The mangrove forest dynamics model mesoFON

Grueters, Uwe; Seltmann, Torsten; Schmidt, H; Horn, Hendrik; Pranchai, Aor; Vovides, Alejandra G.; Peters, Ronny; Vogt, Juliane; Dahdouh‐Guebas, Farid; Berger, Uta

doi:10.1016/j.ecolmodel.2014.07.014

Cited by 26 publications

(21 citation statements)

References 45 publications

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“…Tasks 2,4,5,6 are all related to effects of disturbance regime on community dynamics. Natural disturbance events, such as hurricane and lightning impacts, represent local threats at the stand level (compare Piou et al, 2008;Vogt et al, 2014;Grueters et al, 2014). Yet, the main focus on the natural threat of disturbance does not appear adequate given the plethora of anthropogenic threats that have led to the massive global mangrove loss (Valiela et al, 2001;Duke et al, 2007).…”

Section: Tasks For Mangrove Ibms and Its Critiquementioning

confidence: 99%

“…So far, the investigation of mangrove responses to natural disturbance represents the second major application area of IBMs, such as KiWi and the new IBM mesoFON, which operate on small areas of forest stands Vogt et al, 2014;Grueters et al, 2014). Yet, none of these authors considered that hurricanes do not impact trees by chance (alone).…”

Section: Natural Threatsmentioning

confidence: 99%

“…mesoFON is a new individual-based model of mangrove forest dynamics that was developed from the KiWi model and first described by Grueters et al (2014). The authors provided a full model description according to the ODD (overview, design concepts, details) protocol (Grimm et al, 2006(Grimm et al, , 2010.…”

Section: The Initial Developmentmentioning

confidence: 99%

“…As this was identified to be the most critical biological shortcoming of the SORTIE model (Pacala et al, 1996) its developers made considerable effort to enable simulated crowns to move laterally and to change their shape flexibly or, in other words, to make them plastic (Strigul et al, 2008). Grueters et al (2014) presented results of a first mesoFON application. They examined the long-term interaction of lateral crown displacement and disturbance in mangroves and addressed tasks 2, 5 and 6 thereby (see Table 1).…”

Section: The Initial Developmentmentioning

confidence: 99%

“…The crown plasticity routines in mesoFON take advantage of the fields-of-neighborhood (FON) approach originally proposed by Berger and Hildenbrandt (2000). Grueters et al (2014) duplicated the FON of a tree, one being responsible for above-, the other being responsible for belowground competition. They shift the above-ground FON and the crown (center) together away from the most severe competition in its neighborhood, thereby allowing for lateral crown displacement and competitor avoidance.…”

Section: The Initial Developmentmentioning

confidence: 99%

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Individual-based modeling of mangrove forest growth: MesoFON – Recent calibration and future direction

Grueters

Ibrahim

Satyanarayana

et al. 2019

Estuarine, Coastal and Shelf Science

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A R T I C L E I N F O Keywords:Individual-based model Model purpose Mangrove threats Model calibration Rhizophora apiculata IBMbedding A B S T R A C TWe introduce individual-based models (IBMs) of mangrove forests and criticize the tasks for their development recommended previously for being mostly related to natural threats. This is contrasted with our perspective that the key research question of today's models should be to mitigate anthropogenic threats.Core objective (1) of this article is to provide a review of mangrove threats prioritizing solution-oriented IBM approaches. Because species-specific calibration of IBMs is time-consuming, efficiency is crucial. Globally, we identify an urgent need to parametrize Asian mangrove species.We suggest IBMs to unveil management scenarios with maximum sustainable timber yield to prevent mangrove conversion and over-exploitation. The key model purpose regarding natural threats is to govern the management of mangrove forest stability for coastal protection using a combination of windthrow models and IBMs. We argue for the embedding of IBMs in ecosystem models to achieve purposes regarding eutrophication and altered hydrology/sedimentation. Core objective (2) is to describe the development of the new IBM mesoFON from a task-to a solution-oriented model. Initially, the interaction of lateral crown displacement and hurricane impacts was examined with mesoFON. Later, we introduced propagule production & local dispersal with the task to close the tree life cycle. Here, we describe the change in purpose of mesoFON accompanying its calibration for Rhizophora apiculata in Malaysia. For this we applied a Genetic Algorithm optimizer, used mesoFON as a "way-back machine", initialized it with observed tree diameters/positions and shrank the trees backwards in time.Objective (3) is to describe mesoFON's future direction: Embedding in the General Ecosystem Model (Fitz et al., 1996) and targeting the solution of threats at larger spatial scales. Finally, we demonstrate that the new model simulates overland waterflow qualitatively right even in benchmark settings.

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Section: Tasks For Mangrove Ibms and Its Critiquementioning

confidence: 99%

Section: Natural Threatsmentioning

confidence: 99%

Section: The Initial Developmentmentioning

confidence: 99%

Section: The Initial Developmentmentioning

confidence: 99%

Section: The Initial Developmentmentioning

confidence: 99%

See 3 more Smart Citations

Individual-based modeling of mangrove forest growth: MesoFON – Recent calibration and future direction

Grueters

Ibrahim

Satyanarayana

et al. 2019

Estuarine, Coastal and Shelf Science

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Solving the fourth‐corner problem: forecasting ecosystem primary production from spatial multispecies trait‐based models

Sarker

Reeve

Matthiopoulos

2021

Ecological Monographs

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Examining the commonalities and knowledge gaps in coastal zone vegetation simulation models

Piercy

Charbonneau

Russ

et al. 2023

Earth Surf Processes Landf

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Models can be powerful tools to help scientists, policymakers, and practitioners forecast vegetation dynamics and inform management decisions in a variety of ecosystems. In coastal environments, vegetation moderates physical and hydrodynamic processes that conversely affect vegetation dynamics. Coastal management and the implementation of nature‐based solutions in the coastal environment requires models that can predict vegetation dynamics that are driven by ecological processes as well as physical processes (e.g., storms, sea level rise). To determine models that are capable of simulating biomass dynamics and assess their ability to make predictions within the context of climate change and environmental management, we reviewed coastal dynamic vegetation simulation models across the following coastal zone habitats: tidal wetlands (salt marsh, mangroves, and freshwater wetlands), seagrass beds, coastal forests (maritime and floodplain forests), and dune habitats. Fifty‐four models met the review criteria and were examined to assess their ability to simulate relevant ecological processes and the spatiotemporal scales at which they are applied. These models included a variety of exogenous and endogenous processes and integrate complex ecogeomorphological feedbacks affecting plant and soil community, hydrodynamic, and sediment transport dynamics. Most models reviewed utilized implicit approaches to predicting vegetation biomass and simulated a limited number of processes based on the principal drivers of habitat of interest. Key gaps identified were the exclusion of below‐ground biomass dynamics, limited inclusion of processes such as competition, facilitation, and succession, and the inability to simulate management actions. Future models should seek to move towards process‐based approaches where appropriate to enable their application to different systems and facilitate their use under novel environmental conditions.

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The mangrove forest dynamics model mesoFON

Cited by 26 publications

References 45 publications

Individual-based modeling of mangrove forest growth: MesoFON – Recent calibration and future direction

Individual-based modeling of mangrove forest growth: MesoFON – Recent calibration and future direction

Solving the fourth‐corner problem: forecasting ecosystem primary production from spatial multispecies trait‐based models

Examining the commonalities and knowledge gaps in coastal zone vegetation simulation models

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