Water and stem girdling affect the tracheids’ number more than their shape in Pinus pinaster saplings

AbstractThe pine wilt disease (PWD), for which no effective treatment is available at the moment, is a constant threat to Pinus spp. plantations worldwide, being responsible for significant economic and environmental losses every year. It has been demonstrated that elicitation with chitosan increases plant tolerance to the pinewood nematode (PWN) Bursaphelenchus xylophilus, the causal agent of the PWD, but the biochemical and genetic aspects underlying this response have not been explored. To understand the influence of chitosan in Pinus pinaster tolerance against PWN, a low-molecular-weight (327 kDa) chitosan was applied to mock- and PWN-inoculated plants. Nematode population, malondialdehyde (MDA), catalase, carotenoids, anthocyanins, phenolic compounds, lignin and gene expression related to oxidative stress (thioredoxin 1, TRX) and plant defence (defensin, DEF, and a-farnesene synthase, AFS), were analysed at 1, 7, 14, 21 and 28 days post-inoculation (dpi). At 28 dpi, PWN-infected plants elicited with chitosan showed a sixfold lower nematode population when compared to non-elicited plants. Higher levels of MDA, catalase, carotenoids, anthocyanins, phenolic compounds, and lignin were detected in chitosan-elicited plants following infection. The expression levels of DEF gene were higher in elicited plants, while TRX and AFS expression was lower, possibly due to the disease containment-effect of chitosan. Combined, we conclude that chitosan induces pine defences against PWD via modulation of metabolic and transcriptomic mechanisms related with plant antioxidant system.

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

Forests ◽

10.3390/f12030368 ◽

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.

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Using GEDI lidar data and airborne laser scanning to assess height growth dynamics in fast-growing species: a showcase in Spain

Forest Ecosystems ◽

10.1186/s40663-021-00291-2 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Juan Guerra-Hernández ◽

Adrián Pascual

Keyword(s):

Pinus Radiata ◽

Pinus Pinaster ◽

Laser Scanning ◽

Forest Ecosystems ◽

Height Growth ◽

Growth Patterns ◽

Forest Growth ◽

Airborne Laser Scanning ◽

Fast Growing ◽

Airborne Laser

Abstract Background The NASA’s Global Ecosystem Dynamics Investigation (GEDI) satellite mission aims at scanning forest ecosystems on a multi-temporal short-rotation basis. The GEDI data can validate and update statistics from nationwide airborne laser scanning (ALS). We present a case in the Northwest of Spain using GEDI statistics and nationwide ALS surveys to estimate forest dynamics in three fast-growing forest ecosystems comprising 211,346 ha. The objectives were: i) to analyze the potential of GEDI to detect disturbances, ii) to investigate uncertainty source regarding non-positive height increments from the 2015–2017 ALS data to the 2019 GEDI laser shots and iii) to estimate height growth using polygons from the Forest Map of Spain (FMS). A set of 258 National Forest Inventory plots were used to validate the observed height dynamics. Results The spatio-temporal assessment from ALS surveying to GEDI scanning allowed the large-scale detection of harvests. The mean annual height growths were 0.79 (SD = 0.63), 0.60 (SD = 0.42) and 0.94 (SD = 0.75) m for Pinus pinaster, Pinus radiata and Eucalyptus spp., respectively. The median annual values from the ALS-GEDI positive increments were close to NFI-based growth values computed for Pinus pinaster and Pinus radiata, respectively. The effect of edge border, spatial co-registration of GEDI shots and the influence of forest cover in the observed dynamics were important factors to considering when processing ALS data and GEDI shots. Discussion The use of GEDI laser data provides valuable insights for forest industry operations especially when accounting for fast changes. However, errors derived from positioning, ground finder and canopy structure can introduce uncertainty to understand the detected growth patterns as documented in this study. The analysis of forest growth using ALS and GEDI would benefit from the generalization of common rules and data processing schemes as the GEDI mission is increasingly being utilized in the forest remote sensing community.

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