Considerations for ecosystem receptors

Ecosystem receptor lines of evidence

At Step 3 of the Water Quality Management Framework, you could potentially select 3 ecosystem receptor lines of evidence:

  • biodiversity
  • toxicity
  • biomarkers, including biomarkers of exposure or effect and those that measure bioaccumulation.

Within each line of evidence, one or more indicators could be selected. When designing your monitoring program, consider specific requirements for different biological indicators.

Ecosystem receptors vary widely in nature, ranging from those measured solely using laboratory experiments to those that can only be measured using field surveys.

Here we address common issues relevant to ecosystem receptors when designing a water/sediment quality monitoring program.

Setting decision criteria to assess change

Irrespective of the monitoring program objective, the criteria used to assess change (including trend or ‘impact’) need to be set as part of the study design. Stand-alone guideline values for ecosystem receptors rarely exist so we usually base decision criteria on comparisons with reference or control conditions.
Procedures for setting decision criteria consist of 3 interconnected stages that need inputs from:

  • Steps 1, 2 and 3 of the Water Quality Management Framework, to select potential, relevant and sensitive ecosystem receptors (indicators)
  • Step 5 of the framework, to define water quality and environmental objectives.

Indicators are likely to vary in terms of specificity and cost so it is important to explore these issues when designing a monitoring program.
The terminology we use here will be familiar from traditional frequentist statistical methods. Similar considerations apply to Bayesian methods (Sahu & Smith 2006).

See also:

Expand all

How levels of protection affect decision criteria

The condition of an ecosystem may influence decisions about monitoring study design. We define 3 categories of ecosystem condition in the Water Quality Guidelines:

  • high conservation or ecological value systems
  • slightly to moderately disturbed systems
  • highly disturbed systems.

For high conservation or ecological value systems, the study design will need to set effect sizes and the ratio between α and β so as to be as precautionary as possible. Often there are scant baseline data for ecosystem receptors in such systems. The study design should maximise opportunities to improve baseline knowledge so that natural variation is sufficiently well characterised to allow effect sizes to be set (Mapstone 1995).

Humphrey et al. (1999) criticised aspects of the environmental impact assessment (EIA) process in Australia, saying that too often developments proceeded without adequate baseline data being gathered to detect and assess potential disturbances.

We strongly recommend that parties adopt a precautionary approach and respond wisely and in a timely manner to data gathered for ‘early detection’ indicators.

Slightly to moderately disturbed systems should be treated like high conservation or ecological value systems, acknowledging that there may be negotiated deviations from default guideline values (DGVs) prescribed for high conservation or ecological value systems. Nevertheless, any decisions on effect size should be based on sound ecological principles of sustainability rather than arbitrary relaxation of guideline values determined for high conservation or ecological value systems, or because of resource constraints.

For highly disturbed systems, our philosophy is that, at worst, water quality is maintained so that it can support the values identified by stakeholders (Step 2 of the Water Quality Management Framework). Ideally, the longer-term aim is towards improved water quality, in which case design considerations for remediation become relevant.

Expand all

References

Beyers DW 1998, Causal inference in environmental impact studies, Journal of the North American Benthological Society 17(3): 367–373.

Bourlat SJ, Borja A, Gilbert J, Taylor MI, Davies N, Weisberg SB, et al. 2013, Genomics in marine monitoring: new opportunities for assessing marine health status, Marine Pollution Bulletin 74: 19–31.

Camp RJ, Seavy NE, Gorresen PM & Reynolds MH 2008, A statistical test to show negligible trend: comment, Ecology 89: 1469–1472.

Chapman PM 1990, The sediment quality triad approach to determining pollution-induced degradation (PDF, 781KB), Science of the Total Environment 97–98: 815–825.

Chariton AA, Court LN, Hartley DM, Colloff MJ & Hardy CM 2010, Ecological assessment of estuarine sediments by pyrosequencing eukaryotic ribosomal DNA, Frontiers in Ecology and the Environment 8: 233–238.

Chow S-C & Liu J-P 1992, Design and Analysis of Bioavailability and Bioequivalence Studies, Marcel Dekker, New York.

Dixon PM & Pechmann JH 2008, A statistical test to show negligible trend: reply, Ecology 89: 1473.

Dixon PM & Pechmann JHK 2005, A statistical test to show negligible trend, Ecology 86: 1751–1756.

Downes BJ, Barmuta LA, Fairweather PG, Faith DP, Keough MJ, Lake PS, et al. 2002, Monitoring Ecological Impacts: Concepts and practice in flowing waters, Cambridge University Press, Cambridge.

Erickson WP & McDonald LL 1995, Tests for bioequivalence of control media and test media in studies of toxicity, Environmental Toxicology & Chemistry 14: 1247–1256.

Fairweather PG 1991, Statistical power and design requirements for environmental monitoring, Australian Journal of Marine and Freshwater Research 42: 555–567.

Fox DR 2001, Environmental power analysis — a new perspective, Environmetrics 12: 437–449.

Fox DR, Ben-Haim Y, Hayes KR, McCarthy MA, Wintle B & Dunstan P 2007, An info-gap approach to power and sample size calculations, Environmetrics 18: 189–203.

Green RH 1989, Power analysis and practical strategies for environmental monitoring, Environmental Research 50: 195–205.

Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN et al. 2016, Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations, European Journal of Epidemiology 31(4): 337–350.

Gwinn DC, Beesley LS, Close P, Gawne B & Davies PM 2016, Imperfect detection and the determination of environmental flows for fish: challenges, implications and solutions, Freshwater Biology 61: 172–180.

Hook SE, Gallagher EP & Batley GE 2014, The role of biomarkers in the assessment of aquatic ecosystem health, Integrated Environmental Assessment and Management 10(3): 327–341.

Humphrey CL, Faith DP & Dostine PL 1995, Baseline requirements for assessment of mining impact using biological monitoring, Australian Journal of Ecology 20(1): 150–166.

Humphrey C, Thurtell L, Pidgeon RWJ & van Dam R, Finlayson M 1999, A model for assessing the health of Kakadu's streams, Australian Biologist 12: 33–42.

Kroon F, Streten C & Harries S 2017, A protocol for identifying suitable biomarkers to assess fish health: a systematic review, PloS one 12, e0174762.

Linkov I, Loney D, Cormier S, Satterstrom FK & Bridges T 2009, Weight-of-evidence evaluation in environmental assessment: review of qualitative and quantitative approaches, Science of the Total Environment 407: 5199–5205.

MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LL & Hines JE 2006, Occupancy Estimation and Modeling: Inferring patterns and dynamics of species occurrence, 1st Edition, Elsevier, Burlington.

Mackenzie DI & Royle JA 2005, Designing occupancy studies: general advice and allocating survey effort, Journal of Applied Ecology 42: 1105–1114.

Mapstone BD 1995, Scalable decision rules for environmental impact studies: effect size, Type I, and Type II errors, Ecological Applications 401.

McBride G, Cole RG, Westbrooke I & Jowett I 2014, Assessing environmentally significant effects: a better strength-of-evidence than a single P value? Environmental Monitoring and Assessment 186: 2729–2740.

McBride GB 1999, Applications: equivalence tests can enhance environmental science and management, Australian & New Zealand Journal of Statistics 41(1): 19–29.

McDonald LL & Erickson WP 1998, Testing for bioequivalence in field studies: has a disturbed site been adequately reclaimed? in: Fletcher DJ & Manly BFJ (eds), Statistics in Ecology and Environmental Monitoring 2, University of Otago Press, Dunedin, pp. 183–197.

Ngatia M, Gonzalez D, Julian SS & Conner A 2010, Equivalence versus classical statistical tests in water quality assessments, Journal of Environmental Monitoring 12: 172–177.

Noon BR, Bailey LL, Sisk TD & McKelvey KS 2012, Efficient species-level monitoring at the landscape scale, Conservation Biology 26: 432–441.

Parkhurst DF 2001, Statistical significance tests: equivalence and reverse tests should reduce misinterpretation, Bioscience 51: 1051–1057.

Paul WL, Rokahr PA, Webb JM, Rees GN & Clune TS 2016, Causal modelling applied to the risk assessment of a wastewater discharge, Environmental Monitoring and Assessment 188: 131.

Price SJ, Muncy BL, Bonner SJ, Drayer AN & Barton CD 2015, Effects of mountaintop removal mining and valley filling on the occupancy and abundance of stream salamanders, Journal of Applied Ecology 53: 459–468.

Ryan TP Jr 2013, Sample Size Determination and Power, John Wiley & Sons, Hoboken.

Sahu SK & Smith TMF 2006, A Bayesian method of sample size determination with practical applications, Journal of the Royal Statistical Society: Series A (Statistics in Society) 169: 235–253.

Saxena G, Marzinelli EM, Naing NN, He Z, Liang Y, Tom L et al. 2015, Ecogenomics reveals metals and land-use pressures on microbial communities in the waterways of a megacity, Environmental Science & Technology 49: 1462–1471.

Stewart-Oaten A, Murdoch WW & Parker KR 1986, Environmental impact assessment: “pseudoreplication” in time? Ecology 67: 929–940.

Suter GW & Cormier SM 2011, Why and how to combine evidence in environmental assessments: weighing evidence and building cases, Science of the Total Environment 409: 1406–1417.

Toft CA & Shea PJ 1983, Detecting community-wide patterns: estimating power strengthens statistical inference, American Naturalist 122: 618–625.

Tyre AJ, Tenhumberg B, Field SA, Niejalke D, Parris K & Possingham HP 2003, Improving precision and reducing bias in biological surveys: estimating false-negative error rates, Ecological Applications 13: 1790–1801.

Underwood AJ 1991, Beyond BACI: experimental designs for detecting human environmental impacts on temporal variation in natural populations, Australian Journal of Marine and Freshwater Research 42(5): 569–587.

Underwood AJ 1992, Beyond BACI: the detection of environmental impact on populations in the real, but variable, world, Journal of Experimental Marine Biology and Ecology 161(2), 145–178.

Wasserstein RL & Lazar NA 2016, The ASA’s statement on p-values: context, process, and purpose, The American Statistician 70: 129–133.