Biology subjects

Dufrene, E.

Publications and source records attributed to Dufrene, E..

4 recordsLinked to original sources

bioRxiv2021

" Idol with feet of clay ": reliable predictions of forest ecosystem functioning require flawless climate forcings.

Climate change affects various aspects of the functioning of ecosystem, especially photosynthesis, respiration and carbon storage. We need accurate modelling approaches (impact models) to simulate the functioning, vitality and provision of ecosystem services of forests in a warmer world. These impact models require climate data as forcings, which are often produced by climate models comparing more or less well with observational climate data. The bias percentage of the climate forcings propagates throughout the modeling chain from the climate model to the impact model. In this study, we aimed to quantify these bias percentage, addressing three questions: (1) Do the impact model predictions vary when forcing it with different climate models, and how do the predictions under climate model vs. observational climate forcing differ? (2) Does the variability in the impact climate simulations caused by climate forcings fade out at large spatial scale? (3) How the fact of using simulated climatic data affects the process-based model predictions in the case of stressful events? To answer these questions, we present results obtained over the historical period (e.g. 1970-2010) with the CASTANEA ecophysiological forest model and use the data from three climate models. Our analysis focuses on French forests, studying European beech (Fagus sylvatica), temperate deciduous oaks (Quercus robur and Q. petraea), Scots pine (Pinus sylvestris) and spruce (Picea abies) monospecific stands. We show that prediction of photosynthesis, respiration and wood growth highly depends on the climate model used, whether debiased or not, and also on species and region considered. Overall, we observed an improvement of prediction after a monthly mean bias or monthly quantile mapping correction for three model considered, but not with the same success. Then we highlighted a large variability in the processes simulated by the impact model under different climate forcings when considering the plot (i.e. scale of a few hectares) scale. This variability fades out at larger scale (e.g. the scale of an ecological region, i.e. 100 km2), owing to an aggregation effect. Moreover, process predictions obtained under different climate forcings are more variable during driest years. These results highlight the necessity to quantify bias and uncertainties in climate forcings before predicting fluxes dynamics with process-based model.

ecology↗

bioRxiv2021

Potential of Synthetic Aperture Radar Sentinel-1 time series for the monitoring of phenological cycles in a deciduous forest

Annual time-series of the two satellites C-band SAR (Synthetic Aperture Radar) Sentinel-1 A and B data over five years were used to characterize the phenological cycle of a temperate deciduous forest. Six phenological markers of the start, middle and end of budburst and leaf expansion stage in spring and the leaf senescence in autumn were extracted from time-series of the ratio (VV/VH) of backscattering at co-polarization VV (vertical-vertical) and at cross polarization VH (vertical-horizontal). These markers were compared to field phenological observations, and to phenological dates derived from various proxies (Normalized Difference Vegetation Index NDVI time-series from Sentinel-2 A and B images, in situ NDVI measurements, Leaf Area Index LAI and litterfall temporal dynamics). We observe a decrease in the backscattering coefficient ({sigma}0) at VH cross polarization during the leaf development and expansion phase in spring and an increase during the senescence phase, contrary to what is usually observed on various types of crops. In vertical polarization, {sigma}0VV shows very little variation throughout the year. S-1 time series of VV/VH ratio provides a good description of the seasonal vegetation cycle allowing the estimation of spring and autumn phenological markers. Estimates provided by VV/VH of budburst dates differ by approximately 8 days on average from phenological observations. During senescence phase, estimates are positively shifted (later) and deviate by about 20 days from phenological observations of leaf senescence while the differences are of the order of 2 to 4 days between the phenological observations and estimates based on in situ NDVI and LAI time-series, respectively. A deviation of about 7 days, comparable to that observed during budburst, is obtained between the estimates of senescence from S-1 and those determined from the in situ monitoring of litterfall. While in spring, leaf emergence and expansion described by LAI or NDVI explains the increase of VV/VH (or the decrease of {sigma}0VH), during senescence, S-1 VV/VH is decorrelated from LAI or NDVI and is better explained by litterfall temporal dynamics. This behavior resulted in a hysteresis phenomenon observed on the relationships between VV/VH and NDVI or LAI. For the same LAI or NDVI, the response of VV/VH is different depending on the phenological phase considered. This study shows the high potential offered by Sentinel-1 SAR C-band time series for the detection of forest phenology for the first time, thus overcoming the limitations caused by cloud cover in optical remote sensing of vegetation phenology. HighlightsO_LIWe study S-1 C-band dual polarized data potential to predict forest phenology C_LIO_LISeasonal phenological transitions were accurately described by S-1 time-series C_LIO_LIBudburst and senescence dates from S-1 differ from direct observations by one week C_LIO_LITime-series of S-1 VV/VH, NDVI, LAI and litterfall were also compared C_LIO_LIRelationships VV/VH vs NDVI and LAI show a hysteresis according to the season C_LI

ecology↗

bioRxiv2020

A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

Tree phenology is a major driver of forest-atmosphere mass and energy exchanges. Yet tree phenology has historically not been recorded at flux measurement sites. Here, we used seasonal time-series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (Greenness Chromatic Coordinate), broad-band NDVI, LAI (Leaf Area Index), fAPAR (fraction of Absorbed Photosynthetic Active Radiation), CC (Canopy Closure), fRvis (fraction of Reflected Radiation) and GPP (Gross Primary Productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured very well the interannual variability of spring phenology (R2 > 0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) less than 4 days. For the CC and GCC methods, mid-amplitude (50%) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25% of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 days. GPP provides the most biased estimates. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2 =0.63 and 0.80 for NDVI and CC, respectively), with MAD less than 7 days. Our results show that proximal sensing methods can be used to derive robust phenological indexes. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and decadal trends of mass and energy exchanges. HighlightsO_LIWe used 8 indirect methods to predict the timing of phenological events. C_LIO_LIGCC, NDVI and CC captured very well the interannual variation of spring phenology. C_LIO_LIGCC, NDVI and CC provided the best estimates of observed budburst dates. C_LIO_LINDVI and CC derived-dates correlated with observed leaf senescence dates. C_LI

ecology↗

bioRxiv2019

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

O_LIPhenological 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.\nC_LIO_LIHere 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 7 site-years of image analyses, we report a strong correlation (r{superscript 2}=0.97) between the WPVbb determined with a phenological camera and its quantification through ground observation.\nC_LIO_LIWe 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.\nC_LIO_LIDeploying 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.\nC_LI

ecology↗