climate change ͉ feedbacks ͉ biomass burning ͉ Indonesia ͉ global carbon cycle

The mountain chain of the Sierra Madre de Chiapas in southern Mexico is globally significant for its biodiversity and is one of the most important coffee production areas of Mexico. It provides water for several municipalities and its biosphere reserves are important tourist attractions. Much of the forest cover outside the core protected areas is in fact coffee grown under traditional forest shade. Unless this (agro)forest cover can be sustained, the biodiversity of the Sierra Madre and the environmental services it provides are at risk. We analyzed the threats to livelihoods and environment from climate change through crop suitability modeling based on downscaled climate scenarios for the period 2040 to 2069 (referred to as 2050s) and developed adaptation options through an expert workshop. Significant areas of forest and occasionally coffee are destroyed every year by wildfires, and this problem is bound to increase in a hotter and drier future climate. Widespread landslides and inundations, including on coffee farms, have recently been caused by hurricanes whose intensity is predicted to increase. A hotter climate with more irregular rainfall will be less favorable to the production of quality coffee and lower profitability may compel farmers to abandon shade coffee and expand other land uses of less biodiversity value, probably at the expense of forest. A comprehensive strategy to sustain the biodiversity, ecosystem services and livelihoods of the Sierra Madre in the face of climate change should include the promotion of biodiversity friendly coffee growing and processing practices including complex shade which can offer some hurricane protection and product diversification; payments for forest conservation and restoration from existing government programs complemented by private initiatives; diversification of income sources to mitigate risks associated with unstable environmental conditions and coffee markets; integrated fire management; development of markets that reward sustainable land use practices and forest conservation; crop insurance programs that are accessible to smallholders; and the strengthening of local capacity for adaptive resource management.Response to Reviewers: Major revisions of the paper have been made following the guidance provided by the reviewer: 1)The paper has been shortened through elimination of some non-essential detail, especially in the introduction and the discussion of the adaptation options.2)The methods section has been expanded through a more detailed explanation of the public participation process and of the analytical process. Substantial statistical analysis of the variability among different Global Circulation Models has been added. We now present confidence intervals of 15 GCMs in the maps of predicted future coffee suitability and also a map showing the agreement among models in Figure 3. We also show the prediction of coffee suitability by altitude for individual models, in addition to mean and confidence intervals. Error bars have also been added to ...

Aim The aim of this study is to introduce a structural vegetation map of the Serengeti ecosystem and, based on the map, to test the relative influences of landscape factors on the spatial heterogeneity of vegetation in the ecosystem.Location This study was conducted in the Serengeti-Maasai Mara ecosystem in northern Tanzania and southern Kenya, between 34°and 36°E longitude, and 1°a nd 2°S latitude.Methods The vegetation map was produced from satellite imagery using data from over 800 ground-truthing points. Spatial characteristics of the vegetation were analysed in the resulting map using the fragstats software package. Average patch area and nearest neighbour distance (NND) were determined for grassland, shrubland and woodland vegetation types. The heterogeneity of vegetation types was estimated with Simpson's diversity index (D). Structural equation modelling (SEM) was used to explore the relationships between the spatial characteristics of vegetation and three predictor variables: annual rainfall, coefficient of variation (CV) in annual rainfall, and topographic moisture index (TMI).Results A vegetation map is presented along with a detailed summary of the distribution of land-cover classes and spatial heterogeneity in the ecosystem. Significant relationships were found between vegetation diversity (D) and TMI, and also between D and average rainfall. The average area of grassland patches showed significant relationships with average rainfall, with rainfall CV and with TMI. Grassland NND was positively associated with average rainfall. Woodland patch area showed a unimodal response to average rainfall and a negative linear association with TMI. Woodland NND showed a U-shaped association with annual rainfall and a weaker positive linear association with TMI. An acceptable model that explained variation in shrubland patch characteristics could not be identified. Main conclusionsThe vegetation map and analysis thereof resulted in three significant causal explanatory models that demonstrate that both rainfall and topography are important contributors to the distribution of woodlands and grasslands in the Serengeti. These findings further indicate that changes in patch characteristics have a complex interaction with rainfall and with topography. Our results are concordant with recent studies suggesting that percent woody cover in African savannas receiving less than c. 650 mm year )1 is bounded by average annual rainfall.

Transparent, consistent, and accurate national forest monitoring is required for successful implementation of reducing emissions from deforestation and forest degradation (REDD+) programs. Collecting baseline information on forest extent and rates of forest loss is a first step for national forest monitoring in support of REDD+. Peru, with the second largest extent of Amazon basin rainforest, has made significant progress in advancing its forest monitoring capabilities. We present a national-scale humid tropical forest cover loss map derived by the Ministry of Environment REDD+ team in Peru. The map quantifies forest loss from 2000 to 2011 within the Peruvian portion of the Amazon basin using a rapid, semi-automated approach. The available archive of Landsat imagery (11 654 scenes) was processed and employed for change detection to obtain annual gross forest cover loss maps. A stratified sampling design and a combination of Landsat (30 m) and RapidEye (5 m) imagery as reference data were used to estimate the primary forest cover area, total gross forest cover loss area, proportion of primary forest clearing, and to validate the Landsat-based map. Sample-based estimates showed that 92.63% (SE = 2.16%) of the humid tropical forest biome area within the country was covered by primary forest in the year 2000. Total gross forest cover loss from 2000 to 2011 equaled 2.44% (SE = 0.16%) of the humid tropical forest biome area. Forest loss comprised 1.32% (SE = 0.37%) of primary forest area and 9.08% (SE = 4.04%) of secondary forest area. Validation confirmed a high accuracy of the Landsat-based forest cover loss map, with a producer's accuracy of 75.4% and user's accuracy of 92.2%. The majority of forest loss was due to clearing (92%) with the rest attributed to natural processes (flooding, fires, and windstorms). The implemented Landsat data processing and classification system may be used for operational annual forest cover loss updates at the national level for REDD+ applications.

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