Operational Effect Variables and Functional Ecosystem ... - CiteSeerX

Internat. Rev. Hydrobiol.

92

2007

3

326–357

DOI: 10.1002/iroh.200610931

LARS HÅKANSON, ANDREAS C. BRYHN and THORSTEN BLENCKNER Dept. of Earth Sciences, Uppsala University, Villavagen 16, 752 36 Uppsala, Sweden; e-mail: [email protected]

Operational Effect Variables and Functional Ecosystem Classifications – a Review on Empirical Models for Aquatic Systems along a Salinity Gradient key words: trophic level classification, chlorophyll, Secchi depth, macrophytes, eutrophication

Abstract This paper presents a comparative study based on a very comprehensive set of empirical data from many international data bases including fresh water systems, coastal brackish water areas and marine coastal areas. We present a general trophic level classification system (oligotrophic, mesotrophic, eutrophic and hypertrophic categories) for sites/areas characterised by a wide range of salinities. This classification system targets on the following operational effect variables (bioindicators), which are meant to reflect key structural and functional aspects of aquatic ecosystems and characteristic (median) values for entire defined areas (the ecosystem scale) for the growing season: Secchi depth (as a standard measure of water clarity), chlorophyll-a concentrations (a measure of primary phytoplankton biomass), the oxygen saturation in the deep-water zone (an indicator reflecting sedimentation, oxygen consumption, oxygen concentrations and the habitat conditions for zoobenthos, an important functional group) and the macrophyte cover (an important variable for the bioproduction potential, including fish production, and the “biological value” of aquatic systems). For a wide range of systems, these bioindicators can be predicted using practically useful models, i.e., models based on variables that can be accessed from standard monitoring programs and maps. These bioindicators are regulated by a set of abiotic factors, such as salinity, suspended particulate matter (SPM), nutrient concentrations (N and P), morphometry and water exchange. Empirical data ultimately form the basis for most ecological/environmental studies and this work uses maybe the most comprehensive data set ever related to trophic level conditions. It also gives compilations of empirically-based (statistical) models quantifying how the variables are interrelated and how they reflect fundamental aspects of aquatic ecosystems.

1. Introduction and Aim A given coastal area may be defined and characterised in many ways, e.g., according to territorial boundaries, pollution status, water stratification (thermoclines/haloclines), etc. One such system is shown in Figure 1 as a background to the following trophic level classification, which is the main focus of this work. Figure 1 gives a geographical zonation into the following categories: 1. The drainage area. For example, the drainage area of the Baltic Sea covers 1,700,000 km2, which is more than four times larger than the entire water area (415,266 km2). 2. The coastal zone, i.e., the zone inside the outer islands of the archipelago and/or inside barrier islands. This is the zone in focus in this work. The coastal zone is of special impor* Corresponding author

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Figure 1. A given coastal system, here the Baltic Sea, may be divided into the following three functional zones: the coastal zone, the transition zone and the deep-water area. The focus in this work is on the coastal zone. Modified from HÅKANSON (1991).

tance for recreation, fishing, water planning and shipping and is a zone where different conflicts and demands overlap. The natural processes (water transport, fluxes of material and energy and bioproduction) in this zone are of utmost importance for the entire sea. It may be considered a “nursery and pantry” for the sea (HÅKANSON and ROSENBERG, 1985). 3. The transition zone, i.e., the zone between the coastal zone and the deep-water areas. This is the zone extending down to depths at which episodes of resuspension of fine materials occur in connection with storms and/or current activities (at about 44 m water depth in the Baltic Sea; see HÅKANSON, 1991). The conditions in terms of water dynamics and distribution of pollutants and suspended and dissolved materials in this zone are of great importance for the ecological status of the entire system. This zone geographically dominates the open water areas outside the coastal zone. 4. The deep-water zone, by definition the areas beneath the theoretical wave base. In these areas, there is a continuous deposition of fine materials. This is the “end station” for many types of pollutants and these are the areas in which conditions with low oxygen concentrations are most likely to occur. There are also many other types of classification systems for coastal areas. For example, JOHNSON (1919) and DAVIS (1964, 1972) have presented coast type classifications based on

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geological considerations, e.g., according to form-creating processes. VALENTIN (1952, 1979) based a coastal classification on whether the coast is expanding or retreating. There are also different regional classification systems (OLSSON, 1966; ABRAHAMSEN et al., 1977) generally based on or related to DAVIES’S system. Coastal classifications like these are useful to get a broad overview of the general features and they can explain why the coastal geomorphology looks the way it does, but they do not provide the data necessary for ecological modelling or quantifications, e.g., how and why a given coastal area functions as a “nursery and pantry” for the bioproduction or as a receiving system for water pollutants. The aim of the coastal classification system discussed in this work is to address such issues. The trophic level classification system presented in this work should be based on practically useful, operational effect variables or bioindicators for coastal management. Such bioindicators should meet the following criteria (HÅKANSON and PETERS, 1995; MOLDAN and BILLHARZ, 1997; LIVINGSTON, 2001; BORTONE, 2005): (1) they should be measurable, preferably simply and inexpensively, (2) clearly interpretable and predictable by validated quantitative models, (3) internationally applicable, (4) relevant for the given environmental threat and (5) representative for the given ecosystem. Differential equations are often used to quantify fluxes (g per time), amounts (g) and concentrations (g per volume) of all types of materials, but not generally bioindicators such as the Secchi depth, chlorophyll-a concentrations, the oxygen saturation in the deep-water zone or other types of ecosystem effect variables (Fig. 2). Regressions based on empirical data are often necessary to relate concentrations of chemicals in water or sediments to target bioindicators. Two coastal areas are likely to react differently to a change in the load of nutrients to the system. The classical approach (from VOLLENWEIDER, 1968) to carry out effect-load-sensitivity (ELS) analysis is to use dynamic mass-balance models to predict concentrations of nutrients and empirical models (like regressions) to link these concentrations to measured data on the bioindicators (Fig. 2). In contexts of coastal management, one must generally for practical and economical reasons seek simpler but relevant operational effect variables than the ideal ones related to production or biomasses of functional groups or species. Mean concentrations of a given toxic substance (or substances) in predatory fish, Secchi depth, oxygen concentrations and chlorophyll-a concentrations are examples of simple, operational effect variables. In the absence of validated operational predictive models, coastal management may fail to take the appropriate actions since without such models it is not possible to provide quantitative predictions of consequences of different management strategies or remedial actions at the ecosystem scale (which is the focal scale for coastal managers rather than the conditions at individual sites). This is the main reason why in this work we do not discuss most of the available bioindicators related to the abundance or changes in individual species or bioindicators that require in-depth biological knowledge for measurements and interpretations. The water quality indices and trophic level classification system discussed in this work may be useful tools for enhancing communications between natural scientists, water managers, economists, policymakers and/or the general public. A wide variety of indices for coastal areas have emerged during recent years (SEPA, 2000; AERTBERG et al., 2003; DIAZ et al., 2004; ANDERSEN et al., 2006). The index TRIX (TRophic IndeX) has been described by VOLLENWEIDER et al. (1998) and is also included in Italian legislation regarding coastal management (PENNA et al., 2004). However, TRIX has yet to be critically tested for areas outside the Mediterranean Sea. TRIX is based on chlorophyll-a, oxygen saturation in the deep-water zone, total-P concentration and total-N concentration. It should be noted that the TRIX scale from 0 to 10 gives the impression that one of the ends is good (oligotrophy) and the other is bad (hypertrophy). We argue that a more suitable index for coastal management should instead start from what is regarded as the norm (the normal = reference = target situation), which describes a requested situation (e.g., the situation as it was 50 or 100 years

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Operational Effect Variables Conditions in the sea outside the coast

Population, land use

Sub-model for inflow and outflow

Catchmet area sub-model

Nutrient loading (TN and TP) and suspended particulate mattrer (SPM) loading Handled by differential equations Mass-balance models for TN, TP and SPM Inflow

Outflow Surface water Deep water

Internal processes, sedimentation, resuspension, diffusion, mixing, mineralisation

Indicator 1, Secchi depth from SPM and salinity

Indicator 2, Chlorophyll-a from TN- & TPconcentrations

Often handled by regressions

Indicator 3, Oxygen saturation in deep water from SPM-sedimentation

Morphometry and water dynamic characteristics (Area, mean depth, section area, form, water exchange)

Indicator 4, Macrophyte cover from SPM and morphometry and hydrodynamics

Focus of this work

TN, TP and SPM concentrations

The "biological value" of a given coastal area indicated by the area down to the Secchi depth, Asec

Figure 2. Basic elements in Effect-Load-Sensitivity (ELS) modelling for coastal water eutrophication utilizing mass-balance modelling and regression analyses relating nutrient concentrations to operational effect variables or indicators (Secchi depth, chlorophyll-a concentrations, oxygen saturation in the deepwater zone and macrophyte cover) and focus of this work.

ago). The end value of the index should describe an extremely detrimental anthropogenic impact (the maximum possible deviation from the norm) – in both directions. Such indices are also available for lakes (HÅKANSON et al., 2000), but not, according to our knowledge, for coastal areas. Such an index would account for the fact that artificially low nutrient levels may be just as undesirable as an artificially high nutrient level. The development of a norm-based index for brackish and marine coastal areas would be an apt goal for future scientific work. In this work, we will make a comprehensive comparative study including data from fresh water, brackish water and marine coastal systems to try to find general patterns and criteria

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for classifying water systems into classes useful for sustainable water management and to detect regime shifts. As stressed, the focus is on the ecosystem scale (i.e., on entire coastal areas and lakes, and not on smaller spatial scales, such as sample sites) and on seasonal changes (rather than changes related to smaller temporal scales). The focus here is on empirical models and on criteria to estimate the “biological value” of coastal ecosystems (Fig. 2), i.e., which areas have the highest bioproduction potential, which areas should be protected and preserved, i.e., where should one minimize the construction of marinas, harbours, dredging and dumping activities, and limit sand extractions and emissions of nutrients and contaminants, etc. (HÅKANSON and ROSENBERG, 1985). Operational bioindicators for water management should be comprehensible without expert knowledge. In fact, one reason to develop such measures is so that politicians and the general public can understand the present conditions and future changes in the environment. All bioindicators should not just be understandable by coastal managers and scientists, it should also be possible to predict them using validated models and to calculate changes in the bioindicators and relate such changes to changes in fundamental abiotic driving variables, such as salinity, suspended particulate matter (SPM) concentrations, nutrient loading/concentrations and water exchange. We will discuss and motivate a set of bioindicators and how they may be used for classifications of water systems. Note that classification systems may not in themselves provide answers to the causal reasons for regime shifts, but they can provide a scientific framework for such analyses. The empirical models discussed in this work are meant to address questions of this kind in a general, quantitative manner.

2. Operational Effect Variables and Abiotic Variables for Water Management Table 1 lists the variables in focus in this work. Evidently, different users might have different demands on “water quality” and different criteria to define “water quality”, setting management targets and defining operational effect variables. It is also interesting to note that within the European Water Framework Directive, different European countries (e.g., Finland, Sweden and the Netherlands) have classified a number of standard effect variables into descriptive categories, excellent, good, satisfactory, passable and poor, but have used different reference levels for the given classes (Table 2), which is an evident problem. Hopefully, this work can help to pave the way for future harmonisations in these contexts. Key bioindicators discussed in this work are the Secchi depth (a measure of water transparency), the oxygen concentration/saturation in the deep-water areas (regulating the survival of zoobenthos and diffusion of phosphorus), the chlorophyll-a concentrations (a standard measure of phytoplankton biomass) and the macrophyte cover (a measure of coastal productiv-

Table 1.

Compilation of general operational effect variables (bioindicators) for coastal management and the main abiotic variables influencing them.

Target effect variables (yi)

Key abiotic variables (xi)

1. 2. 3. 4.

1. 2. 3. 4. 5. 6.

Secchi depth Oxygen saturation in deep-water Chlorophyll-a concentration Macrophyte cover

Suspended particulate matter (SPM) Total-P concentration (TP) Total-N concentration (TN) Salinity Morphometry (area, volume, mean depth, max. depth, etc.) Water exchange

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Table 2. Water quality criteria from different countries for mainly coastal areas for chlorophyll-a (Chl), concentrations of total phosphorus (TP), total nitrogen (TN) and Secchi depth from different sources: www.environment.fi/waterquality, www.environ.se and www.env.gov.bc.ca/wat/wq/BCguidelines/nutrients/nutrients.html. Classes I

II

III

IV

V

excellent 20 µg/l

Salinity criteria

F = Fresh water, salinity < 5‰ B = Brackish water, salinity 5-20‰ M = Marine water, salinity > 20‰

Figure 4. Illustration of the relationship between Secchi depth, SPM in surface water and salinity in surface water and trophic categories for fresh water systems (salinity 20‰).

some key arguments to provide a framework for systems where also variations in salinity are important. The conditions in lakes (salinity 0) are used as reference conditions. The class limits for the trophic categories are based on three chlorophyll classes (2, 6 and 20 µg/l), since data on chlorophyll gives basic information on primary phytoplankton production/biomass. Note that these classes also largely correspond to the classes discussed in the mentioned trophic level classifications except that there is no overlap between the classes; the chlorophyll limits used here are meant to illustrate characteristic conditions during the growing season. We will relate lake TP to chlorophyll by the widely used OECD-model (Table 4) and relate TP to Secchi depth by the regression in Figure 5, which is based on data from 936 lakes covering a very wide domain. These regressions have been used to define the class limits shown in Figure 4. So, the chlorophyll classes 2, 6 and 20 µg/l correspond to TP-concentrations of 7.8, 24 and 86 µg/l, Secchi depths of 3.9, 1.9 and 0.9 m and SPM-values of 1.7, 4.6 and 12.4 mg/l. How these limits change for systems with different salinities will be discussed in the following text.

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Table 3. Characteristic features in (A) freshwater systems, (B) brackish coastal systems and (C) marine coastal systems of different trophic levels (modified from OECD, 1982; HÅKANSON and JANSSON, 1983; WALLIN et al., 1992). All data represent characteristic (median) values for the growing season (months 6, 7 and 8) for the surface water zone. Information of fish from MAITLAND and LINSELL (1977) and MOEN and SVENSEN (2004). Trophic level

Chl-a (µg/l)

Secchi (m)

Risk that O2Sat Total-N in DW < 20% (µg/l)

Total-P (µg/l)

Dominant fish

A. Fresh water systems, salinity < 5‰ Oligotrophic Mesotrophic Eutrophic Hypertrophic

20

>11.5 0.9

Note*

TP calculated from Chl-a and salinity using the model in Figure 11. The relationship between TN and TP is from the log-regression in Figure 9; Secchi depths from TP in Figure 5; SPM from Secchi depth and salinity from Eq. 1. Note*: The prevalence of different fish species in marine coastal areas is not first and foremost related to trophic level but rather to the substrate. So, these characteristics refer to species generally found in coasts dominated by rocks (oligotrophic), macrophytes (mesotrophic) and soft sediments (eutrophic and hypertrophic).

If there is a change from one trophic category to another, this may also entail a change in the abundance and/or biomass of key functional organisms, like the fish species illustrated in Table 3A. Most systems to classify lakes into different trophic categories include, like the system given in Table 3A, Secchi depth, since this is a very useful indicator of the effective depth of the photic zone and a key variable needed to calculate primary production in aquatic systems, and hence also secondary production (HÅKANSON and BOULION, 2002). Table 4.

Empirical regression models to calculate chlorophyll (Chl in µg/l) from total-P (TP in µg/l) for lakes.

Chl(OECD): Chl(OECD): Chl(DR): Chl(N): Chl(N).US: Chl(H1)

Chl = 0.64 · TP1.05 Chl = 0.28 · TP0.96 Chl = 0.073 · TP1.451 Chl = 0.56 · TP0.87 Chl = 0.54 · TP0.80 Chl = 0.36 · TP–0.50

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(summer max; from OECD, 1982) (summer average; from OECD, 1982) (DILLON and RIGLER, 1974) (world-wide lakes; from NÜRNBERG, 1996) (N. Am. lakes; from NÜRNBERG, 1996) (soft water lakes; HÅKANSON et al., 2005)

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Operational Effect Variables y = -0.62x + 1.14; r2 = 0.58; n = 936; p < 0.0001 1.2 1 .8

log(Secchi)

.6 .4 .2 0 -.2 -.4 -.6 -.8 (Secchi in m; TP in µg/l) -1

0

.5

1

1.5

2

2.5

3

log(TP) Figure 5.

The relationship between Secchi depth and TP using data from 936 lakes (data described in HÅKANSON and BOULION, 2002).

Trying to find regularities of functional and structural dependencies reflect the holistic approach to the study of aquatic systems. According to ODUM (1986) this approach, contrary to the reductionistic one, aims to consider the phenomena in their integrity. It means that for studies at the scale of the entire ecosystem there is no need to know all the details of the system. It is, however, necessary to recognise the usefulness of combining the holistic and the reductionistic approaches. For example, the very close and well-known relationship between primary production and chlorophyll is shown only if the ranges of the two variables cover a wide domain of systems (the holistic approach). The character of this connection is almost indistinguishable at the small ranges within which much reductionistic or site-specific research is carried out. The data for a given lake/coast can deviate markedly from a general regression. Hence, it is necessary to find out the reasons of such deviations by studies of the individual phenomena in the given deviating ecosystem (the reductionistic approach). 2.1.2.2. Chlorophyll versus Nutrients Figure 6 gives a scatter plot of empirical data between chlorophyll-a and TP. These data emanate from a very comprehensive data set (more than 500 systems; see Table 5) collected by us from many different sources. The range in chlorophyll-a is from 0.12 to 60 µg/l, in salinity from 0 to 275, in TP from < 1 to 1090 µg/l and in TN (total-N) from 1 to 2110 µg/l. All data represent median values for the surface-water layer from the growing

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Table 5. Compilation of main references (A) and of data (B). Note that all data used in this study represent median surface water values for the growing season. This data base was first compiled by HÅKANSON and EKLUND (in prep.). A. Area

Country

Salinity range

Main references

Chesapeake Bay U.S.A. 4 for the two first steps and F = 1 for the third step. D = water depth of the given site (m); Temp = water temperature (°C); Days, 5 = Number of days with winds less than 5 m/s. y = log (SPM). From HÅKANSON and ECKHELL (2005). Step

r2

Model variable

Model

0.74 0.83 0.87

log (TP) pH log (DR)

y = 1.56 · x1 – 1.69 y = 1.47 · x1 + 0.18 · x2 – 2.85 y = 1.148 · x1 + 0.137 · x2 + 0.286 · x3 – 1.985

A. Lakes 1 2 3

B. Sites in the open Baltic Sea 1 2 3

0.59 0.78 0.80

log (Temp) log (Depth) (Days 5)1.3

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y = 1.17 · x1 – 0.77 y = 0.89 · x1 – 0.636 · x2 – 0.59 y = –0.764 · x1 – 0.524 · x2 – 0.0221 · x3 + 0.569

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cles) can be deep. Studies by ANDERSSON (2000) demonstrate that resuspension correlates with winds higher than 7 m/s in four different archipelago areas in the Baltic Proper, whereas studies by ECKHELL et al. (2000) indicate that wind speeds higher than 14 m/s correlate best with resuspension. BURBAN et al. (1989, 1990) have demonstrated that changes in water turbulence, SPM and salinity are also key regulatory factors for the aggregation and flocculation of suspended particles and hence also for the fall velocity. A typical fall velocity for SPM in lakes is 10–100 m/yr depending on the material and this can increase significantly along a salinity gradient (KRANCK, 1973, 1979; LICK et al., 1992). In the summer, when the water temperature is high, the biological production is also high and this affects the SPMconcentration. 2.1.4. Morphometry How should one define the coastal area so that parameters, like the mean depth or the volume development (Vd = 3 · Dm/Dmax; Dm = mean depth; Dmax = maximum depth; Fig. 12), can be relevant as model variables (x) to predict target bioindicators (y)? The approach in this work (from PILESJÖ et al., 1991) assumes that the borderlines are drawn at the topographi-

Sea Deep water retention time (TDW) Surface water retention time (TSW) Section area (At; j=2 openings)

Filter factor [Ff=Σ(Σ(cos(ai)·x i)·At)j ]

ai

xi

Coast Accumulation area (1-ET)

Exposure (Ex=100·At/A)

Erosion transportation area (ET)

Coastal area (A)

Mean depth (Dm=V/A)

Wave base (WB)

Form factor (Vd=3·Dm/Dmax)

Max. depth (Dmax)

Figure 12. Illustration of key coastal parameters in mass-balance modelling which can be determined quickly from digitized bathymetric maps using GIS-methods (GIS = Geographical Information System). The key issue is to define the boundary lines, i.e., where the coastal area ends and the sea or adjacent coastal area begins. The approach used in this modelling is to define the boundary lines so that the topographical openness (the exposure, Ex, defined by the ratio between the section area, At, and the enclosed coastal area, A) attains a minimum value. This is the “topographical bottleneck” method. The filter factor (a measure similar to the fetch) describes how islands and topographical barriers between the defined coastal area and the sea act as energy filter for the wave impact on the coastal area. In this figure, the filter factor is illustrated for one of the two section areas in this coastal area (ai = the angle between two radials sent out from the opening over open water; xi = the length of each radial; i = the number of radials at each opening; j = the number of openings; j = 2 in this example).

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Figure 13. Illustration of relative hyposographic curves (= depth-area curves) for four coasts with different forms (and form factor = volume development = Vd). The form influences the areas above the Secchi depth (Asec), which indicate the production capacity and “biological value” of the coastal system.

cal bottlenecks so that the exposure (Ex = 100 · At/A, where At = the section area and A = the enclosed coastal area) of the coast from winds and waves from the open sea is minimized. Once the coastal area is defined, one can also determine important variables for mass-balance calculations, such as the coastal volume and important morphometric parameters for internal fluxes, such as the mean depth and the water surface area, and variables regulating the water exchange between the coast and the sea, such as the section area, the exposure and the filter factor (HÅKANSON, 2000). This method of defining coastal areas also opens a possibility to use empirical models to estimate, e.g., the theoretical water retention times of the surface water and the deep water (Table 8), and the bottom dynamic conditions (regulating sedimentation, resuspension and diffusion) from morphometrical parameters (such as area, mean depth, form factor and section area). In large enclosed coastal areas, the theoretical water retention time may be relatively long, whereas in small open coasts the value may be just a few days. In such coastal areas, it is evident that the concentrations of nutrients or pollutants are close to the values in the outside sea. Hence, it is also difficult to maintain concentration gradient from point source emissions to the coasts if the water mass is exchanged many times each month. So, concentrations of pollutants in coastal areas often depend much on the conditions outside the given coastal area. The form of the coast (Vd) is important, for example, for the growth of macrophytes and benthic algae and for resuspension. Figure 13 illustrates relative hypsographic curves for four systems with different Vd-values. Coast 1 has a large percentage of the bottom above

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Biomass (g ww/m2)

3000 2500 2000 1500 1000 500 0 0

2

4

6

8

10

Water depth (m) Figure 14. The relationship between the biomass of the mobile epifauna (>0.5 mm) in g m–2 and the water depth at the sampling sites at Tylösand (on the Swedish west coast; data from MÖLLER et al., 1985).

the Secchi depth, which is the water depth where most macrophytes may be found and where the production and biomass of benthic algae and zoobenthos may be very high. This is also indicated in Figure 14, which shows the relationship between the biomass of the mobile epifauna and water depth from a coastal area on the Swedish west coast (salinity > 20‰). An equation expressing the area above the Secchi depth, Asec, as a function of Vd, the area of the coast (Area) and the maximum depth of the coast (Dmax) is also given in Figure 13 (from HÅKANSON, 1999). Deep, U-formed coasts generally have smaller areas above the Secchi depth. The larger Asec, the higher the biological value, and the more important it is to protect and preserve such areas. So, these are areas where one should not build marinas, harbours or discharge contaminants. 2.1.5. Water Exchange The hydrodynamical conditions are generally more dynamic in marine coastal areas than in lakes. A typical surface water retention time is 2–6 days for a Baltic coastal area and about 1 year for a lake (HÅKANSON, 2000). This has, as already stressed, implications for the conditions in the coastal areas, which are greatly influenced by the conditions in the outside sea (MUIR WOOD, 1969; Stanley and SWIFT, 1976; DYER, 1972; MCCAVE, 1981; SEIBOLD and BERGER, 1982; POSTMA, 1982). Emissions of pollutants cannot be calculated into concentrations without knowledge of the water retention time of a given coastal area. If concentrations cannot be predicted, it is also practically impossible to predict the related ecological effects. Thus, it is important to highlight some basic concepts concerning the turnover of water in coastal areas. The water exchange can be driven by many processes, which vary in time and space. The importance of the various processes will vary with the topographical characteristics of the coast, which do not vary in time, but vary widely between different

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coasts. The water exchange sets the framework for the entire biotic spectrum; the prerequisites for life are quite different in coastal waters where the characteristic retention time varies from hours to weeks. Factors influencing the water exchange are listed below (HÅKANSON, 2000). • The fresh water discharge (Q; in m3/sec) is the amount of water entering the coast from tributaries per time unit. In estuaries, Q may be the most important factor for the water retention time. • Tides. When the tidal variation is > 50 cm, it is often a dominating factor for the surface water retention time. • Water level fluctuations always cause a flux of water. These variations may be measured with simple gauges. They vary with the season of the year and are important for the water retention time of shallow coastal areas. Thus, the mean depth is a useful coastal parameter. • Boundary level fluctuations. Fluctuations in the thermocline and the halocline boundary layers may be very important for surface and deep water retention times, especially in deep and open coasts. • Local winds may create a water exchange in all coastal areas, especially in small and shallow coasts. • Thermal effects. Heating and cooling, e.g., during warm summer days and nights, may give rise to water level fluctuations which may cause a water exchange. This is especially true in shallow coasts since water level variations in such areas are more linked to temperature alterations in the air than is the case in open water areas. • Coastal currents are large, often geographically concentrated, shore-parallel movements in the sea close to the coast. They may have an impact on the water retention time, especially in coasts with a great exposure. In theory, it may be possible to distinguish driving and mixing processes. In practice, this is often impossible. Surface water mixing causes a change in boundary conditions, which causes water exchange, etc. The theoretical surface water retention time (T) for a coastal area is the time it would take to fill a coast of volume V if the water input from rivers is given by Q and the net water input from the sea by R, i.e.: T = V/(Q + R). This definition does not account for the fact that actual water exchange normally varies temporally and spatially, e.g., above and beneath the thermocline. It is costly and demanding to empirically determine the theoretical surface or deep-water retention time (TSW and TDW, respectively) in a given coastal area using field methods (HÅKANSON, 2000). TSW may be indefinitely long on a calm summer day and very short (a few hours) in connection with a storm or a sudden change in air pressure. TSW or TDW generally emanate from frequency distributions. As a rule of thumb, one can say that the cost of establishing TSW-value from traditional field measurements (using dye, current meters, etc.) is about 20,000 USD for one coastal area. It is not very meaningful to build management models if it is a prerequisite that such field work first must be carried out to determine TSW as a driving variable before the model can be used. This means that it is of major importance that TSW can be predicted easily from one coastal morphometric variable, the exposure. It has been shown (PERSSON et al., 1994; HÅKANSON and KARLSSON, 2004) that TSW can be predicted very well (r2 = 0.95) with the regression in Table 8, which is based on the exposure (Ex), which, in turn is a function of section area (At) and coastal area (Area) (Fig. 12). The range of this model for TSW is given by the minimum and maximum values for Ex in Table 8. The model should not be used without complementary algorithms if the tidal range is >20 cm/day or for estuaries, where the fresh water discharge must also be accounted for. For open coasts, i.e., when Ex > 1.3, TSW may be calculated not by this equation but from a model based on coastal currents (HÅKANSON, 2000). It is also very expensive to determine the theoretical deep-water retention time, TDW, using, e.g., the dye method (PERSSON and HÅKANSON, 1996). With the approach given in

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Empirical regressions to predict the theoretical surface and deep-water retention times (TSW and TDW in days) from morphometric parameters.

Regression Ex ) + 3.49) ln (TSW) = (–4.33 ·
 TDW = (–251 – 138 · log (At) + 269 · log (Vd))

r2

n

Reference

0.95 0.79

14 15

PERSSON et al. (1994) HÅKANSON and KARLSSON (2004)

Model domain: 0.002 < Ex < 1.3; 0.0006 < At < 0.08; 0.5 < Vd < 1.5; note that TSW and TDW are never permitted to be 120 days. Ex = 100 · At/A; Vd = 3 · Dm/Dmax; At = section area (km2); Vd = the form factor (= 3 · Dm/Dmax; Dm = mean coastal depth (m), Dmax = max. coastal depth (m).

Table 8, TDW also can be predicted from simple morphometric parameters (r2 = 0.79). This empirical model is based on two standard morphometric parameters, the section area (At; the larger the section area, the more open the coast for waves and winds from the sea and the shorter the theoretical deep-water retention time) and the form factor (Vd; the larger the deep-water volume and the longer the theoretical deep-water retention time). The domain of the TDW-model is given by the minimum and maximum values for the two model variables in Table 8. This TDW-formula is only applicable to stratified coastal areas, which are not influenced by tides since it is based on data from Baltic coastal areas. For open and tidal coasts, there are also simple models available to predict the theoretical water retention time (HÅKANSON, 2000). 2.2. Target Operational Effect Variables 2.2.1. Secchi Depth Many factors are known to influence the Secchi depth (VOLLENWEIDER, 1958, 1960; CARL1977, 1980; PREISENDORFER, 1986). Like SPM, Secchi depth depends on, (1) autochthonous production (the amount of plankton, detritus, etc. in the water; more plankton, etc. mean a lower Secchi depth); (2) allochthonous materials (from tributaries); and (3) the amount of resuspended material (HÅKANSON, 2006). These factors are not independent: High sedimentation leads to high amounts of resuspendable materials; high resuspension leads to high internal loading of nutrients and increased production; a high amount of coloured substances in estuaries means a smaller photic zone and a lower production; a high input of allochthonous substances and a high production would mean a high sedimentation, etc. The empirical relationship between Secchi depth and chlorophyll-a largely depends on the chlorophyll-a concentration co-varying with the total amount of suspended particles (KIEFER and AUSTIN, 1974; TILZER, 1988). Figure 4 shows the very strong and logical relationship between Secchi depth, the SPM-concentration and salinity. Several studies have quantified and ranked variables of significance to predict how Secchi depths vary among water systems (WALLIN et al., 1992; NÜRNBERG and SHAW, 1998). Table 9 gives three empirical models for the Secchi depth using data from 23 Baltic coastal areas. One can note the significant logical and negative relationships between Secchi depth and total-N (TN), chlorophyll and sedimentation (the higher the bioproduction, the more SPM, the higher the sedimentation and the lower the Secchi depth). There is also a significant positive relationship between salinity and Secchi depth – the higher the salinity, the clearer the water. The filter factor is an expression for how an archipelago between the given coastal area and the sea may act as SON,

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an energy filter for the waves from the sea – the denser the topographical filter effect, the lower the Secchi depth. The first ladder in Table 9 gives Secchi depth in three steps: • First from mean TN (r2 = 0.83); the higher TN, the higher the phytoplankton biomass, the more turbid the water and the lower the Secchi depth. • Then from the form factor (Vd; r2 = 0.89); Vd relates to resuspension; if the coastal area is very shallow, i.e., if Vd attains a small value, the coastal area will be dominated by resuspension areas with relatively coarse sediments, little fine sediments and little resuspension of SPM. So, the Secchi depth will be high. The main reason for this seems to be that for open coastal areas the fine suspended particles will be transported out of the area and not be trapped in the same manner as in lakes. • The last step gives the section area (At in km2) as a model variable; then r2 reached 0.91. If At is large, the water exchange between the coastal area and the sea will be very important and one should expect that the Secchi depth in the coastal area would be close to the Secchi depth in the sea. The next ladder is based not on TN but on chlorophyll. The steps are: • Chlorophyll enters at step one (r2 = 0.83); the higher the phytoplankton biomass, the lower the Secchi depth. • The filter factor (Fig. 12) appears at the second step (r2 = 0.88). If Ff is large, the coast is open to wind/wave influences from the sea. Then the Secchi depth in the coastal area would be similar to the Secchi depth in the sea. • At the third and last step, one finds the mean depth (Dm; r2 = 0.91). The larger the mean depth, the more SPM will be entrapped and retained in the coastal area and the smaller the Secchi depth. In the third ladder, the filter factor has been omitted because it may not be easily accessed. Then the steps are: • Chlorophyll enters at the first step (r2 = 0.83); as before. • The form factor, as before (r2 = 0.88). These regressions for Secchi depth demonstrate that there are some morphometric parameters (Vd, At and Dm) that are important in understanding and predicting how variables like Table 9. Results of the stepwise multiple regression for Secchi depth (Sec; mean value for the growing season) using the coastal database. n = 23 Baltic coastal areas (salinities between 5 and 7; F > 4). TN = total-N concentration (µg/l), Vd = the form facto (= 3 · Dm/Dmax); At = the section area (m2); Chl = chlorophyll-a concentration (µg/l), Ff = the filter factor (m3). Data from WALLIN et al. (1992). Step

r2

Model variable

Model

A. Actual data on y = Sec and model variables 1 2 3

TN Vd At

y = –0.033 · x1 + 14.5 y = –0.033 · x1 – 1.65 · x2 + 16.3 y = –0.033 · x1 – 1.51 · x2 + 18.3 · x3 + 15.9

log (Chl) Ff log (Dm)

y = –1.03 · x1 + 0.88 y = –0.96 · x1 + 0.0078 · x2 + 0.80 y = –0.090 · x1 – 0.0095 · x2 – 0.30 · x3 + 1.03

0.83 0.89 0.91

B. Transformed data; y = log (Sec) 1 2 3

0.83 0.88 0.91

C. Transformed data; y = log (Sec); Ff = the filter factor omitted 1 2

0.83 0.88

log (Chl) log (Vd)

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y = –1.03 · x1 + 0.88 y = –1.036 · x1 – 0.445 · x2 + 0.887

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Secchi depth, SPM, sedimentation and oxygen saturation/concentration vary among coastal areas. Numerous factors can potentially influence the Secchi depth. It is easy to speculate and qualitatively discuss such relationships. With statistical analyses based on empirical data, it is possible to quantitatively rank such influences and derive predictive models based on just a few, but the most important factors influencing variability in mean Secchi depth among coastal areas. A “natural” or reference Secchi depth can also be estimated from the models given in Table 9. Such a prediction would account for differences in coastal morphometry and TN or chlorophyll. If the actual mean long-term Secchi depth differs from such a predicted reference value, such divergences may be discussed in a quantitative manner. Note that these empirical models only apply for coastal areas of the same type and within the ranges of the variables used in the statistical analyses. In other words, empirical models like these are not generally valid. But within their range of application, empirical models often give better predictions than dynamic models (AHLGREN et al., 1988; PETERS, 1991). Furthermore, it is likely that the same basic principles concerning factors influencing water clarity and Secchi depth apply also for other coastal areas, although the parameter constants, and also the model parameters, may have to be altered to predict Secchi depth for coasts of other types. 2.2.2. Oxygen in the Deep-Water Zone The rationale for using this chemical measure is related to the linkages between the conditions for the bottom fauna, the oxygen concentrations in the sediment/water zone and the load of organic matter to the sediments (PEARSON and ROSENBERG, 1976). When the O2-saturation is lower than about 20% (≈2 mg O2/l), key functional benthic groups are extinct (CORNETT and RIGLER, 1979). This is a threshold value. An empirical model for O2Sat is given in Table 10. One can note: • The more oxygen-consuming matter (i.e., the higher the sedimentation of organic materials) in the deep-water zone, the lower O2Sat, r2 = 0.43 at step 1. • The prevailing bottom dynamic conditions in the coastal area (ET, i.e., the erosion and transport areas for fine sediments; see HÅKANSON and JANSSON, 1983). If variations among

Table 10. Results of the stepwise multiple regression for the oxygen saturation in the deepwater zone (mean O2Sat in the deep-water zone during the growing season in %) using the coastal database from WALLIN et al. (1992) for 23 Baltic (brackish) coastal areas; F > 4. y = log (101 – O2Sat). SedDW = sedimentation in sediment traps placed in the deepest part of the coastal area (g dw/m2 · d); ET = the fraction of ET-areas; TDW = the theoretical deepwater retention time (days); Dm = the mean depth (m). From HÅKANSON (2006). Step 1 2 3 4

r2

Model variable

Model

0.43 0.64 0.74 0.80

log (SedDW)
 ET log (1+TDW)
D m

y = 0.925 · x1 + 0.132 y = 0.974 · x1 – 0.185 · x2 + 1.72 y = 0.866 · x1 – 0.151 · x2 + 0.244 · x3 + 1.39 y = 0.643 · x1 – 0.118 · x2 + 0.301 · x3 + 0.323 · x4 + 0.470

Range of model variables: Dm (m) Min. Max.

3.8 13.8

ET (–)

TDW (days)

SedDW (g/m2 · d)

0.19 0.99

1 128

5.3 82.5

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coastal areas in ET are accounted for, r2 increases from 0.43 to 0.64. If ET is high (say 0.95), the oxygenation in the coastal area is also likely high and O2Sat high, and vice versa. • The theoretical deep-water retention time (TDW); the longer TDW, the lower O2Sat. This is logical and mechanistically understandable. If variations among coastal areas in TDW are accounted for, r2 increases to 0.74. • The mean depth (Dm); the mechanistic reason for this is not so easy to disclose since Dm influences different factors, e.g., (1) resuspension, (2) the volume and hence all SPM-concentrations, (3) stratification and mixing, and (4) the depth of the photic zone and, hence, primary production. If variations among coastal areas in Dm are accounted for, the r2-value increases to 0.80. This O2Sat-model should not be used for coastal areas with characteristics outside the limits given in Table 10 for the model variables, and it should not be used for coastal areas dominated by tides. If the model is used for other coastal areas, the calculation must be regarded with due reservation, as a hypothesis rather than a prediction. When the zoobenthos die at low oxygen concentrations, the biological mixing (= bioturbation) of the sediments will halt (HÅKANSON and JANSSON, 1983). This cessation has profound consequences for the lamination of the sediments and hence also for all interpretations about the age of sediments and the age distribution. 2.2.3. Chlorophyll-a Concentrations Calculating primary phytoplankton production from chlorophyll is a focal issue in aquatic sciences. Generally, chlorophyll-a concentrations are predicted from water temperature, light conditions and nutrients (DILLON and RIGLER, 1974; SMITH, 1979; RILEY and PREPAS, 1985; EVANS et al., 1996; HÅKANSON and BOULION, 2002). Empirical models to predict chlorophyll-a (Chl in µg/l) are exemplified in Table 4 for lakes. Figures 6 and 10 give the relationships between chlorophyll, TP and salinity, which are used to define the class limits for TP in Table 3; Figure 8 gives the regression between chlorophyll and TN using the comprehensive data base. 2.2.4. Macrophyte Cover and the Biological Value of the System Macrophytes can contribute significantly to the total primary production, especially in shallow and enclosed areas (WETZEL, 1983). The macrophytes intercept nutrients, keep them bound for long periods and they also entrap and retain fine sediments settling out from the water phase. Macrophytes provide an important protective environment for small fish. Most benthic algae, macrophytes and zoobenthos appear at water depth smaller than the Secchi depth, since this area is generally well oxygenated and has the highest bioproduction potential. HÅKANSON and BOULION (2002) have used empirical data from many lakes and statistical modelling to rank the factors influencing the variability among the lakes in macrophyte cover (MAcov). No such data are available to us from coastal areas. The macrophyte database includes data on MAcov from lakes covering a wide domain. The macrophyte cover varies from 0.29% to 100%. The light conditions are important for the macrophytes, and the database includes information on Secchi depth, which varies from 0.4 m to 6.7 m. Results of stepwise multiple regressions are given in Table 11. This table gives a ranking of the factors influencing the MAcov-variability among the studied lakes. One can note: • Sec/Dm is the most important factor to statistically explain the variation among these lakes in MAcov; r2 = 0.52 for n = 19 lakes. This is probably also true for coastal areas. © 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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Table 11. Stepwise multiple regression analyses using data from HÅKANSON and BOULION (2002) to calculate (A) the macrophyte cover (Maccov in % of lake area) and (B) the macrophyte production (PRMA in g ww/m2 · yr). Sec = Secchi depth (m), Dm = mean depth (m), Lat = latitude (°N), Dmax = maximum depth (m) and A1a = lake area above 1 m water depth (m2). Step

r2

Model variable

Model

A. y-variable =
 Ma c; cov n = 19 lakes; F > 4 1 2 3 4

0.52 0.67 0.74 0.84

Sec/Dm 90/(90 – Lat)
 Dmax  log (A1)

y = 1.944 + 4.825 · x1 y = 6.757 + 3.83 · x1 – 1.57 · x2 y = 8.31 + 2.57 · x1 – 1.50 · x2 – 0.286 · x3 y = 10.49 + 1.502 · x1 – 1.993 · x2 – 0.422 · x3 + 0.490 · x4

B. y-variable = log (PRMA); n = 35 lakes; F > 4 1 2

0.73 0.80

log (Maccov) 90/(90 – Lat)

y = 0.678 + 1.328 · x1 y = 2.472 + 1.028 · x1 – 0.516 · x2

• The next important factor for MAcov is latitude, which is evidently related to water temperature. If latitude is added, 67% of the variation in MAcov among the lakes can be statistically accounted for. • The third factor is maximum depth; the deeper the system the smaller MAcov, which is logical. • The fourth factor is the area of the lake above 1 m, A1; r2 = 0.84. Several marine studies have also recognised that the water clarity is a crucial factor for the survival and success of macrophytes (ZIMMERMAN et al., 1995; LONGSTAFF and DENNISON, 1999; SAGERT et al., 2005). DUARTE (1991) presented models relating Secchi depth to the lowest colonisation depth for several marine seagrass species.

3. Summary and Conclusions In this work, the focus has been set on entire defined coastal areas (the ecosystem scale) and on four important operational effect variables (bioindicators) for coastal management, Secchi depth, chlorophyll-a, the oxygen saturation in the deep-water zone and the macrophyte cover. “Operational” means that these variables can be measured easily and be understood and used by coastal managers and predicted by simple models that can be run by driving variables accessed from maps and monitoring programs. This work has discussed empirical data and empirical models for these bioindicators and also a set of abiotic factors, which may be used to predict these bioindicators, mainly nutrient concentration, salinity, SPM-concentration, morphometric parameters (area, volume, mean depth, section area and form factor) and water exchange (the theoretical surface-water and deep-water retention times). We have reviewed statistical models for fresh water, brackish and marine systems, and some of these have been used to develop the new general trophic level system in Table 3. One should note that the class limits given in this table in the future may be somewhat altered if and when better empirical data becomes available, especially on Secchi depth in marine systems. One main result of this review is that the variations of many of these variables are very large; Table 6 gives a compilation of coefficients of variation (CV). These high CVs indicate that many samples need to be taken to obtain reliable mean or median values, e.g., for regressions. This work deals with entire coastal areas defined according to

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the topographical bottleneck approach and values from the growing season for the surfacewater layer.

4. Acknowledgements This work has been carried out within the framework of the Thresholds-project and integrated EU project coordinated by Prof. CARLOS M. DUARTE, CSIC-Univ. ILLES BALEARS, Spain, and the author would like to acknowledge the financial support from EU and the constructive cooperation within the project.

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