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Climate change, wine, and conservation

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TLDR
It is demonstrated that, on a global scale, the impacts of climate change on viticultural suitability are substantial, leading to possible conservation conflicts in land use and freshwater ecosystems.
Abstract
Climate change is expected to impact ecosystems directly, such as through shifting climatic controls on species ranges, and indirectly, for example through changes in human land use that may result in habitat loss. Shifting patterns of agricultural production in response to climate change have received little attention as a potential impact pathway for ecosystems. Wine grape production provides a good test case for measuring indirect impacts mediated by changes in agriculture, because viticulture is sensitive to climate and is concentrated in Mediterranean climate regions that are global biodiversity hotspots. Here we demonstrate that, on a global scale, the impacts of climate change on viticultural suitability are substantial, leading to possible conservation conflicts in land use and freshwater ecosystems. Area suitable for viticulture decreases 25% to 73% in major wine producing regions by 2050 in the higher RCP 8.5 concentration pathway and 19% to 62% in the lower RCP 4.5. Climate change may cause establishment of vineyards at higher elevations that will increase impacts on upland ecosystems and may lead to conversion of natural vegetation as production shifts to higher latitudes in areas such as western North America. Attempts to maintain wine grape productivity and quality in the face of warming may be associated with increased water use for irrigation and to cool grapes through misting or sprinkling, creating potential for freshwater conservation impacts. Agricultural adaptation and conservation efforts are needed that anticipate these multiple possible indirect effects.

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Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate

TL;DR: It is demonstrated that grape-associated microbial biogeography is nonrandomly associated with regional, varietal, and climatic factors across multiscale viticultural zones, posing the existence of nonrandom “microbial terroir” as a determining factor in regional variation among wine grapes.
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Berry Phenolics of Grapevine under Challenging Environments

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A bitter cup: climate change profile of global production of Arabica and Robusta coffee

TL;DR: In this paper, the authors used machine learning algorithms to derive functions of climatic suitability from a database of geo-referenced production locations, and the resulting multi-model ensemble suggests that higher temperatures may reduce yields of C. arabica, while C. canephora could suffer from increasing variability of intra-seasonal temperatures.
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The Impact of Climate Change on Viticulture and Wine Quality

TL;DR: In this article, the authors discuss the impact of climate change on wine production and propose adaptation strategies to continue to produce high-quality wines and to preserve their typicity according to their origin in a changing climate.
References
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Journal ArticleDOI

Biodiversity hotspots for conservation priorities

TL;DR: A ‘silver bullet’ strategy on the part of conservation planners, focusing on ‘biodiversity hotspots’ where exceptional concentrations of endemic species are undergoing exceptional loss of habitat, is proposed.
Journal ArticleDOI

Very high resolution interpolated climate surfaces for global land areas.

TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Journal ArticleDOI

Maximum entropy modeling of species geographic distributions

TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Journal ArticleDOI

A statistical explanation of MaxEnt for ecologists

TL;DR: A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
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