Advances and limitations of individual-based models to analyze and predict dynamics of mangrove forests: A review

Abstract

Mangrove ecosystems are considered vulnerable to climate change as coastal development limits the ecosystem services and adaptations important to their survival. Although they appear rather simple in terms of species diversity, their ecology is complex due to interacting geophysical forces of tides, surface runoff, river and groundwater discharge, waves, and constituents of sediment, nutrients and saltwater. These interactions limit developing a comprehensive framework for science-based sustainable management practices. A suite of models have been developed independently by various academic and government institutions worldwide to understand the dynamics of mangrove ecosystems and to provide ecological forecasting capabilities under different management scenarios and natural disturbance regimes. The models have progressed from statistical tables representing growth and yield to more sophisticated models describing various system components and processes. Among these models are three individual-based models (IBMs) (FORMAN, KIWI, and MANGRO). A comparison of models' designs reveal differences in the details of process description, particularly, regarding neighbor competition among trees. Each model has thus its specific range of applications. Whereas FORMAN and KIWI are most suitable to address mangrove forest dynamics of stands, MANGRO focuses on landscape dynamics on larger spatial scale. A comparison of the models and a comparison of the models with empirical knowledge further reveal the general needs for further field and validation studies to advance our ecological understanding and management of mangrove wetlands. #

A Socio-Ecological Assessment Aiming at Improved Forest Resource Management and Sustainable Ecotourism Development in the Mangroves of Tanbi Wetland National Park, The Gambia, West Africa

“…Although these forests have been studied systematically in many parts of the world for flora and fauna composition/distribution, vegetation dynamics, forest loss and evolution, predictable future trends, etc. (Berger et al 2008;Massó i Alemán et al 2010;Satyanarayana et al 2011), the topics evaluating their significance in the local livelihoods are limited (e.g., Walters et al 2008), and none are available for West Africa (although much information exists in French ''grey literature''-reports, thesis research, etc. ).…”

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

“…Hence, remote sensing can provide information on mangroves at multiple scale levels depending on the user's need. By synthesizing the knowledge of the hierarchical structures of mangroves [20,21,23,29] with the empirical study of the optimum pixel size to extract mangrove features from remotely-sensed images [30], we can establish explicit relationships between spatial and temporal scales of mangrove features and the corresponding image spatial resolution at which to map these features ( Figure 1). This relationship guides the image analysts or interpreters to select the optimal image spatial resolution in order to accurately map a specific mangrove feature.…”

Composição E Estrutura Florística Em Bosques De Manguezais Paraenses, Brasil

“…Esta característica de Laguncularia racemosa também foi descrita na costa paraense por Matni, Menezes e Mehlig (2006), Berger et al (2008), Menezes, Berger e Mehlig (2008) e Sales et al (2009). Associa-se a presença dessa espécie a solos mais arenosos e melhor drenados (LONDE et al, 2013) e sua ocorrência às clareiras e bordas de canais, ou ainda colonizando áreas degradadas, apontado por Soares (1999) como indicador de alterações antrópicas, o que, pelos resultados encontrados, parece não ocorrer nos manguezais estudados.…”