Data

Stream mesocosm experiment on benthic macroinvertebrate and algal communities

Terrestrial Ecosystem Research Network
Davies, Peter
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4227/05/54D43B0CF12A1&rft.title=Stream mesocosm experiment on benthic macroinvertebrate and algal communities&rft.identifier=10.4227/05/54D43B0CF12A1&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=The dataset consists of results from two stream mesocosm experiments that were conducted in the summer-autumn of 1996 and 1997 to distinguish the influence of fine sediment loads and nutrient concentrations on benthic macro-invertebrate and algal communities. 11 biological variables were extracted from the results of this experiment and were standardized for the purpose of training neural networks that could be used to diagnose nutrient and fine sediment impacts in field surveys. The 11 variables were selected according to how well they correlated with the experimental treatment levels (high and low values of both nutrients and fine sediments). The 11 variables were: chlorophyll a (mg/m2), macro-invertebrate familial richness, total abundance, and the abundance of Oligochaeta, Leptoperla varia (Gripopterygidae), Nousia spp. (Leptophlebiidae), Austrophlebioides spp. (Leptophlebiidae), Orthocladiinae, Tanypodinae, Tipulidae and larval Scirtidae. These taxa were abundant within and among the stream mesocosm communities and are common in a wide range of Tasmanian rivers. Values for each of 11 biological response variables were standardized by dividing by their average value observed in the experimental controls mesocosm samples from that year. See Magierowski RH, Read SM, Carter SJB, Warfe DM, Cook LS, Lefroy EC, et al. (2015) Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks. PLoS ONE 10(3): e0120901. https://doi.org/10.1371/journal.pone.0120901 https://doi.org/10.1371/journal.pone.0120901. This data was collected for the purpose of training artificial neural networks that could diagnose nutrient and sediment impacts in Tasmanian rivers. Each of the 11 variables were standardized by their average value observed in the experimental control samples from that year and some experimental treatment effects (Light) were ignored to simplify the neural network training process. Therefore, these data should not be used to make conclusions about the impacts of fine sediments and nutrients in Tasmanian rivers. 1)Experimental design: The experiments were conducted using a flow-through water supply via diversion and hydraulic manifold from the Little Denison River, Tasmania (-43.0, 146.8). In each experiment, 32 flow-through mesocosms, each 4 m length × 0.4 m width × 0.4 m depth, were established with cleaned cobbles sourced from the adjacent river and colonised for four months by continuous constant flow-through from the Little Denison River. In a split-plot design, the mesocosms received low or high nutrient concentrations (Low: 0.035 mg/L NO3-N and 0.008 mg/L PO4-P; High: 0.4 mg/L NO3-N and 0.08 mg/L PO4-P), and low or high fine sediment loads (Low: < 5 g/m2 sand (grain size 0.06 -2 mm) initially and fortnightly pulses of < 5 mg/L suspended clay (grain size Progress Code: completedMaintenance and Update Frequency: notPlanned&rft.creator=Davies, Peter &rft.date=2015&rft.edition=1&rft.relation=https://doi.org/10.1371/journal.pone.0120901&rft.coverage=IBRA region: Tasmania South East Single location on the Little Dennison River, Tasmania.&rft.coverage=northlimit=-43; southlimit=-43.1; westlimit=146.7; eastLimit=146.8; projection=EPSG:3577&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_rights=TERN services are provided on an as-is and as available basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure. <br />Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting&rft_rights=Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.&rft_subject=biota&rft_subject=environment&rft_subject=AQUATIC ECOSYSTEMS&rft_subject=EARTH SCIENCE&rft_subject=BIOSPHERE&rft_subject=FRESHWATER ECOSYSTEMS&rft_subject=ENVIRONMENTAL ASSESSMENTS&rft_subject=HUMAN DIMENSIONS&rft_subject=ENVIRONMENTAL GOVERNANCE/MANAGEMENT&rft_subject=GREEN ALGAE&rft_subject=ANIMALS/INVERTEBRATES&rft_subject=BIOLOGICAL CLASSIFICATION&rft_subject=Invertebrate Biology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=ZOOLOGY&rft_subject=Freshwater Ecology&rft_subject=ECOLOGY&rft_subject=chlorophyll a concentration (Milligram per Square Meter)&rft_subject=Milligram per Square Meter&rft_subject=treatment count (Number)&rft_subject=Number&rft_subject=1 km - < 10 km or approximately .01 degree - < .09 degree&rft_subject=Subannual&rft_subject=Austrophlebioides&rft_subject=Leptoperla&rft_subject=Nousia&rft_subject=OLIGOCHAETA&rft_subject=Ecosystem Assessment And Management (9605)&rft_subject=Land And Water Management (9609)&rft.type=dataset&rft.language=English Access the data

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TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting

Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.

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Brief description

The dataset consists of results from two stream mesocosm experiments that were conducted in the summer-autumn of 1996 and 1997 to distinguish the influence of fine sediment loads and nutrient concentrations on benthic macro-invertebrate and algal communities. 11 biological variables were extracted from the results of this experiment and were standardized for the purpose of training neural networks that could be used to diagnose nutrient and fine sediment impacts in field surveys. The 11 variables were selected according to how well they correlated with the experimental treatment levels (high and low values of both nutrients and fine sediments). The 11 variables were: chlorophyll a (mg/m2), macro-invertebrate familial richness, total abundance, and the abundance of Oligochaeta, Leptoperla varia (Gripopterygidae), Nousia spp. (Leptophlebiidae), Austrophlebioides spp. (Leptophlebiidae), Orthocladiinae, Tanypodinae, Tipulidae and larval Scirtidae. These taxa were abundant within and among the stream mesocosm communities and are common in a wide range of Tasmanian rivers. Values for each of 11 biological response variables were standardized by dividing by their average value observed in the experimental controls mesocosm samples from that year. See Magierowski RH, Read SM, Carter SJB, Warfe DM, Cook LS, Lefroy EC, et al. (2015) Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks. PLoS ONE 10(3): e0120901. https://doi.org/10.1371/journal.pone.0120901 https://doi.org/10.1371/journal.pone.0120901. This data was collected for the purpose of training artificial neural networks that could diagnose nutrient and sediment impacts in Tasmanian rivers. Each of the 11 variables were standardized by their average value observed in the experimental control samples from that year and some experimental treatment effects (Light) were ignored to simplify the neural network training process. Therefore, these data should not be used to make conclusions about the impacts of fine sediments and nutrients in Tasmanian rivers.

Lineage

1)Experimental design: The experiments were conducted using a flow-through water supply via diversion and hydraulic manifold from the Little Denison River, Tasmania (-43.0, 146.8). In each experiment, 32 flow-through mesocosms, each 4 m length × 0.4 m width × 0.4 m depth, were established with cleaned cobbles sourced from the adjacent river and colonised for four months by continuous constant flow-through from the Little Denison River. In a split-plot design, the mesocosms received low or high nutrient concentrations (Low: 0.035 mg/L NO3-N and 0.008 mg/L PO4-P; High: 0.4 mg/L NO3-N and 0.08 mg/L PO4-P), and low or high fine sediment loads (Low: < 5 g/m2 sand (grain size 0.06 -2 mm) initially and fortnightly pulses of < 5 mg/L suspended clay (grain size <0.06 mm) during 12 hr of raised flows; High: 1 kg/m2 sand initially and fortnightly pulses of 100 mg/L suspended clay). These concentrations were selected based on field measurements of streams impacted by agriculture and forestry (P.E. Davies, unpubl. data). Nutrient conditions were not sufficiently low to be limiting in the control treatments and in the high nutrient treatments were representative of high mean values in Tasmanian agricultural catchments (baseflow values: TN: 0.01-1.5 mg/L; TP: 0.01-0.05 mg/L). Benthic sediment loads in the high sediment treatments were similar to those observed in Tasmanian streams in highly grazed and cleared agricultural catchments. There were 16 replicates of each treatment combination, and each experiment ran for 90 days. One macroinvertebrate and one composite benthic algal sample was collected from each mesocosm at the end of each experiment by Surber and scrape sampling as detailed above, resulting in 64 samples in total.

2) Data processing: Macroinvertebrate abundance and species composition, and benthic algal biomass (chlorophyll-a) were not significantly different between the two years (P.E. Davies, unpublished data) so the two data sets were combined. A third factor, light, was also manipulated in the mesocosm experiment with the use of commercially available shade-cloth over half of the replicate stream channels. This factor was ignored for the purpose of training ANNs and the ANNs were trained to find patterns in macroinvertebrate community composition correlated with sediment and nutrient concentrations irrespective of light exposure. This decision was made because it was difficult to equate the use of shade cloth with local and catchment scale riparian shading in the gradient survey. Values for each of 11 biological response variables were standardized by dividing by their average value observed in the experimental controls mesocosm samples from that year.

3) Justification for nutrient levels: Australasian and international literature on nitrate and phosphate concentrations limits to stream benthic algal growth was examined (Biggs and Geoff 1987, Biggs 2000a, b, Bowman et al. 2007, Dodds and Welch 2000, Dodds et al 1998, 2002, Mosich et al 2001), along with local Tasmanian data on nutrient-algal relationships (Davies, Cook and O'Brien unpublished data). TP and TN concentrations (as soluble reactive phosphate, or DRP, and nitrate) above 0.05 and 0.1 mg/l respectively, were deemed no longer strongly limiting to benthic algal growth under ambient high light conditions. High nutrient treatments were therefore selected for this experiment to be nominally 0.1 and 0.5 mg/l as DRP-P and NO3-N. Nutrient treatments were applied by continuous addition, from shaded 10 litre Marriott bottles, refilled very two days, of an 3-4 ml/min flow of a solution of NaH2PO4 and KNO3. Water samples for nutrient analysis were taken weekly to fortnightly from the upper end of each trough during each three month trial. Overall mean measured concentrations of 0.06 and 0.37 mg/l PO4-P and NO3-N respectively in the high nutrient (LNs and lNs) treatments (n = 72 samples). The low nutrient treatment consisted of the ambient nutrient levels provided by the source water. Overall mean measured concentrations in the low nutrient treatment troughs were 0.0099 mg/l PO4-P and 0.038 mg/l NO3-N . There were no significant differences in concentrations between years (all p > 0.2 by one-way ANOVA). References: Biggs B.J.F. & Geoff M.P. (1987) A survey of filamentous algal proliferations in New Zealand rivers. New Zealand Journal of Marine and Freshwater Research 21, 175-191. Biggs B.J.F. (2000a) Eutrophication of streams and rivers: Dissolved nutrient-chlorophyll relationships for benthic algae. Journal of the North American Benthological Society 19, 17-31. Biggs B.J.F. (2000b) New Zealand Periphyton Guideline: Detecting, monitoring and managing enrichment of streams. Ministry for the Environment - NIWA, Christchurch, NZ. 122 pp. Biggs B.J.G. (2000) Eutrophication of streams and rivers: dissolved nutrient-chlorophyll relationships for benthic algae. Journal of the North American Benthological Society 19,17-31. Bowman M.F., Chambers P.A. & Schindler D.W. (2007) Constraints on benthic algal response to nutrient addition in oligotrophic mountain rivers. River Research and Applications 2, 858-876. Dodds W.K., Jones J.R. & Welch E.B. (1998) Suggested classification for stream trophic state: distributions of temperate stream types by chlorophyll, total N and P. Water Research 32, 1455-1462. Dodds W.K., Smith V.H. & Lohman K. (2002) Nitrogen and phosphorus relationships to benthic algal biomass in temperate streams. Canadian Journal of Fisheries and Aquatic Science 59, 865-874.

Progress Code: completed
Maintenance and Update Frequency: notPlanned

Notes

Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
Purpose

The Freshwater Ecosystems Project is developing a suite of models to enable managers and planners to explore freshwater ecosystem responses to management interventions and climate change for the Tasmanian Midlands (with an emphasis on the impacts of intensifying agriculture) and the Australian Alps (with an emphasis on alpine and sub-alpine wetlands and bogs). Planners and managers will have the ability to explore responses of freshwater ecosystems to scenarios of management interventions and climate change.

The Landscapes and Policy Hub is a research collaboration that focuses on integrating ecology and social science to provide guidance for policy makers on planning and management of biodiversity at a regional scale. Focusing on two contrasting landscapes, the Tasmanian Midlands and the Australian Alps, the research hub is developing tools, techniques and policy options to integrate biodiversity into regional scale planning. The interdisciplinary research is placing particular emphasis on landscape-scale management of species and communities listed under the under Australia's primary conservation legislation: Environment Protection and Biodiversity Conservation Act 1999. This includes Matters of National Environment Significance like the Tasmanian Midlands Lowland Grasslands communities and the unique alpine wetlands in the Australian Alps. The research hub is hosted by the University of Tasmania and is one of five new national research hubs recently funded to study biodiversity conservation by the National Environmental Research Program (NERP) from 2011-2014.

Created: 1996-01-01

Issued: 2015-02-06

Modified: 2024-05-03

Data time period: 1996-01-01 to 1997-05-01

This dataset is part of a larger collection

146.8,-43 146.8,-43.1 146.7,-43.1 146.7,-43 146.8,-43

146.75,-43.05

text: IBRA region: Tasmania South East Single location on the Little Dennison River, Tasmania.