Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species.

Riparian forests are considered as complex and biologically rich habitats. They play a crucial role for biodiversity conservation, in particular in landscapes under strong management pressure. Tree-related microhabitats (TreMs) support a wide array of biodiversity and some authors have suggested using them as indirect biodiversity indicators in forest ecosystems. This study investigates TreM profiles of a temperate riparian forest as last natural forest ecosystem in a context of intensively managed landscape along the Ciron river and of surrounding pine plantations of the Landes de Gascogne forest (France) and explores their drivers at tree and plot scales. A total of 28 transects perpendicular to the river, representing 84 0.1 ha-plots, were selected along the Ciron river and three of its tributaries. At tree scale, diameter at breast height (dbh), tree species and tree status (living, standing or lying dead) were significant predictors of TreM diversity. At plot scale, Alnus glutinosa was found to significantly contribute to riparian forest TreM profiles by hosting specific TreMs. The same role was highlighted for broadleaved species in pine plantations. At plot scale, the drivers of TreM diversity varied depending on plot location in the transect (riparian forest, plantation forest, intermediate area). We found that the presence of lying dead trees is an important driver of TreM diversity at plot scale, particularly in plantation stand and we thus suggest lying dead trees be included in future studies focusing on TreMs. The rich TreM profile of riparian forests highlights their crucial role in biodiversity conservation.

As studies have underlined the sensitivity of lidar metrics to scan angles, the objective of this study was twofold. Firstly, we further investigated the influence of lidar scan angle on the ABA predictions of stand attributes of riparian (29 field plots), broadleaf (42 field plots), coniferous (31 field plots) and mixed (45 field plots) forest types in France. Secondly, we evaluated the potential of voxelisation approaches to normalise scan angle effects in lidar metrics and mitigate scan angle effects in ABA models. To achieve these objectives, we first selected a model based on four lidar metrics with different sensitivities to lidar scan angle, i.e. mean and variance of canopy height values, gap-fraction, and coefficient of variation of plant area density (PAD) profile. For each plot, we considered the point cloud scanned from one flight line independently and characterised each resulting point cloud by the mean scan angle (MSA) and classified them into one of three classes: A (0 degrees <=MSA < 10 degrees), B (10 degrees<=MSA < 20 degrees) or C (20 degrees<=MSA < 30 degrees). An experimental setup involving nine scenarios was conceived to study the impact of the number of flight lines (sce-narios fl1, fl2 and fl3) and predominant scan angle (scenarios A, B or C) or combination of scan angle directions (scenariosA and B, orA and C, orB and C), on area-based approach (ABA) models. We built ABA models for the same forest plots for 5000 resampled datasets in each scenario to predict three forest attributes, i.e., stem and total volume (Vst and Vtot) and basal area (BA). Three goodness-of-fit criteria were computed for each model (coefficient of determination (R2), relative root mean square error (rRMSE) and mean percentage error (MPE). We compared the distributions of the goodness-of-fit criteria between scenarios to assess the behaviour of the predictive models when: 1) the number of flight lines (i.e., scan angles) increases (fl1, fl2 or fl3); 2) lidar datasets comprise specific scan angle (A, B or C) or combination of scan angles (AB, AC or BC); 3) voxelisation is used to compute Pfand CVPAD. The results show that models built with point clouds scanned from multiple flight lines were more robust, with a lower standard deviation of their goodness-of-fit criteria. On average, across all forest types, compared to fl1, the standard de-viations of R2 distributions were lower for fl2 and fl3 by 42 % and 77 %, respectively. We also observed that a dataset with a predominantly nadir configuration (i.e., scenario A) did not always result in better predictions (mean R2 higher by 0.08, 0.07, 0.04 for scenario B for broadleaf, coniferous and mixed, respectively). For a set of calibration plots, the resulting forest attribute models depend on the acquisition geometry over the plots, as observed in this study, which could result in unreliable wall-to-wall predictions. The risk is particularly high in acquisitions with low overlapping rates, with many areas covered by only one flight line. Using voxel-basedPfand CVPAD together with the mean and variance of heights helped to mitigate the impacts of changes in scan angles by a) increasing the means of the distributions, thereby improving the accuracy of predictions, or b) reducing the standard deviations, thereby increasing prediction precision, or c) both of the above.

This research aims at integrating ecological networks modelling in a participatory approach in order to assess impacts of land-use planning scenarios on landscape connectivity. This approach was applied to the metropolitan area of Bordeaux, a highly dynamic territory that has been modified by several decades of rapid urbanization. Whilst ecological network modelling is widely used in the academic spheres, the concept of ecological network itself also rose within operational stakeholders acting in land-use planning. However, exchanges between scientists and stakeholders about this concept and its modelling and decision-making applications remain rare and generally relate to discussions about results of analyzes carried out by scientists on their own. To our knowledge, no studies to date have involved stakeholders throughout the whole modelling process. In this purpose, we developed an adapted companion modelling approach bringing together scientists and stakeholders for co-constructing (1) a multi-species approach based on ecoprofiles, (2) a conceptual model of the territory’s social-ecological functioning and (3) five land-use planning scenarios over a 15-years horizon. In parallel, we used a landscape graphs approach for modelling ecological networks of ecoprofiles, computing local and global connectivity metrics and estimating scenarios’ impacts on multi-species connectivity. The results globally showed negative impacts of dystopian scenarios or anticipated trends in planning on landscape connectivity (from −20.5% to −8.1% on average, respectively), and in a lesser extent positive impacts of utopian or transformative scenarios (+1.5% and 4.5% on average, respectively). Scenarios’ impacts also varied among ecoprofiles, with some ecoprofiles showing similar or antagonistic effects. These results served as a support of debates between stakeholders on the consequences of policy decisions and actions on connectivity, and on the possibilities of translating connectivity modelling in land-use planning and biodiversity conservation in an urban context.

Lidar scan angle can affect estimation of lidar-derived forest metrics used in area-based approaches (ABAs). As commonly used first-order metrics and various user-developed metrics are computed in the form of a grid or a raster, their response to various scan angles needs to be investigated similarly. The objective of this study was to highlight the impact of scan angles on 11 metrics (9 height-based and 2 other commonly used metrics) at the level of the grid-cell. The study area was divided into a grid of cell size 30 m. In every grid-cell, the flight lines that sampled at least 90% area of the grid-cell were identified. The flight lines and the corresponding point clouds were then classified based on their mean scan angle into four classes 0°–10°, 10°–20°, 20°–30° and 30°–40°. Metrics were computed for one flight line per class for each grid-cell. This resulted in a maximum of four values for a metric in every grid-cell. Comparing these values revealed the evolving nature of the metrics with the scan angle. For the comparison we used a paired t-test and simple linear regression. We observed that most of the metrics were systematically under-estimated with increasing scan angle. Gap-fraction, rumple index were affected more than standard deviation of height while the maximum height was relatively stable. Among the height percentiles, the higher percentiles were relatively more stable compared to the lower percentiles. Scan angles can indeed have an impact on the estimation of lidar derived metrics. Although, many of the metrics studied showed statistically significant differences in their computation for different scan angles, their impact on the accuracies of ABA models needs to be studied further by accounting for the differences as shown in this study.

The phloem-limited poleroviruses infect Arabidopsis thaliana without causing noticeable disease symptoms. In order to facilitate visual infection identification, we developed virus-induced gene silencing (VIGS) vectors derived from Turnip yellows virus (TuYV). Short sequences from the host gene AtCHLI1 required for chlorophyll biosynthesis [42 nucleotides in sense or antisense orientation or as an inverted-repeat (IR), or an 81 nucleotide sense fragment] were inserted into the 3′ non-coding region of the TuYV genome to screen for the most efficient and robust silencing vector. All recombinant viruses produced a clear vein chlorosis phenotype on infected Arabidopsis plants due to the expression inhibition of the AtCHLI1 gene. The introduction of a sense-oriented sequence into TuYV genome resulted in a virus exhibiting a more sustainable chlorosis than the virus containing an IR of the same length. This observation was correlated with a higher stability of the sense sequence insertion in the viral genome. In order to evaluate the impact of the TuYV silencing suppressor P0 in the VIGS mechanism a P0 knock-out mutation was introduced into the recombinant TuYV viruses. They induced a similar but milder vein clearing phenotype due to lower viral accumulation. This indicates that P0 does not hinder the performances of the TuYV silencing effect and confirms that in the viral infection context, P0 has no major impact on the production, propagation and action of the short distance silencing signal in phloem cells. Finally, we showed that TuYV can be used to strongly silence the phloem specific AtRTM1 gene. The TuYV-derived VIGS vectors therefore represent powerful tools to easily detect and monitor TuYV in infected plants and conduct functional analysis of phloem-restricted genes. Moreover this example indicates the potential of poleroviruses for use in functional genomic studies of agronomic plants.

Summary The genetic architecture of plant response to viruses has often been studied in model nonnatural pathosystems under controlled conditions. There is an urgent need to elucidate the genetic architecture of the response to viruses in a natural setting. A field experiment was performed in each of two years. In total, 317 Arabidopsis thaliana accessions were inoculated with its natural Turnip mosaic virus (TuMV). The accessions were phenotyped for viral accumulation, frequency of infected plants, stem length and symptoms. Genome‐wide association mapping was performed. Arabidopsis thaliana exhibits extensive natural variation in its response to TuMV in the field. The underlying genetic architecture reveals a more quantitative picture than in controlled conditions. Ten genomic regions were consistently identified across the two years. RTM3 (Restricted TEV Movement 3) is a major candidate for the response to TuMV in the field. New candidate genes include Dead box helicase 1, a Tim Barrel domain protein and the eukaryotic translation initiation factor eIF3b. To our knowledge, this study is the first to report the genetic architecture of quantitative response of A. thaliana to a naturally occurring virus in a field environment, thereby highlighting relevant candidate genes involved in plant virus interactions in nature.

The long distance movement of potyviruses is a poorly understood step of the viral cycle. Only factors inhibiting this process, referred to as “Restricted TEV Movement” (RTM), have been identified in Arabidopsis thaliana. On the virus side, the potyvirus coat protein (CP) displays determinants required for long-distance movement and for RTM-based resistance breaking. However, the potyvirus CP was previously shown not to interact with the RTM proteins. We undertook the identification of Arabidopsis factors which directly interact with either the RTM proteins or the CP of lettuce mosaic virus (LMV). An Arabidopsis cDNA library generated from companion cells was screened with LMV CP and RTM proteins using the yeast two-hybrid system. Fourteen interacting proteins were identified. Two of them were shown to interact with CP and the RTM proteins suggesting that a multiprotein complex could be formed between the RTM proteins and virions or viral ribonucleoprotein complexes. Co-localization experiments in Nicotiana benthamiana showed that most of the viral and cellular protein pairs co-localized at the periphery of chloroplasts which suggests a putative role for plastids in this process.

Potyvirus is the largest genus of plant viruses causing significant losses in a wide range of crops. Potyviruses are aphid transmitted in a nonpersistent manner and some of them are also seed transmitted. As important pathogens, potyviruses are much more studied than other plant viruses belonging to other genera and their study covers many aspects of plant virology, such as functional characterization of viral proteins, molecular interaction with hosts and vectors, structure, taxonomy, evolution, epidemiology, and diagnosis. Biotechnological applications of potyviruses are also being explored. During this last decade, substantial advances have been made in the understanding of the molecular biology of these viruses and the functions of their various proteins. After a general presentation on the family Potyviridae and the potyviral proteins, we present an update of the knowledge on potyvirus multiplication, movement, and transmission and on potyvirus/plant compatible interactions including pathogenicity and symptom determinants. We end the review providing information on biotechnological applications of potyviruses.

Turnip yellows virus (TuYV), a phloem-limited virus, encodes a 74kDa protein known as the readthrough protein (RT) involved in virus movement. We show here that a TuYV mutant deleted of the C-terminal part of the RT protein (TuYV-∆RTCter) was affected in long-distance trafficking in a host-specific manner. By using the C-terminal domain of the RT protein as a bait in a yeast two-hybrid screen of a phloem cDNA library from Arabidopsis thaliana we identified the calcineurin B-like protein-interacting protein kinase-7 (AtCIPK7). Transient expression of a GFP:CIPK7 fusion protein in virus-inoculated Nicotiana benthamiana leaves led to local increase of wild-type TuYV accumulation, but not that of TuYV-∆RTCter. Surprisingly, elevated virus titer in inoculated leaves did not result in higher TuYV accumulation in systemic leaves, which indicates that virus long-distance movement was not affected. Since GFP:CIPK7 was localized in or near plasmodesmata, CIPK7 could negatively regulate TuYV export from infected cells.

The phloem is a complex tissue composed of highly specialized cells with unique subcellular structures and a compact organization that is challenging to study in vivo at cellular resolution. We used confocal scanning laser microscopy and subcellular fluorescent markers in companion cells and sieve elements, for live imaging of the phloem in Arabidopsis leaves. This approach provided a simple framework for identifying phloem cell types unambiguously. It highlighted the compactness of the meshed network of organelles within companion cells. By contrast, within the sieve elements, unknown bodies were observed in association with the PP2-A1:GFP, GFP:RTM1 and RTM2:GFP markers at the cell periphery. The phloem lectin PP2-A1:GFP marker was found in the parietal ground matrix. Its location differed from that of the P-protein filaments, which were visualized with SEOR1:GFP and SEOR2:GFP. PP2-A1:GFP surrounded two types of bodies, one of which was identified as mitochondria. This location suggested that it was embedded within the sieve element clamps, specific structures that may fix the organelles to each another or to the plasma membrane in the sieve tubes. GFP:RTM1 was associated with a class of larger bodies, potentially corresponding to plastids. PP2-A1:GFP was soluble in the cytosol of immature sieve elements. The changes in its subcellular localization during differentiation provide an in vivo blueprint for monitoring this process. The subcellular features obtained with these companion cell and sieve element markers can be used as landmarks for exploring the organization and dynamics of phloem cells in vivo.

Background: To identify plant genes involved in various key traits, QTL mapping is a powerful approach. This approach is based on the use of mapped molecular markers to identify genomic regions controlling quantitative traits followed by a fine mapping and eventually positional cloning of candidate genes. Mapping technologies using SNP markers are still rather expensive and not feasible in every laboratory. In contrast, microsatellite (also called SSR for Simple Sequence Repeat) markers are technologically less demanding and less costly for any laboratory interested in genetic mapping.Results: In this study, we present the development and the characterization of a panel of 96 highly polymorphic SSR markers along the Arabidopsis thaliana genome allowing QTL mapping among accessions of the Versailles 24 core collection that covers a high percentage of the A. thaliana genetic diversity. These markers can be used for any QTL mapping analysis involving any of these accessions. We optimized the use of these markers in order to reveal polymorphism using standard PCR conditions and agarose gel electrophoresis. In addition, we showed that the use of only three of these markers allows differentiating all 24 accessions which makes this set of markers a powerful tool to control accession identity or any cross between any of these accessions.Conclusion: The set of SSR markers developed in this study provides a simple and efficient tool for any laboratory focusing on QTL mapping in A. thaliana and a simple means to control seed stock or crosses between accessions.

Association mapping fo quantitative responses to [i]Turnip Mosaic Virus[/i] (TuMV) infection trait loci in [i]Arabidopsis thaliana[/i] through evaluation of biomass, viral accumulation, and metabolic profiles. GDR Genetique Quantitative dans les Populations Naturelles