Evidence-Based Management of the Anzali Wetland System (Northern Iran) Based on Innovative Monitoring and Modeling Methods

Roghayeh Sadeghi Pasvisheh, Marie Anne Eurie Forio, Long Tuan Ho and Peter L. M. Goethals
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Roghayeh Sadeghi Pasvisheh: Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium
Marie Anne Eurie Forio: Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium
Long Tuan Ho: Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium
Peter L. M. Goethals: Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium

Sustainability, 2021, vol. 13, issue 10, 1-16

Abstract: As an “international aquatic ecosystem” in Northern Iran, the Anzali wetland is a nursery for fish and a breeding and wintering area for a wide variety of waterfowl. The wetland is threatened by human activities (deforestation, hunting, tourism, and urbanization), leading to habitat destruction, eutrophication, and sediment accumulation. To stop the degradation and to set up effective protection and restoration in line with the Sustainable Development Goals, scientific insights must be integrated into a practical framework for evidence-based support for policymakers and managers of the Anzali wetland. In this study, the Drivers–Pressure–State–Impact–Response (DPSIR) framework is used as a suitable tool to link human pressures and state changes to derive an overview of the potential impacts. Population growth, intensive agriculture, increased urbanization, and industrialization are the major driving forces that have led to a complex cascade of state changes. For instance, during recent years, water quality deterioration, habitat degradation, and the overgrowth of invasive species in the Anzali wetland watershed have caused negative socio-economic and human health impacts. Integrated and innovative monitoring programs combined with socio-environmental modeling techniques are needed for a more evidence-based management approach as part of a multiresponse strategy for the sustainable development of the wetland system. In this respect, there is a critical gap in useful information concerning biological composition and innovative monitoring methods. Moreover, the relation of biota with human activity and environmental conditions needs to be better quantified. Therefore, ecological modeling techniques based on machine learning and statistics were reviewed for their advantages and disadvantages. The overview of approaches presented here can serve as the basis for scientists, practitioners, and decision-makers to develop and implement evidence-based management programs for the Anzali wetland.

Keywords: DPSIR framework; integrated monitoring; ecological modeling; evidence-based management; aquatic ecosystem management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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