Contribution of deep soil layers to the transpiration of a temperate deciduous forest: Implications for the modelling of productivity.

Climate change is imposing drier atmospheric and edaphic conditions on temperate forests. Here, we investigated how deep soil (down to 300 cm) water extraction contributed to the provision of water in the Fontainebleau-Barbeau temperate oak forest over two years, including the 2018 record drought. Deep water provision was key to sustain canopy transpiration during drought, with layers below 150 cm contributing up to 60% of the transpired water in August 2018, despite their very low density of fine roots. We further showed that soil databases used to parameterize ecosystem models largely underestimated the amount of water extractable from the soil by trees, due to a considerable underestimation of the tree rooting depth. The consensus database established for France gave an estimate of 207 mm for the soil water holding capacity (SWHC) at Fontainebleau-Barbeau, when our estimate based on the analysis of soil water content measurements was 1.9 times as high, reaching 390 ± 17 mm. Running the CASTANEA forest model with the database-derived SWHC yielded a 185 gC m-2 y-1 average underestimation of annual gross primary productivity under current climate, reaching up to 687 ± 117 gC m-2 y-1 under climate change scenario RCP8.5. It is likely that the strong underestimation of SWHC that we show at our site is not a special case, and concerns a large number of forest sites. Thus, we argue for a generalisation of deep soil water content measurements in forests, in order to improve the estimation of SWHC and the simulation of the forest carbon cycle in the current context of climate change.

The within-population variability of leaf spring and autumn phenology is influenced by temperature in temperate deciduous trees.

Leaf phenology is a major driver of ecosystem functioning in temperate forests and a robust indicator of climate change. Both the inter-annual and inter-population variability of leaf phenology have received much attention in the literature; in contrast, the within-population variability of leaf phenology has been far less studied. Beyond its impact on individual tree physiological processes, the within-population variability of leaf phenology can affect the estimation of the average budburst or leaf senescence dates at the population scale. Here, we monitored the progress of spring and autumn leaf phenology over 14 tree populations (9 tree species) in six European forests over the period of 2011 to 2018 (yielding 16 site-years of data for spring, 14 for autumn). We monitored 27 to 512 (with a median of 62) individuals per population. We quantified the within-population variability of leaf phenology as the standard deviation of the distribution of individual dates of budburst or leaf senescence (SDBBi and SDLSi, respectively). Given the natural variability of phenological dates occurring in our tree populations, we estimated from the data that a minimum sample size of 28 (resp. 23) individuals, are required to estimate SDBBi (resp. SDLSi) with a precision of 3 (resp. 7) days. The within-population of leaf senescence (average SDLSi = 8.5 days) was on average two times larger than for budburst (average SDBBi = 4.0 days). We evidenced that warmer temperature during the budburst period and a late average budburst date were associated with a lower SDBBi, as a result of a quicker spread of budburst in tree populations, with a strong species effect. Regarding autumn phenology, we observed that later senescence and warm temperatures during the senescence period were linked with a high SDLSi, with a strong species effect. The shares of variance explained by our models were modest suggesting that other factors likely influence the within-population variation in leaf phenology. For instance, a detailed analysis revealed that summer temperatures were negatively correlated with a lower SDLSi.

Patterns of local, intercontinental and interseasonal variation of soil bacterial and eukaryotic microbial communities.

Although ongoing research has revealed some of the main drivers behind global spatial patterns of microbial communities, spatio-temporal dynamics of these communities still remain largely unexplored. Here, we investigate spatio-temporal variability of both bacterial and eukaryotic soil microbial communities at local and intercontinental scales. We compare how temporal variation in community composition scales with spatial variation in community composition, and explore the extent to which bacteria, protists, fungi and metazoa have similar patterns of temporal community dynamics. All soil microbial groups displayed a strong correlation between spatial distance and community dissimilarity, which was related to the ratio of organism to sample size. Temporal changes were variable, ranging from equal to local between-sample variation, to as large as that between communities several thousand kilometers apart. Moreover, significant correlations were found between bacterial and protist communities, as well as between protist and fungal communities, indicating that these microbial groups change in tandem, potentially driven by interactions between them. We conclude that temporal variation can be considerable in soil microbial communities, and that future studies need to consider temporal variation in order to reliably capture all drivers of soil microbiome changes.

"Green pointillism": detecting the within-population variability of budburst in temperate deciduous trees with phenological cameras.

Phenological cameras have been used over a decade for identifying plant phenological markers (budburst, leaf senescence) and more generally the greenness dynamics of forest canopies. The analysis is usually carried out over the full camera field of view, with no particular analysis of the variability of phenological markers among trees. Here we show that images produced by phenological cameras can be used to quantify the within-population variability of budburst (WPVbb) in temperate deciduous forests. Using seven site-years of image analyses, we report a strong correlation (r2 = 0.97) between the WPVbb determined with a phenological camera and its quantification through ground observation. We show that WPVbb varies strongly (by a factor of 4) from year to year in a given population and that those variations are linked with temperature conditions during the budburst period, with colder springs associated to a higher differentiation of budburst (higher WPVbb) among trees. Deploying our approach at the continental scale, i.e., throughout phenological cameras networks, would improve the understanding of the spatial (across populations) and temporal (across years) variations of WPVbb, which have strong implications on forest functioning, tree fitness and phenological modelling.