Spatio-temporal patterns of climatic variability have effects on the environmental conditions of a given land territory and consequently determine the evolution of its productive activities. One of the most direct ways to evaluate this relationship is to measure the condition of the vegetation cover and land-use information. In southernmost South America there is a limited number of long-term studies on these matters, an incomplete network of weather stations and almost no database on ecosystems productivity. In the present work, we characterized the climate variability of the Magellan Region, southernmost Chilean Patagonia, for the last 34 years, studying key variables associated with one of its main economic sectors, sheep production, and evaluating the effect of extreme weather events on ecosystem productivity and sheep production. Our results show a marked multi-decadal character of the climatic variables, with a trend to more arid conditions for the last 8 years, together with an increase in the frequency of extreme weather events. Significant percentages of aboveground net primary productivity (ANPP) variance is explained by high precipitation, mesic temperatures, and low evapotranspiration. These conditions are, however, spatially distributed in the transition zone between deciduous forests and steppe and do not represent a general pattern for the entire region. Strong precipitation and wind velocity negatively affect lamb survival, while temperature and ANPP are positively correlated. The impact of extreme weather events on ANP and sheep production (SP) was in most of the cases significantly negative, with the exception of maximum temperature that correlated with an increase of ANPP, and droughts that showed a non-significant negative trend in ANPP. The examination of these relationships is urgent under the current scenario of climate change with the acceleration of the environmental trends here detected.
Monitoring seasonal and interannual variations of gross primary productivity, net primary productivity and net ecosystem productivity using a diagnostic model and remotely-sensed data
