Preface: Climate Change Impact on Plant Ecology

Climate change likely represents the major modifying agents of functional and structural processes in terrestrial and marine ecosystems [...]

CO 2 . However, new evidence suggests that ecosystem adaptation through plant-microbe symbioses could alleviate some nitrogen limitation. The warming of soils and increased litter inputs will accelerate carbon losses through microbial respiration. The thawing of high latitude/altitude permafrost will increase rates of SOC loss and change the balance between CO 2 and CH 4 emissions. The balance between increased plant respiration in warmer climates and carbon uptake from enhanced plant growth is a key uncertainty for the size (and the sign) of the future land carbon sink (or source) capacity.
Environmental modelling with particular regard to primary producers of ecosystems and the estimation of their present and future productive potential fits into this absolutely partial framework of knowledge of the effects of climate change. The models are necessarily a simplified representation of the reality that surrounds us, but, nevertheless, they can provide scenarios useful for understanding the ecosystems' founding processes and their criticality when the pressure of environmental drivers becomes particularly strong, as we are observing right now. One of the limitations of ecological models, however, is that they depend on our level of knowledge and this is true for both theoretical and operational models. The variety and the number of ecological models is impressive, and several fields of exact sciences have been called upon to provide the technical and informatics tools that have made it possible to define their current and future developments. But even taking into consideration only a part of the ecosystem, such as the one assigned to primary production (first trophic level, photo-autotrophic compartment), our ability to simulate the processes that underlie carbon fixation in plants is limited by our current knowledge, determining a quantity of information or variability not explained by the model used, which underlies a sort of 'Uncertainty Principle' valid for the ecological sciences. The design of Nature and its state of apparent disorder at the various levels of hierarchical, spatial and temporal scales is still far from being fully discovered. Although the word 'uncertainty' resonates widely in this paper, it can represent a very key source of information and the force that pushes us to try other ways to increase our level of knowledge and make our simulation and forecasting ability more and more accurate in a complex world.
In this brief introduction, some aspects have been touched on that should be further explored through the papers of this Special Issue. The complexity of the real world requires us to undertake methodological approaches that are capable of integrating functional processes that operate within structures that have adapted to the external environment and simulate their adaptability to changes in time and space. As an example, the simple simulation of the trajectory of a light beam within a tree canopy collides with a structural complexity such that the model's simplifications are necessary to alleviate the computational load that would be used to trace the trajectory of the light. We need, in parallel to the biophysical and biochemical computation, new adaptive probabilistic or fuzzy logic algorithms that are able to understand the maximum variability, as is possible of the light path. Even the simple temporal dynamics of a process such as that of carbon fixation can lead to high computational difficulties when considering the self-shading of forest canopies, which varies according to the ecological status, physiology, stress, specific biodiversity, the age and position of individuals, the state of ecological succession, the spatial scale, etc. The leaf patchiness and the internal water status (water chemical potential) are functional and structural parameters that greatly influence the gas exchange both at the leaf level and at individual and community canopy levels. The models that try to simulate these processes are also limited by our mechanistic knowledge that currently is able to consider only a part of the observed variability.
Funding: This research received no external funding.

Conflicts of Interest:
The authors declare no conflict of interest.