Comment les arbres répondent-ils aux variations du climat ?

Summary

The annual biomass produced by a forest is divided among the trees' different compartments: the trunk, the crown and also the fruit. Due to a tree's long life span, its radial growth is considered to be a major indicator of response to climatic changes. Similarly, leaf phenology is a good indicator of a tree's capacity to survive since leaf dynamics are highly selective. A tree's ability to produce fruit is also essential to the long-term survival of the species. These different environmental markers are now being recorded though the causes of their inter-annual variations have yet to be fully understood.

The data collected by the RENECOFOR network for the past 25 years have been instrumental in helping to understand the links between environmental characteristics (mostly climatic) and radial growth, leaf phenology and fruiting in the major temperate-zone forest tree species.

The diagram below summarises various phenological events and the underlying climatic variables influencing them for three deciduous species (Sessile and pedunculate oak and European beech).

Periods and variables influencing the radial growth, the flushing and fruiting for three deciduous species (Sessile and pedunculate oak and European beech) on RENECOFOR net work.

In oak stands, on average leaf burst begins around mid-April and fall yellowing occurs around mid-October. In eastern France, the growing season is shorter (180-190 days as opposed to 210-220 days in western France) due to later leaf burst (delayed by 2 days for each degree of longitude) and earlier yellowing (5-10 days).
For beech, the growing season starts later, around the end of the third week in April. Fall yellowing occurs at the beginning of October, resulting in an average growing season of 180 days. Temperatures during the months of March and April play a determining role in leaf burst (see Figure). For example, 1°C more in March moves leaf burst forward by two to five days. For fall yellowing, temperatures in October or November have the most influence, with higher temperatures resulting in delayed senescence (Delpierre et al., 2009; Lebourgeois et al., 2008, 2010).

For annual growth, the soil water balance is what most influences the development and width of the ring.
For beech, for instance, an early summer drought (especially during June) results in a thin ring (see Figure). Beech trees growing in lowland plains are particularly sensitive to drought when soils are superficial (maximum available soil water capacity below 100 mm) or annual precipitation is low (less than 700 mm) (Lebourgeois et al., 2005).

For oak, the main limiting factor for growth is a lack of available water over the entire growing season (July to October), with key periods varying greatly depending on the stand and local conditions.

Differences between the two oak species are slight, even though pedunculate oak seems to be more sensitive to unusually severe drought events. Comparatively, sessile oak appears more sensitive to summer drought in hot, dry climates. This explains why sessile oak reacts strongly and negatively to summer drought and warm autumn temperatures in the dry oceanic climate of western France, whereas it is barely affected by summer drought in the cooler, wetter conditions offered by the semi-continental climate in eastern France (P > 800 mm) (Lebourgeois, 2006; Mérian et al., 2011).

Finally, as far as fruiting is concerned, oak stands produce on average 251 kg per ha per year as compared to 171 kg for beech, yet for both oak and beech, fruits represent a mere 5% of the annual litter accumulation. Beechnut production follows a biannual cycle which appears to be synchronised among stands. Oak, on the contrary, has a much more irregular acorn production cycle with different stands producing asynchronously (Lebourgeois et al., submitted).

For oaks, masting depends on autumn temperatures during the previous growing season, on April temperatures and on the amount of carbon accumulated by the end of summer (see Figure). For beech as for oak, April is a key period determining nut production, though winter temperatures also influence masting. At the end of the growing season, favourable autumn conditions (temperatures and high carbon storage) favour acorn production (in terms of both quantity and biomass). For beech, analyses have shown that beechnut production closely follows the quantity of pollen produced (this relationship has not been observed for oak).
Masting also correlates with growth: the correlation is positive for oak (high-growth years correspond to heavy masting years), while it is negative for beech (heavy masting = reduced growth).

Summary

Climate change affects the environment at all ecological scales, from individuals to populations and from species and ecosystems to whole biomes (Valladares, 2008). Climatic conditions also profoundly influence the ecological niche occupied by forest tree species by closely affecting tree physiological processes. Indeed, the impact of climate change on tree species is already visible: changes have been observed in individual tree physiology (Saxe et al., 2001), in particular related to phenology (Lebourgois et al., 2008; Morin et al., 2010), tree species geographical distribution (Lenoir et al., 2008) and community composition (Bertrand et al., 2011). Such modifications in turn alter global biodiversity, and functions in the ecosystems stemming from it (Kinzig et al., 2002; Morin et al., 2011).

Faced with such a deluge of impacts, we must improve our understanding of the processes involved to better anticipate the effects of climate change; we must develop our capacity to integrate the complexities of ecosystem functioning into our knowledge and to identify the parameters that best reveal how ecosystems respond to the climate (Morin, 2006). With this goal in mind, long-term observations, such as those carried out by the RENECOFOR network for the past 25 years, become exceedingly important. Indeed, only this type of data makes it possible to reveal the effects of climate on ecological processes such as species phenological responses or individual growth, and to take these effects into account in robust predictive models for the future.

In this presentation, I will give an overview of the current strategies being used to understand these processes and to simulate the impact of climate (and modifications in the climate) on forest trees. I will particularly focus on the effects of climate on tree species distribution and on the relative growth of forest tree species in mono-specific or mixed stands. More precisely, I will attempt to show how long-term monitoring data allows us to answer the following questions:

• What changes in the distribution ranges of forest tree species can be expected?

• To what degree can species mixtures improve a forest's adaptability to climate, taking into account the link between species community composition and ecosystem functioning?

The answers to these questions are based on results provided by the PHENOFIT (Chuine & Beaubien, 2001; Cheaib et al., 2012) (as shown in the figure below) and ForCEEPS (Morin et al., in prep) models.

Sample simulation of the distribution range for beech in 2055 produced by the PHENOFIT model

Summary

Plant phenology is the study of periodical events such as bud burst, flushing, senescence and leaf fall. Climate is the governing factor here and phenology is the first visual indicator of variations in climate.
Phenology is a precision science which necessitates exhaustive sampling in order to take into account the diversity of the many plant species and the variability of the climate. Field observations, while necessary, are laborious and do not guarantee large-scale spatial representation; consequently, they are difficult to extrapolate to broader scales.

Remote sensing by satellite has often been presented as an alternative. The first such observations began several decades ago thanks to daily images provided by the AVHRR radiation-detection imager aboard U.S. NOAA (National Oceanic and Atmospheric Administration) satellites. The estimates resulting from this new source of data, though subject to high levels of uncertainty, made it possible to develop the first global maps of phenological events taking place in the different terrestrial biomes.
In the last fifteen years, significant improvements have been made thanks to the Moderate Resolution Imaging Spectro-radiometer (MODIS), a payload on the TERRA and AQUA satellites (NASA, USA), which is designed to continuously track the vital signs of the terrestrial biosphere. Not only has spatial resolution improved (250 m-1 km for MODIS vs. 1 km for AVHRR), the number of spectral bands has increased (36 vs. 6 for AVHRR), GPS positioning is more precise (50 m vs. 1 to 2 km) and data quality is higher. MODIS data are used to generate (and distribute?? -ceci me semble étrange même en français: comment MODIS peut distribuer les cartes?) a variety of cartographic products at the planetary scale, notably including phenological maps with resolutions of 500 m (MCDI2Q2) and one km (MOD12Q2).

In general, phenological estimates reflect temporal dynamics based on certain indices, called vegetation indices, which are sensitive to plant biomass. It should be kept in mind that these indices can only distinguish changes in foliage, and only when those changes are well marked. The Normalized Difference Vegetation Index (NDVI) is the most commonly-used spectral index. It takes advantage of the strong contrast between the amount of near infrared (NIR) and red (RED) light waves reflected by vegetation, a difference due to plants' high absorption of light in the red wavelength. The index is noted as follows: NDVI = (NIR - RED) / (NIR + RED). The figure below shows the changes over one year in NDVI recorded in situ and the main phenological phases for foliage.

Intra-annual dynamics of NDVI on a deciduous forest

Studies testing the accuracy of the estimated dating of phenological events based on data from the MODIS spectro-radiometer have mainly relied on field observations recorded within the framework of the RENECOFOR network (Soudani et al. 2008; Hmimina et al. 2013; Testa et al., submitted).

Results show that the springtime inflection point on the NDVI curve (d2) is the best marker of the observed bud burst date. Prediction error is 8.5 days with a slight positive bias of 3.5 days. However, the MOD12Q2 programme provides estimates which differ considerably from observations; uncertainty reaches 20 days with a negative bias of 17 days. Leaf bud burst and flushing occur relatively quickly (20 to 30 days between bud burst and the maximum foliage index value for beech and oak in temperate forests). The senescence phase, on the other hand, is more gradual, and also coincides with the return of unfavourable autumn weather conditions which make remote sensing less accurate. Here, prediction error is 20 days in the best case scenario (Testa et al., in press).

In short, the observation data acquired by the RENECOFOR network through long-term monitoring and with standardised protocols offer an unequalled opportunity to validate and calibrate remote sensing products. This calibration has become even more crucial since the launch of new satellites for the European Space Agency's (ESA) Sentinel Mission, which combines very high-resolution spatial and temporal remote-sensing, especially dedicated to observing the planet Earth and to continuously providing data for a variety of actors. The degree of uncertainty for the estimates furnished must be evaluated to avoid erroneous interpretations and to define the limits of the use of remote-sensing (Soudani & François, 2014).