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scientifique

Clinal variation along precipitation gradients in Patagonian temperate forests: unravelling demographic and selection signatures in three Nothofagus spp. / Carolina Soliani in Annals of Forest Science [en ligne], Vol 77 n° 1 (mars 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 77 n° 1 (mars 2020) . - 17 p. Titre : Clinal variation along precipitation gradients in Patagonian temperate forests: unravelling demographic and selection signatures in three Nothofagus spp. Type de document : Article/Communication Auteurs : Carolina Soliani, Auteur ; Maria Marta Azpilicueta, Auteur ; Maria Veronica Arana, Auteur ; Paula Marchelli, Auteur Année de publication : 2020 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] génétique forestière [Termes descripteurs IGN] nothofagus (genre) [Termes descripteurs IGN] nothofagus pumilio [Termes descripteurs IGN] Patagonie [Termes descripteurs IGN] Pléistocène [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] prévision [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] stress hydrique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message: Past demographic changes and current selection pressures determine the genetic variation displayed by Nothofagus species along rainfall gradients. Based on the diversity trends observed at candidate genes associated to drought stress, we inferred a differential species’ adaptive potential. Context: Clinal genetic variation in natural populations could reflect either recent demographic history or the evolution of adapted genotypes along heterogeneous environments. Aims: We describe genetic variation patterns in three Nothofagus species of South American temperate forests, growing along steep rainfall gradients. Our hypothesis is that the selection pressure along this gradient reinforces the genetic structure previously shaped by Pleistocene climate oscillations. Methods: We screened variation along gradients at putative adaptive markers: candidate genes involved in response to drought, and EST-SSRs linked to drought stress genes. Genomic SSRs (gSSRs) were used to decouple the incidence of demographic events in the genetic structure. Results: Genetic diversity at SSRs agreed with the putative location of cryptic Pleistocene refugia in Nothofagus. In addition, each species showed different trends for nucleotide diversity at candidate genes. Unbiased heterozygosity significantly correlated with precipitation at EST-SSRs in Nothofagus nervosa. We found evidences of balancing selection and several SNPs departed from neutral expectations. Conclusions: Nothofagus genetic variability shows a strong imprint of demographic changes that reveals refugia location for the species during Pleistocene. This variability is modelled by environmental conditions across natural gradients, which impose selection pressure at genome regions related to stress response, providing clues about inter-specific differences in adaptive potential to water deficit. Numéro de notice : A2020-032 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0908-x date de publication en ligne : 10/01/2020 En ligne : https://doi.org/10.1007/s13595-019-0908-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94489 [article]

Effects of Quercus rubra L. on soil properties and humus forms in 50-year-old and 80-year-old forest stands of Lombardy plain / Chiara Ferré in Annals of Forest Science [en ligne], Vol 77 n° 1 (mars 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 77 n° 1 (mars 2020) . - 19 p. Titre : Effects of Quercus rubra L. on soil properties and humus forms in 50-year-old and 80-year-old forest stands of Lombardy plain Type de document : Article/Communication Auteurs : Chiara Ferré, Auteur ; Roberto Comolli, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] boisement artificiel [Termes descripteurs IGN] carbone [Termes descripteurs IGN] conversion forestière [Termes descripteurs IGN] humus [Termes descripteurs IGN] Lombardie [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] quercus (genre) [Termes descripteurs IGN] quercus rubra [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Key message: Besides the well-known effects on the native plant community, red oak may also impact the soil; the effects of afforestation with red oak involve both organic layers and mineral soil, resulting in changes in organic carbon quantity and quality and in soil acidification. Context: Many alien species have become widespread in Europe; among these, red oak is a common invader of temperate forests. Aims: The effects of substitution of natural mixed forest by red oak forest on humus forms and soil properties were investigated in two paired plots: a 50-year-old (Bosco Vacaressino) and 80-year-old (Bosco Ginestre) forest stand. Methods: Soil sampling was performed from 3 layers at 40 and 49 points in Bosco Vacaressino and Bosco Ginestre respectively to determine humus forms, soil pH, organic carbon stock, carbon-nitrogen ratio (C:N), available phosphorus, and texture. Results: Red oak resulted in a shift from Mull to Moder humus forms; soil acidification, higher C:N ratio, and soil organic carbon stock were observed compared with mixed forests. Conclusion: The major changes were reflected in a change toward less active humus forms; the effects of vegetation conversions were also visible in mineral layers; many of the modifications were more evident with increasing stand age. Numéro de notice : A2020-033 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0893-0 date de publication en ligne : 10/01/2020 En ligne : https://doi.org/10.1007/s13595-019-0893-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94490 [article]

Warming effects on morphological and physiological performances of four subtropical montane tree species Authors Authors and affiliations / Yiyong Li in Annals of Forest Science [en ligne], Vol 77 n° 1 (mars 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 77 n° 1 (mars 2020) . - 11 p. Titre : Warming effects on morphological and physiological performances of four subtropical montane tree species Authors Authors and affiliations Type de document : Article/Communication Auteurs : Yiyong Li, Auteur ; Yue Xu, Auteur ; Ting Wu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 11 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] diagnostic foliaire [Termes descripteurs IGN] effet thermique [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] montagne [Termes descripteurs IGN] morphologie [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] stress hydrique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message: In a downward transplantation experiment, warming stimulated growth and photosynthesis of Schima superba Gardn. et Champ., Syzygium rehderianum Merr. et Perry and Itea chinensis Hook. et Arn. via increased stomatal conductance. Warming had no effect on growth of Machilus breviflora (Benth.) Hemsl., indicating species-specific differences in response to warming. Context: Climate change has been shown to shift species composition and community structure in subtropical forests. Thus, understanding the species-specific responses of growth and physiological processes to warming is essential. Aims: To investigate how climate warming affects growth, morphological and physiological performance of co-occurring tree species when they are growing at different altitudes. Methods: Soils and 1-year-old seedlings of four subtropical co-occurring tree species (Schima superba Gardn. et Champ., Syzygium rehderianum Merr. et Perry, Itea chinensis Hook. et Arn. and Machilus breviflora (Benth.) Hemsl.) were transplanted to three altitudes (600 m, 300 m and 30 m a.s.l.), inducing an effective warming of 1.0 °C and 1.5 °C. Growth, morphological, and physiological performances of these seedlings were monitored. Results: When exposed to warmer conditions, aboveground growth of the four species except M. breviflora was strongly promoted, accompanied by increased light-saturated photosynthetic rate and stomatal conductance. Warming also significantly increased concentrations of non-structural carbohydrates in leaves of S. rehderianum and M. breviflora, stems of S. superba and S. rehderianum, and roots of I. chinensis. However, we did not detect any effect of warming on stomatal length and stomatal density. Conclusion: Our results provide evidence that climate warming could have species-specific impacts on co-occurring tree species, which might subsequently shift species composition and forest structure. Numéro de notice : A2020-037 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0910-3 date de publication en ligne : 10/01/2020 En ligne : https://doi.org/10.1007/s13595-019-0910-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94491 [article]

Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])  

Public [article]  inGeocarto international > Vol 35 n° 1 [02/01/2020] . - pp 17 - 28 Titre : Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; Ilker Erkanli, Auteur Année de publication : 2020 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] carbone [Termes descripteurs IGN] Fagus (genre) [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] Turquie Résumé : (auteur) The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values. Numéro de notice : A2020-017 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1499817 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1499817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94410 [article]

Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 219 - 230 Titre : Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees Type de document : Article/Communication Auteurs : Hamid Hamraz, Auteur ; Nathan B. Jacobs, Auteur ; Marco A. Contreras, Auteur ; Chase H. Clark, Auteur Année de publication : 2019 Article en page(s) : pp 219 - 230 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] houppier [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points Résumé : (auteur) The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees segmented from airborne LiDAR data. To enable processing by a deep convolutional neural network (CNN), we designed two discrete representations using leaf-off and leaf-on LiDAR data: a digital surface model with four channels (DSM × 4) and a set of four 2D views (4 × 2D). A training dataset of tree crowns was generated via segmentation of tree crowns, followed by co-registration with field data. Potential mislabels due to GPS error or tree leaning were corrected using a statistical ensemble filtering procedure. Because the training data was heavily unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced sub-samples. Benchmarked against multiple traditional shallow learning methods using manually designed features, the CNNs improved accuracies up to 14%. The 4 × 2D representation yielded similar classification accuracies to the DSM × 4 representation (~82% coniferous and ~90% deciduous) while converging faster. Further experimentation showed that early/late fusion of the channels in the representations did not affect the accuracies in a significant way. The data augmentation that was used for the CNN training improved the classification accuracies, but more real training instances (especially coniferous) likely results in much stronger improvements. Leaf-off LiDAR data were the primary source of useful information, which is likely due to the perennial nature of coniferous foliage. LiDAR intensity values also proved to be useful, but normalization yielded no significant improvement. As we observed, large training data may compensate for the lack of a subset of important domain data. Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR. In domains like remote sensing and biomedical imaging, where the data contain a large amount of information and are not friendly to human visual system, human-designed features may become suboptimal. As exemplified by this study, automatic, objective derivation of optimal features via deep learning can improve prediction tasks in such domains. Numéro de notice : A2019-547 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.10.011 date de publication en ligne : 03/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.10.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94192 [article]

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