Welcome to Estuary Trophic Index (ETI) Tools

Although nutrient enrichment threatens many New Zealand estuaries, guidance on how to assess the extent of eutrophication (including indices and indicators that are useful for management) is limited. As a result, it has been difficult to:

  • Determine the current state of estuaries with regard to eutrophication;
  • Assess the effects of the recent landuse intensification and change on estuaries;
  • Gauge the consequences for estuaries of nutrient limits for freshwater (e.g., the National Policy Statement for Freshwater Management, NPSFM, 2014); and
  • Set nutrient load limits to achieve estuarine objectives.

In response, regional council coastal scientists sought advice via the coastal Special Interest Group (cSIG), with funding through Envirolink Tools Grant (Contract No. C01X1420), on the development of a nationally consistent approach to the assessment of estuary eutrophication, including nutrient load thresholds. The purpose of this project, called the NZ Estuary Trophic Index (ETI), is to assist regional councils in determining the susceptibility of an estuary to eutrophication, assess its current trophic state, and assess how changes to nutrient load limits may alter its current state. The project does this by providing tools for determining estuary eco-morphological type, where an estuary sits along the ecological gradient from minimal to high eutrophication, and providing stressor-response tools (e.g., empirical relationships, nutrient models) that link the ecological expressions of eutrophication (measured using appropriate trophic state indicators) with nutrient loads (e.g., macroalgal biomass/nutrient load relationships).

In terms of the regional council planning framework, the ETI provides vital supporting guidance for underpinning the ecological health component of regional plans by identifying relevant estuary attributes and outcomes for inclusion in plans, defining methods and indicators to measure ecosystem health attributes, and providing guidelines to assess whether or not the outcomes are being met.

The ETI provides three tools:

Details of when to use each tool and the knowledge underpinning the tools can be found on the Welcome and Background Information tabs, respectively, for each tool.


Concept diagram of the ETI

Concept diagram of the ETI, showing relationships between ETI Tools 1, 2 and 3. Tool 1 provides information on estuary susceptibility to eutrophication based on estuary type, its physical attributes and nutrient loading. Tool 2 provides scores for estuary trophic health based on measured trophic indicator values. Tool 3 provides trophic scores under under scenarios of changed land use or load limits, and/or when values of trophic state indicators are lacking.

Disclaimer

Whilst NIWA has used all reasonable endeavours to ensure that the information contained in this website is accurate, NIWA does not give any express or implied warranty as to the accuracy of the information contained herein. This website has been reviewed internally by NIWA and meets NIWA standards for website delivery.

Links to the ETI Tools


Please email us at eti-tools@niwa.co.nz for more information about the ETI Tools or to provide feedback. We welcome comments about how to make this webtool more useful.

Suggested citation

Zeldis, J., Storey, R., Plew, D., Whitehead, A., Madarasz-Smith, A., Oliver, M., Stevens, L., Robertson, B., Dudley, B. (2017). The New Zealand Estuary Trophic Index (ETI) Tools: Tool 3 - Assessing Estuary Trophic State using a Bayesian Belief Network. Ministry of Business, Innovation and Employment Envirolink Tools Contract: C01X1420. https://shiny.niwa.co.nz/Estuaries-Screening-Tool-3/

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Glossary



Bayesian Belief Network (BBN): A way of determining the probability of outcome X from decision Y, given all the knowledge and beliefs about the system.

Conditional probability distribution: The probability of state B occurring when state A is known to be a particular value. Estimated from known relationships or expert opinion.

Decision node: Nodes in a BBN that are set a priori based on management decisions. Decision nodes may be tuned, for example, with changed land-use or limit settings.

Estuary type: the ETI toolset has adopted a simple four-category typology specifically suited to the assessment of estuarine eutrophication susceptibility in New Zealand:

  1. Shallow intertidal dominated estuaries (SIDEs)
  2. Shallow, short residence time tidal river and tidal river with adjoining lagoon estuaries (SSRTREs)
  3. Deeper subtidal dominated, longer residence time estuaries (DSDEs)
  4. Coastal lakes

Intermittently closed/open estuary states (ICOEs) are subtypes of SIDEs and SSRTREs that describe estuary closure state.

Intermediate node: Nodes in a BBN which show outcomes arising from decision and situation node states. Intermediate nodes may also be set based on prior knowledge of their states.

Performance node: Nodes in a BBN which show outcomes arising from intermediate node states.

Primary indicators: Indicators that are considered to exhibit direct symptoms of eutrophication. These include macroalgae and phytoplankton biomass, and cyanobacteria.

Secondary indicators: Also known as supporting indicators, these have variable or indirect relationships with eutrophication but are useful in its measurement.

Situation node: Node that sets the known state of a system. In the context of Tool 3, this is the closure state of an estuary and the percentage of surface area that is intertidal.


Welcome to ETI Tool 3

This tool uses knowledge of the ecological connections between drivers of estuary trophic condition (e.g., estuary type, nutrient loads, estuary closure state) and responses of indicators (e.g., macroalgal and macrobenthic health indices) to calculate an ETI score using a Bayesian Belief Network (BBN).

When to use ETI Tool 3

The ETI scoring is identical to that of Tool 2, but the Tool 3 BBN can operate when no or few values are known for primary and secondary indicators (see Glossary for definitions of words in bold). It therefore is most useful when either:

  1. you have little or no knowledge of the state of indicators for an estuary;
  2. you wish to test the response of estuary trophic condition to changes in loads resulting from altered land use or point sources;
  3. you wish to test the response of estuary trophic condition to load limit-setting scenarios in upstream catchments.

ETI Tool 3 contains a BBN suitable for use with shallow intertidal dominated estuaries (SIDEs) and shallow, short residence time tidal river and tidal river with adjoining lagoon estuaries (SSRTREs). ETI Tool 3 is applicable to any SIDE or SSRTRE estuary for which data are available for its decision nodes. If supplementary data on observed values of primary or secondary indicators are available, they can be used to inform, and potentially improve, the predictions of the BBN. However, these are not necessary to run the BBN tool. As well as for permanently open estuaries, Tool 3 can be applied to intermittently closed / open estuaries (ICOEs).


Concept diagram of the BBN for SIDES

Figure 1. A conceptual map of a BBN for shallow intertidal-dominated estuaries (SIDEs) and shallow, short residence time river estuaries (SSRTREs). Boxes (nodes) are connected by arrows that show the directional relationships between nodes. Potential [TN] = Potential Total Nitrogen; RPD = Redox Potential Depth; Sed. TOC = Sediment Total Organic Carbon.

Links to the ETI Tools


Please email us at eti-tools@niwa.co.nz for more information about the ETI Tools or to provide feedback. We welcome comments about how to make this webtool more useful.

Suggested citation

Zeldis, J., Storey, R., Plew, D., Whitehead, A., Madarasz-Smith, A., Oliver, M., Stevens, L., Robertson, B., Dudley, B. (2017). The New Zealand Estuary Trophic Index (ETI) Tools: Tool 3 - Assessing Estuary Trophic State using a Bayesian Belief Network. Ministry of Business, Innovation and Employment Envirolink Tools Contract: C01X1420. https://shiny.niwa.co.nz/Estuaries-Screening-Tool-3/

Download citation

Glossary



Bayesian Belief Network (BBN): A way of determining the probability of outcome X from decision Y, given all the knowledge and beliefs about the system.

Conditional probability distribution: The probability of state B occurring when state A is known to be a particular value. Estimated from known relationships or expert opinion.

Decision node: Nodes in a BBN that are set a priori based on management decisions. Decision nodes may be tuned, for example, with changed land-use or limit settings.

Estuary type: the ETI toolset has adopted a simple four-category typology specifically suited to the assessment of estuarine eutrophication susceptibility in New Zealand:

  1. Shallow intertidal dominated estuaries (SIDEs)
  2. Shallow, short residence time tidal river and tidal river with adjoining lagoon estuaries (SSRTREs)
  3. Deeper subtidal dominated, longer residence time estuaries (DSDEs)
  4. Coastal lakes

Intermittently closed/open estuary states (ICOEs) are subtypes of SIDEs and SSRTREs that describe estuary closure state.

Intermediate node: Nodes in a BBN which show outcomes arising from decision and situation node states. Intermediate nodes may also be set based on prior knowledge of their states.

Performance node: Nodes in a BBN which show outcomes arising from intermediate node states.

Primary indicators: Indicators that are considered to exhibit direct symptoms of eutrophication. These include macroalgae and phytoplankton biomass, and cyanobacteria.

Secondary indicators: Also known as supporting indicators, these have variable or indirect relationships with eutrophication but are useful in its measurement.

Situation node: Node that sets the known state of a system. In the context of Tool 3, this is the closure state of an estuary and the percentage of surface area that is intertidal.


How to use Tool 3

The steps needed to run Tool 3 are described below. However, we also recommend that you check out our brief introduction to the BBN approach for more information about BBNs. In addition, there are some tutorials available on the Netica website to help you understand how to run the Netica software.

Downloading and running the BBN

  1. Download and install the freely available Netica software. Note that this does not require you to have administration rights on your computer and you will be able to run the ETI BBN model using the free Limited Mode version of Netica.
  2. Download the BBN data file and open in Netica.
  3. Set the situation (grey boxes) and decision nodes (blue boxes) to mimic a known or potential state for a SIDE or SSRTRE estuary to estimate its ETI score at the performance node (yellow box). Nodes can be set by clicking on the value for the desired state (Figure 1). Please download the Tool 3 metadata for information about each of the nodes and their associated units. More information about setting the situation nodes is described in the section below.
  4. In some cases, information may be known about the intermediate nodes (red boxes) and these can also be set to the known state. For some estuaries, this information may be available from data that is available for download in Tool 2. Note that manually setting the state within the intermediate nodes can occasionally result in an error message where, based on the parameterisation of the BBN, the value of the intermediate state is not possible given the selected values of the situation and decision nodes. If this occurs, please select different values for the decision or intermediate nodes.
  5. To save an image of the BBN, go to File > Export Graphics. Results from a given BBN setup can also be saved to Excel using instructions provided on the Netica website.
  6. To reset the BBN to its original state, right-click on the whitespace and select Remove findings.


BBN for SIDES

Figure 1. View of the Netica file for the Bayesian Belief Network created for shallow intertidal-dominated estuaries (SIDEs) and shallow, short residence time rivers estuaries (SSRTREs). Known or potential values for decision (blue), situation (grey) and intermediate (red) nodes can be set by selecting the desired value range in the appropriate box, with the likely ETI score displayed in the performance node (yellow box). Bars represent the conditional probability distributions for the range of values within each node.

Setting the situation nodes

Percent intertidal

This situation node alters whether the macroalgal or phytoplankton intermediate node is used to derive the ETI score. For estuaries with intertidal areas less than 5%, the BBN selects the phytoplankton primary indicator as the driver of the ETI primary node. If the intertidal area is greater than 40%, the macroalgae node is used as the driver of the ETI primary node. If the intertidal area is between 5% and 40%, the BBN considers both macroalgal and phytoplankton indicators, and the ETI primary node is scored using the worst of the macroalgae and phytoplankton indicators. Intertidal areas for most New Zealand estuaries are provided in Tool 1. Please refer to the Background information tab for more information.

Closure duration

Set the closure duration to 'always open' unless you are modelling an ICOE (see below for more information).

Modelling ICOES

ICOES can also be modelled using the BBN as follows:

  1. The ‘Closure duration’ situation node (grey box in Figure 1) should be set to ‘short close’ (i.e., a few days) or ‘long close’ (i.e., weeks to months). This setting affects only the state of the Mud intermediate node.
  2. Information also needs to be included using the Potential [N] and Flushing decision nodes. Data for setting these decision nodes for ICOEs iin their closed state can be gathered from the input and output data provided in Tool 1. as shown in Table 1. Please refer to the Background information tab for more information.

Table 1. The appropriate output data from Tool 1. to use for setting the decision nodes (blue boxes) for ICOEs in the BBN, depending on the ICOE closure duration. Note that the user must enter the ICOE closure duration (Tl) into Tool 1. to get an estimate for TN_ICOE.

Links to the ETI Tools


Please email us at eti-tools@niwa.co.nz for more information about the ETI Tools or to provide feedback. We welcome comments about how to make this webtool more useful.

Suggested citation

Zeldis, J., Storey, R., Plew, D., Whitehead, A., Madarasz-Smith, A., Oliver, M., Stevens, L., Robertson, B., Dudley, B. (2017). The New Zealand Estuary Trophic Index (ETI) Tools: Tool 3 - Assessing Estuary Trophic State using a Bayesian Belief Network. Ministry of Business, Innovation and Employment Envirolink Tools Contract: C01X1420. https://shiny.niwa.co.nz/Estuaries-Screening-Tool-3/

Download citation

Glossary



Bayesian Belief Network (BBN): A way of determining the probability of outcome X from decision Y, given all the knowledge and beliefs about the system.

Conditional probability distribution: The probability of state B occurring when state A is known to be a particular value. Estimated from known relationships or expert opinion.

Decision node: Nodes in a BBN that are set a priori based on management decisions. Decision nodes may be tuned, for example, with changed land-use or limit settings.

Estuary type: the ETI toolset has adopted a simple four-category typology specifically suited to the assessment of estuarine eutrophication susceptibility in New Zealand:

  1. Shallow intertidal dominated estuaries (SIDEs)
  2. Shallow, short residence time tidal river and tidal river with adjoining lagoon estuaries (SSRTREs)
  3. Deeper subtidal dominated, longer residence time estuaries (DSDEs)
  4. Coastal lakes

Intermittently closed/open estuary states (ICOEs) are subtypes of SIDEs and SSRTREs that describe estuary closure state.

Intermediate node: Nodes in a BBN which show outcomes arising from decision and situation node states. Intermediate nodes may also be set based on prior knowledge of their states.

Performance node: Nodes in a BBN which show outcomes arising from intermediate node states.

Primary indicators: Indicators that are considered to exhibit direct symptoms of eutrophication. These include macroalgae and phytoplankton biomass, and cyanobacteria.

Secondary indicators: Also known as supporting indicators, these have variable or indirect relationships with eutrophication but are useful in its measurement.

Situation node: Node that sets the known state of a system. In the context of Tool 3, this is the closure state of an estuary and the percentage of surface area that is intertidal.


Background to ETI Tool 3

The information underpinning the connections between the nodes in the BBN has been accumulated from local knowledge based on New Zealand estuarine science, or from studies made overseas (Table 1). This knowledge is used to determine a) relationships of drivers (e.g., nutrient load) to responses (e.g., macroalgal biomass) and b) the probabilities that various states of the drivers will cause the responses to occupy particular states (bands). These probabilities are set within conditional probability distributions, an example of which is shown in Figure 1.

ICOE subtypes of SIDEs and SSRTREs are determined by mouth closure state and duration and are differentiated using the closure duration situation node. This determines which CLUES-Estuaries dilution model is employed for calculating potential nitrogen concentration and flushing decision node values. Decision nodes may be tuned, for example, with changed land-use or limit settings. Nodes which respond to the situation and decision nodes are intermediate nodes, and their primary and secondary scores are combined according to the ETI Tool 2 methodology to calculate the ETI score (performance node).

Banding is provided for both macroalgae and phytoplankton eutrophication potentials in the BBN. However, the main effects of phytoplankton eutrophication are oxygen depletion and high light attenuation in deeper and often stratified estuarine systems, which typically do not occur in New Zealand SIDEs when they are permanently open. Phytoplankton effects are more likely in SSRTREs, particularly those with longer flushing times. Using the Tool 1 database, we have found that the great majority of estuaries with intertidal areas less than 20% are SSRTREs, while the great majority of SIDEs have intertidal areas greater than 40%. To prevent the phytoplankton primary indicator having effect when operating the BBN for estuaries with intertidal areas greater than 40% (i.e., for SIDEs), the BBN selects the macroalgal primary indicator as the driver of the ETI primary node. For estuaries with intertidal areas less than 5% the BBN selects the phytoplankton primary indicator as the driver of the ETI primary node. If the intertidal area is between 5% and 40%, the BBN considers both macroalgal and phytoplankton indicators, and the ETI primary node is scored using the worst of the macroalgae and phytoplankton indicators.

Although the percent intertidal setting affects whether the ETI primary node is driven by macroalgae or phytoplankton, it does not affect how the nutrient and flushing decision nodes affect the macroalgae and phytoplankton nodes. Therefore, if the estuary is a SIDE, but is known to have areas that have deep holes with high nutrients and low flushing, the user may wish to consider the results of phytoplankton primary indicator in decision-making. Conversely, if the estuary is an SSRTRE, but is known to have small but important intertidal areas, the user may wish to consider results of the macroalgae node.


Example conditional probability distribution

Figure 1. An example conditional probability distribution (CPD). This example shows how potential total nitrogen concentration affects macroalgal ecological quality rating (EQR; Robertson et al. 2016b). The body of the table shows the probabilities that various total nitrogen states will cause particular responses in macroalgae. These responses can then be given bands, represented here as letters. The knowledge used to set the CPD’s for the BBN is described in Table 1.


Table 1. Background knowledge for the Cumulative Probability Distributions (CPDs) underpinning the Bayesian Belief network (BBN).

Links to the ETI Tools


Please email us at eti-tools@niwa.co.nz for more information about the ETI Tools or to provide feedback. We welcome comments about how to make this webtool more useful.

Suggested citation

Zeldis, J., Storey, R., Plew, D., Whitehead, A., Madarasz-Smith, A., Oliver, M., Stevens, L., Robertson, B., Dudley, B. (2017). The New Zealand Estuary Trophic Index (ETI) Tools: Tool 3 - Assessing Estuary Trophic State using a Bayesian Belief Network. Ministry of Business, Innovation and Employment Envirolink Tools Contract: C01X1420. https://shiny.niwa.co.nz/Estuaries-Screening-Tool-3/

Download citation

Glossary



Bayesian Belief Network (BBN): A way of determining the probability of outcome X from decision Y, given all the knowledge and beliefs about the system.

Conditional probability distribution: The probability of state B occurring when state A is known to be a particular value. Estimated from known relationships or expert opinion.

Decision node: Nodes in a BBN that are set a priori based on management decisions. Decision nodes may be tuned, for example, with changed land-use or limit settings.

Estuary type: the ETI toolset has adopted a simple four-category typology specifically suited to the assessment of estuarine eutrophication susceptibility in New Zealand:

  1. Shallow intertidal dominated estuaries (SIDEs)
  2. Shallow, short residence time tidal river and tidal river with adjoining lagoon estuaries (SSRTREs)
  3. Deeper subtidal dominated, longer residence time estuaries (DSDEs)
  4. Coastal lakes

Intermittently closed/open estuary states (ICOEs) are subtypes of SIDEs and SSRTREs that describe estuary closure state.

Intermediate node: Nodes in a BBN which show outcomes arising from decision and situation node states. Intermediate nodes may also be set based on prior knowledge of their states.

Performance node: Nodes in a BBN which show outcomes arising from intermediate node states.

Primary indicators: Indicators that are considered to exhibit direct symptoms of eutrophication. These include macroalgae and phytoplankton biomass, and cyanobacteria.

Secondary indicators: Also known as supporting indicators, these have variable or indirect relationships with eutrophication but are useful in its measurement.

Situation node: Node that sets the known state of a system. In the context of Tool 3, this is the closure state of an estuary and the percentage of surface area that is intertidal.


References
  • Bricker, S., Ferreira, J., Simas, T. (2003) An integrated methodology for assessment of estuarine trophic status. Ecological Modelling 169: 39-60. [view online]

  • Burkholder, J., Glasgow, H.J., Cooke, J.(1994) Comparative effects of water-column nitrate enrichment on eelgrass Zostera marina, shoalgrass Halodule wrightii, and widgeongrass Ruppia maritima. Marine Ecology Progress Series 105: 121-138. [view online]

  • Elliott, A.H., Semadeni-Davies, A.F., Shankar, U., Zeldis, J.R., Wheeler, D.M., Plew, D.R., Rys, G.J., Harris, S.R. (2016) A national-scale GIS-based system for modelling impacts of land use on water quality. Environmental Modelling & Software 86: 131-144. [view online]

  • Eppley, R. (1969) Half-saturation constants for uptake of nitrate and ammonium by marine phytoplankton. Limnolology and Oceanography 14: 912-920. [view online]

  • Green, L., Sutula, M., Fong, P. (2012) How much is too much? Identifying benchmarks of adverse effects of macroalgae on the macrobenthic communtiy in estuarine intertidal flats. Pages 171-187 in S. W. K. Miller., editor. Southern California Coastal Water Research Project 2012 Annual Report. Southern California Coastal Water Research Project, Costa Mesa, CA. [view online]

  • Hume, T. (2018) The fit of the ETI trophic state susceptibility typology to the NZ coastal hydrosystems typology. NIWA Client Report 2017007CH: 34. [view online]

  • Hume, T., Gerbeaux,P., Hart, D., Kettles, H., Neale, D. (2016) A classification of New Zealand's coastal hydrosystems. NIWA Client Report HAM2016-062: 112.

  • Hume, T., Snelder, T., Weatherhead, M., Liefting, R. (2007) A controlling factor approach to estuary classification. Journal of Ocean and Coastal Management 50: 905-929. [view online]

  • Matheson, F., Wadhwa, S. (2012) Seagrass in Porirua Harbour Preliminary assessment of restoration potential. NIWA Client Report HAM2012-037: 35. [view online]

  • Pelletier, M.C., Campbell, D.E., Ho, K.T., Burgess, R.M., Audette,C.T., Detenbeck, N.E. (2011) Can sediment total organic carbon and grain size be used to diagnose organic enrichment in estuaries? Environmental Toxicology and Chemistry 30: 538-547. [view online]

  • Plew, D.R., Zeldis, J.R., Shankar, U., Elliott, A.H. (2018). Using simple dilution models to predict New Zealand estuarine water quality. Estuaries and Coasts 41: 1643-1659. [view online]

  • Robertson, B.P., Savage, C., Gardner, J.P.A., Robertson, B.M., Stevens, L.M. (2016a) Optimising a widely-used coastal health index through quantitative ecological group classifications and associated thresholds. Ecological Indicators 69: 595-605. [view online]

  • Robertson, B., Stevens, L., Robertson, B., Zeldis, J., Green, M., Madarasz-Smith, A., Plew, D., Storey, R. Oliver, M. (2016b) NZ Estuary Trophic Index Screening Tool 2. Determining Monitoring Indicators and Assessing Estuary Trophic State. Prepared for Envirolink Tools Project: Estuarine Trophic Index, MBIE/NIWA Contract No C01X1420: 68. [view online]

  • Sutula, M., Green, L., Cicchetti, G., Detenbeck, N., Fong, P. (2014) Thresholds of Adverse Effects of Macroalgal Abundance and Sediment Organic Matter on Benthic Habitat Quality in Estuarine Intertidal Flats. Estuaries and Coasts 37: 1532-1548. [view online]




Links to the ETI Tools


Please email us at eti-tools@niwa.co.nz for more information about the ETI Tools or to provide feedback. We welcome comments about how to make this webtool more useful.

Suggested citation

Zeldis, J., Storey, R., Plew, D., Whitehead, A., Madarasz-Smith, A., Oliver, M., Stevens, L., Robertson, B., Dudley, B. (2017). The New Zealand Estuary Trophic Index (ETI) Tools: Tool 3 - Assessing Estuary Trophic State using a Bayesian Belief Network. Ministry of Business, Innovation and Employment Envirolink Tools Contract: C01X1420. https://shiny.niwa.co.nz/Estuaries-Screening-Tool-3/

Download citation

Glossary



Bayesian Belief Network (BBN): A way of determining the probability of outcome X from decision Y, given all the knowledge and beliefs about the system.

Conditional probability distribution: The probability of state B occurring when state A is known to be a particular value. Estimated from known relationships or expert opinion.

Decision node: Nodes in a BBN that are set a priori based on management decisions. Decision nodes may be tuned, for example, with changed land-use or limit settings.

Estuary type: the ETI toolset has adopted a simple four-category typology specifically suited to the assessment of estuarine eutrophication susceptibility in New Zealand:

  1. Shallow intertidal dominated estuaries (SIDEs)
  2. Shallow, short residence time tidal river and tidal river with adjoining lagoon estuaries (SSRTREs)
  3. Deeper subtidal dominated, longer residence time estuaries (DSDEs)
  4. Coastal lakes

Intermittently closed/open estuary states (ICOEs) are subtypes of SIDEs and SSRTREs that describe estuary closure state.

Intermediate node: Nodes in a BBN which show outcomes arising from decision and situation node states. Intermediate nodes may also be set based on prior knowledge of their states.

Performance node: Nodes in a BBN which show outcomes arising from intermediate node states.

Primary indicators: Indicators that are considered to exhibit direct symptoms of eutrophication. These include macroalgae and phytoplankton biomass, and cyanobacteria.

Secondary indicators: Also known as supporting indicators, these have variable or indirect relationships with eutrophication but are useful in its measurement.

Situation node: Node that sets the known state of a system. In the context of Tool 3, this is the closure state of an estuary and the percentage of surface area that is intertidal.