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Chloroplast haplotypes of Northern red oak (Quercus rubra L.) stands in Germany suggest their origin from Northeastern Canada / Jeremias Götz in Forests, vol 11 n° 9 (September 2020)  

Public [article]  inForests > vol 11 n° 9 (September 2020) . - n° 1025 Titre : Chloroplast haplotypes of Northern red oak (Quercus rubra L.) stands in Germany suggest their origin from Northeastern Canada Type de document : Article/Communication Auteurs : Jeremias Götz, Auteur ; Konstantin V. Krutovsky, Auteur ; Ludger Leinemann, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 1025 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne [Termes IGN] arbre aléatoire minimum [Termes IGN] Canada [Termes IGN] génétique forestière [Termes IGN] gestion forestière durable [Termes IGN] Quercus rubra [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Northern red oak (Quercus rubra L.) is one of the most important foreign tree species in Germany and considered as a major candidate for prospective sustainable forestry in the face of climate change. Therefore, Q. rubra was subject of many previous studies on its growth traits and attempts to infer the origin of various populations of this species using nuclear and chloroplast DNA markers. However, the exact geographic origin of German red oak stands has still not been identified. Its native range widely extends over North America, and the species can tolerate a broad range of environmental conditions. We genotyped individual trees in 85 populations distributed in Germany and North America using five chloroplast microsatellite and three novel chloroplast CAPS markers, resulting in the identification of 29 haplotypes. The new marker set enabled the identification of several new red oak haplotypes with restricted geographic origin. Some very rare haplotypes helped us narrow down the origin of Q. rubra stands in Germany, especially some stands from North Rhine-Westphalia, to the northern part of the species’ natural distribution area including the Peninsula of Nova Scotia, where the most similar haplotype composition was observed, compared to distinct German stands. Numéro de notice : A2020-751 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091025 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.3390/f11091025 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96427 [article]

Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)  

Public [article]  inForests > vol 11 n° 9 (September 2020) . - N° 1016 Titre : Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh Type de document : Article/Communication Auteurs : Mohammad Emran Hasan, Auteur ; Biswajit Nath, Auteur ; A.H.M. Raihan Sarker, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] automate cellulaire [Termes IGN] Bangladesh [Termes IGN] classification par maximum de vraisemblance [Termes IGN] couvert forestier [Termes IGN] déboisement [Termes IGN] dégradation de l'environnement [Termes IGN] détection de changement [Termes IGN] gestion forestière durable [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] mangrove [Termes IGN] modèle de Markov [Termes IGN] modèle de simulation [Termes IGN] occupation du sol [Termes IGN] réserve forestière [Termes IGN] réserve naturelle [Termes IGN] santé des forêts [Termes IGN] série temporelle [Termes IGN] système d'information géographique Résumé : (auteur) Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF. Numéro de notice : A2020-752 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091016 Date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.3390/f11091016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96432 [article]

Use of Bayesian modeling to determine the effects of meteorological conditions, prescribed burn season, and tree characteristics on litterfall of pinus nigra and pinus pinaster stands / Juncal Espinosa in Forests, vol 11 n° 9 (September 2020)  

Public [article]  inForests > vol 11 n° 9 (September 2020) . - N° 1006 Titre : Use of Bayesian modeling to determine the effects of meteorological conditions, prescribed burn season, and tree characteristics on litterfall of pinus nigra and pinus pinaster stands Type de document : Article/Communication Auteurs : Juncal Espinosa, Auteur ; Óscar Rodríguez de Rivera, Auteur ; Javier Madrigal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse [Termes IGN] classification bayesienne [Termes IGN] données météorologiques [Termes IGN] Espagne [Termes IGN] estimation bayesienne [Termes IGN] incendie de forêt [Termes IGN] intégrale de Laplace [Termes IGN] modèle linéaire [Termes IGN] Pinus nigra [Termes IGN] Pinus pinaster [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Research Highlights: Litterfall biomass after prescribed burning (PB) is significantly influenced by meteorological variables, stand characteristics, and the fire prescription. Some of the fire-adaptive traits of the species under study (Pinus nigra and Pinus pinaster) mitigate the effects of PB on litterfall biomass. The Bayesian approach, tested here for the first time, was shown to be useful for analyzing the complex combination of variables influencing the effect of PB on litterfall. Background and Objectives: The aims of the study focused on explaining the influence of meteorological conditions after PB on litterfall biomass, to explore the potential influence of stand characteristic and tree traits that influence fire protection, and to assess the influence of fire prescription and fire behavior. Materials and Methods: An experimental factorial design including three treatments (control, spring, and autumn burning), each with three replicates, was established at two experimental sites (N = 18; 50 × 50 m2 plots). The methodology of the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP forests) was applied and a Bayesian approach was used to construct a generalized linear mixed model. Results: Litterfall was mainly affected by the meteorological variables and also by the type of stand and the treatment. The effects of minimum bark thickness and the height of the first live branch were random. The maximum scorch height was not high enough to affect the litterfall. Time during which the temperature exceeded 60 °C (cambium and bark) did not have an important effect. Conclusions: Our findings demonstrated that meteorological conditions were the most significant variables affecting litterfall biomass, with snowy and stormy days having important effects. Significant effects of stand characteristics (mixed and pure stand) and fire prescription regime (spring and autumn PB) were shown. The trees were completely protected by a combination of low-intensity PB and fire-adaptive tree traits, which prevent direct and indirect effects on litterfall. Identification of important variables can help to improve PB and reduce the vulnerability of stands managed by this method. Numéro de notice : A2020-753 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091006 Date de publication en ligne : 18/09/2020 En ligne : https://doi.org/10.3390/f11091006 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96433 [article]