS1) indicate that remote sensing and as-sociated analyses are a promising tool for mapping the distribution of shallow marine habitat types. Further evaluation of the usage of remote sensing is currently in process within WP2.
Table 1. Analytical approaches applied in habitat mapping. Coverage Env.data
required Data format
Direct mapping Same as input data No All Interpolation Higher than input
data No (op-tional)
Continuous, Ordinal
Criteria analysis Higher than input data
Yes Ordinal, Nominal
Statistical model-ling
Higher than input data
Yes All
2.1.1 Spatial interpolation The term interpolation refers here to spatial predictions that are based only on informa-tion available in the response variable itself at its spatial positions. Thus, predictions
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are made in geographic space, rather than considering properties of the ambient envi-ronment. Spatial interpolation techniques are based on the assumption that adjacent sites are spatially autocorrelated, which means that a variable at one site is more likely to have the value of an adjacent site than that of a more distant site. Methods for interpolation are based on mathematical functions that may be defined as either exact or approximate. Exact methods preserve the values of the input data, while approximate methods produce smoother surfaces. For biological data, approximate methods are often more appropriate, because they may account for local uncertainties in the input data. Common approximate methods for interpolating point data are inverse distance weighted averages, trend surface analyses and kriging, all of which are usually implemented in common GIS software. Interpolation may be useful for producing layers of environmental data, such as esti-mating the probable depth or salinity in unsampled areas. For biological data, interpola-tion may be used for generalising the distribution of species at large, regional scales. At small scales, interpolation may be motivated when the environment is non-patchy with respect to the habitat demands of the species.
2.1.2 Criteria analysis In criteria analysis, spatial predictions of the occurrence of a response variable are made in relation to a set of categorical (nominal or ordinal) layers of environmental variables. The environmental layers are arranged on top of each other, and combined to one layer where all possible combinations of the input layers are represented. The occurrence of the response variable within each combination is then defined from its observed frequency in field samples from corresponding environmental settings. For example, combinations of bottom substrate and wave exposure that correspond to cer-tain abundances of a certain species may be identified. The environmental layers should have full coverage and data for the response variable should be collected so that the relevant environmental gradients are covered. Although the procedure is basically non-statistical, the relevance of the output increases if the layers and categories applied are properly statistically defined and known to be rele-vant for the distribution of the response variable. Criteria analyses are easy to apply and to communicate. Robust results can potentially be achieved, but the accuracy of the prediction may be low unless a high number of classes are defined within each layer. Thus, the procedure may require high amounts of data processing, especially when predicting the distribution of individual species and when a large part of the environmental data is originally in continuous data format.
2.1.3 Statistical modelling Statistical modelling is technically more demanding, but may provide a way of utilising more (or all) of the information available on the relationship between predictor and re-sponse variables. The output is numerical, for example the abundance of a species, or the probability of finding a certain species or life-stage at a site may be estimated. Predictions are based on a mathematical function describing the relationship between the response variable and relevant environmental variables. This function is then used to estimate the value of the same response variable at other sites based on existing in-formation in environmental layers. The response variable should be represented by point data, and the environmental predictor variables must have full coverage over the
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area to be predicted. The environmental predictor variables should preferably be casu-ally related to response variable (see section 2.2.2) and cover a gradient from the minimum to maximum for the response variable along each of the predictor variable gradients. The quality of the output depends on the level of compatibility between the properties of the applied data sets and the regression method. Commonly applied methods vary in their response curve and assumed error functions (Table 2). The simplest way of de-scribing the relationship between predictors and the response variable is multiple linear regression, while generalized linear models (GLM) and generalized additive models (GAM) are more technically complex, they are more flexible and better suited for de-scribing non-linear relationships (Guisan & Zimmerman 2000, Guisan et al. 2002, Leh-mann et al. 2002, Garza-Pérez et al. 2004, Francis et al. 2005). GAM has the advan-tage of high flexibility, as the response curves are not pre-defined but may be explored and fitted to observed response distribution along environmental gradients. This makes GAM potentially better suited for modelling the spatial distribution of species with asymmetrical or polynomial distributions, which are often observed in real data (Leh-mann et al. 2002). GRASP (Generalized Regression Analysis and Spatial Prediction) is a statistical software for spatial prediction based on regression analyses (GAM) that is implemented as an interface and a collection of functions in the statistical software S-plus and R. It also has the advantage of being compatible with the GIS-software Arc-View 3.x. In some cases other methods may be preferred. For example classification and regres-sion trees (CART) are more suitable for modelling interactions, although it does not al-low observation of response shapes and is restricted to assuming Gaussian distribu-tions (Lehmann et al. 2002). Another flexible modelling tool is artificial neural networks (ANN), which has the advantage of providing a way of modelling assemblages of spe-cies, as several response variables can be modelled simultaneously (Brosse et al. 1999, Lek & Guégan 1999, Joy & Death 2004). In datasets with many zero values in the response variable multivariate analytical models such as Canonical correspon-dence analyses (CCA), may be more appropriate than models of individual species (Austin 2002). As an alternative, the included data sets can be cut down to only repre-sent the realized distribution of the response variable (Lehmann et al. 2002), or a “presence only” approach can be applied, using only the sites where the species is present (Zaniewski et al. 2002).
Table 2. Examples of regression analyses used to make spatial predictions Method Statistic distribution Response curve Least Square Regression (LSR) Gaussian Parametric, quadratic Logistic regression
Binomial Parametric, quadratic
Generalized linear models (GLM) Gaussian, binomial, Pois-son…
Parametric, quadratic
Generalized additive models (GAM) Gaussian, binomial, Pois-son…
Non-parametric, smoothed, any shape
Classification and regression trees (CART)
Gaussian Non-linear, non-additive
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2.2 Variables in habitat mapping
There is a wide range of variables applicable for mapping marine habitats and many different approaches depending on sector, country etc. The range of variables will be presented below.
2.2.1 Response variables
The fundamental ecological response variable is the species. Modelling of individual species is conceptually appropriate, because the direct relationship between response and predictor variables is the potentially least complex. The habitat of a certain species is then defined by the most relevant environmental predictors for that species. Focus-ing on the species level agrees with ecological awareness that the distributional dy-namics of species are typically independent of each other, and that communities or as-sociations are unlikely to move as an entity along temporal and spatial gradients (Guisan & Zimmermann 2000). However, mapping of pre-defined habitat types has a high applied relevance and is the most common basis for management decisions. Using the species approach, other re-sponse variables, such as species richness and certain community or assemblage types, may be derived by compiling maps of individual species distributions (Lehmann et al 2003, Austin 2002). The species approach is also motivated by the fact that many habitat types are defined by the presence of major structuring species. Thus, maps with full coverage of structuring species are an important input in for example the Fin-nish national habitat classification system BalMar, and the EUNIS classifications. For species with discrete life-stages, which are ecologically as well as spatially distinct, a combination of several stage-specific models may be required for complete habitat definition. For example, several fish species have a number of well-defined life-stages, which are separated by physiological or ontogenetic shifts leading to changes in habitat preference and, thus, spatial distribution. In other cases, sufficient data on species dis-tributions may not be available, and the distribution of functional groups, habitats or other ecological units of organisation is used as the primary focus for mapping.
2.2.2 Environmental predictor variables
Statistical models and criteria analyses are based on observations on correlations be-tween a response variable and a set of environmental predictor variables. Thus, spatial predictions may potentially be achieved at high precision also in cases where the model has no direct ecological process basis. However, the spatial and temporal ro-bustness of purely correlative predictions are potentially weaker than predictions based on relevant underlying ecological knowledge. Environmental predictors may be defined as being either proximal or distal, based on the position of the predictor in the chain of processes that links it to the species. Using a similar concept, environmental gradients may be defined into three idealized and not exclusive categories (Guisan and Zimmerman, 2000, Austin 2002):
1. Indirect gradients, based on variables that have no physiological effect on
growth or competition (for example depth, latitude). These are typically de-scribed by distal variables.
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2. Direct gradients, based on variables that have a direct physiological influence (for example temperature, salinity and oxygen). These predictor variables can be either proximal or distal.
3. Resource gradients, based on variables that are consumed (ex light, prey, nu-trients). These are typically described by proximal variables.
Models based on proximal variables are potentially more robust and general, whereas species distribution models using distal variables will often primarily be of local value (Austin 2002). However, the relative importance of distal and proximal variables varies with ecological context. Proximal variables are usually more successful predictors where the environment changes slowly and a species occupies its optimal realized niche. Where gradients are steep and environments are extreme, simple distal vari-ables will be as successful as environmental process based models (Austin 2002). Sometimes, GIS coverage for proximal variables are not easily obtained, which in-creases the relative importance of distal predictor variables in habitat modelling. In these cases, distal variables may be used to replace a combination of different proxi-mal variables in a simple way (Guisan and Zimmermann, 2000). For example, wave exposure can be used as a distal variable assumed to at least partially replace proxi-mal variables such as water temperature and bottom substrate, but wave exposure may also be a relevant proximal variable for some species. The shape of the response function will vary with the nature of the environmental pre-dictor variables. Responses to a predictor variable describing an indirect gradient could take any shape. For example, two predictor variables with unimodal shape may cause a bimodal shape in the response variable. In biological variables, skewed distributions are commonly seen, as different predictor variables may affect the abundance of the response variable at different ends of its range of distribution. Additionally, environ-mental predictor variables may interact which can also influence the response curve.
Table 3 Main environmental gradients structuring the distribution of marine species in the BSR. Many variables may be considered either proximal or distal, depending on context, as exemplified in footnotes for the variables nutrients and wave exposure. Scale of prediction Predictor type Larger Smaller Proximal Distal Climate X X Depth X X Latitude X X Light X X Nutrients X X1 X2
Oxygen X X Salinity X X X Substrate type X X Temperature X X Wave exposure X X3 X4
1for primary producers 2for secondary producers 3for species/habitats affected by physical disturbance 4when used as a proxy for, e.g. temperature (see text, section 2.2.2)
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3 GENERAL GUIDELINES FOR DATA INTERCALIBRATION
When large-scale maps are based on data originating from different surveys, potential differences in the approaches used for measuring and estimating data variables should be considered before pooling data sets. Initial evaluations should verify that data sets are compatible regarding sampling strategy, method efficiency and data format. Data sets may then be either pooled directly, after data processing, or after data reduction. As an alternative, datasets collected by different methods may be used for a model building dataset and a validation dataset, respectively. For validation of models focus-ing on relative abundances, intercalibrated data sets may not be required. However, this is only advisable for methodologically similar datasets that do not differ in catch ef-ficiency (see section 3.3).
3.1 Data types
The following terms are used for data types: 1) numerical, 2) ordinal, 3) nominal includ-ing presence/absence, and 4) presence only. Data types should be compatible among data sets and, in case of modelling, with the particular assumptions in the applied analytical approach. In the case of ordinal and nominal data, common intervals and definitions should be applied or developed. Data sets using different data types should be intercalibrated by data reduction.
3.2 Method efficiency
Differences among data sets regarding survey design and sampling methodology may affect the potential accuracy of quantitative estimates, and which data formats to apply. Methods should be compatible regarding gear efficiency, and size of area sampled. This may be verified by referring to common standards (e.g. HELCOM or ICES rec-ommendations), or alternatively, a field intercalibration of gears may be used or re-ferred to in order to ensure compatibility. Field intercalibration may be used to estimate quantitative differences among methods, and subsequently to recalculate estimates and combine datasets. For ordinal and nominal data formats, and in cases where different methodological ap-proaches clearly vary in their level of efficiency, data reduction may be a means for en-suring a common level of accuracy. Special attention should be paid to the efficiency of different methods in estimating absences, since this is highly dependent on the area sampled, sample efficiency and species in target. In such cases, data reduction to presence only should be considered (Zaniewski et al. 2002). A definition of terms is presented in table 4.
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3.3 Definitions of terms related to marine habitat mapping
Table 4. Definitions of terms related to marine habitat mapping Term Definition Accuracy The degree to which an estimated mean differs from the true mean Assemblage Coexisting populations within the same trophic level
Classification The grouping of a data set or a set of variables into predefined cate-
gories Community A group of populations that coexist and interact
Criteria analysis A non-statistical overlay analysis based on criteria developed within
the current and/or earlier studies Data reduction Removing spatial or numerical details in a data set
Data types The classification of data according to their characteristics
Environmental envelope The realized ecological niche of a species
Georeferenced data Data with spatial attributes, such as position coordinates
Habitat Ecological niche referring to a certain species and life stage, e g habi-
tat for perch, spawning habitat of cod or sprat larvae Habitat types Environment with similar structural characteristics and composition of
associated species. Intercalibration The process of correcting data collected by different means so that
they may be comparable with each other. Interpolation The estimation of values of a variable at non-sampled sites from
measurements made at surrounding sites Layer A logical separation of mapped information according to themes, can
be in the form of vector or raster data. (syn. GIS coverage) Marine landscape Geological and biological large-scale spatial characteristics of the ma-
rine environment Nominal data Data type where the variable assumes one of several alternative
states, e g presence/absence or turbid/clear Numerical data Data type where the variable assumes continuous values or values
with equal intervals from each other Ordinal data Data type where the variable assumes values with unknown or un-
equal intervals, e.g. temperature. Overlay analyses Spatial analyses where data is interpreted by superimposing several
thematic layers on each other Population Interbreeding individuals of the same species in a given area
Resolution The smallest size that can be mapped or sampled as one unit. Also
applicable on GIS raster layers when referring to pixel/grain size. Spatial interpolation Spatial predictions based on information present only in the variable
itself and its position Spatial modelling Spatial predictions based on statistical models
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3.4 Data sets used in BALANCE: Compilation of method descrip-tions
A questionnaire on sampling methodology was sent out to all involved BALANCE part-ners, and was responded on by May 2006. In order to ensure compatibility among method descriptions, a common template for biological and geological data collection was designed at the workshop in Uppsala 8-10 November, 2005. The focal points of the questionnaire were to address: 1. Technical descriptions of gears and instruments 2. Sampling designs & comparability of methods from a habitat mapping perspective 3. Suitability for habitat modelling Details of the obtained method descriptions are presented in appendices 1-5. The main part of the descriptions was allocated to either of four main target biota; fish, macro-fauna, zooplankton and phytobenthos (Table 5). These were submitted for further evaluation to experts of each field, according to common guidelines, as described in table 6. Method descriptions for phytoplankton and hydrographic data were additionally provided. These are described in the appendix.
Table 5. Grouping of biological data collected Target biota Methodological sub-groups Number of
methods de-scribed
De-scribed within pilot area
Evaluator
Fish Spawning habitats 2 1, 2, 3 DIFRES, Egg habitats 2 2, 3 SBF Larvae habitats 2 2, 3 Nursery areas 6 1, 3 Foraging habitats 3 2, 3 Macrofauna Soft bottom macrofauna 5 1, 4 EMI Zooplankton Zooplankton 2 2, 4 IFM-GEOMAR Phytobenthos Macrovegetation and sessile
animals on hard substrates 7 2, 3, 4 NIVA
Additional method descriptions provided Phytoplankton Species composition 1 4 - Hydrography Temperature, oxygen, salinity 3 2 -
Table 6. Points addressed in the evaluation and harmonisation of methods for data col-lection. Principles for data intercalibration (focal point number 3 in Table 6) concern cases where more than one method was described 1. Biological aspects General biological characteristics
General method requirements 2. Methods for data collection Description of gear, methodology, special adaptations and
details. 3. Data intercalibration Identification of data sets to intercalibrate; Evaluation of how
to intercalibrate data sets; Recommended data format, accu-racy, and spatial resolution
4. Data applicability for habi-tat mapping
May the data be spatially generalized in order to be represen-tative of larger areas, and if so, how? Do the data sets con-form to the modelling approaches preferred? Main environ-mental data required and its availability
5. Suggestions for forthcom-ing data collection
How should forthcoming data collection be modified/ de-signed in order to facilitate mapping and modelling?
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4 HARMONISATION OF FISH DATA
In order to harmonise fish information for a marine region such as the Baltic Sea Re-gion, it is important to take biological aspects, sampling methodologies and how raw data are handled and interpreted into account.
4.1 Biological aspects
The horizontal and vertical distribution of different fish species depend on whether they are pelagic or demersal, occupy coastal or open water, or if they prefer fresh or saline water. In addition, different life stages, such as the eggs, larvae, juveniles and adults of various fish species may be associated with different ecological niches and habitat characteristics (hydrograhy, bathymetry, substrate etc.). For example for cod, the eggs occupy the mid water layer in the deep basins, the larvae live in the open sea surface layers, juveniles inhabit the layer near bottom in shallow water so-called nursery areas, while immature and adult cod live near the bottom at deeper water depths. In addition, the foraging and spawning habitats of the adult fishes often differ causing seasonal mi-grations between different areas. One of the scopes of BALANCE is to identify spawning and nursery areas, which are important for recruitment of fish species and important aspects when designating MPAs (Marine Protected Areas). The location, size and seasonality of the spawning and nursery areas will be defined by mapping distributions of eggs, larvae and juvenile fish stages. Also, the preferred foraging areas of adult fish in comparison to spawning ar-eas will be mapped. In all cases, the spatial and temporal distributions of different life forms depend on environmental variability influencing the size and location of habitats over time. The choice of survey design, methods and gears, timing and frequency consequently differ among species and life stages (Table 7 & 8). For example, the abundance of ju-venile fish in nursery areas is both temporally and spatially highly variable, which af-fects sampling efficiency at the scale of hours to seasons.
4.2 Description of methods for data collection
Diving surveys Used by SBF. Surveys of perch (Perca fluviatilis L) egg strand abundance in bays and lagoons are conducted in spawning time during April-June. Mapping of egg strands is performed by snorkelling along parallel transect lines drawn perpendicular from the shore to the opposite shore. The first line is placed 5 m from and parallel with the inner-most shore, or as near as possible of it if the depth conditions allow it. The second line is placed at 50 m distance from the first one, the third at 100 m distance from the sec-ond one, and the following ones at 100 m distance from the previous one until the entire area is surveyed. Additional lines are placed 5 m from the shoreline between the starting and end points of the first and second line, respectively. All egg strands within one me-tre on both sides of the transect lines are registered. The substrate on which the strand is attached, depth and distance from nearest shore is noted. Temperature is measured at start, mid-point and end of each transect line 20 cm above the bottom.
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Table 7. Potential biological layers in the mapping of fish, biological characteristics that relate to differences in sampling strategy, and methods used for data collection Potential habitat layer
Potential species layers
Temporal varia-tion within a year
Main habitat Method
Cod, subadult and adult
Constant/seasonal Demersal, depth > 30 m
Demersal and pe-lagic trawl surveys
Sprat, herring
Constant/seasonal
Pelagic, basin slopes
Hydroacoustics, pelagic trawl sur-veys
Foraging habi-tats
Perch, pike, whitefish, sander, roach, bream (>8cm)
Sample scale (hours-days), seasonal
Coastal areas 0-20 m depth
Nordic nets of coastal survey type
Cod
Seasonal
Pelagic/demersal
Demersal and pe-lagic trawl surveys
Spawning habi-tats
Sprat Seasonal Pelagic, open sea Hydroacoustics Pelagic trawl sur-veys
Perch egg strands
Seasonal
Sheltered, partly vegetated, 0-6 m depth
Diving survey Egg habitats
Cod eggs Seasonal Pelagic open sea Bongo survey
Larvae of pike, roach, burbot
Sample scale (hours-days), Seasonal
Sheltered, partly vegetated, 0-2 m depth
White plate and scoop
Larval habitats
Cod larval stages
Seasonal
Pelagic, open sea
Bongo survey
Juvenile plaice, sole, flounder, turbot
Sample scale (hours-days), Seasonal
Sandy/muddy, 0-6 m depth
Beamtrawl, Drop trap, Pushnet, Trawl sampling
Nursery areas (juveniles, age 0+ and 1+)
Juvenile perch, pike, roach, sander, bream
Sample scale (hours-days), Seasonal
Sheltered, partly vegetated, 0-6 m depth
Beach seine, LIPS
Juvenile white-fish
Sample scale (hours-days), Seasonal
Moderately ex-posed, sand, boul-ders, 0-6 m depth
Beach seine, pot. LIPS
White plate and scoop Used by FGFRI. This method is used to sample and observe pike, burbot and cyprinid larvae among and in shallow vegetated shores. The plate is a 20*30 cm white plastic plate fixed to a 1 m long arm. The plate is slowly moved at a depth of 10-40 cm and the typically 13-25 mm long larvae are easily detected against the white background. The scoop is an ordinary white 2-3 l water scoop, which is used for sampling and counting larvae against the white background. Surveys are performed during the latter half of
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May and June. Sampling sites are randomly selected 100 m long shorelines, and the method produces data on presence/absence of larvae on the sites.
Drop-trap This gear can be designed in several ways (see Pihl & Rosenberg 1982, Wennhage et al. 1997 for different designs) and is used not only for flatfish sampling but also for various benthic organisms. At DIFRES the size of the trap is 1 m2 whereas in other studies a size of 0.5 m2 has been used. It is generally assumed that the efficiency of the gear is 100 % and that it is non-selective for different fish sizes. It is possible to ac-complish 5-10 samples per hour (Elliot & Hemingway 2002). The sampling procedure is as follows. The drop-trap is carried out to the desired locations and quickly dropped at the bottom. Then the area within the drop-trap is fished through several times with a small net in order to catch all flatfish enclosed by the drop-trap. The use of drop-traps is restricted to smooth bottom substrates.
Beam-trawl There are several different designs of beam-trawls. The size and weight of the beam has been altered as well as the numbers and sizes of chains in front of the beam. (see Riley & Corlett 1966, Kuipers 1975, Rogers & Lockwood 1989, Kuipers et al. 1992, Kaiser et al. 1994, Wennhage et al. 1997 for different designs). Besides different de-signs the beam trawl can be used from a boat or it can be pulled by hand in shallow ar-eas. The beam-trawl used by DIFRES is 2-m wide and has a 7.4 m rope at each end of the beam for dragging of the gear. In front of the gear an iron-chain is mounted in order to chase the flatfish up from the sediment. This chain also has the purpose of smooth-ing the bottom and thereby ensuring the best contact between the gear and the bottom. The use of beam trawls is restricted to relatively smooth bottom substrates.
Push-net (Standard and Improved) Even though the push-net was originally designed to catch shrimps it has also been used for sampling juvenile flatfish in the coastal zone (Elliot & Hemingway 2002, figure 1). It has the advantage of being very easy to use and that the sampling can be carried out by a single person. A commonly used push-net is the Riley push-net (Elliot & He-mingway 2002), which is 1.5 m wide. This gear resembles a beam-trawl but is pushed instead of being dragged. Push-nets are divided into two different types, one is a stan-dard push-net designed for catching shrimps whereas the other one is a push-net modified by Else Nielsen from DIFRES in order to improve the efficiency for catching flatfish. The two different push-nets are both 63 cm wide, the only difference being that the standard push-net has a sharp edge, and the improved push net has a round edge, which makes it easier to keep a steady fast speed. The use of push-nets is restricted to smooth bottom substrates.
Beach seine Used by SBF and FGFRI. A beach seine is an active gear designed to catch small fishes and shrimps. It is made up by two long leading net-arms joining in a cod end with a slightly lower mesh size. The beach seine is pulled towards the shoreline on relatively smooth substrates. It is possible to use at a low or moderately low coverage of vegeta-tion, although it has the disadvantage of damaging some species of vegetation to a smaller extent. The beach seines used by SBF and FGFRI are 2 m deep, have 10 m long arms and 5 mm mesh size in the arms and 2 mm in the cod end.
Juvenile trawl In fishing for juvenile flatfish in the coastal zone with boat a standard juvenile trawl is towed for 10 min at 1 knot (40 m min-1). Total length of the net is 10.8 m and the width
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is 4.5 m. The trawl is attached directly to 14 kg otter-boards measuring 85x50 cm sepa-rated by a 5.5 m long chain. The height of the opening is estimated at 36 cm. The body is divided into three sections with decreasing stretched mesh size of 10, 8 and 6 mm. The stretched mesh size of the cod end is 5 mm. The use of the juvenile trawl is re-stricted to relatively smooth substrates.
A) B)
C) D)
Fig. 2. Man-powered gears used for catching flatfish in the coastal zone. A) drop trap, B) Beam trawl, C) Standard push-net and D) Improved push-net
Low impact pressure wave sampling (LIPS) Used by SBF and FGFRI. Data on abundance of juvenile fish is collected by point sampling using small detonation-capsules (0.94g have been used at FGFRI, 0,94 plus 10g at SBF) that stun small fish within an area of c. 15-100 m2. This method allows sampling of fish sized 15-150 mm with well-developed swim bladders. A capsule is detonated at a depth of 0.5 m from a boat using a long fishing rod. The capsule is ig-nited through an ignition cord (figure 2a). After detonation, all stunned fish are netted from the surface and collected from the bottom by snorkelling/diving. Fish sampling is preferably randomly stratified by depth, wave exposure and vegetation composition. A maximum daily effort is c. 10-30 detonations depending on travelling distance between sites. To assure that sampling stations do not interfere with each other, they should be at least 30 m apart. Sampling is conducted in late July-August during daytime.
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Fig. 2a. Low impact pressure wave sampling Fig. 2b. Bongo with Babybongo
Nordic nets of coastal survey type Used for national coastal fish monitoring by SBF. Coastal surveys of adult fish are per-formed using Nordic nets of coastal survey type. The nets are 1.8 m deep and 45 m long, and are composed of nine sections of five meters length, with mesh sizes of 10, 12, 15, 20, 24, 30, 38, 47, and 60 mm. The sampling design is random stratified within depth intervals of 0–3 m, 3–6 m, 6–10 m, and 10-20 m. The main part of the surveys is performed within two weeks in August each year. Ten-fifteen stations are fished at each depth interval, except for the deepest stratum where 5 stations are fished,. Nets are laid at 14-17 pm and taken up at 7-10 am the following day.
Bongo with Babybongo Gear used for sampling of ichthyoplankton (and zooplankton) used by IFM-GEOMAR and DIFRES (figure 2b). It consists of 4 net frames (2x0.6m diameter plus 2x0.2m di-ameter), with the large nets normally equipped with 300 and 500µm mesh size for sampling fish eggs and larvae, while the small nets with 150 and 50µm mesh sizes are used to sample different life stages of zooplankton organisms. The Bongo is towed ap-plying double-oblique tows within five meters of bottom or to a maximum depth of 200 meters with a ship speed of 3 knots. Filtered volumes are measured either by me-chanic or electronic flow-meters or estimated via deployment time. Sampling in the Bornholm Basin is performed on a regular station grid covering the deep basin limited by the 60m depth line at a distance of approximately 10nm. Between 3 and 6 surveys per year are conducted, encompassing the main period of productivity (cod & sprat, sprat spawning time) from April to August/September.
Demersal trawl Bottom trawling is conducted with different trawl gears applying bobbins, enforced bot-tom etc. usually towed for 30-60 min at 3.5 knots. The trawls have different proportions depending on the size of the research vessel and surveys type. The International Baltic trawl survey (BITS) coordinated by ICES use a standard gear (TV3 trawl) since 1999. The surveys are intercalibrated and the present gear has been calibrated against the formerly used gear types that varied among countries. Commonly used to fish fishes inhabiting the zone close to the bottom, e.g. cod, flatfishes, etc. Area and volume sam-pled by trawling depends on the gear type and trawl time.
Pelagic trawl Pelagic trawling is conducted with different trawl gears usually towed for 30-60 min at 3.5 knots. The trawls have different proportions depending on the size of the research vessel and surveys. The International Baltic Acoustic Survey coordinated by ICES use
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a standard gear to catch fish species inhabiting the upper and midwater layers (e.g. sprat and herring) obtain biological data to validate the hydroacoustic measurements. Area and volume sampled by trawling depends on the gear type and trawl time.
Hydroacoustics Hydroacoustic survey methodologies are used to provide rapid estimates of fish stock distribution and biomass independent of fisheries data. The method surveys pelagic fish species that swim in large schools utilising most of the water column, in contrast to demersal species. The method is based on instruments, which emit pressure waves in the water and detect part of this energy reflected by fish targets. Hydroacoustic surveys are routinely conducted as part of the Baltic International Acoustic Surveys (BIAS) to estimate the abundance of sprat and herring. The survey methods are described in the Manual for the Baltic International Acoustic Surveys, or BIAS (ICES 2003). Table 8. Sampling methods used and details of data obtained Method Obtained
variables Obtained unit Area covered by
sample Pilot area involved
Diving survey Presence of egg strands
Presence/absence 0.5x0.5m 3
White plate and scoop
Catch rates Presence/absence ~100 m2 3
Drop trap Abundance n/m2 1m2 1 Beam trawl Catch rates n/effort 50m2 1 Push net Catch rates n/effort 20m2 1 Beach seine Catch rates n/effort 100-200m2 3 Juvenile trawl Catch rates n/effort 1800m2 1 LIPS Catch rates n/m2 16 and 100m2 3 Coastal survey nets Catch rates n/effort - 3 Bongo Abundance n/l Volume water fil-
tered is measured 2
Demersal trawl types Catch rates n/effort Varies 2 Pelagic trawl types Catch rates n/effort Varies 2 Hydroacoustics Biomass t/volume Varies 2
4.3 Data Intercalibration
Intercalibration of fish data needs to take into account all the habitats where a species can be found in various life stages.
4.3.1 Nursery areas Estimating fish abundance in nursery areas from their catch-rates is complex due to high temporal and spatial variability of the target species, and due to high variability in the efficiency of different gears. Although the abundance of the target species may be estimated as numbers per area or per known effort (Table 9), the main target species may differ and the relationship between observed catch-rates and the actual abun-dance of fish in an area is unknown in most cases. One way of dealing with this problem is to estimate the relationship between the catch-rates of different gears by intercalibration experiments. Calibration experiments are quite straightforward as long as the gears calibrated are in use at the same location. In an intercalibration study carried out by DIFRES, the efficiency of the most commonly used gears was estimated to vary between 10% and 100%, depending on gear type, but also on the physical characteristics of the environment, such as depth, sediment, and in some cases wave-height. As a consequence, potential differences in method ef-
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ficiency should be considered not only when comparing different gears, but also when collating data collected by the same gear but in different areas. Method efficiency varies among target species and may also vary with the size of the target species. In a study with releases of a known number of turbot, Sparrevohn and Støttrup (in press.) estimated that the efficiency of a boat-driven young-fish trawl was approximately 50 % for sizes 7-12 cm and decreasing to around 10 % for 17 cm large turbot. Similar results have been found in various other studies with flounder and plaice. In some cases, intercalibration of data sets is not at all possible, because several gears and methods cannot be used on all types of substrates. As an example, trawling may not be used in areas where sea-grass is abundant. Intercalibration experiments are po-tentially realistic among gears that target the same species and habitat types, provided that local environmental differences may be accounted for in the intercalibration design. An estimation of the catch efficiency of different methods in relation to target species, size of target species, bottom substrate, season and vegetation is presented in 9. When catch efficiency is strongly affected, data reduction to presence only is recom-mended. In the case of similar target species and low effects on catch efficiency, data sets may potentially be pooled quantitatively however this should be done with caution and only following situation-specific intercalibration.
Examples of method efficiency evaluations Within DIFRES, intercalibration experiments have been conducted regarding gears used for collecting individuals and sampling abundance of juvenile flatfish in the coastal zone. The methods compared were man-powered (drop-trap, beam-trawl and push-net) and vessel-powered (juvenile trawl) methods. These gears are typically used at different depths. Manpowered method can only be applied in the very shallow areas of less than 1-1.5 m depth, whereas vessel powered methods may reach a sample depth of 1 meter at the shallow end, depending on the boat used. However, in general, man-powered methods are recommended for depths less than 1.5 meters. In the intercali-bration experiment, it was assumed that the drop-trap catch 100 % of all fish. The effi-ciency of the other gears in relation to this was estimated to approximately 9% (SD 9.4) for beamtrawl, 31% (SD 18.9) for standard push-net, 28 (SD 26) for improved push-net and 29% (SD 21) for beam-trawl.
The efficiency of low impact pressure wave sampling (LIPS) in sampling perch, pike and roach was evaluated for vegetated and non-vegetated areas by Snickars et al. (submitted manuscript). Method efficiency was measured as the effective area in which >95% of the individuals present were affected, and varied from 7 to 28 m2 using a 0.94g detonator. The study indicates that calibrations among areas should be done with reference to sampled depth, presence or absence of vegetation and detonation size.
Table 9. Target species of the different gears used in surveys of fish nursery areas, together with indication on whether size of the fish caught, bottom-substrate, and vegetation are likely to affect gear catch efficiency (+) or not (0). Method Species Size Substrate Vegetation
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Diving survey Perch and other species with easily recognisable egg strands
Ns + 0
White plate and scoop
Larvae Ns 0 0
Drop trap Mainly flatfish
0 + +
Beam trawl Mainly flatfish
+ + +
Push net Demersal species
+ + +
Beach seine Demersal species
+ + +
Juvenile trawl Demersal species
+ + +
LIPS Fish with swim bladders
+ 0 0
Coastal survey nets
The majority of occurring species
0 0 0
4.3.2 Foraging and spawning areas (Pilot area 1, study area 2) Data intercalibration is needed in order to estimate population size as absolute density or abundance indices based on catch rates from fish surveys conducted by different vessels and using different gear types, sampling strategies etc. The Baltic International Trawl Surveys (BITS) and Baltic International Acoustic Surveys (BIAS) are conducted in spring and autumn by different research vessels from the various countries sur-rounding the Baltic Sea and Kattegat to obtain fisheries independent data for stock as-sessment of cod, herring and sprat. The surveys, which originally used different gears and methodology, are since 1999 coordinated by ICES and use similar gear and stan-dardised methods. (ICES, 2003). The vessels, gear and coverage differ calibration ex-periments have been conducted and calibration factors are used to calibrate catch rates per species, size etc. These data can also be used on a temporal scale to com-pare distribution of cod, sprat and herring over a range of years in relation to environ-mental changes determining their habitat volume and quality. Data from Danish and German fish surveys not related to BITS and BIAS are addition-ally used to map cod spawning areas and spawning time. Comparison of relative com-position catch rates from different surveys can be made without calibration of gear catchability and efficiency as long as the sampling strategy used is similar, e.g. per cent female cod in spawning condition per haul can be plotted for an area and be com-pared to similar data from other surveys, in order to identify spawning locations and timing of spawning independent of trawl gear used. The sub-sampling of fish from the catches may differ among surveys, e.g. representative sampling of the catch versus systematic sampling by length groups) but this can be corrected by up-weighting the samples to total catch. Sampling of ichthyoplankton using Bongo and Babybongo is made using standard gear in a standardised way and data are independent of vessel.
4.4 Data applicability for habitat mapping
Data on fish abundance collected using different methods and gears should generally be viewed as not directly quantitatively comparable. This is mainly due to differences in
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the target species and overall catching efficiency, as explained in the sections above (figure 3a-d). Because of high spatial and temporal variability of target species, method efficiency varies with environmental setting, and quantitative comparisons between studies using the same gear should also be done with caution. Also, data on fish abun-dance are often highly skewed with non-normal frequency distributions, because sev-eral species and/or life stages have a strong schooling behaviour that results in strongly zero-inflated catch data. The schooling behaviour may also be dependent on the environmental conditions. As an example, juvenile fishes have been found to alter their tendency to school as a response to changes in habitat complexity (Rangeley & Kramer, 1998). Thus, the following recommendations may be made:
1. Generally, data reduction to “presence only” is recommended for large-scale
mapping. 2. In all instances where the predictor variable influences the schooling ten-
dency of the target species, presence/absence models may be seriously bi-ased and should be used with care.
3. Presence/absence data may be applicable for methods with similar target species and catch efficiency.
4. Quantitative pooling is only recommended on smaller scales and after situa-tion-specific intercalibration exercises.
5. In statistical spatial modelling, presence/absence data or presence only data of good quality is preferred over more detailed data of uncertain relevance, as the estimated output may still be expressed as the numerical probability for occurrence of the predictor variable.
4.5 Suggestions for forthcoming data collection
Standards for fish sampling methods are under continuous development within ICES and EU, to ensure use of data beyond the national and regional level. New standards are in progress for monitoring of fish communities in lakes and streams and standards for trawling of pelagic and benthic species in off-shore areas with larger vessels are developed and set within ICES. However, similar standards for monitoring and mapping of young fishes in shallow coastal areas are not available. Since the sampling techniques are highly specialised for certain species or habitats, such standards are harder to develop for shallow coastal areas. A first step towards harmonisation would be to conduct transnational in-tercalibration efforts, comparing and evaluating fish sampling surveys in shallow coastal areas within the BSR. Although such an attempt is relevant and highly recom-mended, it would require a large effort and should be conducted over a longer time pe-riod and is thus not possible to include in BALANCE. Currently, for shallow nursery areas, the sampling methods drop-traps, LIPS and coastal survey nets (gill nets) appear the most general by the aspects analysed in 9. However, these are limited in their potential areas of use, and it may not be possible to agree on one single gear type or method to be used throughout the Baltic Sea. Drop-traps can only be used in very shallow waters, and LIPS will only catch fish with swim bladders, which means that flatfish will not be caught. Gill-nets, again, do not easily al-low quantitative abundance estimates as the catch method is passive, which means that the catch rate of different species will depend on their level of activity in the survey area. Also, gillnetting can be very time consuming during the summer season in the more saline areas of the Baltic Sea due to large catches of crabs. In most cases, small trawls or push-nets are preferred for sampling at sandy beaches, as these gears are
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generally quite efficient and economically feasible. However, they cannot be used at the whole range of different bottom types and vegetation coverage. From the perspective of habitat mapping, fish surveys that are a priori designed for sampling of the most likely habitats of the target species are relevant for mapping, but have limited value for modelling unless the survey also covers habitats were the target species is less likely to be found. This is because the predictive strength of the statisti-cal models relies on full inclusion of the whole potential distributional range of predicted species in order to properly define the environmental envelope, and not only the opti-mal habitats. Thus, developing methods that enable sampling of fish in the majority of potential habitats in the BSR is thus an important challenge for future fisheries related research in coastal areas.
Fig. 3a. Gadus morhua, an adult cod in a pe-lagic habitat. Photo from www.fishbase.de.
Fig. 3b. Esox lucius, a juvenile pike caught in a near-shore habitat. Photo: The Swedish Board of Fisheries.
Fig. 3c. Trigla lucerna, a red garnet in a deep sea mud habitat. Photo: Orbicon.
Fig. 3d. Pleuronectes platessa, a flatfish in a shallow water macroalgae habitat. Photo: Or-bicon.
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5 HARMONISATION OF MACROFAUNA DATA
Macrozoobenthos, or benthic macrofauna, is defined as benthic invertebrates that are retained on 1 mm sieve. Benthic macrofauna represent important ecological functions and contribute significantly major ecological processes in the Baltic Sea (figure 4). Ad-ditionally, benthic fauna species have a value as indicators in water quality assessment and in modelling of habitat types.
5.1 Biological aspects
Three main components may be considered as potentially useful parameters in map-ping and modelling, as described in table 10 and below.
1. Key macrozoobenthic species and/or functions that can be used to pre-dict various habitat types in the Baltic Sea area As a group, benthic macrofauna is functionally very diverse and includes practically all major animal phyla. However, as the Baltic Sea is isolated, has short developing time, low salinity and temperature, only a limited number of macrozoobenthic species have been able to adapt to the local conditions. A mixture of marine and lacustrine organ-isms characterizes the communities. Specific brackish-water or endemic forms are rare. In its northern and north-eastern ends the number of benthic invertebrate species is particularly low and often each ecosystem function is represented by a single spe-cies. Thus, the loss of a species may correspond to the loss of ecosystem function (Segerstråle 1957, Järvekülg 1979, Hällfors et al. 1981). As the number of benthic in-vertebrate species is low, each ecosystem function is often represented by a single species. Thus, modelling of the distribution of the key species gives good representa-tion of the distribution of the habitats of interest. For example certain herbivorous inver-tebrate species are only found within Zostera marina meadows and the invasive am-phipod species Gammarus tigrinus indicates the presence of the endangered charophyte communities.
2. Species that may be used as biological indicators It is essential to develop biodiversity and eutrophication relevant indicators for the BSR. This can be done by statistical analyses assessing responsiveness and robustness. Links between ecosystem components can be analyzed using multivariate statistics and/or modelling. Multivariate statistics can also provide insight into the performance and statistical power of the data available and show which biodiversity components are essential for inclusion in monitoring activities. The ecosystems of the Baltic Sea are very dynamic and characterized by high physical and biological disturbances. Wave induced currents and ice scraping are the prevailing physical disturbance in the shallower areas and semi-natural periods of hypoxia/anoxia in the deeper areas (Kotta et al. 1999, Laine et al. 1997). Eutrophication induced blooms of phytoplankton and macroalgae and their decomposition are ranked among the most severe biological disturbances affecting local invertebrate distribution (Norkko & Bonsdorff 1996ab, Paalme et al. 2002). Benthic invertebrate communities represent an intermediate trophic level and nutrient additions affect them in many ways. According to the Pearson-Rosenberg model (Pearson & Rosenberg, 1978) the bio-mass of benthic invertebrates increases gradually to a maximum as the load of organic matter increases. After this, the biomass falls and often shows a secondary peak but lower than the first maximum. Increasing nutrient loads enhance the production of ben-
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thic and/or pelagic microalgae (Granéli & Sundbäck 1985, Howarth, 1988) and thereby increase available food for benthic grazers, suspension feeders or deposit feeders. As a consequence, abundance and growth responses of invertebrates are observed from an initial increase of nutrients (Posey et al., 1999). Further eutrophication leads to hy-poxia and the disappearance of benthic invertebrates. Thus, benthic communities are highly sensitive to eutrophication, which makes them a good indicator of water quality (Pearson & Rosenberg, 1978, Grall & Chauvaud, 2002, Gray et al., 2002). The effects of eutrophication are more pronounced in coastal areas where they are ex-pressed as an excessive growth of filamentous algae (Rosenberg 1985, Hull 1987, Gray 1992, Kolbe et al. 1995), changes in herbivore assemblages (Kotta et al., 2000), and the development of dense populations of filter-feeding mussels (Barnes & Hughes 1988, Kautsky et al. 1992, Kautsky 1995). Owing to their large filtration capacity, popu-lations of filter-feeding mussels are able to filter major parts of the water column each day (Riisgård & Møhlenberg 1979, Kautsky & Evans 1987), and thereby directly control the standing stock of pelagic primary producers. Consequently, filter-feeders are con-sidered to play a key role in the stability of coastal ecosystems (Herman & Scholten 1990).
3. Invasive species Biological invasions are considered another key factor affecting the dynamics of ben-thic fauna (Jansson 1994, Olenin & Leppäkoski 1999). Due to environmental instability, a low number of species and an increasing intensity of freight transportation, the eco-system of the Baltic Sea is very exposed to invasions, and biological invasions have resulted in relatively large-scale ecological changes. Examples from invasions in the 1980s and 1990s have shown that successful invasive species may render previously stable systems unbalanced and unpredictable (Leppäkoski 1991, Carlton & Geller 1993, Mills et al. 1993, Carlton 1996, Ruiz et al. 1999) and may severely affect biologi-cal diversity in an area (Baker & Stebbins 1965, Gollasch & Leppäkoski 1999, Gollasch et al. 1999). A number of benthic animals that presently live in the BSR have only re-cently invaded the area, some of them only in the last decades (Kotta 2000, Lep-päkoski and Olenin 2001). A few of the non-native animals add unique ecological func-tions for the species-poor Baltic Sea ecosystem (Leppäkoski et al. 2002) whereas others share the same food resources with the local species and, thus, may reduce the native biological diversity (Kotta et al. 2001, Kotta and Ólafsson, 2003). Among the most recent newcomers is the amphipod Gammarus tigrinus which has caused strong impacts on the Baltic Sea ecosystem including the disappearance of native amphipods and potentially a change in fish diet (Kotta, unpublished data).
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Table 10. Potential biological layers in the mapping of macrofauna, biological character-istics that relate to differences in sampling strategy, and methods used for data collec-tion. Examples are given for planned continuous benthic invertebrate layers in pilot area 4. Potential habitat layer
Potential species layers
Temporal variation
Main habitat Method
Key invertebrate species repre-sentative of pilot area
e.g. Mytilus tros-sulus
seasonal and/or an-nual
e.g. M. trossulus, exposed, hard bot-tom substrate, sa-linity above 5 psu, mainly photic zone
Core sampling by diving on mixed bot-toms, van Veen grab sampling on soft bottoms
Biological indi-ces (biodiversity, eutrophication)
e.g. biomass of deposit feeders indicating state of eutrophication
annual soft accumulation bottoms, depth 10-40 m
een and/or Ekman type grabs
Key invasive species
e.g. Gammarus tigrinus
seasonal Sheltered partly vegetated, 0-6 m depth
Core sampling by divers, Van Veen and/or Ekman type grabs
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5.2 Description of methods for data collection
This section describes the usage of grabs (Van Veen, Ekman type grabs) and core samplers (remote or diver operated) in macrozoobenthos sampling (Table 11). The 0.1 m2 Van Veen grab is the standard gear for benthic macrofauna sampling in the Baltic Sea, because of its very good reliability and manoeuvrability at sea. In some cases, however, the use of devices with smaller sampling area may be appropriate, e.g. if the fauna is very dense and uniform. In areas where the burrowing depth of the fauna is beyond the penetration depth of the grabs, or in sites where that type of gear cannot be used, remote core samplers may be advisable. Alternatively, the core sampler can be operated by diver and is usually used at depths less than 20 m.
Grabs The empty van Veen grab sampler should weigh about 30 kg when used for fine grain sizes and up to 80 kg in sandy bottoms. The settling down and the closing of the grab must be done as gently as possible. Winch operation should be standardized, in order to reduce the shock wave and the risk of sediment loss as a result of lifting the grab be-fore completed closure. The wire angle must be kept as small as possible to ensure that the grab is set down and lifted up vertically. If, as often happens on sandy bottom or erosion sediments, less than 5L of sediment is collected by a van Veen grab (the critical volume is smaller for other grab types), the sample should be regarded as not quantitative, and a new sample should be taken after loading the grab with an extra weight. This may as much as double the effective sampling depth of the grab. The evi-dence of this problem may be different in different parts of the Baltic Sea, depending on, e.g., how deep in the sediment the species live. In the northern parts of the Baltic Sea, benthic macrofauna rarely penetrate sediment deeper than 10 cm except for the nonindigenous polychaete Marenzelleria neglecta. Criteria for rejection of samples col-lected by grabs are given by Rees et al. (1991) and in the HELCOM guidelines (http://sea.helcom.fi/Monas/ CombineManual2/PartC/ CFrame.htm).
Core samplers The design of the corer and its usage is described by Kangas (1972). The corer has potential to be used as standard gear in vegetated or unvegetated soft or mixed sedi-ments because of its very good reliability and because it is easy to handle underwater.
General methodological guidelines The standard sieve for the Baltic Sea area has a mesh size of 1.0 x 1.0 mm. In order to collect quantitatively developmental stages of the macrofauna and abundant smaller species, additional smaller sieve with mesh size of 0.25 x 0.25, 0.4 x 0.4 or 0.5 x 0.5 mm are recommended. On the representative stations, at least 3 to 5 samples should be taken, depending on area and species composition, to enable the investigator to reach a certain level of precision by sorting as many samples as necessary. The same procedure is strongly recommended for all other benthos stations unless another sam-pling strategy (area sampling) is employed in national/coastal monitoring programs The choice of sample size and number of samples is always a compromise between the need for statistical accuracy and the effort, which can be put into the study. One way to do this is to calculate an index of precision. The ratio of standard error to arith-metic mean may be used (Elliott, 1983). A suggested reasonable error is 0.2 ± 20%. Biomass determination should be carried out for each taxon separately. All polychaetes should be removed from the tubes, other methods have to be explicitly stated (e.g. for large numbers of polychaetes). The dry weight should be estimated after drying the
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formalin material at 60°C to constant weight (for 12-24 hours, or an even longer time, depending on the thickness of the material). The total wet and/or dry weight of the ani-mals in each sample is usually calculated for an area of 1 m2. Sampling on shallow stations is recommended to be conducted during daytime, since some benthic species have semipelagic activity during the night. Exact positioning and correct depths when sampling should be noted in the protocols to avoid comparisons between samples taken at different localities (although noted as the same station in the protocols). Experienced and well-trained personnel are a prime basis for maintaining quality stan-dards on a high level. Allocation of resources for proper training and education of field and laboratory personnel is important. Ring tests and intercalibration exercises at least on a regional basis should be undertaken regularly basis and be obligatory. They should be open to all institutions including private industry. Technicians who carry out the actual procedures rather than managing scientists should take part in the exer-cises. Regional taxonomical workshops should be held on a regular basis and be attended by every laboratory. A checklist of species in the area should be developed, distributed to the participating laboratories and updated regularly. It is advisable, even with routine samplings, to place some specimens of each taxon under museum curatorship to make later taxonomic checks possible.
Table 11. Methods applied and details of data obtained Method Obtained variables Obtained unit Area cov-
ered by sample
Pilot area involved
Van Veen grab sampling
Abundance and bio-mass per species
n/m2 and ww/m2 or dw/m2
0.1m2 4
Ekman grab sam-pling
Abundance and bio-mass per species
n/m2 and ww/m2 or dw/m2
0.02m2 4
Core sampling Abundance and bio-mass per species
n/m2 and ww/m2 or dw/m2
0.0143m2 1
Core sampling diver operated
Abundance and bio-mass per species
n/m2 and ww/m2 or dw/m2
0.03 m2 4
5.3 Data intercalibration
The Van Veen grab sampler combined with 1mm mesh sieving, conform to an estab-lished international standard in the Baltic Sea area. However, all methods described are basically similar and result in the same data format and units. The basic differences between gears are in the size of the sampled area and in depth penetration, which should be accounted for by intercalibration before pooling data sets. Intercalibration of gears should be done within and between regions and on the specific sediment types concerned. Alternatively, literature data on the intercalibration of methods conducted in different basins and on different sediments can be used, when appropriate. The gears described have a relatively similar catch efficiency of invertebrate species, especially for sessile groups (HELCOM, 1982; http://sea.helcom.fi/Monas/ Combine-Manual2/PartC/ CFrame.htm). Differences in grab-specific catch efficiency among spe-cies should, however be noticed in particular for species with high mobility. When com-paring different grabs and corers used in the northern Baltic Sea, the major difference is seen in that the diver operated corer has a better catch efficiency of mobile necto-
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benthic invertebrates than the remote methods. Also, deep digging invertebrates such as Marenzelleria neglecta can basically only be effectively sampled by core sampler or Van Veen grab type sampler. Also, if datasets are pooled, the size of sieve used should be similar. However, inverte-brate biomasses may be compared even if sieves of smaller size are used, as the ma-jority of biomass is derived from larger clams, polychaetes and crustaceans.
5.4 Data applicability for habitat mapping
The current report compiles the methodology of datasets used for different monitoring programs in the Baltic Sea area. The purpose of monitoring is mainly to assess the quality of water rather than to perform habitat mapping, and does not include straight-forward suggestions for the spatial resolution. Thus, the datasets obtained by monitor-ing activities need to be combined with other research surveys before any valid interpo-lation based habitat prediction can be done. Alternatively, monitoring data can be used in models, and the model based relationships between environment and invertebrate communities can be used to predict the benthic fauna in areas outside of sampling programs. Especially at shallow sites, the level of information on geomorphologic, hydrographic and biological variables is insufficient for producing reliable habitat maps. Other Euro-pean (e.g. LIFE project: 05 NAT/LV/000100) or national monitoring projects related to Water Framework Directive may partly reduce this limitation in the near future. Predicted variables As the Baltic Sea is isolated, has short developing time, low salinity and temperature the natural diversity is low in the region. Each habitat is often represented by one (or a few) benthic invertebrate species. Thus, modelling of species distribution has high po-tential to predict the distribution of habitats. After this, the distributions of species that are indicative of certain habitat types should be selected and combined in order to pre-dict the distribution of habitat types of interest. As the number of samples within the regions is often low, interpolation techniques (such as kriging) typically result in poor habitat maps. However, using existing point data is advisable in order to initially establish the functional relationships between physical and biological environment and benthic invertebrate communities. This tech-nique together with GIS modelling can be regarded as the most rewarding method for the prediction of species and/or habitats. In case there is no sufficient data to perform interpolation or other modelling the point data may be represented as such. The spatial and temporal resolution of data is essential, as different processes operate at different spatial scales, thus, modelling needs to be done at a predefined spatial scale in order to make the correct comparisons between regions. The sampling resolu-tion is far too low to cover small-scale habitat heterogeneity in most studied areas. Bio-logical processes operating at smaller scale impact the formation of habitats at larger scales. Thus, there is a need for establishing process pattern relationships at various spatial scales in order to achieve accurate modelling of habitats Environmental variables Wave exposure, fronts, salinity, hypoxia, nutrients, depth, sediment type, bottom slope are likely key environmental variables for predicting the presence and/or densities of the key macrozoobenthos species. However, knowledge on the relationships between
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distribution patterns and ecosystem processes is currently more qualitative than quanti-tative (Hector et al. 2001). Experimental studies manipulating functional diversity and abiotic environment are needed to quantify how environmental variability modifies the relationships between distribution of species and ecosystem processes (Loerau et al. 2001). Strong fluctua-tion in abiotic factors such as oxygen concentration or wave induced disturbance is ex-pected to reduce the importance of biotic interactions within communities (Laine et al. 1997, Kotta et al. 1999, Worm et al. 2002) and physical control of ecosystem proc-esses is more commonplace (Flöder & Sommer 1999, Buckling et al. 2000). There is also some experimental evidence that physical disturbance override the effect of biotic interactions in terms of community development in the northern Baltic Sea (Kotta, unpublished data). The number of macrofauna significantly decreases with in-creasing exposure. Exposed areas with high sediment mobility are known as poor habi-tats for biota as compared to sheltered areas, which generally host diverse benthic in-vertebrate communities (Gray 2002). Thus, it is likely that the distribution of the key invertebrate species can be predicted using primarily abiotic environmental factors. Consequently, data describing the physical environment is needed to predict large scale distribution of macrofauna (fig. 5).
Fig. 5. A satellite image illustrating very high small-scale variability of geomorphologic and hy-drological conditions (represented as different colours in the figure) within the eastern part of the pilot area 4 coupled with insufficient availability of macrozoobenthos data (stars). The type of environmental data required depends on the scale of the modelling exer-cises. As an example, salinity is essential at larger scales, whereas bottom slope and exposure might be the most interesting variable at smaller scales. Also, specific needs may depend on the location of modelled area, for example ice scraping may be the prime structuring parameter in the northern Baltic Sea whereas accumulation rate of organic matter is probable in the southern Baltic Sea. On the smallest scales, diversity and structure of macrophyte communities may be the best predictor of macrofauna, especially in coastal areas. Both attached and drifting macroalgae and sedimentation of macroalgal debris are likely to modify coastal benthic invertebrate communities.
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5.5 Suggestions for forthcoming data collection
In general many shallow sites lack the information on geomorphologic, hydrographic and biological variables that is needed to produce reliable habitat maps. In these un-derrepresented sites, photographic and video records are recommended as a comple-ment to traditional sampling methods. Sediment profile imaging (see e.g. Rhoads and Germano (1982), Rumohr (1995)) may provide a useful means for rapid surveys and classification of sediment structure and bioturbation depth. Side-scan sonar images will provide information on bottom topography and substrate type, which can be useful in the planning of benthos monitoring programs or in the interpretation of data. Images should be verified by ground-truthing by underwater video recording and/or grab sam-pling of sediments. Descriptive and experimental studies should focus on the effects of habitat fragmenta-tion on habitat functionality and seek the relationships between habitat units, number of habitats, natural biodiversity and functional diversity. This information is needed for the investigation of potential blue corridors and for evaluation of the NATURA 2000 net-work. Macrofauna can be large and the complexity of the habitat will be reflected in the sampling methods (figure 6a-d).
Fig. 6a. Crangon crangon, a large individual hiding in a crevice in a submarine structure made by leaking gas. Photo: Orbicon.
Fig. 6b. Metridium senile, dead man’s fingers attached to the rock on a deep-water boulder field. Photo: Orbicon.
Fig. 6c. Nephros norwegicus, a Norwegian lobster in a deep-water mud habitat. Photo: Orbicon.
Fig. 6d. A log worm burrow in a sandy/soft bottom habitat. Photo: Orbicon.
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6 HARMONISATION OF ZOOPLANKTON DATA
Mesozooplankton is the only size group of zooplankton for which data availability al-lows the mapping of their distribution. Usually, the mesozooplankton community is dominated by copepods, mainly Pseudocalanus acuspes, Temora longicornis and the group of Acartia spp., and cladocerans.
6.1 Biological aspects
The different copepod species are characterized by differences in their life-cycle and vertical distribution (Dippner et al. 2000, Möllmann et al. 2000). Pseudocalanus acus-pes is a marine copepod, or perhaps a glacial relict, which displays on average one generation per year with peak reproduction in early spring and an overwintering stock of mainly C4-5 copepodites (Hansen et al. 2006, Renz and Hirche 2006). Further, an ontogenetic vertical distribution has been shown with the later copepodites and adults dwelling in the deep water of the Baltic basins, while younger stages occur in the ther-mocline region (Hansen et al. 2006, Renz and Hirche 2006). Long-term changes of the copepod are related to variable salinity and/or oxygen levels in the deep water, which are dependent on major Baltic inflow events (Möllmann et al. 2003, Renz and Hirche 2006). In contrast to P. acuspes, the taxa T. longicornis and Acartia spp. have multiple generations per year and produce resting eggs to overcome adverse winter conditions. These copepods are mainly distributed in the vicinity of the thermocline and their long-term development is steered by climate-induced tempera-ture changes (Dippner et al. 2000, Möllmann et al. 2000).
6.2 Description of methods for data collection
Mesozooplankton data are generally collected using different types of net samplers. Abundance and biomass values integrated over the water column are determined us-ing either gears towed by a research vessel in double-oblique hauls from the surface to close to the seafloor, e.g. Bongo, or in vertical hauls on a fixed position, e.g. Judai, WP-2. Vertically resolved data may be achieved using opening/closing nets (e.g. Multi-net, BIOMOC, BIONESS), but also Judai and WP-2, either towed by a ship or vertically operating on a fixed position. Values per square or cubic meter are derived by applying the filtered volume from flowmeters and the sampling depth. Different mesh sizes be-tween 50 and 300 µm are used which is contingent on targeted species or stages. In the future either modern underwater videotechnology (Video-Plankton Recorder, VPR) or sampling by ships of opportunity (Continuous Plankton Recorder, CPR) will be in-creasingly used for monitoring zooplankton. For the central Baltic Sea, different kinds of zooplankton datasets are available.
1. A long-term dataset >1960 is held by the Latvian Fish Resources Agency (LATFRA). Samples are collected seasonally (usually February, May, August and November) by a Judai-Net (net opening 0.36m; mesh-size 160µm) at varying depth intervals and on a varying number of stations (Möllmann et al. 2000).
2. Horizontally resolved samples are available from IFM-GEOMAR, based on
Bongo sampler (net opening 0.30m; mesh-size 150µm) on station grids in the Bornholm Basin with a varying number of stations per year and month since the late 1980s.
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3. Monthly to bimonthly surveys in the Bornholm Basin have been conducted be-
tween May 2002 and 2003 using the Bongo-sampler and a 0.5 m2 Multinet (mesh-size 50µm), which provided horizontally and vertically resolved data.
6.3 Recommendations for data intercalibration
The catchability of the different zooplankton species and their stages varies strongly according to gear and mesh-size used. Consequently, no single gear/mesh-size can be recommended for mapping as a combination of mesh-sizes would be necessary to capture all species/stages representatively. A wide range of gears is thus used in moni-toring and process investigation programmes depending on institute, country or target species.
6.4 Data applicability for habitat mapping
Mapping of zooplankton species is difficult due to their patchy small-scale distribution which is very unstable due to advective transport. Consequently, a spatially and tempo-rarily very highly resolved sampling would be necessary to reliably model their horizon-tal distribution. This is probably not possible with the data available from net sampling, and requires VPR or CPR sampling (see above). However, recent studies from the Bal-tic have shown that general features of the horizontal distribution of the copepod spe-cies and their relationship to the physical environment can be derived from spatial sampling (Hansen et al. 2004, Hansen et al. 2006, Renz and Hirche 2006). The datasets available have a limited suitability for spatial modelling. The data set by LATFRA cannot be used for distribution modelling for single points in time (seasons) due to the low spatial resolution of the sampling. However, by merging data sets over different periods, for example decades, general Baltic wide patterns of distribution for the main mesozooplankton species in relation to hydrography can be derived. IFM-GEOMAR datasets are more suitable for spatial modelling as they frequently represent a station grid within the Bornholm Basin. From these data species- and stage-specific species-environment relationships are in principal achievable.
6.5 Suggestions for forthcoming data collection
Future data collection of zooplankton for reliable spatial modelling can only be con-ducted by highly resolved net sampling with different mesh-sizes and an agreed stan-dard gear, which is very costly. Future alternatives will be VPR- and CPR-sampling, depending on the progress of VPR technology and installation of CPR-lines in the Bal-tic.
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7 HARMONISATION OF PHYTOBENTHIC DATA
Phytobenthic species include macroalgae and submersed phanerogams. Since the group consists to a large part of photosynthetic organisms, depth distribution is ulti-mately limited by light availability. However, the limiting factor of individual species may as well be limited by lack of appropriate substrate. Phanerogams and charophytes are found on muddy to sandy sediments, while macroalgae are mainly found on stable substrates such as rock and boulders. Mobile substrates are generally less favourable for phytobenthic species. Since the vegetation changes over the year, repeated studies and monitoring are generally conducted during the same time of the year in order to be comparable.
7.1 Biological aspects
Mapping of phytobenthic species is interesting from a biodiversity perspective, but also because some species may be useful indicators of water quality. For example, some species are only found in clear waters, whereas high abundance of other species may indicate eutrophication. Also, importantly for habitat mapping, many abundant phyto-benthic species are important habitat formers in themselves, e g Fucus belts (wracks), or blue mussel beds. The distribution of large structure-forming species can generally be surveyed by coarse methods, such as UW video techniques, whereas closer obser-vations through diving or sampling are required for estimations of biodiversity and for determining the distribution of more cryptic species. A trade-off between quality and quantity may be seen among these methods, and the final choice of method will de-pend on the target taxa and the aim of the study (Table 12). Table 12. Methods applied for data collection in the phytobenthos and their po-tential biological layers in mapping Data collection method
Potential layer in map
Depth limi-tations
Ranked accuracy (1=highest)
Ranked effi-ciency (1=fastest)
Scuba diving Any species 30 m 1 4 Free diving Any species 4 m 2 3 UW-video and ROV Habitat forming
species Unlimited 3 2
Remote sensing by satellite
Habitat forming species
6 m 4 1
7.2 Description of methods for data collection
Scuba diving Diving surveys are used as method for registration of species on hard bottoms in sev-eral coastal monitoring programs since they are accurate and repeatable. One method is to dive along transects from deep water to the surface, observing the species vertical distributions and abundance (e.g. Norwegian program and the Swedish East coast monitoring programs). Another approach is to collect point-data at a number of posi-tions (e.g. the Danish coastal monitoring program). The inventory units are percentage cover or abundance classes (Appendix 2). Each monitoring program results in high quality data, and there are benefits and disadvantages of each approach. Underwater video and ROV
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Underwater video equipments have developed strongly during the recent decade and are now a relevant alternative to diving for some studies. Compared to diving, under-water video is not as accurate since species can not be studied closely and verification samples can not be collected. However, much larger areas can be covered since the video may be dragged after a boat for many hours, or lowered at a large number of sites. Remotely operated vehicles (ROV) are capable of collecting data of higher qual-ity than UW-video due to their manoeuvrability, but the obtained data is still of poorer quality than data collected by diving. On the other hand, a ROV can not cover as large areas as the UW-video. Free diving Free diving may be as accurate as SCUBA diving, but are only reliable at shallow sites, or down to 3-4 meters depth. Compared to SCUBA diving, free diving requires less equipment and personnel and is thereby more efficient, and is also more practical at very shallow depths. National recommendations for surveying vegetation in shallow inlets have been developed in Sweden in connection to the Natura2000 baseline inven-tories (Persson and Johansson 2005). Remote sensing Satellite images may be used to obtain habitat maps over large areas, and a grain size down to 10m or less can be achieved. One main objective within Balance WP2 is to evaluate the possibility of using remote sensing by satellite imagery for characterisation and identification of shallow marine coastal habitats in the Baltic Sea, and to evaluate if satellite based information can complement existing information and hence be incorpo-rated in management strategies. Currently, methodological procedures have been de-veloped (and are under evaluation, and are to be reported by the end of 2006.
7.3 Recommendations for data intercalibration
Unfortunately, there are no international standards for surveying benthic vegetation. The national monitoring programs of each country are not synchronized which make their data less comparable, especially for annual species. The applied methodology also differs, which has to be dealt with when combining data sets from different sur-veys. These differences relate to both sampling design and accuracy. When combining data of different quality and units for a modelling task the least com-mon denominator should therefore be used, which is presence only or pres-ence/absence in the case of data presented in appendix 2. Unfortunately a lot of infor-mation is lost when this is done. An alternative approach is to make multiple models taking the full advantage of each dataset, which will result in a set of models and pre-dictions that vary in quality. A European standard for marine biological surveys on littoral and sublittoral hard bot-tom is currently under development. However, national monitoring programs are still likely to continue without major changes in order not to loose comparability with earlier data series.
7.4 Data applicability for habitat mapping
Several species should be able to model since they are included the national datasets. These include the brown alga Fucus vesiculosus, the red alga Furcellaria lumbricalis, the phanerogam Zostera marina and charophytes, which are also important habitat building species that equally could be modelled as habitats. The general distribution of phytobenthic species could also be modelled.
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7.5 Suggestions for forthcoming data collection
The monitoring programs are unfortunately not designed for spatial modelling activities since they do not cover full environmental gradients. Surveys directed only at the opti-mal habitat of the target species do not provided an adequate basis for spatial ecologi-cal models, but suboptimal environments should also be registered. This is also an es-sential aspect in marine spatial planning, where potential effects of different actions are to be analysed. There are research projects in which diving inventories have been de-signed for modelling studies (e.g. Isæus 2004). Irrespective of which method is used, the surface substrate, level of sedimentation and water depth should be registered together with the species data. This gives the possi-bility to validate the indata layers used for modelling and to analyse the errors of the predictions. The Secchi-depth should also be measured at each station as a proxy for light availability. There are many types of phytobenthic organisms in the Baltic Sea and Kattegat all characterising different habitats and requiring different sampling method-ologies (figure 7a-d).
Fig. 7a. Fucus vesiculosus, the dominating submerged brown seaweed in the Baltic Sea. Photo: The Natural Heritage Service, Finland.
Fig. 7b. Dilsea carnosa, a red seaweed in central Kattegat. It distribution is limited by the low salinity in the Baltic Sea. Photo: Orbicon.
Fig. 7c. Chara sp., a green algae living in shallow sheltered soft sediment habitats. Photo: The Natural Heritage Service, Finland.
Fig. 7d. Zostera marina, the dominating sea-grass in the Baltic Sea. Photo: The National Environmental Research Institute.
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8 SUMMARY AND CONCLUSIONS
In general, there are many challenges to be met if ecological relevant maps are to be produced for larger marine areas, such as the Baltic Sea Marine Region. Some of the challenges related to various types of biological information available are discussed be-low. These are challenges that should be met, as ecological maps are essential for in-formed marine spatial planning and thus for the long-term sustainable development within the Baltic Sea region.
8.1 Fish
Data on fish abundance collected using different methods and gears should generally be viewed as not directly quantitatively comparable. This is mainly due to differences in the target species and overall catchability. Because of high spatial and temporal vari-ability of target species, method efficiency varies with environmental setting, and quan-titative comparisons between studies using the same gear should also be done with caution. Also, data on fish abundance are often highly skewed with non-normal fre-quency distributions, because several species and/or life stages have a strong school-ing behaviour that results in strongly zero-inflated catch data. The schooling behaviour may also be dependent on the environmental conditions. As an example, juvenile fishes have been found to alter their tendency to school as a response to changes in habitat complexity. Thus, the following recommendations may be made:
• Generally, data reduction to “presence only” is recommended for large-scale
mapping. • In all instances where the predictor variable influences the schooling tendency
of the target species, presence/absence models may be biased and should be used with care.
• Presence/absence data may be applicable for methods with similar target species and catch efficiency.
• Quantitative pooling is only recommended on smaller scales and after situa-tion-specific intercalibration exercises.
• In statistical spatial modelling, presence/absence data or presence only data of good quality is preferred over more detailed data of uncertain relevance, as the estimated output may still be expressed as the numerical probability for occurrence of the predictor variable.
8.1.1 Suggestions for future data collection Standards for fish sampling methods are under continuous development within ICES and EU, to ensure use of data beyond the national and regional level. New standards are in progress for monitoring of fish communities in lakes and streams and standards for trawling of pelagic and benthic species in off-shore areas with larger vessels are developed and set within ICES. However, similar standards for monitoring and mapping of juvenile fish in shallow coastal areas are not available. Since the sampling techniques are highly specialised for certain species or habitats, such standards are harder to develop for shallow coastal areas. A first step towards harmonisation would be to conduct transnational in-tercalibration efforts, comparing and evaluating fish sampling surveys in shallow coastal areas within the BSR. Although such an attempt is relevant and highly recom-mended, it would require a large effort and should be conducted over a longer time pe-riod and is thus not possible to include in BALANCE.
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Currently, for shallow nursery areas, the sampling methods drop-traps, LIPS and coastal survey nets (gill nets) appear the most general by the aspects analysed in table 9. However, these are limited in their potential areas of use, and it may not be possible to agree on one single gear type or method to be used throughout the Baltic Sea. Drop-traps can only be used in very shallow waters, and LIPS will only catch fish with swim bladders, which means that flatfish will not be caught. Gill-nets, again, do not easily al-low quantitative abundance estimates as the catch method is passive, which means that the catch rate of different species will depend on their level of activity in the survey area. Also, gillnetting can be very time consuming during the summer season in the more saline areas of the Baltic Sea due to large catches of crabs. In most cases, small trawls or push-nets are preferred for sampling at sandy beaches, as these gears are generally quite efficient and economically feasible. However, they cannot be used at the whole range of different bottom types and vegetation coverage. From the perspective of habitat mapping, fish surveys that are a priori designed for sampling of the most likely habitats of the target species are relevant for mapping, but have limited value for modelling unless the survey also covers habitats were the target species is less likely to be found. This is because the predictive strength of the statisti-cal models relies on full inclusion of the whole potential distributional range of predicted species in order to properly define the environmental envelope, and not only the opti-mal habitats. Thus, developing methods that enable sampling of fish in the majority of potential habitats in the BSR is thus an important challenge for future fisheries related research in coastal areas.
8.2 Macrofauna
Benthic macrofauna represent important ecological functions and contribute signifi-cantly to major ecological processes in the Baltic Sea. Additionally, benthic fauna spe-cies have a value as indicators in water quality assessment and in modelling of habitat types. Three main components may be considered as potentially useful parameters in mapping and modelling: 1) key macrozoobenthic species and/or functions that can be used to predict various habitat types in the Baltic Sea area, 2) species that may be used as biological indicators, 3) invasive species. Data on benthic macrofauna usually comes from environmental monitoring pro-grammes. The purpose of monitoring is mainly to assess the quality of water rather than to perform habitat mapping, and sampling is therefore often not sampled in a way that is directly applicable for mapping. The datasets obtained by monitoring activities may need to be combined with other research surveys before any valid interpolation based habitat prediction can be done. Alternatively, monitoring data can be used in models, and the model based relationships between environment and invertebrate communities can be used to predict the benthic fauna in areas outside of sampling programs.
8.2.1 Suggestions for future data collection In general many shallow sites lack the information on geomorphologic, hydrographic and biological variables that is needed to produce reliable habitat maps. In these un-derrepresented sites, photographic and video records are recommended as a comple-ment to traditional sampling methods. Sediment profile imaging may provide a useful means for rapid surveys and classification of sediment structure and bioturbation depth. Side-scan sonar images will provide information on bottom topography and sub-strate type, which can be useful in the planning of benthos monitoring programs or in
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the interpretation of data. Images should be verified by ground-truthing by underwater video recording and/or grab sampling of sediments. Descriptive and experimental studies should focus on the effects of habitat fragmenta-tion on habitat functionality and seek the relationships between habitat units, number of habitats, natural biodiversity and functional diversity. This information is needed for the investigation of potential blue corridors and for evaluation of the NATURA 2000 net-work.
8.3 Zooplankton
The catchability of the different zooplankton species and their stages varies strongly according to gear and mesh-size used. Consequently, no single gear/mesh-size can be recommended for mapping as a combination of mesh-sizes would be necessary to capture all species/stages representatively. A wide range of gears is thus used in moni-toring and process investigation programmes depending on institute, country or target species. Mapping of zooplankton species is difficult due to their patchy small-scale distribution, which is very unstable due to advective transport. Consequently, a spatially and tempo-rally very highly resolved sampling would be necessary to reliably model their horizon-tal distribution. This is probably not possible with the data available from net sampling, and requires VPR or CPR sampling. However, recent studies from the Baltic have shown that general features of the horizontal distribution of the copepod species and their relationship to the physical environment can be derived from spatial sampling. The datasets available have a limited suitability for spatial modelling. The data set by LATFRA cannot be used for distribution modelling for single points in time (seasons) due to the low spatial resolution of the sampling. However, by merging data sets over different periods, for example decades, general Baltic wide patterns of distribution for the main mesozooplankton species in relation to hydrography can be derived. Datasets more suitable for spatial modelling would be data collected from a frequently visited station grid. From these data species- and stage-specific species-environment relation-ships are in principal achievable.
8.3.1 Suggestions for future data collection Future data collection of zooplankton for reliable spatial modelling can only be con-ducted by highly resolved net sampling with different mesh-sizes and an agreed stan-dard gear, which is very costly. Future alternatives will be VPR- and CPR-sampling, depending on the progress of VPR technology and installation of CPR-lines in the Bal-tic.
8.4 Benthic vegetation
There are no international standards for surveying benthic vegetation. The national monitoring programs of each country are not synchronized which make their data less comparable, especially for annual species. The applied methodology also differs, which has to be dealt with when combining data sets from different surveys. These differ-ences relate to both sampling design and accuracy. When combining data of different quality and units for a modelling task the least com-mon denominator should therefore be used, which is presence only or pres-ence/absence in the case of data presented in appendix 2. Unfortunately a lot of infor-mation is lost when this is done. An alternative approach is to make multiple models
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taking the full advantage of each dataset, which will result in a set of models and pre-dictions that vary in quality. A European standard for marine biological surveys on littoral and sublittoral hard bot-tom is currently under development. However, national monitoring programs are still likely to continue without major changes in order not to loose comparability with earlier data series. Several species should be possible to model since they are included the national data-sets. These include the brown alga Fucus vesiculosus, the red alga Furcellaria lumbri-calis, the phanerogam Zostera marina and charophytes, which are also important habi-tat building species that equally could be modelled as habitats. The general distribution of phytobenthic species could also be modelled.
8.4.1 Suggestions for future data collection The monitoring programs are unfortunately not designed for spatial modelling activities since they do not cover full environmental gradients. Surveys directed only at the opti-mal habitat of the target species do not provided an adequate basis for spatial ecologi-cal models, but suboptimal environments should also be registered. This is also an es-sential aspect in marine spatial planning, where potential effects of different actions are to be analysed. There are research projects in which diving inventories have been de-signed for modelling studies. The diversity of phytobenthic organisms and habitats in the Baltic Sea and Kattegat re-quires different methodologies for efficient sampling. Irrespective of which method is used, the surface substrate, level of sedimentation and water depth should be regis-tered together with the species data. The Secchi-depth should also be measured at each station as a proxy for light availability. This gives the possibility to validate the in-data layers used for modelling and to analyse the errors of the predictions.
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9 REFERENCES
Austin, M. P. 2002. Spatial prediction of species distribution: an interface between eco-logical theory and statistical modelling. Ecological Modelling 157:101-118. Baker, H. & Stebbins, G. 1965. The Genetics of Colonizing Species. Academic Press, New York. Barnes, R. S. K. & Hughes, R. N. 1988. An Introduction to Marine Ecology. Blackwell Scientific Publications, Oxford. Brosse, S., Guegan, J-F., Tourenq, J-N., Lek, S. (1999). The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecological Modelling 120: 299-311. Buckling A., Kassen R., Bell G., Rainey P. B., 2000, Disturbance and diversity in ex-perimental microcosms, Nature, 408, 961–964. Carlton J. T. & Geller, J. B. 1993. Ecological roulette: the global transport of nonindi-genous marine organisms. Science (Wash.), 261, 78–82. Carlton, J. T. 1996. Pattern, process, and prediction in marine invasion ecology. Biol. Conserv., 78, 97–106. Dahl et al. 2003- Stenrev, havbundens oaser. (http:www2.dmu.dk/1_viden/2_publikationer/3_miljobib/raporter/MB02.pdf) Dippner, J.W., Kornilovs, G., and Sidrevics, L. 2000. Long-term variability of mesozoo-plankton in the central Baltic Sea. Journal of Marine Systems, 25: 23-32. Dybern I., Ackefors H., Elmgren R. 1976. Recommendation on method for Marine biological studies in the Baltic Sea. BMB, 98p Elliot, M. & Hemmingway, K. L. (2002). Fishes in Estuaries. Blackwell Science. Elliott, J. M., 1983. Some Methods for the Statistical Analysis of Samples of Benthic In-vertebrates. Freshwater Biological Association - Scientific Publication No. 25. 159 pp. Flöder S., Sommer U., 1999, Diversity in planktonic communities: An experimental test of the intermediate disturbance hypothesis, Limnol. Oceanogr., 44, 1114–1119. Francis, M. P., Morrison, M.A., Leathwick, J., Walsh, C., Middleton, C. (2005). Predic-tive models of small fish presence and abundance in northern New Zealand harbours. Estuarine Coastal and Shelf Science 64: 419-435. Garza-Pérez, J. R., Lehmann, A., Arias-Gonzalez, J. E. (2004). Spatial prediction of coral reef habitats: integrating ecology with spatial modelling and remote sensing. Ma-rine Ecology Progress Series 269: 141-152. Gollasch, S. & Leppäkoski, E (Eds.). 1999. Initial Risk Assessment of Alien Species in Nordic Coastal Waters. Nordic Council of Ministers, Copenhagen.
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Gollasch, S., Minchin, D., Rosenthal, H. & Voigt, M (Eds.). 1999. Exotics across the Ocean Case Histories on Introduced Species. Department of Fishery Biology, Institute for Marine Science, University of Kiel, Germany.
Grall, J. & L. Chauvaud, 2002. Marine eutrophication and benthos: the need for new approaches and concepts. Global Change Biology 8: 813–830. Granéli, E. & K. Sundbäck, 1985. The response of planktonic and microbenthic algal assemblages to nutrient enrichment in shallow coastal waters, southwest Sweden. Journal of Experimental Marine Biology and Ecology 85: 253–268. Gray J. S., 2002, Species richness of marine soft sediments, Mar. Ecol. Prog. Ser., 244, 285–297. Gray, J. S. 1992. Eutrophication in the sea. In Marine Eutrophication and Population Dynamics. Proc. 25th Eur. Mar. Biol. Symp. (Columbo, G. C., Ferrari, I., Ceccherelli, V. U. & Rossi, R., Eds.). Olsen & Olsen, Fredensborg, 3–15. Gray, J. S., R. S. Wu & Y. Y. Or, 2002. Effects of hypoxia and organic enrichment on the coastal marine environment. Marine Ecology Progress Series 238: 249–279. Grip, et al (in prep) Inventering av marina naturtyper på utsjöbankar. Naturvårdsverket rapport. Guisan, A., & Zimmerman, N.E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling 135: 147-186. Guisan, A., Edwards, Jr., T. C., Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Model-ling, 157, 89-100. Hansen, F., Möllmann, C., Schütz, U. and Hinrichsen, H.-H. 2004. Spatio-temporal dis-tribution of Oithona similis in the Bornholm Basin (Central Baltic Sea). Journal of Plank-ton Research, 26: 659-668. Hansen, F.C., Möllmann, C., Schütz, U. and Neumann, T. 2006. Spatio-temporal distri-bution and production of calanoid copepods in the central Baltic Sea. Journal of Plank-ton Research, 28: 39-54. Hector A., Joshi J., Lawler S. P., Spehn E. M., 2001, Conservation implications of the link between biodiversity and ecosystem functioning, Oecologia, 129 (4), 624–628. HELCOM, 1998. Manual for Marine Monitoring in the COMBINE Programme of HELCOM HELCOM, 1982. Baltic Sea Environmental Proceedings, Second biological intercalibra-tion workshop, No. 9, Marine Pollution Laboratory and Marine Division of the National Agency of Environmental Protection, Denmark August 17-20, 1982, Rønne, Denmark Herman, P. M. J. & Scholten, H. 1990. Can suspension-feeders stabilize estuarine ecosystems? In Trophic Relationships in the Marine Environment. Proc. 24th Eur. Mar. Biol. Symp. (Barnes, M. & Gibson, R. N., Eds.). Aberdeen University Press, Aberdeen, 104–116. Howarth, R. W. 1988. Nutrient limitation of net primary production in marine ecosys-tems. Annual Review of Ecology and Systematics 19: 89–110.
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Hull, S. C. 1987. Macroalgal mats and species abundance: a field experiment. Estuar. Coast. Shelf Sci., 25, 519–532. Hällfors, G., Niemi, Å., Ackefors, H., Lassig, J. & Leppäkoski, E. 1981. Biological oceanography. In The Baltic Sea (Voipio, A., Ed.). Elsevier Oceanography Series, Am-sterdam, 30, 219–274. ICES. 2003: Manual for the Baltic International Acoustic Surveys (BIAS) (ICES 2003). In: Report of the Baltic International Fish Survey Working Group. ICES CM 2003/G:05 Ref.: D. H.: 213 – 258. Jansson, K. 1994. Alien Species in the Marine Environment. Introductions to the Baltic Sea and the Swedish West Coast. Report 4357. Swedish Environmental Protection Agency. Joy, M. K, Death, R. G. (2004). Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks. Freshwater Biology 49: 1036–1052. Järvekülg, A. 1979. Benthic Fauna of the Eastern Baltic Sea. Valgus, Tallinn (in Rus-sian). Kaiser, M. J., Rogers, S. I. & McCandless, D. T. (1994). Improving quantitative surveys of epibenthic communities using a modified 2 m beam trawl. Marine Ecology Progress Series, 106: 131-138. Kangas, P., 1972. Quantitative sampling equipment for the littoral benthos II. IBP i Nor-den 10: 9–16. Kautsky, H., 1995. Diving inventory of the vegetation of the shallow bottoms in Stock-holm archipelago 1994 (In Swedish). Department of Systems Ecology, Stockholm Uni-versity. Kautsky, H., Kautsky, L., Kautsky, N., Kautsky, U. & Lindblad, C. 1992. Studies on the Fucus vesiculosus community in the Baltic Sea. Acta Phytogeogr. Suec., 78, 33–49. Kautsky, N. & Evans, S. 1987. Role of biodeposition by Mytilus edulis in the circulation of matter and nutrients in a Baltic coastal ecosystem. Mar. Ecol. Prog. Ser., 38, 201–212. Kautsky, U. 1995. Ecosystem Processes in Coastal Areas of the Baltic Sea. Doctoral dissertation, Stockholm University, Sweden. Kolbe, K., Kaminski, E., Michaelis, H., Obert, B. & Rahmel, J. 1995. Macroalgal mass development in the Wadden Sea: first experiences with a monitoring system. Helgol. Meeresunters., 49, 1–4. Kotta J, Ólafsson E (2003) Competition for food between the introduced exotic poly-chaete Marenzelleria viridis and the resident native amphipod Monoporeia affinis in the Baltic Sea. J Sea Res. 342:27–35 Kotta J, Orav H, Sandberg-Kilpi E (2001) Ecological consequence of the introduction of the polychaete Marenzelleria viridis into a shallow water biotope of the northern Baltic Sea. J Sea Res. 46:273–280
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Kotta, J. 2000. Impact of eutrophication and biological invasions on the structure and functions of benthic macrofauna. Dissertationes Biologicae Universitatis Tartuensis, 63, Tartu University Press, 1–160. Kotta, J., Kotta, I. & Kask, J. 1999. Benthic animal communities of exposed bays in the western Gulf of Finland (Baltic Sea). Proc. Est. Acad. Sci. Biol. Ecol., 48, 107–116. Kotta, J., Kotta, I. Viitasalo, I. 2000. Effect of diffuse and point source nutrient supply on the low diverse macrozoobenthic communities of the northern Baltic Sea. Bor. Env. Res., 5, 235–242. Kuipers, B. (1975). On the efficiency of a two-metre beam trawl for juvenile plaice (Pleuronectes platessa). Netherlands Journal of Sea Research, 9 (1): 69-85. Krause-Jensen, D. et al. Tekninske anvisninger for marin overvågning. NOVANA Laine, A., Sandler, A. O., Andersin, A.-B. & Stigzelius, J. 1997. Long-term changes of macrozoobenthos in the eastern Gotland Basin and the Gulf of Finland (Baltic Sea) in relation to the hydrographical regime. J. Sea Res., 38, 135–159. Lehmann, A., Overton, J. Leathwick, J. R. (2002). GRASP: generalized regression analysis and spatial prediction: Ecological Modelling 157: 189-207. Lek, S., & Guégan, J. F., (1999). Artificial neural networks as a tool in ecological mod-elling, an introduction. Ecological Modelling 120: 65-73. Leppäkoski E, Olenin S, Gollasch S (2002) The Baltic Sea – a Field laboratory for inva-sion biology. In Leppäkoski E, Olenin S, Gollasch S (ends) Invasive Aquatic Species of Europe. Kluver Acad. Publ., Dordrecht, Boston, London, pp 253–259 Leppäkoski E., Olenin S., 2001. The meltdown of biogeographical peculiarities of the Baltic Sea: the interaction of natural and man-made processes, Ambio, 30, 202–209. Leppäkoski, E. 1991. Introduced species – resource or threat in brackish water seas? Examples from the Baltic and the Black Sea. Mar. Pollut. Bull., 23, 219–223. Loreau M., Naeem S., Inchausti P., Bengtsson J., Grime J.P., Hector A., Hooper D. U., Huston M.A., Raffaelli D., Schmid B., Tilman D., Wardle D.A., 2001, Biodiversity and ecosystem functioning: current knowledge and future challenges, Science, 294, 804-808. Mills, E. L., Leach, J. H., Carlton, J. T. & Secor, C. L. 1993. Exotic species in the Great Lakes: a history of biotic crises and anthropogenic introductions. J. Great Lakes Res., 19, 1–54. Möllmann, C., G. Kornilovs, and L. Sidrevics. 2000. Long-term dynamics of main mesozooplankton species in the central Baltic Sea. Journal of Plankton Research, 22: 2015–2038. Möllmann, C., F.W. Köster, G. Kornilovs, and L. Sidrevics. 2003. Interannual variability in population dynamics of calanoid copepods in the central Baltic Sea. ICES Marine Science Symposia, 219: 294-306 Moy, F. Aure, J. et al. 2005. Langtidsovervåkning av miljökvaliteten i kystområdene av Norge. Årsapport for 2004. F Moy. Oslo, SFT, NIVA.
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Norkko, A. & Bonsdorff, E. 1996b. Rapid zoobenthic community responses to accumu-lations of drifting algae. Mar. Ecol. Prog. Ser., 131, 143–157. Olenin, S. & Leppäkoski, E. 1999. Non-native animals in the Baltic Sea: alteration of benthic habitats in coastal inlets and lagoons. Hydrobiologia, 393, 233–243. Paalme, T., Kukk, H., Kotta, J., Orav, H. 2002. “In vitro” and “in situ” decomposition of nuisance macroalgae Cladophora glomerata and Pilayella littoralis. Hydrobiologia, 475/476, 469–476. Persson, J., Johansson, G. 2005. Manual för basinventering av marina habitat (1150, 1160, 1650), Naturvårdsverket. 27pp. (www.naturvardsverket.se) Pearson, T. H. & R. Rosenberg, 1978. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanography and Marine Biol-ogy: An Annual Review 16: 229–311. Pihl, L. & Rosenberg, R. (1982) Production, abundance, and biomass of mobile epiben-thic marine fauna in shallow waters, Western Sweden. Journal of Experimental Marine Biology and Ecology, 57: 273-301. Pitkänen, Timo. 2006. The use of GIS methods in planning and carrying out an under-water nature inventory. Univ. Turku, 98 pp Posey, M. H., T. D. Alphin, L. Cahoon, D. Lindquist & M. E. Becker, 1999. Interactive effects of nutrient additions and predation on infaunal communities. Estuaries 22: 785–792. Rees, H. L., C. Heip, M. Vincx and M. M. Parker, 1991. Benthic communities: use in monitoring point-source discharges. ICES Techniques in Marine Environmental Sci-ences No. 16, 70 pp. Renz, J., and H.J. Hirche. 2006. Life cycle of Pseudocalanus acuspes giesbrecht (Co-pepoda, Calanoida) in the central Baltic Sea: I. Seasonal and spatial distribution. Ma-rine Biology, 148: 567. Rhoads, D.C. and Germano, J.D., 1982. Characterisation of organism-sediment rela-tions using sediment profile imaging: an efficient method of remote ecological monitor-ing of the seafloor (REMOTS system). Mar. Ecol. Prog. Ser., 8, pp. 115-128. Riisgård, H. U. & Møhlenberg, F. 1979. An improved automatic recording apparatus for determining the filtration rate of Mytilus edulis as a function of size and algal concentra-tion. Mar. Biol., 52, 61–67. Riley, J. D. & Corlett, J. (1966). The numbers of 0-group plaice in Port Erin Bay, 1964-1966. Marine Biological Station, University of Liverpool, Port Erin, Isle of Man, Annual Report No. 78 for 1965. Rogers, S. I. & Lockwood, S. J. (1989). Observations on the capture efficiency of a two-metre beam trawl for juvenile flatfish. Netherlands Journal of Sea Research 23(3): 347-352
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10 APPENDICES
10.1 Appendix 1. Methods for sampling fish data
AP
PE
ND
IX 1
. PA
GE
1a/
3M
ETH
OD
/ EQ
UIP
MEN
TD
rop
trap
Pus
hnet
Man
pow
ered
2m
eter
be
amtra
wl
Traw
l sa
mpl
ing
of
youn
g fis
h
Beac
h S
eine
Div
ing
LIP
SW
hite
pla
tes
and
scoo
psG
ill n
et
surv
eys
Info
rmat
ion
prov
ided
by
CR
S /D
IFR
ES
CR
S
/DIF
RES
CR
S /D
IFR
ESC
RS
/DIF
RE
SAS
/SB
FAS
/SBF
AS
/S
BFA
L
/F
GFR
IAS
/SBF
PILO
T A
REA
11
11
33
33
3TA
RG
ET S
PEC
IES
Spe
cies
/life
sta
geYo
ung
flatfi
shYo
ung
flatfi
shYo
ung
flatfi
shYo
ung
flatfi
shYo
ung
fish,
fre
shw
ater
sp
ecie
s
Youn
g fis
h,
fresh
wat
er
spec
ies
Youn
g fis
h,
fresh
wat
er
spec
ies
Fish
larv
aeFi
sh >
8cm
Mob
ility
type
activ
e (k
m)
activ
e (k
m)
activ
e (k
m)
activ
e (k
m)
activ
e (k
m)
sess
ileac
tive
(km
)ac
tive
(km
)ac
tive
(km
)
Tem
pora
l var
iatio
nse
ason
alse
ason
alse
ason
alse
ason
alse
ason
alse
ason
alse
ason
alse
ason
alse
ason
alO
UTP
UT
VAR
IAB
LES
Nam
eab
unda
nce
catc
h ra
teca
tch
rate
catc
h ra
teca
tch
rate
pres
ence
abun
danc
epr
esen
ceca
tch
rate
Uni
tn/
m2
n/ef
fort
n/ef
fort
n/ef
fort
n/ef
fort
pres
./abs
.n/
m2
pres
./abs
.n/
effo
rtD
eriv
ed v
aria
ble
unit
none
none
none
none
none
none
none
none
none
Pos
ition
ing
accu
racy
3-20
m3-
20m
3-20
m3-
20m
3-65
mM
ETH
OD
DET
AIL
S A
ND
REF
EREN
CES
Orig
inal
pur
pose
of s
tudy
Map
ping
Map
ping
Map
ping
Mon
itorin
gM
appi
ngM
appi
ngM
appi
ngM
appi
ngM
onito
ring
Are
a/vo
lum
e co
vere
d by
eac
h sa
mpl
e1
sqm
20 s
qm50
sqm
1800
sqm
varie
s0.
25 s
qm16
or 1
00
sqm
ca 1
00 s
qmno
t est
imab
le
Est
imat
ed a
ccur
acy
EXAC
TG
OO
DG
OO
DM
OD
ERAT
EG
OO
DG
OO
DVE
RY
GO
OD
VER
Y G
OO
DM
OD
ERA
TE
How
is e
valu
atio
n ac
hiev
ed?
inte
rcal
ibra
tion
with
in
DIF
RE
S
inte
rcal
ibra
tion
with
in
DIF
RES
inte
rcal
ibra
tion
with
in
DIF
RE
S
inte
rcal
ibra
tion
with
in
DIF
RE
S
-ow
n es
timat
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rs e
t al
in p
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own
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ate
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in S
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e te
xt in
pr
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xt in
pr
esen
t rep
ortse
e te
xt in
pr
esen
t rep
ortse
e te
xt in
pr
esen
t rep
ortFr
anci
s et
al.
2005
, mod
if ac
c H
udd
(200
0)
Snic
kars
et
al. I
n pr
epSn
icka
rs in
pr
ep,
Sand
strö
m
2005
Kje
llman
20
04S
öder
berg
et
al 2
005
BALANCE Interim Report No. 32
44
AP
PE
ND
IX 1
. PA
GE
1b/
3M
ETH
OD
/ EQ
UIP
MEN
TBo
ngo
Dem
ersa
l tra
wl (
TV3
traw
l)H
ydro
acho
ust
ics
Pela
gic
traw
l
Info
rmat
ion
prov
ided
by
RV
, GK
/IF
M-
GE
OM
AR
JT, H
P /D
IFR
ES
JT, H
P /D
IFR
ESJT
, HP
/DIF
RES
PILO
T A
REA
22
22
TAR
GET
SPE
CIE
SS
peci
es/li
fe s
tage
Ear
ly L
ife
Stag
es o
f cod
an
d sp
rat
Cod
, fla
tfish
Spr
at, h
errin
gSp
rat,
herri
ng
Mob
ility
type
activ
e/pa
ssiv
eac
tive
(km
)ac
tive
(km
)ac
tive
(km
)
Tem
pora
l var
iatio
nse
ason
alan
nual
annu
alan
nual
OU
TPU
T VA
RIA
BLE
SN
ame
abun
danc
e/pr
odu
ctio
nca
tch
rate
biom
ass
catc
h ra
te
Uni
tn/
m²
n/ef
fort
t/vol
ume
n/ef
fort
Der
ived
var
iabl
e un
itno
neno
neno
neno
neP
ositi
onin
g ac
cura
cy10
m3
dec.
min
.3
dec.
min
.3
dec.
min
.M
ETH
OD
DET
AIL
S A
ND
REF
EREN
CES
Orig
inal
pur
pose
of s
tudy
Mon
itorin
gM
appi
ngM
appi
ngM
appi
ngA
rea/
volu
me
cove
red
by e
ach
sam
ple
250
km**
2va
ries
varie
sva
ries
Est
imat
ed a
ccur
acy
GO
OD
GO
OD
GO
OD
How
is e
valu
atio
n ac
hiev
ed?
subj
ectiv
ely
judg
edsu
bjec
tivel
ysu
bjec
tivel
y
Met
hod
refe
renc
eBI
TS (I
CES
)B
IAS
(ICES
)BI
AS
(ICES
)
BALANCE Interim Report No. 32
45 A
PP
EN
DIX
1. P
AG
E 2
a/3
MET
HO
D/ E
QU
IPM
ENT
Dro
p tra
pP
ushn
etM
anpo
wer
ed
2met
er
beam
traw
l
Traw
l sa
mpl
ing
of
youn
g fis
h
Bea
ch S
eine
Div
ing
LIP
SW
hite
pla
tes
and
scoo
psG
ill n
et
surv
eys
ECO
NO
MY
Tim
e in
Fie
ld/s
ampl
eno
t est
imat
edno
t est
imat
edno
t est
imat
edno
t est
imat
ed6-
10
sam
ples
/day
*2
pers
25ha
/day
*2
pers
7-15
sa
mpl
es/d
ay
*2pe
rs
20-3
0 m
in/s
ite6-
12 n
ets/
day
*2pe
rs
Tim
e in
Lab
/sam
ple
(not
incl
udin
g re
gist
ratio
n an
d nu
mer
ical
ana
lyse
s)lit
tle-n
one
little
-non
elit
tle-n
one
none
none
none
none
little
-non
eno
ne
Labo
rato
ry a
naly
sis
taxo
nom
ic
valid
atio
nta
xono
mic
va
lidat
ion
taxo
nom
ic
valid
atio
nta
xono
mic
va
lidat
ion
INTE
ND
ED U
SE IN
BA
LAN
CE
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apYE
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ssib
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odel
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. Var
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odel
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el V
alid
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e: m
ovin
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indo
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ON
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Pla
nned
met
hod
for s
patia
l mod
ellin
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GR
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a pr
oces
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al
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TIM
ATI
ON
OF
ENVI
RO
NM
ENTA
L VA
RIA
BLE
S A
FFEC
TIN
G D
ISTR
IBU
TIO
N O
F TA
RG
ET S
PEC
IES
Sal
inity
MO
DM
OD
MO
DM
OD
MO
DM
OD
MO
DM
OD
MO
DW
ave
expo
sure
MO
DM
OD
MO
DM
OD
MO
DM
OD
MO
DM
OD
MO
DD
epth
HIG
HH
IGH
HIG
HH
IGH
HIG
HH
IGH
HIG
HH
IGH
HIG
HLi
ght
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
Nut
rient
sM
OD
MO
DM
OD
MO
DM
OD
MO
DM
OD
MO
DM
OD
Oxy
gen
cont
ent
HIG
HH
IGH
HIG
HH
IGH
HIG
HH
IGH
MO
DH
IGH
MO
DTe
mpe
ratu
reH
IGH
HIG
HH
IGH
HIG
HH
IGH
HIG
HH
IGH
HIG
HH
IGH
BALANCE Interim Report No. 32
46
A
PP
EN
DIX
1. P
AG
E 2
b/3
MET
HO
D/ E
QU
IPM
ENT
Bon
goD
emer
sal t
raw
l (TV
3 tra
wl)
Hyd
roac
hous
tic
sP
elag
ic tr
awl
ECO
NO
MY
Tim
e in
Fie
ld/s
ampl
e2h
not e
stim
ated
not e
stim
ated
not e
stim
ated
Tim
e in
Lab
/sam
ple
(not
incl
udin
g re
gist
ratio
n an
d nu
mer
ical
ana
lyse
s)2h
none
none
none
Labo
rato
ry a
naly
sis
INTE
ND
ED U
SE IN
BA
LAN
CE
Hab
itat m
apYE
SYE
SYE
SYE
SR
esp.
Var
in M
odel
-N
ON
ON
OE
nv. V
ar in
Mod
el -
YES
YES
YES
Mod
el V
alid
atio
n -
NO
NO
NO
Pla
nned
met
hod
for s
patia
l in
terp
olat
ion
- -
- -
Pla
nned
met
hod
for s
patia
l mod
ellin
g -
to c
ome
to c
ome
to c
ome
Dat
a pr
oces
sing
refe
renc
esto
com
eto
com
eto
com
eto
com
e
ESTI
MA
TIO
N O
F EN
VIR
ON
MEN
TAL
VAR
IAB
LES
AFF
ECTI
NG
DIS
TRIB
UTI
ON
OF
TAR
GET
SPE
CIE
SS
alin
ityH
IGH
HIG
HH
IGH
HIG
HW
ave
expo
sure
LOW
HIG
HH
IGH
HIG
HD
epth
HIG
HH
IGH
HIG
HH
IGH
Ligh
tM
OD
LOW
LOW
LOW
Nut
rient
sM
OD
LOW
LOW
LOW
Oxy
gen
cont
ent
HIG
HH
IGH
LOW
LOW
Tem
pera
ture
HIG
HM
OD
MO
DM
OD
BALANCE Interim Report No. 32
47
AP
PE
ND
IX 1
. PA
GE
3a/
3M
ETH
OD
/ EQ
UIP
MEN
TD
rop
trap
Pus
hnet
Man
pow
ered
2m
eter
be
amtra
wl
Traw
l sa
mpl
ing
of
youn
g fis
h
Bea
ch S
eine
Div
ing
LIP
SW
hite
pla
tes
and
scoo
psG
ill n
et
surv
eys
SAM
PLIN
G D
ETA
ILS
Sam
plin
g st
rate
gyst
ratif
ied
or
rand
om w
ithin
m
uddy
/san
dy
subs
trate
s
stra
tifie
d or
ra
ndom
with
in
mud
dy/s
andy
su
bstra
tes
stra
tifie
d or
ra
ndom
with
in
mud
dy/s
andy
su
bstra
tes
aim
at h
igh
geog
raph
ic
cove
rage
rand
om w
ithin
sh
allo
w b
ays
of th
e st
udy
area
stra
tifie
d ra
ndom
stra
tifie
d ra
ndom
stra
tifie
d ra
ndom
stra
tifie
d ra
ndom
Sam
plin
g pe
riod
varie
s w
ith
stud
yva
ries
with
st
udy
varie
s w
ith
stud
yva
ries
with
st
udy
Jul-A
ugA
pr-J
unJu
l-Sep
Apr
-May
Aug
(mai
nly)
Oth
er d
ata
colle
cted
with
in s
ame
stud
y -
- -
leng
th,
som
etom
es
age
vege
tatio
n so
met
imes
, te
mpe
ratu
re,
salin
ity,
turb
idity
dept
h, te
mp,
ve
geta
tion,
sa
linity
, nu
trien
ts,
turb
idity
dept
h, te
mp,
ve
geta
tion,
sa
linity
, tu
rbid
ity,
subs
trate
tem
p, s
al, v
eg
type
dept
h, te
mp,
se
cchi
ap
prox
, le
ngth
, age
, se
xD
ata
inte
rpre
tatio
n in
com
bina
tion
with
ot
her m
etho
ds -
- -
- -
- -
- -
Is th
e st
udy
desi
gned
to g
eogr
aphi
cally
cov
er th
e fo
llow
ing
grad
ient
s?S
alin
ityva
ries
varie
sva
ries
varie
sN
ON
ON
OYE
SN
OW
ave
expo
sure
varie
sva
ries
varie
sva
ries
NO
mod
erat
ely
YES
YES
NO
Dep
thva
ries
varie
sva
ries
varie
sN
OYE
SYE
SN
OYE
SLi
ght
varie
sva
ries
varie
sva
ries
NO
NO
som
etim
esN
ON
ON
utrie
nts
varie
sva
ries
varie
sva
ries
NO
NO
NO
NO
NO
Oxy
gen
cont
ent
Tem
pera
ture
varie
sva
ries
varie
sva
ries
NO
NO
NO
YES
NO
Doe
s th
e st
udy
cove
r are
as o
utsi
de th
e pr
imar
y di
strb
utio
n ar
ea o
fthe
targ
et s
peci
es?
SUPP
LEM
ENTA
L IN
FOR
MA
TIO
N
BALANCE Interim Report No. 32
48
APP
EN
DIX
1. P
AG
E 3
b/3
MET
HO
D/ E
QU
IPM
ENT
Bon
go n
et, 6
0cm
di
amet
er w
ith
335
and
500µ
m
hi
Dem
ersa
l tra
wl (
TV3
traw
l)H
ydro
acho
ust
ics
Pel
agic
traw
l
SAM
PLIN
G D
ETA
ILS
Sam
plin
g st
rate
gygr
idde
pth
stra
tifie
dde
pth
stra
tifie
dde
pth
stra
tifie
d
Sam
plin
g pe
riod
3-5
surv
eys
per
year
Mar
-Oct
5-8
sur
veys
aro
und
the
year
5-8
sur
veys
ar
ound
the
year
5-8
sur
veys
aro
und
the
year
Oth
er d
ata
colle
cted
with
in s
ame
stud
yhy
drog
raph
y,
traw
l sur
vey
biom
ass,
le
ngth
freq
uenc
y,
wei
ght,
sex,
mat
urity
-bi
omas
s,
leng
th fr
eque
ncy,
w
eigh
t, se
x, m
atur
ity
Dat
a in
terp
reta
tion
in c
ombi
natio
n w
ith
othe
r met
hods
- -
-
Is th
e st
udy
desi
gned
to g
eogr
aphi
cally
cov
er th
e fo
llow
ing
grad
ient
s?S
alin
ityYE
SYE
SYE
SYE
SW
ave
expo
sure
NO
NO
NO
NO
Dep
thYE
SYE
SYE
SYE
SLi
ght
NO
NO
NO
NO
Nut
rient
sN
ON
ON
ON
OO
xyge
n co
nten
tYE
STe
mpe
ratu
reYE
SYE
SYE
SYE
SD
oes
the
stud
y co
ver a
reas
out
side
tYE
SN
OSU
PPLE
MEN
TAL
INFO
RM
ATI
ON
Add
ition
al g
ears
us
ed: I
KS
and
m
ultin
ets
BALANCE Interim Report No. 32
49
10.2 Appendix 2. Methods for sampling macrofauna data
Appendix 2, page 1/2METHOD/ EQUIPMENT van VEEN grab van VEEN grab EKMAN grab Core sampler,
diver operatedCore sampler, remote
Information provided by JA /IAE JK /EMI JK /EMI JK /EMI JH /NERIPILOT AREA 4 4 4 4 1TARGET SPECIESSpecies/life stage all observed all observed all observed all observed all observedMobility type active (meters) active (meters) active (meters) active (meters) active (meters)Temporal variation seasonal-annual seasonal-annual seasonal-annual seasonal-annual seasonal-annual
OUTPUT VARIABLESName biomass abund., biomass abund., biomass abund., biomass abund., biomass
Unit gWW/m2 n/m2, gDW/m2 n/m2, gDW/m2 n/m2, gDW/m2 n/m2, gWW/m2Derived variable unit gWW/m2 gWW/m2 gWW/m2Positioning accuracy 20-100 (<50m) 3-20m 3-20m 3-20m 2-5 mMETHOD DETAILS AND REFERENCESSiev/mesh 0.5 mm 1 and 0.25 mm 0.25 mm 0.25 mmOriginal purpose of study monitoring monitoring monitoring monitoring Mapping /ground
truthingArea/volume of sample >4L and 0.1m2 >4L and 0.1m2 0.02m2 0.0083m2 0.0143m2Estimated accuracy moderate good good good moderateHow is evaluation achieved? Periodic
intercalibration exercises
Field intercalibrations
Field intercalibrations
Field intercalibrations
Method reference 1, 2 3 3 3 4ECONOMYTime in Field/sample not estimated 25 stations/week
*2 pers7-20 stations/day *1-2 pers
7-20 stations/day *1-2 pers
60-70 samples/day *3
Time in Lab/sample (not including registration and numerical analyses)
0.5 samples/ day *1pers
3 samples/ day *1pers
2 samples/day *1pers
2 samples/day *1pers
Laboratory analysis sorting, counting and weighing
sorting, counting and weighing
sorting, counting and weighing
sorting, counting and weighing
sorting, counting and weighing
INTENDED USE IN BALANCEHabitat map YES YES YES YES YESResp.Var in Model not decided yet YES YES YES YESEnv. Var in Model Pot. Yes NO NO NO NOModel Validation Pot. Yes YES YES YES NOPlanned method for spatial interpolation
not decided yet kriging in well sampled areas
kriging in well sampled areas
kriging in well sampled areas
not decided yet
Planned method for spatial modelling
- - - - -
Data processing references - - - - -ESTIMATION OF ENVIRONMENTAL VARIABLES AFFECTING DISTRIBUTION OF TARGET SPECIESSalinity MODERATE LOW-HIGH LOW-HIGH LOW-HIGH MODERATEWave exposure MOD-HIGH MOD-HIGH HIGH HIGH MODERATEDepth MODERATE HIGH MOD-HIGH MOD-HIGH HIGHLight MODERATE LOW MOD-HIGH MOD-HIGH LOWNutrients MODERATE HIGH HIGH HIGH LOWOxygen content HIGH HIGH HIGH HIGH HIGHTemperature LOW-HIGH MOD-HIGH MODERATE MODERATE LOW
BALANCE Interim Report No. 32
50
Appendix 2, page 2/2METHOD/ EQUIPMENT van VEEN grab van VEEN grab EKMAN grab Core sampler,
diver operatedCore sampler, remote
SAMPLING DETAILSSampling strategy arbitrary-
representative of basins
stratified by waterbodies and/or grid
stratified by waterbodies and/or grid
stratified by waterbodies and/or grid
transect -random
Sampling period May Late spring Ice free season Ice free season AprilPrincipal study area and size Approx
25.000km2Estonian coastline
Varies: 25 km2, or Estonian coastline
Varies: 25 km2, or Estonian coastline
Acc to GEUS, SGU output
Other data collected within same study
water transparency, standard hydrology and hydrochemistry
phytoplankton, zooplankton, bottom vegetation, sediment type, organic matter, temperature profiles, salinity, oxygen, water nutrients
vegetation, sediment type, sediment organic matter content
vegetation, sediment type
sediment type (phi scale)
Data interpretation in combination with other methods
plankton, nutrients, salinity, oxygen, grain size, depth
phytoplankton, nutrients, salinity, oxygen, sediment, human disturbances
phytoplankton, nutrients, salinity, oxygen, sediment, human disturbances
phytoplankton, nutrients, salinity, oxygen, sediment, human disturbances
sediment type
Is the study designed to geographically cover the following gradients?Salinity NO YES YES YES NOWave exposure NO YES YES YES NODepth YES YES YES YES YESLight NO NO YES YES NONutrients NO YES YES YES NOOxygen contentTemperature NO YES No NO NODoes the study cover areas outside the primary distrbution area ofthe target species?
NO YES YES YESSUPPLEMENTAL INFORMATION 5, 6Footnotes: 1) HELCOM 1998
2) Dybern et al 19763) http://sea.helcom.fi/Monas/CombineManual2/PartC/CFrame.htm4) Kanneworf & Nicolaisen 1973, Jensen 19975) Sediment type is classified visually into broad categories (e.g. silt, clay, fine sand, medium sand, coarse sand, pebbles); homogenous/mixed; oxygen conditions estimated by colour 6) Data is normally not classified into assemblage types but instead is analysed using various multivariate statistics; the rationale behind it is the lack of clear assemblages but instead the presence of continuous changes of communities according to the changes in
BALANCE Interim Report No. 32
51
10.3 Appendix 3. Methods for sampling zooplankton data
Appendix 3, page 1/2METHOD/ EQUIPMENT BONGO MULTINETS JUDAI NETInformation provided by RV /IFM-GEOMAR,
CM /DIFRESRV /IFM-GEOMAR, CM /DIFRES
RV /IFM-GEOMAR, CM /DIFRES
JA /IAE
PILOT AREA 2 2 2 4TARGET SPECIESSpecies/life stage copepods,
cladoceranscopepods, cladocerans
copepods, cladocerans
copepods, cladocerans
Mobility type active/passive active/passive active/passive active/passiveTemporal variation weeks weeks weeks weeksOUTPUT VARIABLESName abundance abundance abundance abundanceUnit n/m3 n/m3 n/m3 n/m3Derived variable unit biomass mg/m3 biomass mg/m3 biomass mg/m3 biomass mg/m3Positioning accuracy 50mMETHOD DETAILS AND REFERENCESOriginal purpose of study Monitoring Monitoring Monitoring MonitoringEquipment details Bongo net (30cm
diameter,150µm mesh size)
Multinet (0.5m2, 50µm mesh size)
Judai (36cm diameter, 160µm mesh size)
WP-2 net (100 microns)
Area/volume covered by each sample not estimated not estimated not estimated not estimated
Estimated accuracy GOOD GOOD GOOD GOODHow is evaluation achieved? subjectively subjectively subjectively subjectivelyMethod reference HELCOM,
COMBINEECONOMYTime in Field/sample 2h 2h 2h not estimatedTime in Lab/sample (not including registration and numerical analyses)
2h 2h 2h
Laboratory analysis counting and size estimations
INTENDED USE IN BALANCEHabitat map YES YES YES not decided yetResp.Var in Model YES YES YESEnv. Var in Model NO NO NOModel Validation NO NO NOPlanned method for spatial interpolation
not decided yet
Planned method for spatial modelling not decided yet
Data processing references
BALANCE Interim Report No. 32
52
Appendix 3, page 2/2METHOD/ EQUIPMENT BONGO MULTINETS JUDAI NETESTIMATION OF ENVIRONMENTAL VARIABLES AFFECTING DISTRIBUTION OF TARGET SPECIESSalinity HIGH HIGH HIGH LOW/MODWave exposure LOW LOW LOW LOWDepth HIGH HIGH HIGH LOWLight MOD MOD MOD LOWNutrients LOW LOW LOW LOWOxygen contentTemperature HIGH HIGH HIGH MOD/HIGHSAMPLING DETAILSSampling strategy grid line line arbitrary-
representativeSampling period monthly monthly seasonally annuallyPrincipal study area, name and size ICES Sub-division
25, 26, 28ICES Sub-division 25, 26, 28
ICES Sub-division 25, 26, 28
Gulf of Riga
Other data collected within same study Hydrography Hydrography Hydrography Hydrography
Data interpretation in combination with other methods
hydrological and hydrochemical data
Is the study designed to geographically cover the following gradients?Salinity YES YES YES NOWave exposure NO NO NO NODepth NO YES YES NOLight NO NO NO NONutrients YES YES YES NOOxygen content YES YES YES NOTemperature YES YES YES NODoes the study cover areas outside the primary distrbution area ofthe target species?
NO NO NO -
BALANCE Interim Report No. 32
53
10.4 Appendix 4. Methods for sampling phytobenthos data
App
endi
x 4,
pag
e 1/
3M
ETH
OD
/ EQ
UIP
MEN
TD
ivin
gS
kin
divi
ngD
rop-
Vid
eoS
atel
lite
data
Div
ing
UW
-vid
eoD
ivin
gIn
form
atio
n pr
ovid
ed b
yK
D /N
ER
IA
S /A
BF
MB
/ME
TSÄ
SW
/ME
TRIA
MI /
NIV
AM
I /N
IVA
MI /
NIV
API
LOT
AR
EA1
33
1-4
1B
altic
Sea
, not
K
atte
gatt
Bal
tic S
ea, n
ot
Kat
tega
ttTA
RG
ET S
PEC
IES
Spe
cies
/life
sta
geal
ldo
min
ant
dom
inan
tdo
min
ant
all
all
all
Mob
ility
type
sess
ilese
ssile
sess
ilese
ssile
sess
ilese
ssile
sess
ileTe
mpo
ral v
aria
tion
annu
al-
pere
nnia
lan
nual
-pe
renn
ial
annu
al-
pere
nnia
lan
nual
-pe
renn
ial
annu
al-
pere
nnia
lan
nual
-pe
renn
ial
annu
al-
pere
nnia
lO
UTP
UT
VAR
IAB
LES
Nam
eab
unda
nce
(l
)ab
unda
nce
(l
)ab
unda
nce
(l
)ab
unda
nce
(l
)ab
unda
nce
(l
)ab
unda
nce
(l
)ab
unda
nce
(l
)U
nit
% c
ont.
scal
ecl
ass
% o
rdin
al s
cale
% o
rdin
al s
cale
clas
s%
ord
inal
sca
le%
ord
inal
sca
le
Sca
le in
terv
als
1,2,
3…99
,100
%sc
aled
1-7
, 1-4
, or
0-4
0,1,
3,10
,20,
..90,
100%
0-20
, 20-
80, 8
0-10
0%0-
40,
1, 2
, 5, 1
0,
25,..
.75,
100
%0,
1, 2
, 5, 1
0,
25,..
.75,
100
%P
ositi
onin
g ac
cura
cy1-
2m3-
20m
5-1
0m2-
3m1-
5 m
1-5
m1-
5 m
MET
HO
D D
ETA
ILS
AN
D R
EFER
ENC
ESO
rigin
al p
urpo
se o
f stu
dyM
appi
ng
/Mon
itorin
gM
appi
ngM
appi
ngM
appi
ngM
onito
ring
Map
ping
Map
ping
Are
a/vo
lum
e co
vere
d by
eac
h sa
mpl
e25
m2
0.25
m2
and
20m
2m
in 2
0m2
1 sc
ene
60X6
0km
/res
ol.
10 m
100m
2 ob
serv
atio
n un
it30
-100
m
trans
ects
, ca
5-10
m w
idth
Est
imat
ed a
ccur
acy
Goo
dG
ood
Goo
dU
nder
ev
alua
tion
Goo
d -
-
How
is e
valu
atio
n ac
hiev
ed?
Rea
l tim
e te
levi
sion
from
di
ver t
o sh
ip
Fiel
d te
sts
with
in
deve
lopm
ent o
f m
anua
l
Div
ing
Gro
und
truth
ing
Inte
rcal
ibra
tion
betw
een
dive
rsN
ot e
valu
ated
Not
eva
luat
ed
Met
hod
refe
renc
eD
ahl e
t al 2
003,
K
raus
e-Je
nsen
20
05
Per
sson
&
Joha
nsso
n 20
05P
itkän
en 2
006
Wen
nber
g, in
pr
epM
oy e
t al 2
005
Kau
tsky
199
5
BALANCE Interim Report No. 32
54
App
endi
x 4,
pag
e 2/
3M
ETH
OD
/ EQ
UIP
MEN
TD
ivin
gS
kin
divi
ngD
rop-
Vid
eoS
atel
lite
data
Div
ing
UW
-vid
eoD
ivin
gEC
ON
OM
YTi
me
in F
ield
/sam
ple
6-9d
ives
/day
14ha
/day
*2 p
ers
1,5k
m2/
d*2p
ers
(gro
und
truth
ing)
20 d
ays/
yr
Tim
e in
Lab
/sam
ple
(not
incl
udin
g re
gist
ratio
n an
d nu
mer
ical
ana
lyse
s)lit
tlelit
tle1
sqkm
/day
Labo
rato
ry a
naly
sis
som
e ta
xono
mic
va
lidat
ion
som
e ta
xono
mic
va
lidat
ion
vide
o an
alys
es,
som
e ta
xono
mic
va
lidat
ion
imag
e in
terp
reta
tion
som
e ta
xono
mic
va
lidat
ion
noso
rting
and
dry
w
eigh
t of
sam
ples
INTE
ND
ED U
SE IN
BA
LAN
CE
Hab
itat m
apYE
SYE
SYE
SYE
SYE
SYE
SN
OR
esp.
Var
in M
odel
YES
YES
pot y
espo
t yes
YES
YES
NO
Env
. Var
in M
odel
NO
YES
NO
pot y
esN
OYE
SN
OM
odel
Val
idat
ion
NO
YES
pot y
espo
t yes
NO
NO
NO
Pla
nned
met
hod
for s
patia
l in
terp
olat
ion
not d
ecid
ed y
etm
ovin
g w
indo
w
(som
e)A
rcG
IS to
pogr
id,
pot o
ther
sns
(ful
l co
vera
ge)
NO
NO
NO
Pla
nned
met
hod
for s
patia
l m
odel
ling
none
GR
AS
P,
regr
essi
on tr
eepo
t GR
AS
Ppo
t GR
AS
Ptim
e se
ries,
G
RAS
Pcr
iteria
ana
lysi
scr
iteria
ana
lysi
s
Dat
a pr
oces
sing
refe
renc
es -
Lehm
ann
et a
l 20
02B
ALM
AR
Wen
nber
g, in
pr
epLe
hman
n et
al
2002
Grip
et a
l, in
pr
epG
rip e
t al,
in
prep
ESTI
MA
TIO
N O
F EN
VIR
ON
MEN
TAL
VAR
IAB
LES
AFF
ECTI
NG
DIS
TRIB
UTI
ON
OF
TAR
GET
SPE
CIE
SS
alin
ityH
IGH
HIG
HH
IGH
HIG
HM
ED
--
Wav
e ex
posu
reH
IGH
HIG
HH
IGH
HIG
HH
IGH
HIG
HH
IGH
Dep
thH
IGH
HIG
HH
IGH
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BALANCE Interim Report No. 32
55
App
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x 4,
pag
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3M
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reN
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ON
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ON
ON
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OD
oes
the
stud
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ver a
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out
side
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ofth
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?N
ON
ON
ON
ON
ON
ON
O
BALANCE Interim Report No. 32
56
10.5 Appendix 5. Methods for sampling phytoplankton, hydrography & hydrochemistry data
Appendix 5, page 1/2METHOD/ EQUIPMENT CTD profiling CTD casts Water sampler HoseInformation provided by HHH /IFM-GEOMAR JA /IAE JA /IAE JA /IAE
PILOT AREA 2 4 4 4TARGET SPECIESSpecies/life stage HYDROGRAPHY HYDROGRAPHY HYDROCHEMISTRY PHYTOPLANKTON
Mobility type passive passive passive passiveTemporal variation days/weeks days/weeks days-months days/weeksOUTPUT VARIABLESName Temperature,
Salinity, Oxygen content
Temperature, Salinity, depth
N02, NO3, N-tot, NH4, PO4, P-tot, Si
species abundance
Unit °C, PSU, ml/l °C, PSU umol/L n/LDerived variable unit - - - biomass mg/m3Positioning accuracy 10m 50m 50m 50mMETHOD DETAILS AND REFERENCESOriginal purpose of study Mapping Monitoring Monitoring MonitoringEquipment details CTD-OTS80 and
CTD-ADMCTD Arop 500 MBL TPN (Transparent
plastic Nansen) with reverse thermometers
Area/volume covered by each sample 250 km**2 Integrated hose sample 0-10 m depth, subsample of 200mL preserved
Estimated accuracy GOOD GOODHow is evaluation achieved? - -Method reference Grasshof 1976 HELCOM 2001,
Ütermöhl 1958
ECONOMYTime in Field/sample 0.25 hours not estimated not estimatedTime in Lab/sample (not including registration and numerical analyses)
no no not estimated
Laboratory analysis no no Standard spectrophotometer
d C l L b
counting and size estimations
INTENDED USE IN BALANCEHabitat map COD/SPRAT map not decided yet not decided yetResp.Var in ModelEnv. Var in Model model input for
circulation modelModel ValidationPlanned method for spatial interpolation
not decided yet
Planned method for spatial modelling not decided yet
Data processing references
BALANCE Interim Report No. 32
57
Appendix 5, page 2/2METHOD/ EQUIPMENT CTD profiling CTD casts Water sampler HoseESTIMATION OF ENVIRONMENTAL VARIABLES AFFECTING DISTRIBUTION OF TARGET SPECIESSalinity HIGH LOW/MODWave exposure LOWDepth HIGH LOWLight HIGHNutrients HIGHOxygen content HIGH MOD/HIGHTemperature LOWSAMPLING DETAILSSampling strategy grid representative representative arbitrary-
representativitySampling period monthly annuallyPrincipal study area, name and size Bornholm Basin
12000 km**2Other data collected within same study ichthyoplankton hydrology,
hydrochemistryData interpretation in combination with other methods
Manually analysed water samples
hydrological and hydrochemical data
Is the study designed to geographically cover the following gradients?Salinity YES NOWave exposure NO NODepth YES YES NOLight NO NONutrients NO NOOxygen content YES NOTemperature YES NODoes the study cover areas outside the primary distrbution area ofthe target species? -
BALANCE Interim Report No. 32
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