ZimFlores: A model to advise co-management of the mafungautsi forest in Zimbabwe

ZimFlores (version 4) is the outcome of a participatory modelling process and seeks to provide a shared factual basis for exploring land-use options for the communal lands surrounding the Mafungautsi forest. The ZimFlores experience underscores the importance of a sharing a common problem and a common location in which all participants have an interest. Participatory modelling has proved an effective way to consolidate a diverse body of knowledge and make it accessible. Results demonstrate the importance of model outputs that are diagnostic, and which offer insights into the issues under consideration. Many people contributed in many ways to help realize ZimFlores. Some of the people who contributed include Elias Ayuk, Ivan Bond, Ephraim Chabayanzara, Muyeye Chambwera, Vijay Datadin, Wil de Jong, Peter Frost, James Gambiza, Jode Garbe, Peter Gondo, Emmanuel Guveya, Chiedza Gwata, John Hansell, Gideon Kamumvuri, Godwin Kowero, Dominic Kwesha, Chris Legg, Tim Lynam, Hilton Madevu, Simba Mandota, Everisto Mapedza, Frank Matose, Gutura Matoso, Chipo Mlambo, John Mudekwe, Maxwell Mukwekwerere, Dorcas Mungwari, Tendayi Mutimukuru, Mkhululi Ngwenya, Richard Nyirenda, Bill Ritchie, Lizwelabo Sibanda, Manasa Sibanda, Fergus Sinclair, Jivas Sithutha, Jasper Taylor, Michael Thomas, Zivanai Tsvuura and Steve Twomlow. We are grateful to the UK’s Department for International Development (DFID) and the European Community for financial support of this project.     

The challenges of effective model scoping: A FLORES case study from the mafungautsi forest margins,Zimbabwe

This paper explores the challenge of defining the scope of a systems model, emphasising three aspects: boundary, granularity and conceptual scope. The significance of these is illustrated by reference to a model of land-use decisions made in villages bordering on the Mafungautsi forest in Zimbabwe. The purpose of this model was to help policy players (Forestry Commission staff, non-governmental organisations, researchers and local people) to understand the impact of policy interventions on local people’s livelihoods. Scoping decisions that were made in building the Mafungautsi model were deliberately liberal, to encompass the interests of all participants in the modelling process. These decisions now present a range of serious challenges: the difficulty of model calibration, the computational expense of running simulations, and the difficulty for new users to understand the model. Facilitators of modelling teams need to consider the serious implications of giving everyone what they want and including all participants’ ideas in a model. In the long run, it may be better to be tough and reject many suggestions at the outset. The former approach is unlikely to lead to a tractable model, while the latter may ultimately offer greater satisfaction for all. We are grateful to the UK’s Department for International Development (DFID) and the European Community for financial support of this project. Thanks also to all of the ACM team who have contributed ideas on the process and content of the modelling work and who make it fun to undertake collaborative research. Thanks also to the participants in the Zimbabwe FLAC workshops for their input into the specification of the Mafungautsi model. Finally thanks to Robert Muetzelfeldt, Jasper Taylor, Jonathan Massheder, Fergus Sinclair and Jerry Vanclay for helping us to ride on the FLORES bandwagon.     

谈建设国家森林公园的生态意义社会意义及经济意义

本文从生态环境的可持续发展、森林资源的保护的角度,论述了建设国家森林公园的生态意 义、社会意义及经济意义。     

Use of 3-PG and 3-PGS to simulate forest growth dynamics of Australian tropical rainforests: II. An integrated system for modelling forest growth and scenario assessment within the wet tropics bioregion

Regional model analyses of forest growth are critical for capturing global aspects of tropical rainforest carbon exchange. This research presents the development of a multi-model approach for assessing forest growth and biomass accumulation within the wet tropics bioregion (WTB) based on 10 years of available data and existing model parameter sets. The Tropical Rainforest Growth (TRG) model system employs the 3-PG and 3-PGS models to account for both old-growth rainforest and forest regeneration from seedlings in response to human-induced and natural disturbances. Above-ground biomass (AGB) stocks of the mature forest throughout the WTB for 2000 were estimated to be ∼202 t C/ha. Replacement of areas of old-growth with commercial timber plantations decreased overall AGB stocks to approximately 146 t C/ha. However, plantation carbon accumulation rates were higher than the mature rainforest, representing their potential to accumulate more biomass over a longer analysis time period. As tropical cyclones may significantly alter the carbon stocks of old-growth rainforests, the effect of tropical cyclone Rona on the WTB was assessed. The cyclone had a minimal impact on total AGB stocks within the region, yet these systems are an important factor to be considered in carbon and forest regeneration modelling activities in the tropics. The TRG system is an advanced modelling tool providing a rapid process-based assessment of biomass stocks and accumulation dynamics within Australia's tropical rainforest bioregion and has the potential for application in tropical forest ecosystems at both national and international levels.     

Spatio-temporal analysis of forest fire events in the Margalla Hills,Islamabad,Pakistan using socio-economic and environmental variable data with machine learning methods

Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study consid-ers both environmental (altitude,precipitation,forest type,terrain and humidity index) and socioeconomic (popula-tion density,distance from roads and urban areas) factors to analyze how human behavior affects the risk of forest fires.Maximum entropy (Maxent) modelling and random forest (RF) machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills.The receiver operating characteristic(ROC) curve and the area under the ROC curve (AUC) were used to compare the models.We studied the fire history from 1990 to 2019 to establish the relationship between the prob-ability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999s,2009s,and 2019s were 0.532,0.569,and 0.518,respectively;using RF,they were 0.782,0.825,and 0.789,respectively.Fires were mainly distributed in urban areas and their probability of occurrence was related to acces-sibility and human behaviour/activity.AUC principles for validation were greater in the random forest models than in the Maxent models.Our results can be used to establish preventive measures to reduce risks of forest fires by consid-ering socio-economic and environmental conditions.