Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020–2050), representing assessment priorities for ex situ conservation; (2) identifies ‘core localities’ that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing plantations will be neagtively impacted by climate change.
Coffee farming provides livelihoods for around 15 million farmers in Ethiopia and generates a quarter of the country's export earnings. Against a backdrop of rapidly increasing temperatures and decreasing rainfall, there is an urgent need to understand the influence of climate change on coffee production. Using a modelling approach in combination with remote sensing, supported by rigorous ground-truthing, we project changes in suitability for coffee farming under various climate change scenarios, specifically by assessing the exposure of coffee farming to future climatic shifts. We show that 39-59% of the current growing area could experience climatic changes that are large enough to render them unsuitable for coffee farming, in the absence of significant interventions or major influencing factors. Conversely, relocation of coffee areas, in combination with forest conservation or re-establishment, could see at least a fourfold (>400%) increase in suitable coffee farming area. We identify key coffee-growing areas that are susceptible to climate change, as well as those that are climatically resilient.
The Pacific Equatorial dry forest of Northern Peru is recognised for its unique endemic biodiversity. Although highly threatened the forest provides livelihoods and ecosystem services to local communities. As agro-industrial expansion and climatic variation transform the region, close ecosystem monitoring is essential for viable adaptation strategies. UAVs offer an affordable alternative to satellites in obtaining both colour and near infrared imagery to meet the specific requirements of spatial and temporal resolution of a monitoring system. Combining this with their capacity to produce three dimensional models of the environment provides an invaluable tool for species level monitoring. Here we demonstrate that object-based image analysis of very high resolution UAV images can identify and quantify keystone tree species and their health across wide heterogeneous landscapes. The analysis exposes the state of the vegetation and serves as a baseline for monitoring and adaptive implementation of community based conservation and restoration in the area.
Itigi thicket is a spatially restricted ecosystem only present in Zambia and Tanzania. It is thought to be highly threatened and therefore we need to urgently assess the threats to this ecosystem as well as extent and rates of change to derive its true conservation status. In this study we focus on the Itigi-Sumbu thicket surrounding Lake Mweru Wantipa in Zambia, which occurs both inside and outside a National Park (IUCN category II). Earth observation data archives provide the means to assist the conservation assessment process by allowing the monitoring of the ecosystem over time. In particular, the Landsat archive offers over 40 years of imagery at a resolution suited to the distribution of this ecosystem. In this study we exploit this archive and extend it back 50 years using historical aerial photography. The remote-sensing data were classified according to presence of thicket at five dates across a 50-year period and these outputs were combined to produce a deforestation map. Crucially, this map was assessed for accuracy using a novel approach to expert knowledge, which shows that the resultant map is highly accurate (93% overall accuracy). A confusion matrix was used to provide a confidence interval to the deforestation figures. Results indicate that 64% of the Itigi-Sumbu thicket around Lake Mweru Wantipa has been cleared over the last 50 years and that the largest area of remaining thicket is currently situated within the Mweru Wantipa National Park. This deforestation figure provides the means to assess the conservation status of Itigi-Sumbu thicket as part of the Red List of Ecosystem as Endangered (EN). Fifty Years Monitoring of Itigi-Sumbu thicket Susana Baena et al.
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