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Indicators -

  • An indicator is a statistic based upon a time trend data set which is relevant to a particular issue of concern (Vandermeulen 1998).

  • Indicators can be used to present the data in a way that represents the broader significance or implications of the data compared to the raw data on its own (Vandermeulen 1998).

  • Can be a useful tool within management frameworks for purposes of communication and decision making (Vandermeulen 1998).

  • Can provide information on trends in the conditions of a phenomenon and has significance extending beyond that associated with the properties of the statistics itself (Vandermeulen 1998).

  • Indicators are used to describe complex systems more simply.

Ecological indicators -

  • Ecological indicators are used to detect and describe the effects of fishing on marine ecosystems (Fulton et al 2005).
  • Ecological indicators are used to communicate information about ecosystems and the impact human activities have on ecosystems to the public or government policy makers (wikipedia).

Environmental Indicators -

  • 'are selected key statistics that represent or summarise a significant aspect of the state of the environment, natural resource sustainability and related human activities' (Vandermeulen 1998).

Size-based indicators (SBIs) -

  • is the statistical summary of the size distribution of fish assemblages and populations. The size of an organism is a central factor to key ecological processes. The changes in size distribution may have many ecological or physical causes, including genetic or environment variability in life history characteristics, predictor-prey relationship, and competitive interactions (Shin et al 2005).
  • Fishing is always size selective (ie, nets used, target species and by-catch).
  • 'Are typically used to describe teh response of communities or individuals populations to exploitation and may contribute to the development of an ecosystem approach to fisheries' (Shin et al 2005).
  • Size based indicators are evaluated for their relevance by Shin et al (2005). The theoretical and empirical modelling bases are examined, then their measurability (sampling constraints, availability of size data, potential biases), sensitivity to fishing effects, responsiveness (time of response) and specificity (discrimination of fishing and environmental effects) are examined.

Population indicators -

  • (e.g. mortality rate, exploitation rate, or average length) are the most operationally useful indicators because their meaning is clear and the expected effect of fisheries on them is well understood, , ie. reference points can be set (Rochet and Trenkel 2003).
  • first step from single-species to community assessment and management. These indicators have the goal of preserving population states, structures and dynamics (Rochet and Trenkel 2003).

Assemblage indicators -

  • used to refer to populations living together in a biotope, ignoring interactions. These indicators describe the range of populations and the distribution of their characteristics that may be effected by fishing (Rochet and Trenkel 2003).
  • the target of these indicators is to preserve diverse assemblages.

Community indicators -

  • are networks of interacting populations or individuals
  • the goals of these indicators is to assess the effect of fishing on the interactions, the tropic paths, and the biomass flows in the community
  • and for the preserving of the above functions of the community (Rochet and Trenkel 2003). 
  • ways of using community indicators:
    • do not worry about using reference points and instead examine whether the indicator is currently changing
    • determine theoretical reference points
    • develop an empirical reference system, which can be developed by gathering indicator estimates from several communities

Descriptive indicators -

  • reflect an actual situation in a given system (Degnbol and Jarre 2004).

Performance indicators -

  • compare actual conditions with a specific set of reference conditions (Degnbol and Jarre 2004).
  • they measure the difference between target and current situation to see how policy is working or not (Degnbol and Jarre 2004).

Efficiency indicators -

  • relate environmental pressures to human activities and are therfore most relevant for policy-making (Degnbol and Jarre 2004).

Total welfare indicators -

  • measure overall sustainability (Degnbol and Jarre 2004).

Contextual indicators -

  • contextual (or audit or descriptive) indicators provide information on the background conditions, which may include conditions that humans have no control (Perry et al 2010).

Management indicators -

  • management (or control) indicators are information on conditions that humans have some direct control, and should be applicable to measure the results of management actions (Perry et al 2010). 
  • also called performance indicators which compare actual conditions with some desired set of conditions (Perry et al 2010). 
  • other possible management indicators are total welfare indicators, which are some measure of overall sustainability and efficiency indicators that relate the environmental pressure to human activities (Perry et al 2010).

Classes of indicators

Traffic Light System -

  • is a way of using multiple indicators and their critical reference points (RP) by Caddy (1999) for managing populations of invertebrates where age structure information and stock-recruit relationship (SRR) based RPs were not available (Caddy and Seijo 2005).
  • by using a range of simple indicators to measure quantifiable life-history characteristics, and divide (or score) them into red or green categories, depending on which side they fell of a value which analysis, past experience of biological studies suggest is an appropriate limit reference point (LRP) (Caddy and Seijo 2005). 
  • later Halliday et al (2001) added a yellow category  as a transition between green and red to represent a precautionary or uncertain condition of stock (Caddy and Seijo 2005).

Quantitative Status -

Feedback -


Analysing trends in indicators -

  • if analysis concludes that a true trend is occurring, but there really is not it is a type I error
  • if analysis concludes that there is no trend occurring , but there really is a trend it is a type II error.
  • Type I error -
    • can lead to manager responding to noise rather than a signal. which can result in considerable waste of resources to provide action (Jennings 2005).
    • can results in conservation priorities being focused on short-term noise rather than longer term signals, with a waste of scarce resources (Maxwell and Jennings 2005).
  • Type II error -
    • can lead to mangers failing to act when they should (Jennings 2005).
    • can have adverse consequences on the success of conservation efforts due to the lack of immediacy and lack of management action (Maxwell and Jennings 2005).

Properties and Process of selecting indicators

There is not likely to be a suite of indicators that will work for all regions. Instead need to find a balance of indicators that can be used over a wide range of regions but acknowledge that some smaller regions may need additional indicators.

Properties of indicators

A table for the general properties of indicators (ie not just for fisheries) by Perry et al (2010) but based on Rice and Rochet (2005).

  • Concrete property, or abstract concept? 
  • Measurable units, or relative scale?
  • Directly observable, or output of model?
Theoretical basis
  • Basis credible, or debated?
  • If derived from empirical observations, are the concepts consistent with established theory?
Public awareness
  • Does it have high public awareness already?
  • Is its meaning readily understood?
  • Already enshrined in legislation somewhere?
  • Use measurement tools that are widely available and low cost?
  • Can variance and bias be estimated? Is it high or low?
  • Are the accuracy and precision of data collection methods known?
  • Is it subject to vagaries of different sampling gears?
  • Is it highly variable seasonally? geographically?
  • Does it have high taxonomic specificity?
Historical data
  • Are historical data available? From low large an area?
  • Are the uncertainties of these historical data known?
  • Are these historical data freely available?
  • Does the indicator respond smoothly, monotonically, and with high slope?
  • Does the indicator respond rapidly (eg within 1-3 years) of changes, or on longer (eg decadal) scales?
  • How specific is the indicator to the process being indexed?

Process of selecting indicators

A suggestion for selecting ecosystem indicators (more general not just fisheries oriented) (Rice and Rochet 2005).

1determine user needs
2develop a list of candidate indicators
3determine screening criteria
4score candidate indicators against the screening criteria
5summarise the scoring results
6decided how many indicators are needed
7make final selection
8report on the chosen suite of indicators


This table is from Jennings (2005). This one is fisheries oriented.

1Set operational management objectives based on existing policy commitments and knowledge of potential threats to sustainability (based on past and present experience and expectations for the future).
2Identify the fishing impacts on components and attributes that may compromise each operational objective (based on past and present experience and expectations for the future).
3Ranking fishing impacts from high and low probability that they will compromise the achievement of objectives
4Based on step 3 and criteria for selecting good state indicators, identify state indicators (one or more) for fishing impacts most likely to compromise each objective. Specificity, complexity and number of indicators selected will reflect resources available for management.
5Identify/develop potential pressure and response indicators to describe the pressures and management responses that influence the value of each state indicator.
6Identify/develop methods/models that link potential pressure to state and potential response indicators (to predict whether and how changes in pressure and/or response can be used to achieve desired state).
7If links described in step 6 cannot be adequately described than initiate relevant research
8Identify/describe appropriate monitoring programmes for measuring the values of state, pressure and response indicators.
9Based on the operational objective, set reference points, directions or trajectories for each state, pressure and response indicator. Determine response times to specific changes in true values.
10Confirm that it is feasible to meet reference points, directions or trajectories for all indicators simultaneously (and hence that is possible to meet all objectives simultaneously). Revise reference points, directions or trajectories if not.
11If steps 4-10 cannot be completed for the indicators relating to an operational objective then prioritise research to improve existing indicators or identify new ones
12Repeat and review process 1-11 regularly, to take account of new research and to continually improve the management system.

EBFM consensus indicators

The following is (based on Fulton et al 2004) from a table in Perry et al (2010):

  1. Relative biomass- of gelatinous zooplankton, cephalopods, small pelagics, demersals, piscivores, top predators
    1. "specifically of gelatinous zooplankton, cephalopods, small pelagics, scavengers, demersal fish, piscivores, top predators (TL 4+, which synthesise over large temporal and spatial scale), and biogenic habitat (cover forming species). Ease of sampling differs strongly between these groups (for instance external bodies, such as NGOs, often monitor the charismatic top predators, tourism can focus attention on coral reef fishes, snapshots of mangrove forests and shallow benthic habitat may be available from satellite images), but it is possible to find proxies for even some of the most difficult groups. For example, changes in relative biomass of gelatinous zooplankton may be captured by frequency of bloom events, as this is a much more easily obtainable statistic due to the ability to generate it from expert information and fisher interviews." (Medley et al 2009).

  2. Biomass ratios- piscivore:planktivore, pelagic:demersal, infauna:epifauna
    1. "in particular the biomass ratios of piscivore: planktivore (PS:ZP), pelagic: demersal (P:D) and infauna: epifauna. The last of these is probably not feasible in data poor situations, but the other two can be done. More importantly there are existing published values for PS:ZP and P:D that can guide reference direction (if not reference point) definition (Bianchi et al., 2000; Caddy, 2000; Caddy and Garibaldi, 2000)." (Medley et al 2009).

  3. Habitat-forming taxa- eg proportional area covered by these epifauna and or macrophytes

  4. Size spectra- Slopes of community size spectra and thier changes can be particularly strong indicators of community level changes
    1. "which give an indication of perturbation in system structure (using the slope of the curve), but can also highlight changes in system productivity (via the intercept)." (Medley et al 2009).
  5. Taxonomic diversity (richness)- eg based on species counts
    1. "while a highly controversial topic in the ecological and conservation literature, it remains an informative and fundamental piece of information about the system. Alternative measures of diversity or system structure (e.g. Whitakker plots), ABC plots, Kempton Q index (Ainsworth and Pitcher, in press)) may need to be considered too, depending on which operating model is used or what empirical data is available. One possibility is considering the value of changes in species-area curves (e.g. the slope and asymptote of the curve) through time (or spatially if under a perturbation gradient). It is likely that some simple or ordinal data can be collected on this from the fishers themselves given that they are acutely aware of what they catch (Minte-Vera and Medley, pers.comm.). Looking at changes in these rankings could be highly informative." (Medley et al 2009).

  6. Total fishery removals- catch + discards + bycatch
    1. "this indicator considers the total biomass removed from the system versus what is left cycling in the system. While similar in concept to the widely used comparison of primary production vs. removals from the system, there is concern that it will not be suitable for data poor and dispersed small scale fisheries due to the lack of data on removals let alone discards." (Medley et al 2009).

  7. Maximum (or mean) length- maximum (or mean) length across all species in the catch
    1. "this indicator has been observed to work in practice even if simple rules of thumb regarding L∞ are used. Caution is needed regarding market driven changing in preferred sizes, however." (Medley et al 2009).

  8. Size-at-maturity- example of main target species, bycatch and top predators
    1. "while this can be a strong means of detecting change in the system and stock structure, it may be too difficult to use in data poor situations" (Medley et al 2009).

  9. Tropic level or tropic spectrum of the catch - average tropic level or spectra of the catch (eg Gascuel et al 2005) may require that diet data be updated periodically.
  10. Biophysical characteristics- eg temperature, salinity, sea ice (where present), chlorophyll a, primary production, atmospheric indices (eg PDO)
    1. "(Chlorophyll a, temperature, dissolved inorganic nitrogen, and level of contaminants): these may be drawn from water quality monitoring programs or remotely sensed data sets, but are needed even if they aren’t already being collected as they are the means of teasing out causation (is a non-fisheries sector producing the changes?). Indicators respond to any system change, including those caused by sectors other than fisheries, so any information that helps elucidate causation is extremely helpful." (Medley et al 2009).


Others indicators include:

  1. contaminants
  2. marine transport and non indigenous invasive species
  3. aquaculture and development
  4. ocean productivity

Another list:

  • mean length (cm)
  • tropic level of landings
  • proportion and under and moderately exploited species
  • proportion of predatory fish
  • mean lifespan (years)
  • 1/CV biomass (coefficient of variation)


Bianchi, G., H. Gislason, K. Graham, L. Hill, X. Jin, K. Koranteng, S. Manickchand-Heileman, I. Paya, K. J. Sainsbury, F. Sanchez, and K. Zwanenburg. 2000. Impact of fishing on size composition and diversity of demersal fish communities. ICES Journal of Marine Science 57: pp 558-71.

Caddy, J.F. (1999) Deciding on precautionary management measures for a stock based on a suite of limit reference points (LRPs) as a basis for multi-LRP harvest laws. NAFP Sci. Coun. Stud. 32, 55-68.

Caddy, J. F. 2000. Marine catchment basin effects versus impacts of fisheries on semi-enclosed seas. ICES Journal of Marine Science 57: pp 628-40.

Caddy, J. F., and L. Garibaldi. 2000. Apparent changes in the trophic composition of world marine harvests: the perspective from the FAO capture database. Ocean & Coastal Management 43: pp 615-55.

Caddy. J.F. and Seijo, J.C. (2005). The responsibility for sustainable fisheries. Philosophical Transactions of the Royal Society B, 360, 59-75.

Degnbol, P., et al. (2004). Review of indicators in fisheries management - a development perspective. African Journal of Marine Science 26: 303-326.

Fulton, E.A., Fuller,M., Smith, A.D.M., and Punt, A. (2004) Ecological indicators of the ecosystem effects of fishing: Final report. AFMA Final Research Report, report Number R99/1546.

Fulton, E.A., Smith, A.D.M., Webb, H., and Slater, J. (2004a) Ecological indicators for the impacts of fishing on non-target species, communities and ecosystems: Review of potential indicators. AFMA Final Research Report, report Number R99/1546.

Fulton, E.A., Smith, A.D.M. and Punt, A.E. (2005) Which ecological indicators can robustly detect effects of fishing? ICES Journal of Marine Science. 62, 540-551.

Gascuel, D. Bozec, Y.-M., Chassot, E., Colomb, A. and Laurans, M. (2005) The trophic spectrum: theory and application as an ecosystem indicator. ICES Journal of Marine Science 62: pp 443-452.

Halliday, R.G., Fanning, L.P. and Mohn, R.K. (2001) Use of the traffic light method in fishery management planning. Canadian Science Advisory Secretariat, Res. Doc. 2001/108.

Jennings, S. (2005) Indicators to support an ecosystem approach to fisheries. Fish and Fisheries, 6, 212-232.

Maxwell, D. and Jennings, S. (2005) Power of monitoring programmes to detect decline and recovery of rare and vulnerable fish. Journal of Applied Ecology, 42, 25-37.

Medley, P.; Cheung, W.; Fulton, B.; Minte-Vera, C. (2009) Multispecies and ecosystem indicators, and biomass-fleet dynamics stock assessment: an initial evaluation. FAO Fisheries and Aquaculture Circular. No. 1045. Rome, FAO. 28p

Perry, R.I., Livingston, P., and Fulton, E.A. (2010) Ecosystem indicators. In G. Jamieson, P. Livingston, and C.I. Zhang (eds) Report of Working group 19 on Ecosystem-based management Science and its Application to the North Pacific. PICES Science Report no. 37, Sydney, Canada, 83-89.

Rice, J.C. and Rochet, M-J. (2005) A framework for selecting a suite of indicators for fisheries management. ICES Journal of Marine Science, 62, 516-527.

Rochet, M-J. and Rice, J.C. (2005) Do explicit criteria help in selecting indicators for ecosystem-based fisheries management? ICES Journal of Marine Science. 62, 528-539.

Rochet, M-J. and Trenkel, V.M. (2003) Which community indicators can measure the impact of fishing? A review and proposals. Canadian Journal of Fisheries and Aquatic Sciences, 60 (1) 86-99.

Shin, Y-J., Rochet, M-J., Jennings, S., Field, J.G., and Gislason, H. (2005) Using size-based indicators to evaluate the ecosystem effects of fishing.  ICES Journal of Marine Science. 62, 384-396.

Vandermeulen, H. (1998) The development of marine indicators for coastal zone management. Ocean & Coastal Management, 39, 63.71.

Background reading

References that were not used on this page but may be of interest for back ground reading

Caddy, J. F., et al. (2005). "This is more difficult than we thought! The responsibility of scientists, managers and stakeholders to mitigate the unsustainability of marine fisheries." Philosophical Transactions of the Royal Society B-Biological Sciences 360(1453): 59-75.

Cury, P.M. and Christensen, V. (2005) Quantitative ecosystem indicators for fisheries management. ICES Journal of Marine Science 62: 307-310.

Cury, P. M., et al. (2005). "Viability theory for an ecosystem approach to fisheries." ICES Journal of Marine Science 62: 577-584.

Degnbol and P. (2005). "Indicators as a means of communicating knowledge." ICES Journal of Marine Science 62: 606-611.

Dulvy, N. K., et al. (2006). "Threat and decline in fishes: an indicator of marine biodiversity." Canadian Journal of Fisheries and Aquatic Sciences 63(6): 1267-1275.

Fischer, A.S., Hall, J., Harrison, D.E. Stammer, D. and Benveniste, J. (2009) Ocean information for society: sustaining the benefits, realizing the potential. OceanObs'09 Conference Summary.

Fu, C., Gaichas, S., Link, J.S., Bundy, A., Boldt, J.L., Cook, A.M., Gamble, R., Utne, K.R., Liu, H., and Friedland, K.D. (2012) Relative importance of fisheries, trophodynamic and environmental drivers in a series of marine ecosystems. Marine Ecology Progress Series, 459, 169-184.

Hall, S. J., et al. (2006). "A length-based multispecies model for evaluating community responses to fishing " Canadian Journal of Fisheries and Aquatic Sciences 63: 1344-1359.

Jennings, S., and Dulvy, N.K. (2005). "Reference points and reference directions for size-based indicators of community structure." ICES Journal of Marine Science 62: 397-404.

Link, J.S.,Megrey, B.A., Miller, T.J., Essington, T., Boldt, J., Bundy, A., Moksness, E., Drinkwater, K.F. and Perry, R.I. (2010) Comparative analyssi of marine ecosystems: international production modelling workshop. Biology Letters 6, 723-726.

Link, J.S., Gaichas, S., Miler, T.J., Essington, T., Bundy, A., Boldt, J., Drinkwater, K.F. and Moksness, E. (2012) Synthesizing lessons learned from comparing fisheries production in 13 northern hemisphere ecosystems: emergent fundamental features. Marine Ecology Progress Series, 459, 293-302.

Livingston, P. A., et al. (2005). "A framework for ecosystem impacts assessment using an indicator approach." ICES Journal of Marine Science 62: 592-597.

Nicholson, M. D., et al. (2004). "Testing candidate indicators to support ecosystem-based management: the power of monitoring surveys to detect temporal trends in fish community metrics." Ices Journal of Marine Science 61(1): 35-42.

Pranovi, F., Link, J., Fu, C., Cook, A.M., Lui, H., Gaichas, S., Freidland, K.D., Utne, K.R., and Benoît, H.P. (2012) Trophic-level determinants of biomass accumulation in marine ecosystems. Marine Ecology Progress Series, 459, 185-201.

Shin, Y-J., Bundy, A., Shannon, L.J., Simier, M., Coll, M., Fulton, E.A., Link, J.S., Jouffre, D., Ojaveer, H., Mackinson, S., Heymans, J.J. and Raid, T. (2010) Can simple be useful and reliable? Using ecological indicators to represent and compare the states of marine ecosystems. ICES Journal of Marine Science, 67, 717-731.

Shin, Y-J. and Shannon, L.J. (2010) Using indicators for evaluating, comparing and communicating the ecological status of exploration marine ecosystem. ICES Journal of Marine Science, 67,.




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