Projects / Programmes
Ecophysiological, morphological and growth response of fir and beech along geographical gradient – basis for predicting future development trends
| Code |
Science |
Field |
Subfield |
| 4.01.00 |
Biotechnical sciences |
Forestry, wood and paper technology |
|
| Code |
Science |
Field |
| 4.01 |
Agricultural and Veterinary Sciences |
Agriculture, Forestry and Fisheries |
beech, silver fir, uneven aged forests, silviculture, latitudinal gradient, response, predicting future trends, climate change
Data for the last 5 years (citations for the last 10 years) on
March 28, 2023;
A3 for period 2017-2021
Data for ARRS tenders (
04.04.2019 – Programme tender,
archive
)
| Database |
Linked records |
Citations |
Pure citations |
Average pure citations |
| WoS |
326 |
11,591 |
10,362 |
31.79 |
| Scopus |
328 |
12,461 |
11,241 |
34.27 |
Researchers (10)
Organisations (2)
Abstract
Along the Carpathian ridge, distributed from Czech Republic to Romania seven permanent plots will be established in the optimally developed managed beech and fir adult forest stands, located at elevations above 800 m. Locations within old-growth reserves will also be selected. On every plot, three categories of different light intensities will be defined based on the analysis of hemispherical photos: under closed canopy, at the forest edge and in the open. In every light category assimilation, morphological response of young beech and fir trees as well as the soil respiration soil biodiversity will be measured during two consecutive growing seasons. Growth response and stable isotope analysis will be evaluated on 15 adult beech and fir trees from same plots based on dendrochronological analysis. Parallel samples (cores) of beech and fir will be collected for quantitative wood anatomy analysis. Time series analyses on remote sensing satellite data will be conducted for beech and fir forest response along the studied research sites. Apart from ecophysiological and morphological traits / responses, which will be paralleled with climate and radial growth, in addition growth responses will be paralleled with different indexes obtained from satellite images along the studied geographical gradient. Obtained/ measured parameters with climatic data will be used to develop response-model for both tree species and provide future response scenarios with novel, sophisticated machine learning algorithms, suitable for the future forest growth and functioning predictions. Project is organized in five work packages, evenly distributed among the Czech and Slovenian research teams according to their established professional expertise. The synergy between research teams will provide new insight into the processes affecting the future existence of complex European forests in a new, comprehensive way. Workflow: At least seven research locations will be selected on the outer part of the Carpathian arc in managed and old growth adult silver fir-beech forests. Selected locations represent the basis for the first three work packages (Wp1- 3). On every location three light categories will be defined based on the hemispherical photo analysis (Wp1). In every light category and all locations during two consecutive growing seasons ecophysiological measurements in controlled conditions and morphological responses will be performed during optimal growing conditions within two-week period. The same approach will be used for the soil respiration and soil biodiversity measurements. On same locations samples from adult beech and fir trees will be sampled for the dendrochronological, stable isotopic analysis (Wp2) and wood anatomy analysis (Wp3). Sampling for both Wp2 and Wp3 will be performed at once. Based on selected location time series analyses on remote sensing satellite data (Wp4) will be conducted for beech and fir forest response in low spatial and high temporal resolution and in high spatial and moderate temporal resolution to yield remote-sensing based indicators of beech and fir forest conditions. Twenty years of the MODIS data with meteorological parameters (precipitation and temperature) will be used to compare annual forest development at selected sites and to extract phenology-based metrics. Field data obtained from Wp1-3 and remote sensing data (Wp4) will be analyzed in advanced modelling tools based on machine learning algorithms (Wp5) to develop response models and ecological amplitude response-models that will explain the variation of studied parameters in relation to climate and to obtain climate scenarios and project future performance of studied parameters.
