Multi-scale estimation of the effects of pressures and drivers on mangrove forest loss globally

Turschwell, Mischa P.; Tulloch, Vivitskaia; Sievers, Michael; Pearson, Ryan M.; Andradi-Brown, Dominic A.; Ahmadia, Gabby N.; Connolly, Rod M.; Bryan-Brown, Dale; Lopez‐Marcano, Sebastian; Adame, María Fernanda; Brown, Christopher J.

doi:10.1016/j.biocon.2020.108637

Cited by 52 publications

(31 citation statements)

References 61 publications

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“…Several researches have abundantly performed the assessment of mangrove health status [165] and trend loss of mangrove forests [166], or to categorize the conservation index of mangrove forests [167]. Faridah-Hanum, et al [165] classified the status of mangrove forests comprehensively in an ecological-socioeconomic approach using biotic integrity, soil, marine, hydrology, and socio-economic factors.…”

Section: ) Related Studies To Develop a Mangrove Assessment Index Or Frameworkmentioning

confidence: 99%

“…On the other hand, Turschwell, et al [166] captured the multi-scale interactions of mangrove losses at different impacts for different nations through landscape differences and state changes by the application of an Bayesian hierarchical model within a Drivers, Pressures, State changes, Impact, and Response (DPSIR) framework. The Pressure-State-Response framework is a useful tool to understand the changes in environment caused by human activities [169].…”

Section: ) Related Studies To Develop a Mangrove Assessment Index Or Frameworkmentioning

confidence: 99%

“…The Pressure-State-Response framework is a useful tool to understand the changes in environment caused by human activities [169]. The correlative relationships of the DPSIR variables defined by Turschwell, et al [166] are weak and contains uncertainty since complex political and socio-ecological perspectives are involved. The gap in this study is the lack of predictors for human related activities such as the likelihood of land conversion for agriculture, aquaculture and plantation, or the effects of pollution and ecosystem threats.…”

Section: ) Related Studies To Develop a Mangrove Assessment Index Or Frameworkmentioning

confidence: 99%

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Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

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Section: ) Related Studies To Develop a Mangrove Assessment Index Or Frameworkmentioning

confidence: 99%

Section: ) Related Studies To Develop a Mangrove Assessment Index Or Frameworkmentioning

confidence: 99%

Section: ) Related Studies To Develop a Mangrove Assessment Index Or Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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“…Forest fragmentation due to all sorts of anthropogenic disturbances is herein translated by socioeconomic parameters that can cause effective loss of mangroves because of demographic pressure influence. Despite the essential role played by mangroves in the health of the environment, approximately 0.55% of mangrove area -for each 1% increase in human population density -is lost for agricultural conversion in one of the biggest mangroves in the world (Turschwell, et al, 2020).…”

Section: Factors Related To Natural Hazardous Socio-economy and The Environmentmentioning

confidence: 99%

Effects of anthropic activity dynamics on mangrove ecosystems’ degradation in Caracol County – Haiti

Morancy

Cunha

2021

Preprint

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Anthropization process and climatic changes, mainly deforestation and sea level rise, are factors significantly contributing to the most evident loss of mangrove ecosystems. The aim of our study is to analyze variables related to the effects of the dynamics of human activities closely associated with these ecosystems’ degradation. The Geographic Information System tool allowed identifying and comparing soil use and occupation variations based on information provided by 140 randomly chosen participants in Caracol County - Haiti. Interviews were carried out in loco between March and November 2020. Based on results statistically analyzed through multivariate regression tests, the most significant exploratory variable for the mangroves’ degradation process (p<0.05) was “mangrove occupation”, regardless of age, sex, schooling, time in the same residence, profession, home distance from the mangrove, landslide events, and risk of floods. We have concluded that distance from home, ecological function, intervention in biodiversity conservation, as well as water-climatic and geo-physical threats are factors closely correlated to mangroves’ environmental conservation and management (p<0.05).

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“…A recent multi-scale estimation on mangrove cover loss of 77 countries using a DPSIR model con rmed that the protected areas slowed the loss rates between 1996-2016 while the regulatory quality determined the mangrove covers outside of the protected areas (Turschwell et al 2020) using the global mangrove watch database (GMW, Bunting et al 2018). Population density was another important driver for mangrove cover loss in countries with low regulatory quality (Turschwell et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic Patterns for Global Mangrove Cover Changes with Multivariate Analyses

Lin

Shen

2021

Preprint

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Socioeconomic patterns beyond the land conversions of global mangrove cover changes were few discussed. This study integrated databases of global mangrove covers, i.e., a multi-database in 1980-2016 with a combination of FAO country-survey and the global mangrove watch, and a mangrove biome cover database in 2000-2012. Annual mangrove cover change (%) of each mangrove holding country or territory was incorporated with its socioeconomic indicators in the two periods to testify possible socioeconomic patterns beyond the mangrove deforestation. The socioeconomic indicators consisted of GDP per capita and urban population percentile, and aquacultural indicators (production per capita, sales value per capita, contribution to the national GDP, and product price). Results indicated that annual mangrove cover change was interactively driven by multi-factors of national economy measures, urbanization, and aquaculture, instead of aquacultural dominance or GDP per capita alone. The multi-factor driven patterns of annual mangrove changes differed geographically among the continents. Temporal change of aquacultural product price was significantly and positively correlated to annual mangrove cover change (increase), especially in Asia and Oceania for both the periods, suggesting positive feedbacks of mangrove conservation and restoration in the two continents although the price was used to a motivator of mangrove deforestation, such as currently in Africa. These findings preliminarily bridge the knowledge gap between land conversion losses and socioeconomics of global mangrove cover changes.

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Multi-scale estimation of the effects of pressures and drivers on mangrove forest loss globally

Cited by 52 publications

References 61 publications

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Effects of anthropic activity dynamics on mangrove ecosystems’ degradation in Caracol County – Haiti

Socioeconomic Patterns for Global Mangrove Cover Changes with Multivariate Analyses

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