Detecting long temporal trends of photosystem II herbicides (PSII) in the Great Barrier Reef lagoon.

Monitoring programs are fundamental to understanding the state and trend of aquatic ecosystems. Sampling designs are a crucial component of monitoring programs and ensure that measurements evaluate progress toward clearly stated management objectives, which provides a mechanism for adaptive management. Here, we use a well-established marine monitoring program for inshore water quality in the Great Barrier Reef (GBR), Australia to investigate whether a sampling re-design has increased the program’s capacity to meet its primary objectives. Specifically, we use bootstrap resampling to assess the change in statistical power to detect temporal water quality trends in a 15-year inshore marine water quality data set that includes data from both before and after the sampling re-design. We perform a comprehensive power analysis for six water quality analytes at four separate study areas in the GBR Marine Park and find that the sampling re-design (i) increased power to detect trends in 23 of the 24 analyte-study area combinations, and (ii) resulted in an average increase in power of 34% to detect increasing or decreasing trends in water quality analytes. This increase in power is attributed more to the addition of sampling locations than increasing the sampling rate. Therefore, the sampling re-design has substantially increased the capacity of the program to detect temporal trends in inshore marine water quality. Further improvements in sampling design need to focus on the program’s capability to reliably detect trends within realistic timeframes where inshore improvements to water quality can be expected to occur.

Sampling re-design increases power to detect change in the Great Barrier Reef’s inshore water quality

[Extract] Indicators representative of ecosystem condition are required for the long-term monitoring of the Great Barrier Reef (GBR) in a Reef Integrated Monitoring and Reporting Program (RIMREP), which tracks progress towards Reef 2050 Plan targets and objectives. Seagrass meadows are highly sensitive to climatic conditions and environmental pressures such as water quality, as seen through recent (past 10 years) changes in abundance in the GBR (McKenzie, et al., 2016). Due to these impacts, GBR seagrass meadows underwent a period of decline from 2009 to 2011. Widespread loss of seagrass occurred, but in 2015 many meadows had started recovering. 

The storage reserves within seagrass rhizomes were tested for suitability as a complimentary indicator in the MMP/RIMREP because previous studies had suggested that they are good indicators. We set out to test the relationships between total non-structural carbohydrates (TNSC) and seagrass condition (i.e. trend in abundance, either declining pre 2011 or recovering post 2011), seagrass abundance, water temperature and daily light in a temporal analysis using linear models. Samples were collected quarterly from 2008 to 2015 from four locations (8 sites) for three species (917 samples in total) in the Wet Tropics and Burdekin regions. TNSC was significantly (p<0.001) lower pre 2011 during the period of decline (181and 192 mg gDW-1for intertidal sites pooled and subtidal sites pooled, respectively) than post 2011 during recovery (277 and 289 mg gDW-1) for H. uninervis. A similar trend was observed for T. hemprichii, which occurred at intertidal sites only (168 mg gDW-1 in decline and 208 mg gDW-1in recovery), but not for C. serrulata which had the fewest available data points. The differences were even greater when investigating individual sites. TNSC were also correlated (p<0.001) to seagrass abundance during both the decline and recovery phases. TNSC was positively correlated to water temperature, though the period being assessed was relatively mild in terms of temperature extremes. Therefore, light was the main pressure assessed in this project. A direct effect of light limitation (daily light, average of 30 days prior to TNSC collection) on TNSC was not observed, in fact there was a slight negative effect of light in some analyses. This was contrary to our hypothesis, as low light, at least in part, drove declines in seagrass abundance from 2009 –2011. In an additional spatial analysis, differences in TNSC among regions and habitat types were assessed from 39 sites collected in late 2014 across the GBR. This spatial analysis was carried out to explore representativeness of the sites used in the temporal analysis. There was little difference in TNSC among habitats; however, TNSC varied among NRMs and were lowest in the Mackay Whitsunday and Fitzroy NRMs. 

This exploration of storage reserves, undertaken at a time of dynamic meadow changes, has yielded exciting results on their variation with meadow condition and abundance. However, we did not provide conclusive evidence to support the inclusion of TNSC as an indicator in monitoring programs such as the MMP at this stage, because the link to the main environmental pressure tested –light –was not demonstrated by this analysis. Irrespective of this, TNSC was an indicator of cumulative stress (being correlated to abundance and condition), but the specific pressure(s) could not be identified. This provides justification for further inquiry into the effect of other pressures (e.g. nutrients and flood plume exposure), other biological processes (e.g. reproduction and meadow expansion) and to obtain further data on other species. 

We also tested the relationship between %cover and biomass, with the aim of developing biomass calibration formulae. Above-ground biomass and %cover was measured in seven mono-specific meadows for four species and four habitat types. Above ground biomass was highly correlated (p<0.001) to % cover, and the correlation was further improved (lower AIC) by factoring canopy height into the calibration. Even after canopy height was included in the calibration, canopy height strongly affected the calibration values and highlighted the importance of habitat/morphology-specific calibration formulae. Further work is required to capture all species and habitat/morphology combinations that are routinely monitored. With further work, these calibration values will enable integration among seagrass monitoring programs including Queensland Ports Seagrass Monitoring Program and GBR historical baseline data.

/pdf/developing-and-refining-biological-indicators-for-condition-6cj60oxg4v.pdf

Developing and refining biological indicators for condition assessments in an integrated monitoring program

Organic contaminants, in particular persistent organic pollutants (POPs), adversely affect water quality and aquatic food webs across the globe. As of now, there is no globally consistent information available on concentrations of dissolved POPs in water bodies. The advance of passive sampling techniques has made it possible to establish a global monitoring program for these compounds in the waters of the world, which we call the Aquatic Global Passive Sampling (AQUA-GAPS) network. A recent expert meeting discussed the background, motivations, and strategic approaches of AQUA-GAPS, and its implementation as a network of networks for monitoring organic contaminants (e.g., POPs and others contaminants of concern). Initially, AQUA-GAPS will demonstrate its operating principle via two proof-of-concept studies focused on the detection of legacy and emerging POPs in freshwater and coastal marine sites using both polyethylene and silicone passive samplers. AQUA-GAPS is set up as a decentralized network, which is...

/pdf/aquatic-global-passive-sampling-aqua-gaps-revisited-first-40eil101cb.pdf

Aquatic Global Passive Sampling (AQUA-GAPS) Revisited: First Steps toward a Network of Networks for Monitoring Organic Contaminants in the Aquatic Environment

/pdf/review-of-the-marine-monitoring-program-mmp-3ectgpw60j.pdf

Review of the Marine Monitoring Program (MMP)

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

F. Kroon

Author Tools

Chat about Author

Papers

Review of the Marine Monitoring Program (MMP)