scholarly journals Genetic variation of chemical and mechanical traits of maritime pine (Pinus pinaster Aiton). Correlations with wood density components

2011 ◽  
Vol 68 (2) ◽  
pp. 255-265 ◽  
Author(s):  
Maria J. Gaspar ◽  
Ana Alves ◽  
José L. Louzada ◽  
José Morais ◽  
Antonio Santos ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Wood Density ◽  
Pinus Pinaster ◽  
Maritime Pine

Genetic resources in maritime pine (Pinus pinaster Aiton): molecular and quantitative measures of genetic variation and differentiation among maternal lineages

2004 ◽  
Vol 197 (1-3) ◽  
pp. 103-115 ◽  
Author(s):  
Santiago C González-Martı́nez ◽  
Stéphanie Mariette ◽  
Maria Margarida Ribeiro ◽  
Christian Burban ◽  
Annie Raffin ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Genetic Resources ◽  
Pinus Pinaster ◽  
Maritime Pine

scholarly journals Can wood density be efficiently selected at early stage in maritime pine (Pinus pinaster Ait.)?

2008 ◽  
Vol 65 (1) ◽  
pp. 106-106 ◽  
Author(s):  
Laurent Bouffier ◽  
Céline Charlot ◽  
Annie Raffin ◽  
Philippe Rozenberg ◽  
Antoine Kremer
Keyword(s):  
Wood Density ◽  
Pinus Pinaster ◽  
Early Stage ◽  
Maritime Pine ◽  
Pinus Pinaster Ait

scholarly journals Genetic variation in susceptibility to pine wilt disease of maritime pine (Pinus pinaster Aiton) half-sib families

2018 ◽  
Vol 75 (3) ◽  
Author(s):  
Isabel Carrasquinho ◽  
Ana Lisboa ◽  
Maria L. Inácio ◽  
Elsa Gonçalves
Keyword(s):  
Genetic Variation ◽  
Pinus Pinaster ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Maritime Pine ◽  
Pine Wilt ◽  
Sib Families

Valorisation of non-timber by-products from maritime pine (Pinus pinaster, Ait) for particleboard production

2021 ◽  
Vol 168 ◽  
pp. 113581
Author(s):  
J. Santos ◽  
J. Pereira ◽  
N. Ferreira ◽  
N. Paiva ◽  
J. Ferra ◽  
...  
Keyword(s):  
Pinus Pinaster ◽  
Maritime Pine ◽  
Pinus Pinaster Ait ◽  
By Products

scholarly journals Modelling Maritime Pine (Pinus pinaster Aiton) Spatial Distribution and Productivity in Portugal: Tools for Forest Management

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 368
Author(s):  
Cristina Alegria ◽  
Natália Roque ◽  
Teresa Albuquerque ◽  
Paulo Fernandez ◽  
Maria Margarida Ribeiro
Keyword(s):  
Spatial Distribution ◽  
Forest Management ◽  
Species Distribution ◽  
Pinus Pinaster ◽  
Landscape Planning ◽  
Environmental Data ◽  
Maritime Pine ◽  
Future Climate Change ◽  
Sequential Simulation ◽  
Model Species

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.

Does phylogeographical structure relate to climatic niche divergence? A test using maritime pine (Pinus pinaster Ait.)

2015 ◽  
Vol 24 (11) ◽  
pp. 1302-1313 ◽  
Author(s):  
M. J. Serra-Varela ◽  
D. Grivet ◽  
L. Vincenot ◽  
O. Broennimann ◽  
J. Gonzalo-Jiménez ◽  
...  
Keyword(s):  
Pinus Pinaster ◽  
Maritime Pine ◽  
Climatic Niche ◽  
Niche Divergence ◽  
Pinus Pinaster Ait

scholarly journals Inducibility of Plant Secondary Metabolites in the Stem Predicts Genetic Variation in Resistance Against a Key Insect Herbivore in Maritime Pine

2018 ◽  
Vol 9 ◽  
Author(s):  
Xosé López-Goldar ◽  
Caterina Villari ◽  
Pierluigi Bonello ◽  
Anna Karin Borg-Karlson ◽  
Delphine Grivet ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Secondary Metabolites ◽  
Plant Secondary Metabolites ◽  
Maritime Pine ◽  
Insect Herbivore
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