Adjusting tropical marine water quality guideline values for elevated ocean temperatures (NESP TWQ 2.1.6, NESP TWQ 5.2 and NESP TWQ 3.1.5, AIMS)

This data contains consists of one Excel spreadsheet file containing the thermal stress data used to develop the Species Sensitivity Distribution described in the publication: Negri, A.P., Smith, R.A., King, O., Frangos, J., Warne, M. St-J., Uthicke, S. (2019) Adjusting tropical marine water quality guideline values for elevated ocean temperatures Environmental Science and Technology 54: 1102-1110 DOI: 10.1021/acs.est.9b05961. The increased frequency of heatwaves and poor water quality represent two of the most prevalent and severe pressures faced by coral reefs globally. While these pressures often co-occur, their potential risk to tropical marine species are usually considered alone. The potential effects of heatwaves on water quality guideline values are sometimes addressed by including an arbitrary “safety factor” or are not considered at all. In this study, we applied a mixture toxicity approach to predict how water quality guideline values should change at elevated temperatures. We used the common contaminants copper and diuron (a common herbicide) as examples. We first developed temperature-stress relationship for 41 tropical benthic marine species, using methods adapted from water quality guideline derivation. This enabled us to predict effects on tropical communities as temperature increased. The protection temperature values we calculated were similar to temperatures known to initiate coral bleaching and are suitable for application in multi-stressor risk assessments. This method enabled the adjustment of current water quality guideline values for copper and diuron to account for heatwave events. This approach could be extended to other ecosystems and to sediment, low salinity, light limitation, anoxia and ocean acidification, offering an alternative approach for adjusting environmental guidelines, reporting and risk assessments to account for multiple stressors. Methods: Species sensitivity distribution (SSD) for thermal stress Developing thermal SSD for marine communities requires temperature stress data for multiple species across diverse taxa (de Vries et al. 2008). We conducted a literature search for studies that quantified the effects of increasing temperatures on coral reef marine taxa. The Web of Science and Google Scholar were used to search for literature published after 1980 using the following terms: (aquatic OR marine OR estuar* OR coral) AND (temperature OR thermal OR temp*) AND (ecotoxic* OR toxic*) AND (tropical OR sub- tropical OR subtropical). We also included papers cited in recent publications (Ban et al. 2014, Uthicke et al. 2016). In order to apply the multi substance-potentially affected fraction (ms-PAF) approach to estimate the effects of multiple stressors, SSDs need to be derived from consistent endpoint metrics and exposure durations (Traas et al. 2002). Contaminant SSDs used in this study were derived by applying the Australian and New Zealand water quality guideline (ANZ) protocols (Hobbs et al. 2005, Warne et al. 2018a) – the thermal SSD was therefore derived using consistent protocols. Only “acceptable” quality data according to (Warne et al. 2018a)) were included in the thermal SSD. This endured ensure reliable and consistent temperature stress values. For consistency with the contaminant SSDs used in this study, only thermal effects data that caused a 10% ecologically relevant effect (ET10) or no observed effect (NOET) for each species were applied. The temperature threshold (TTx) for each species was the maximum temperature above acclimation at which negative affects did not occur: TTx = Tx – Ta Ta (°C) is the acclimation temperature while Tx (°C) is the highest temperature exceeding the Ta where there is no statistically significant effect (P > 0.05) on species x (i.e. NOET) or the effect is not greater than 10% (i.e. ET10). The thermal SSD was derived using the Burrlioz 2.0 software (CSIRO 2019). This software applies the Burr type III statistical distribution that best fits the effect TTx data of multiple species, and calculates the potentially affected fraction of species in a community (PAF (%). Burrlioz 2.0 calculated temperatures for several levels of ecosystem protection: the protective temperature (PTx) for 80%, 90%, 95% and 99% of species in a community (PT80, PT90, PT95 and PT99, respectively). This thermal SSD, derived from chronic data, will be relevant to extended periods of increased sea surface temperature (e.g. summer heatwave conditions of several weeks), but not short-term temperature fluctuations nor to the longer-term increases to basal sea surface temperatures associated with climate change. SSD for contaminants copper and diuron: Data to generate the SSD for copper were obtained from the Australian and New Zealand water quality guidelines (ANZG 2018). Ecotoxicity data to generate the SSD for diuron were obtained from recently proposed water quality guidelines (Warne et al. 2018b). The SSDs for these contaminants were modelled using Burrlioz 2.0 (CSIRO 2019). ms-PAF to predict the joint effects of thermal stress and contaminants using the Independent Action model. The independent action (IA) model can be used to derive joint effects for chemicals that have different modes of action but do not interact (de Zwart and Posthuma 2005, Traas et al. 2002). IA was the most appropriate model to calculate the combined effect of temperature on the two contaminants because the modes of action are likely to be different and because IA is generally more conservative (predicts lower joint effects) than the concentration addition approach (Traas et al. 2002). The “multiple stressor” potentially affected fraction of a community (ms-PAFIA) was calculated by combining the PAF of each stressor(Traas et al. 2002) as follows: ms-PAFIA = PAFA + PAFB - (PAFA × PAFB) where PAFA and PAFB are the PAF from two stressors (e.g. temperature and copper or temperature and diuron), scaled from 0 (0% of species in the community affected) to 1 (100% of species in the community affected). For more detailed methods see the associated publication, available as open access from 2021: Format: One Excel file: Adjusting water quality GVs for heatwaves 2020 data eatlas.xlsx. The first tab in the Excel file is an overview and definition of terms. The second tab contains the data used to construct the thermal species sensitivity distribution presented in the publication. Data Dictionary: Common name: of organisms tested Species: species tested Phylum: of species tested Class: of species tested Life stage: of species tested Endpoint: the measured indication of stress in the organism Ta: the acclimation temperature (°C). Tx: is the highest temperature (°C) exceeding the Ta where there is no statistically significant effect on species x Preferential selection grouping: is the priority score of data quality for inclusion in the species sensitivity distribution based on Warne, et a (2018). References: Ban, S. S.; Graham, N. A. J.; Connolly, S. R. Evidence for multiple stressor interactions and effects on coral reefs. Global Change Biology. 2014, 20: 681?697. de Vries, P.; Tamis, J. E.; Murk, A. J.; Smit, M. G. D. Development and application of a species sensitivity distribution for temperature-induced mortality in the aquatic environment. Environ. Toxicol. Chem. 2009, 27: 2591?2598. de Zwart, D.; Posthuma, L. Complex mixture toxicity for single and multiple species: proposed methodologies. Environmental Toxicology and Chemistry 2005, 24:2665?2676. Hobbs, D. A.; Warne, M. St. J.; Markich, S. J. Evaluation of criteria used to assess the quality of aquatic toxicity data. Integr. Environ. Assess. Manage. 2005, 1, 174?180. Uthicke, S.; Fabricius, K.; De’ath, G.; Negri, A.; Warne, M. St. J.; Smith, R.; Noonan, S.; Johansson, C.; Gorsuch, H.; Anthony, K. Multiple and cumulative impacts on the GBR: assessment of current status and development of improved approaches for management. Final Report Project 1.6 Report to the National Environmental Science Programme. Reef and Rainforest Research Centre Limited, Cairns (144 pp.), 2016, 2016/08/NESP-TWQ-1.6-FINAL-REPORTA.pdf. Negri, A.P., Smith, R.A., King, O., Frangos, J., Warne, M. St-J., Uthicke, S. (2019) Adjusting tropical marine water quality guideline values for elevated ocean temperatures Environmental Science and Technology 54: 1102-1110 DOI 10.1021/acs.est.9b05961. Traas, T. P.; Van de Meent, D.; Posthuma, L. H. T.; Kater, B. J.; de Zwart, D.; Aldenberg, T. The potentially affected fraction as a measure of ecological risk. In Species Sensitivity Distributions in Ecotoxicology; Posthuma, L.; Suter, G. W., II.; Traas, T. P., Eds.; CRC Press: Lewis, Boca Raton, FL, USA, pp 315?344. 2002 Warne, M.S.J., Batley, G.E., van Dam, R.A., Chapman, J.C., Fox, D.R., Hickey, C.W. and Stauber, J.L. (2018) Revised method for deriving Australian and New Zealand Water Quality Guideline Values for toxicants - update of the 2015 version. Prepared for the revision of the Australian and New Zealand Guidelines for Fresh and Marine Water Quality, Australian and New Zealand Governments and Australian state and territory governments, Canberra, ACT, 48 pp. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\NESP\3.1.5_Pesticide-guidelines-GBR

Principal Investigator
Andrew Negri (Dr) Australian Institute of Marine Science
Co Investigator
Sven Uthicke (Dr) Australian Institute of Marine Science
Co Investigator
Michael Warne (Assoc. Prof) University of Queensland - School of Earth and Environmental Sciences
Co Investigator
Rachel Smith Queensland Department of Environment and Science
Co Investigator
Julius Frangos Queensland Department of Environment and Science
Co Investigator
Olivia King Griffith University - School of Environment and Science
Point Of Contact
Andrew Negri Australian Institute of Marine Science

Data collected from 20 Feb 2016 until 20 Oct 2019

Data Usage Constraints
  • Attribution 3.0 Australia