Physical sciences, Earth sciences

How forests respond to climate change

How forests respond to climate change varies between different regions of the world. EU-funded scientists investigated how a changing climate affects primary temperate forests in southern South America (SSA).

There is still a patchy scientific understanding of how forests in SSA respond to a changing climate. The 'Assessing climate change impacts over large areas of primary forests in southern South America' (FORECOFUN-SSA) project addressed this question.

Researchers developed a dynamic modelling framework to analyse forest–climate interactions and the ecological mechanisms controlling the response of tree species and forest stands to climate change. The framework was used to create a model of the forest ecosystem in order to investigate multiple and interacting impacts on SSA forests on a broad scale.

Field studies were carried out to obtain data on forest structure and composition. In addition, scientists identified traits that account for major variation in dominant species along a large climatic gradient. The results were used to define the parameters controlling the response of different tree species to climate.

Project partners also modelled forest dynamics at the stand scale, and at the regional scale to assess forest composition and species distribution. Data from simulations indicated that a drier climate will strongly alter forest structure, leading to a dramatic decrease in above-ground biomass.

FORECOFUN-SSA also contributed to a database of morphological and functional plant traits. The database will enable scientists to understand how ecosystems adapt to global change.

Computer simulations revealed that human-made changes in fire regimes affect the resilience of the threatened conifer Pilgerodendron oviferum. They also showed that Andean species-rich forests are likely to be replaced eastwards by woodlands dominated by a single species, and eventually by steppe.

Project results also contributed to an increased knowledge of rare and less-studied tree species, thereby reducing the level of uncertainty in predictive models. This allowed large areas of primary forest in SSA to be accurately modelled and facilitated the production of high-quality baseline data for future research.

last modification: 2015-02-23 15:47:10
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