A broad-scale assessment of the risk to coastal seagrasses from cumulative threats

Grech, Alana; Coles, Robert G.; Marsh, Helene

doi:10.1016/j.marpol.2011.03.003

Cited by 98 publications

(62 citation statements)

References 24 publications

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“…They can be used in future to quantitatively assess ecosystems at greatest risk from plume water contaminants and, therefore, prioritise investment into water quality improvement. Table S1: Operational bio-optical algorithms tested for the retrieval of WQ data gradients [1]; Table S2: Summary of water quality variables (WQv) and risk assessment classes defined in Brodie et al risk assessment framework [10]. Magnitude and Likelihood categories (VR: Very Rare, R: Rare, O: Occasional, F: Frequent, VF: Very Frequent) and final risk categories (likelihood x magnitude): VL: Very Low, L: Low, M: Medium, H: High, VH: Very High are from published values or estimated by expert opinion.…”

Section: Discussionmentioning

confidence: 99%

“…Magnitude and Likelihood categories (VR: Very Rare, R: Rare, O: Occasional, F: Frequent, VF: Very Frequent) and final risk categories (likelihood x magnitude): VL: Very Low, L: Low, M: Medium, H: High, VH: Very High are from published values or estimated by expert opinion. Likelihood categories for TSS and Chl-a are based on frequency of exceedance of the water quality threshold using remote sensing data and PSII categories are based on a recent assessment of to PSII (modified from [10,11]); Table S3: Proportion of macroalgue in the algal communities (MAp) measured through the MMP. Interpolated data are indicated in italic and with an asterisk; Table S4: Seagrass cover data measured through the MMP.…”

Section: Discussionmentioning

confidence: 99%

“…More than 30 rivers drain into the GBR and are a major source of land-sourced contaminants delivered to the marine environment [9]. The sediments, nutrients and herbicides discharged as agricultural runoff through river plumes have been identified as the contaminants of greatest concern with regards to their potential impacts on GBR key ecosystems, including coral reefs and seagrass meadows (e.g., [9,10]). Different river plume water types (hereafter plume water types), so called "Primary" for the inshore plume waters, "Secondary" for the midshelf-plume waters and "Tertiary" for the offshore plume waters, have been described in the GBR.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating the Exposure of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating a Simple Satellite Risk Framework with Environmental Data

et al. 2016

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River runoff and associated flood plumes (hereafter river plumes) are a major source of land-sourced contaminants to the marine environment, and are a significant threat to coastal and marine ecosystems worldwide. Remote sensing monitoring products have been developed to map the spatial extent, composition and frequency of occurrence of river plumes in the Great Barrier Reef (GBR), Australia. There is, however, a need to incorporate these monitoring products into Risk Assessment Frameworks as management decision tools. A simple Satellite Risk Framework has been recently proposed to generate maps of potential risk to seagrass and coral reef ecosystems in the GBR focusing on the Austral tropical wet season. This framework was based on a "magnitudeˆlikelihood" risk management approach and GBR plume water types mapped from satellite imagery. The GBR plume water types (so called "Primary" for the inshore plume waters, "Secondary" for the midshelf-plume waters and "Tertiary" for the offshore plume waters) represent distinct concentrations and combinations of land-sourced and marine contaminants. The current study aimed to test and refine the methods of the Satellite Risk Framework. It compared predicted pollutant concentrations in plume water types (multi-annual average from 2005-2014) to published ecological thresholds, and combined this information with similarly long-term measures of seagrass and coral ecosystem health. The Satellite Risk Framework and newly-introduced multi-annual risk scores were successful in demonstrating where water conditions were, on average, correlated to adverse biological responses. Seagrass meadow abundance (multi-annual change in % cover) was negatively correlated to the multi-annual risk score at the site level (R 2 = 0.47, p < 0.05). Relationships between multi-annual risk scores and multi-annual changes in proportional macroalgae cover (as an index for coral reef health) were more complex (R 2 = 0.04, p > 0.05), though reefs incurring higher risk scores showed relatively higher proportional macroalgae cover. Multi-annual risk score thresholds associated with loss of seagrass cover were defined, with lower risk scores (ď0.2) associated with a gain or little loss in seagrass cover (gain/´12%), medium risk scores (0.2-0.4) associated with moderate loss (´12/´30%) and higher risk scores (>0.4) with the greatest loss in cover (>´30%). These thresholds were used to generate an intermediate river plume risk map specifically for seagrass meadows of the GBR. An intermediate river plume risk map for coral reefs was also developed by considering a multi-annual risk score threshold of 0.2-above which a higher proportion of macroalgae within the algal communities can be expected. These findings contribute to a long-term and adaptive approach to set relevant risk framework and thresholds for adverse biological responses in the GBR. The ecological thresholds and risk scores used in this study will be refined and validated through ongoing monitoring and assessment. As uncertainties are red...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating the Exposure of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating a Simple Satellite Risk Framework with Environmental Data

et al. 2016

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“…The losses reported by Coles et al (2015) were proposed to have been caused by a string of severe cyclones and floods on the Queensland coast. Decreases in seagrass distribution in the overall GBR region have been primarily attributed to anthropogenic activities such as agricultural, urban and industrial runoff, and urban and port developments, particularly in inshore areas (Grech et al 2011). If the loss observed between 1995 and 2011 is accurate, and it occurred consistently during the 16-year time interval, the rate of loss would have been 2.9% year -1 , 60% less than the rate reported for seagrass globally (7% year -1 ; Waycott et al 2009).…”

Section: Seagrass Species Distribution In 2011 and Comparison To Pattmentioning

confidence: 99%

Spatial and temporal variability of seagrass at Lizard Island, Great Barrier Reef

Saunders¹,

Bayraktarov

Roelfsema

et al. 2015

Botanica Marina

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Increasing threats to natural ecosystems from local and global stressors are reinforcing the need for baseline data on the distribution and abundance of organisms. We quantified spatial and/or temporal patterns of seagrass distribution, shoot density, leaf area index, biomass, productivity, and sediment carbon content in shallow water (0-5 m) at Lizard Island, Great Barrier Reef, Australia, in field surveys conducted in December 2011 and October 2012. Seagrass meadows were mapped using satellite imagery and field validation. A total of 18.3 ha of seagrass, composed primarily of Thalassia hemprichii and Halodule uninervis, was mapped in shallow water. This was 46% less than the area of seagrass in the same region reported in 1995, although variations in mapping methods may have influenced the magnitude of change detected. There was inter-annual variability in shoot density and length, with values for both higher in 2011 than in 2012.Seagrass properties and sediment carbon content were representative of shallow-water seagrass meadows on a mid-shelf Great Barrier Reef island. The data can be used to evaluate change, to parameterize models of the impact of anthropogenic or environmental variability on seagrass distribution and abundance, and to assess the success of management actions.

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“…Because of its proximity to a heavily populated urban zone, and the relative shallowness of the coastal zone, seagrass meadows are prone to heavy impact from human activities (Grech et al, 2011). This decline of seagrass meadows around the world have been extensively documented , the drivers of which include eutrophication (Cardoso et al, 2004), the overharvesting of top predators (releasing herbivores to feed unimpeded) Valentine, 2007, 2006), and direct impact from mostly motorized vessels Dunton and Schonberg, 2002).…”

Section: Introductionmentioning

confidence: 99%

The Ecology and Economics of Seagrass Community Structure

Dewsbury¹

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ACKNOWLEDGMENTSThis dissertation document is a culmination of over a decade of intellectual discovery at Florida International University. Having received my Masters from the Department of Biological Sciences here at FIU, and then spending a subsequent year working as a technician; I view my dissertation as having built on the foundations I laid for myself in those early years. For all the trials and learning curves I have been through in order to complete this work I owe a debt of gratitude to many people. If I were to list every single individual who made a meaningful contribution to this work, this section will run multiple pages. So, to those who are not heretofore mentioned, please note that I am eternally grateful for all the contributions received including field help, manuscript comments, analysis advice, and general support over the years. typically a chief provider of some of these economic goods and services. Many previous studies have documented the ecological functions of this seagrasses. Unfortunately, our increasing knowledge of seagrass structure and function has not been fully incorporated into economic models estimating their value. In this dissertation, I focus on the seagrass ecosystem in southern Biscayne Bay, and simultaneously study the ecological dynamics of the seagrass beds, and estimate its economic value. This value is based on recent ecological models in the literature as well as data I collected from the system. I focused on Biscayne Bay due to, 1) the relevance that this question had to the relationship between Biscayne Bay and the Miami metropolis, and 2) the lack of existing reliable models that explore this relationship in this area. More specifically, I became very vi interested in this question while working for Biscayne National Park, where such a model would have improved seagrass restoration work taking place there.I found that southern Biscayne Bay is dominated by Thalassia testudinum, with other seagrasses following a spatial pattern primarily determined by salinity and water column nutrient distribution. Syringodium filiforme was mostly found east of the islands, Halodule wrightii was mostly found near the shoreline, and Halophila engelmenii was spotted at only two of the 190 sites visited. T. testudinum distribution was largely unaffected by nutrient enrichment at all sites, but it appeared to induce severe herbivory further from the coastline. For the calendar year 2004, we deduced using a Total Ecosystems Valuation (TEV) model that seagrass ecosystems potentially contributed over $198 million US dollars to the local economy. We argue that a simultaneous understanding and use of both ecological and economic models is important for future conservation efforts of seagrass ecosystems.

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A broad-scale assessment of the risk to coastal seagrasses from cumulative threats

Cited by 98 publications

References 24 publications

Estimating the Exposure of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating a Simple Satellite Risk Framework with Environmental Data

Estimating the Exposure of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating a Simple Satellite Risk Framework with Environmental Data

Spatial and temporal variability of seagrass at Lizard Island, Great Barrier Reef

The Ecology and Economics of Seagrass Community Structure

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