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Feng J, Yu H, Wu L, Yuan C, Zhao X, Sun H, Cheng C, Li Y, Sun J, Li Y, Wang X, Shang Y, Xu J, Zhang T. Evaluating Ecosystem Characteristics and Ecological Carrying Capacity for Marine Fauna Stock Enhancement Within a Marine Ranching System. Animals (Basel) 2025; 15:165. [PMID: 39858165 PMCID: PMC11758635 DOI: 10.3390/ani15020165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2024] [Revised: 12/29/2024] [Accepted: 01/02/2025] [Indexed: 01/27/2025] Open
Abstract
China has recently launched extensive marine ranching projects, highlighting the need for scientific evaluation of ecosystem structure and function to guide their development. This study established two energy flow models and an evaluation index system to assess the structure, function, carrying capacity, and ecological status of both a marine ranching ecosystem and a nearby control site in the Beibu Gulf. The results show that the ranching ecosystem outperformed the control ecosystem in terms of food chain length, system size, and ecological carrying capacity of economically important species. The ranching ecosystem was classified as "relatively good", while the control ecosystem was deemed "relatively poor", which may confirm the success of the ranching efforts. Mussels, large crabs, and scorpaenidae were identified as key species for stock enhancement based on their biomass potential. Scenario simulations using Ecosim, driven by biomass and fishing factors, indicate that stock enhancement strategies targeting MOB (mussels, oysters, and barnacles) significantly improved the ranching ecosystem, raising its status to "good". However, the simulations also revealed that stock enhancement had limited effects on optimizing food web structure, system organization, and energy transfer efficiency, suggesting that a combination of strategies is necessary for further improvement.
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Affiliation(s)
- Jie Feng
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Key Laboratory of Ecological Prewarning, Protection and Restoration of Bohai Sea, Ministry of Natural Resources, Qingdao 266033, China
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Haolin Yu
- Laboratory for Marine Science and Technology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China; (H.Y.); (C.C.); (Y.S.)
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Engineering Laboratory for Marine Ranching, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
| | - Lingjuan Wu
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Chao Yuan
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Xiaolong Zhao
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Huiying Sun
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Cheng Cheng
- Laboratory for Marine Science and Technology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China; (H.Y.); (C.C.); (Y.S.)
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Engineering Laboratory for Marine Ranching, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
| | - Yifei Li
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Jingyi Sun
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Yan Li
- Guangxi Academy of Oceanography, Nanning 530022, China;
| | - Xiaolong Wang
- Marine Science Research Institute of Shandong Province, Qingdao 266104, China;
| | - Yongjun Shang
- Laboratory for Marine Science and Technology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China; (H.Y.); (C.C.); (Y.S.)
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Engineering Laboratory for Marine Ranching, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
| | - Jiangling Xu
- North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China; (J.F.); (L.W.); (C.Y.); (X.Z.); (H.S.); (Y.L.); (J.S.)
- Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
| | - Tao Zhang
- Laboratory for Marine Science and Technology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China; (H.Y.); (C.C.); (Y.S.)
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Engineering Laboratory for Marine Ranching, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266400, China
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Zeng Y, Liu G, Li J, Zhao Y, Yang W. Ecological threshold of phosphorus load in Baiyangdian Lake based on a PCLake model and ecological network analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:170091. [PMID: 38224883 DOI: 10.1016/j.scitotenv.2024.170091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 01/17/2024]
Abstract
Ecological thresholds are a useful indicator for implementing ecological management. Many studies determine the thresholds for nutrient loads in lakes based on the maximum allowable concentration of chlorophyll a (Chla), although this neglects the overall performance of the ecosystem. A PCLake model of Baiyangdian (BYD) Lake in northern China was constructed with six ecological network analysis (ENA) indicators that characterized the ecosystem function, system maturity, and food web structure to quantify the overall status of the BYD ecosystem. To my knowledge, this is the first study on the system level responses of the BYD Lake to phosphorus load interference. Different phosphorus load scenarios were designed to simulate the ecological responses of BYD Lake. The simulated results were employed to calculate the ENA indicators. Ecological thresholds were determined through the driving response relationship between the phosphorus load gradient and the ENA indicators. The results show a non-linear transition response of ENA indicator under phosphorus load gradient. As phosphorus load increases, D/H, SOI, and FCI decreases while A/DC, TPP/TR, and TPP/TB increases. This indicates that the overall structure and function of the ecosystem will deteriorate if phosphorus load increases. The phosphorus load thresholds for the overall performance of BYD Lake were 0.50-1.32 mg m-2 d-1, slightly wider than that of Chla (0.53-1.26 mg m-2 d-1). The model results clearly indicate that there is a time-lag phenomenon at the switch points in the response of ENA indicators compared to that of single functional group. In addition, the A/DC, TPP/TR, SOI, and FCI present more time-lag than that of other ENA indicators. These time-lag effects provide a particular opportunity for biodiversity conservation. Therefore, a possible management strategy is proposed to combine system-level and function group-level thresholds, with the ENA-based threshold as the bottom line and the phytoplankton's threshold as the early-warning indicator. This design is expected to be more precise and efficient, by exploiting the advantages of two thresholds, and may benefit for ecological management practices.
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Affiliation(s)
- Yong Zeng
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Oil & Gas Pollution Control, College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China.
| | - Gaiguo Liu
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Oil & Gas Pollution Control, College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China
| | - Jiaxin Li
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Oil & Gas Pollution Control, College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China
| | - Yanwei Zhao
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Wei Yang
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
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Romero J, Alonso H, Freitas L, Granadeiro JP. Food web of the oceanic region of the archipelago of Madeira: The role of marine megafauna in the subtropical northeast Atlantic ecosystem. MARINE ENVIRONMENTAL RESEARCH 2024; 195:106382. [PMID: 38309039 DOI: 10.1016/j.marenvres.2024.106382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
Many oceanic areas are still in need of baseline information on their structure and functioning. This is particularly important due to the ever-increasing impacts of global changes, which have led to the decline of marine life, and top predators in particular. The study of the structure and functioning of food webs can help understand the consequences of the disappearance of this group in marine ecosystems. Here, we develop a mass-balanced model for the marine Exclusive Economic Zone of the archipelago of Madeira, with emphasis on the role of marine megafauna in this ecosystem. A total of 50 functional groups were defined, representing coastal and open ocean areas, and epipelagic and deep-sea levels. The total biomass of the Madeira system was calculated at 52.68 t km-2, with lower trophic level organisms comprising 89.9 % of its biomass. Marine megafauna, namely pelagic sharks and coastal birds had the highest impacts across other trophic levels and were classified as keystone species, together with monk seals. The food web was characterized by a linear-like food chain, with a large proportion of specialized organisms, including dolphins, shearwaters, and large pelagic fish. The low mean trophic level of the system was 2.03, much lower than that of fisheries (4.3) targeting mainly tunas and Black scabbardfish. Considering the importance of marine megafauna in this food web and the threats they are facing; monitoring studies of key species in the region should be a priority. This study can now be used to build a needed ecosystem-based fisheries management and integrate conservation measures to declining species.
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Affiliation(s)
- Joana Romero
- Centre for Environmental and Marine Studies (CESAM), Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016, Lisbon, Portugal.
| | - Hany Alonso
- Sociedade Portuguesa para o Estudo das Aves, Av. Alm. Gago Coutinho 46A, 1700-031, Lisbon, Portugal
| | - Luís Freitas
- Museu da Baleia da Madeira, Rua Garcia Moniz 1, 9200-031, Caniçal, Madeira, Portugal
| | - José Pedro Granadeiro
- Centre for Environmental and Marine Studies (CESAM), Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
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Ecosystem Network Analysis in a Smallholder Integrated Crop–Livestock System for Coastal Lowland Situation in Tropical Humid Conditions of India. SUSTAINABILITY 2020. [DOI: 10.3390/su12125017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The integrated crop–livestock system (ICLS) is a farming strategy that helps to sustain agrobiodiversity, ecosystem services, and restores environmental sustainability. Furthermore, ICLS provides food and nutritional security to the small and marginal farmers in developing nations. In this context a mass-balanced ecosystem model was constructed for a smallholder ICLS along the Indian west coast to analyze the agro-ecological performance in terms of sustainability, resource use, nutrient balance and recycling. Thirteen functional groups were defined in the ICLS model with trophic levels ranging from 1.00 (detritus and benthic nitrogen fixers) to 3.00 (poultry and ruminants). The total system throughput index was estimated to be 1134.9 kg N ha−1 year−1 of which 60% was from consumption, 15% from exports, 10% from respiration, and the remaining 15% eventually flowing into detritus. The gross efficiency of the ecosystem was estimated to 0.3, which indicated higher growth rates and low maintenance energy costs. The higher food self-sufficiency ration of 7.4 indicated the integration of crop–livestock as an imperative system to meet the food and nutritional requirement of the farm family. The indices such as system overhead (60%), Finn’s cycling index (16.6) and mean path length (3.5) denoted that the ICLS is a small, resource-efficient, stable, maturing and sustainable ecosystem in terms of the ecosystem principles and recycling. The present model will serve as the first model on the ICLS from the humid tropics and will help in the evaluation of the other agro-ecological systems using the Ecopath modelling approach. In conclusion, farm intensification through crop and animal diversification has the highest impact on farm productivity, food self-sufficiency and resource-use-efficiency of the smallholder’s livelihood security.
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Impacts of Mainstream Hydropower Dams on Fisheries and Agriculture in Lower Mekong Basin. SUSTAINABILITY 2020. [DOI: 10.3390/su12062408] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The riverine ecosystems of the Mekong River Basin possess the world’s most productive inland fishery and provide highly productive food crops for millions of people annually. The development of hydropower potential in the Mekong River has long been of interest to governments in the region. Among the existing 64 dams, 46 dams have been built in the Lower Mekong Basin (LMB) to produce up to 8650 MW of electricity. Additionally, of the 123 proposed built hydropower dams, eleven hydropower plants have been nominated for the river mainstream and are expected to install a total of 13,000 MW in the LMB countries. However, serious concerns have intensified over the potential negative economic consequences, especially on fisheries and agriculture in Cambodia and Vietnam. To date, most of the concerns have concentrated on the impacts on hydrology, environment, livelihood, and diversity in the LMB attributed to hydropower development. This paper, however, discusses the fishery and agricultural sectors of the LMB and focuses on the downstream floodplains of Cambodia and Vietnam. The dam construction has caused greater losses of biodiversity and fisheries than climate change in the LMB. The reduction of 276,847 and 178,169 t of fish, 3.7% and 2.3% of rice, 21.0% and 10.0% of maize will contribute to a decrease of 3.7% and 0.3% of the GDP of Cambodia and Vietnam, respectively. Lao PDR may benefit the most revenue from electricity generation than the other country in the LMB, as most of the proposed dams are projected in the country. Cambodia burdens 3/4 of the reduction of total capture fishery destruction, whilst Lao PDR, Thailand, and Vietnam endure the remaining 1/3 losses. The tradeoff analyses reveal that losses of capture fisheries, sediment or nutrients, and social mitigation costs are greater than the benefits from electricity generation, improved irrigation, and flood control of the LMB region. The socioeconomic and environmental damage caused by hydropower dams in developing countries, including the Mekong, is greater than the early costs in North America and Europe. It is proposed that dam construction for hydropower in the Mekong River, as well as other rivers in developing countries, should be gradually removed and shifted toward solar, wind, and other renewable resources.
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Wang Y, Kao YC, Zhou Y, Zhang H, Yu X, Lei G. Can water level management, stock enhancement, and fishery restriction offset negative effects of hydrological changes on the four major Chinese carps in China’s largest freshwater lake? Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.03.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Althor G, Mahood S, Witt B, Colvin RM, Watson JE. Large-scale environmental degradation results in inequitable impacts to already impoverished communities: A case study from the floating villages of Cambodia. AMBIO 2018; 47:747-759. [PMID: 29460255 PMCID: PMC6188964 DOI: 10.1007/s13280-018-1022-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2017] [Revised: 12/04/2017] [Accepted: 01/29/2018] [Indexed: 06/08/2023]
Abstract
Cambodian subsistence communities within the Tonle Sap Great Lake area rely on resource extraction from the lake to meet livelihood needs. These fishing communities-many of which consist of dwellings floating on the lake-face potentially profound livelihood challenges because of climate change and changing hydrology due to dam construction for hydroelectricity within the Mekong Basin. We conducted interviews across five village communities, with local subsistence fisher people in the Tonle Sap in 2015, and used thematic analysis methods to reveal a fishery system that is undergoing rapid ecological decline, with local fishing communities increasingly experiencing reductions in available fish stocks. As a result, over 100 000 people living in these communities are experiencing a direct loss of well-being and livelihood. We discuss these losses and consider their implications for the future viability of Cambodian floating village communities.
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Affiliation(s)
- Glenn Althor
- School of Earth and Environmental Sciences, University of Queensland, Queensland, 4072 Australia
| | - Simon Mahood
- Wildlife Conservation Society Cambodia Program, #21, Street 21, Sangkat Tonle Bassac, PO Box 1620, Phnom Penh, Cambodia
- Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT 0909 Australia
| | - Bradd Witt
- School of Earth and Environmental Sciences, University of Queensland, Queensland, 4072 Australia
| | - Rebecca M. Colvin
- Climate Change Institute, Australian National University, Post: Building 141, Linnaeus Way, Canberra, ACT 2601 Australia
| | - James E.M. Watson
- School of Earth and Environmental Sciences, University of Queensland, Queensland, 4072 Australia
- Global Conservation Program, Wildlife Conservation Society, Bronx, NY 10460 USA
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Guo C, Chen Y, Li W, Xie S, Lek S, Li Z. Food web structure and ecosystem properties of the largest impounded lake along the eastern route of China's South-to-North Water Diversion Project. ECOL INFORM 2018. [DOI: 10.1016/j.ecoinf.2017.12.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Robson BJ, Lester RE, Baldwin DS, Bond NR, Drouart R, Rolls RJ, Ryder DS, Thompson RM. Modelling food-web mediated effects of hydrological variability and environmental flows. WATER RESEARCH 2017; 124:108-128. [PMID: 28750285 DOI: 10.1016/j.watres.2017.07.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 07/14/2017] [Accepted: 07/15/2017] [Indexed: 06/07/2023]
Abstract
Environmental flows are designed to enhance aquatic ecosystems through a variety of mechanisms; however, to date most attention has been paid to the effects on habitat quality and life-history triggers, especially for fish and vegetation. The effects of environmental flows on food webs have so far received little attention, despite food-web thinking being fundamental to understanding of river ecosystems. Understanding environmental flows in a food-web context can help scientists and policy-makers better understand and manage outcomes of flow alteration and restoration. In this paper, we consider mechanisms by which flow variability can influence and alter food webs, and place these within a conceptual and numerical modelling framework. We also review the strengths and weaknesses of various approaches to modelling the effects of hydrological management on food webs. Although classic bioenergetic models such as Ecopath with Ecosim capture many of the key features required, other approaches, such as biogeochemical ecosystem modelling, end-to-end modelling, population dynamic models, individual-based models, graph theory models, and stock assessment models are also relevant. In many cases, a combination of approaches will be useful. We identify current challenges and new directions in modelling food-web responses to hydrological variability and environmental flow management. These include better integration of food-web and hydraulic models, taking physiologically-based approaches to food quality effects, and better representation of variations in space and time that may create ecosystem control points.
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Affiliation(s)
- Barbara J Robson
- CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601, Australia.
| | - Rebecca E Lester
- Centre for Regional and Rural Futures, Deakin University, Locked Bag 20000, Geelong, Vic, 3220, Australia.
| | - Darren S Baldwin
- CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601, Australia; The Murray-Darling Freshwater Research Centre, La Trobe University, PO Box 821, Wodonga, Vic, 3689, Australia; Charles Sturt University, Thurgoona, NSW, 2640, Australia
| | - Nicholas R Bond
- The Murray-Darling Freshwater Research Centre, La Trobe University, PO Box 821, Wodonga, Vic, 3689, Australia
| | - Romain Drouart
- CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601, Australia; Ecole des Mines d'Alès, 6 Avenue de Clavières, 30319, Alès Cedex, France
| | - Robert J Rolls
- Institute for Applied Ecology, University of Canberra, Canberra, ACT, 2601, Australia
| | - Darren S Ryder
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
| | - Ross M Thompson
- Institute for Applied Ecology, University of Canberra, Canberra, ACT, 2601, Australia
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Yurek S, DeAngelis DL, Trexler JC, Klassen JA, Larsen LG. Persistence and diversity of directional landscape connectivity improves biomass pulsing in simulations of expanding and contracting wetlands. ECOLOGICAL COMPLEXITY 2016. [DOI: 10.1016/j.ecocom.2016.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Banerjee A, Banerjee M, Mukherjee J, Rakshit N, Ray S. Trophic relationships and ecosystem functioning of Bakreswar Reservoir, India. ECOL INFORM 2016. [DOI: 10.1016/j.ecoinf.2016.09.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Seasonal Changes in the Inundation Area and Water Volume of the Tonle Sap River and Its Floodplain. HYDROLOGY 2016. [DOI: 10.3390/hydrology3040033] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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