Mountain ecosystems are sensitive and reliable indicators of climate change. Long-termstudiesmay be extremely useful in assessing the responses of high-elevation ecosystems to climate change and other anthropogenic drivers from a broad ecological perspective. Mountain research sites within the LTER (Long-Term Ecological Research) network are representative of various types of ecosystems and span a wide bioclimatic and elevational range. Here, we present a synthesis and a review of themain results fromecological studies inmountain ecosystems at 20 LTER sites in Italy, Switzerland and Austria covering inmost casesmore than two decades of observations.We analyzed a set of key climate parameters, such as temperature and snow cover duration, in relation to vascular plant species composition, plant traits, abundance patterns, pedoclimate, nutrient dynamics in soils and water, phenology and composition of freshwater biota.
The overall results highlight the rapid response of mountain ecosystems to climate change, with site-specific characteristics and rates. As temperatures increased, vegetation cover in alpine and subalpine summits increased as well. Years with limited snow cover duration caused an increase in soil temperature and microbial biomass during the growing season. Effects on freshwater ecosystemswere also observed, in terms of increases in solutes, decreases in nitrates and changes in plankton phenology and benthos communities. Thiswork highlights the importance of comparing and integrating long-term ecological data collected in different ecosystems for a more comprehensive overviewof the ecological effects of climate change. Nevertheless, there is a need for (i) adopting co-located monitoring site networks to improve our ability to obtain sound results from cross-site analysis, (ii) carrying out further studies, in particular short-term analyses with fine spatial and temporal resolutions to improve our understanding of responses to extreme events, and (iii) increasing comparability and standardizing protocols across networks to distinguish local patterns from global patterns.
https://www.sciencedirect.com/science/article/pii/S0048969717335817
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