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      Spatio-Temporal Variability of Aquatic Vegetation in Taihu Lake over the Past 30 Years

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          Abstract

          It is often difficult to track the spatio-temporal variability of vegetation distribution in lakes because of the technological limitations associated with mapping using traditional field surveys as well as the lack of a unified field survey protocol. Using a series of Landsat remote sensing images (i.e. MSS, TM and ETM+), we mapped the composition and distribution area of emergent, floating-leaf and submerged macrophytes in Taihu Lake, China, at approximate five-year intervals over the past 30 years in order to quantify the spatio-temporal dynamics of the aquatic vegetation. Our results indicated that the total area of aquatic vegetation increased from 187.5 km 2 in 1981 to 485.0 km 2 in 2005 and then suddenly decreased to 341.3 km 2 in 2010. Similarly, submerged vegetation increased from 127.0 km 2 in 1981 to 366.5 km 2 in 2005, and then decreased to 163.3 km 2. Floating-leaf vegetation increased continuously through the study period in both area occupied (12.9 km 2 in 1981 to 146.2 km 2 in 2010) and percentage of the total vegetation (6.88% in 1981 to 42.8% in 2010). In terms of spatial distribution, the aquatic vegetation in Taihu Lake has spread gradually from the East Bay to the surrounding areas. The proportion of vegetation in the East Bay relative to that in the entire lake has decreased continuously from 62.3% in 1981, to 31.1% in 2005 and then to 21.8% in 2010. Our findings have suggested that drastic changes have taken place over the past 30 years in the spatial pattern of aquatic vegetation as well as both its relative composition and the amount of area it occupies.

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          Chesapeake bay: an unprecedented decline in submerged aquatic vegetation.

          Data on the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay indicate a significant reduction in all species in all sections of the bay during the last 15 to 20 years. This decline is unprecedented in the bay's recent history. The reduction in one major species, Zostera marina, may be greater than the decline that occurred during the pandemic demise of the 1930' s.
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            Positive Feedbacks in Seagrass Ecosystems – Evidence from Large-Scale Empirical Data

            Positive feedbacks cause a nonlinear response of ecosystems to environmental change and may even cause bistability. Even though the importance of feedback mechanisms has been demonstrated for many types of ecosystems, their identification and quantification is still difficult. Here, we investigated whether positive feedbacks between seagrasses and light conditions are likely in seagrass ecosystems dominated by the temperate seagrass Zostera marina. We applied a combination of multiple linear regression and structural equation modeling (SEM) on a dataset containing 83 sites scattered across Western Europe. Results confirmed that a positive feedback between sediment conditions, light conditions and seagrass density is likely to exist in seagrass ecosystems. This feedback indicated that seagrasses are able to trap and stabilize suspended sediments, which in turn improves water clarity and seagrass growth conditions. Furthermore, our analyses demonstrated that effects of eutrophication on light conditions, as indicated by surface water total nitrogen, were on average at least as important as sediment conditions. This suggests that in general, eutrophication might be the most important factor controlling seagrasses in sheltered estuaries, while the seagrass-sediment-light feedback is a dominant mechanism in more exposed areas. Our study demonstrates the potentials of SEM to identify and quantify positive feedbacks mechanisms for ecosystems and other complex systems.
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              Indicators of regime shifts in ecological systems: what do we need to know and when do we need to know it?

              Because novel ecological conditions can cause severe and long-lasting environmental damage with large economic costs, ecologists must identify possible environmental regime shifts and pro-actively guide ecosystem management. As an illustrative example, we applied six potential indicators of impending regime shifts to S. R. Carpenter and W. A. Brock's model of lake eutrophication and analyzed whether or not they afforded adequate advance warning to enable preventative interventions. Our initial analyses suggest that an indicator based on the high-frequency signal in the spectral density of the time-series provides the best advance warning of a regime shift, even when only incomplete information about underlying system drivers and processes is available. In light of this result, we explored two key factors associated with using indicators to prevent regime shifts. The first key factor is the amount of inertia in the system; i.e., how fast the system will react to a change in management, given that a manager can actually control relevant system drivers. If rapid, intensive management is possible, our analyses suggest that an indicator must provide at least 20 years advance warning to reduce the probability of a regime shift to < 5%. As time to intervention is increased or intensity of intervention is decreased, the necessary amount of advance warning required to avoid a regime shift increases exponentially. The second key factor concerns the amount and type of variability intrinsic to the system, and the impact of this variability on the power of an indicator. Indicators are considered powerful if they detect an impending regime shift with adequate lead time for effective management intervention, but not so far in advance that interventions are too costly or unnecessary. Intrinsic "noise" in the system obscures the "signal" provided by all indicators, and therefore, the power of the indicators declines rapidly with increasing within- and between-year variability in measurable variables or parameters. Our results highlight the key role of human decisions in managing ecosystems and the importance of pro-active application of the precautionary principle to avoid regime shifts.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                18 June 2013
                : 8
                : 6
                : e66365
                Affiliations
                [1]Department of Biological Science and Technology, Nanjing University, Nanjing, P R China
                Catalan Institute for Water Research (ICRA), Spain
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DZ SA. Performed the experiments: DZ DX HJ YC ML. Analyzed the data: DZ. Wrote the paper: DZ HJ.

                Article
                PONE-D-12-39625
                10.1371/journal.pone.0066365
                3688898
                23823189
                ddbc0ab9-ec16-4832-b64d-a2c8de3f9f8f
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 December 2012
                : 4 May 2013
                Page count
                Pages: 7
                Funding
                This research was supported by the National Natural Science Foundation of China (31000226) and Environmental Monitoring Research Foundation of Jiangsu Province (1014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Ecology
                Ecological Environments
                Aquatic Environments
                Plant Science
                Botany
                Earth Sciences
                Environmental Sciences
                Environmental Engineering
                Geography
                Geoinformatics
                Marine and Aquatic Sciences
                Freshwater Ecology
                Water Quality

                Uncategorized
                Uncategorized

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