scholarly journals Parameterization of Productivity Model for the Most Common Trees Species in European Part of Russia for Simulation of Forest Ecosystem Dynamics

2019 ◽  
Vol 14 (1) ◽  
pp. 54-76 ◽  
Author(s):  
V.N. Shanin ◽  
P.Ya. Grabarnik ◽  
S.S. Bykhovets ◽  
O.G. Chertov ◽  
I.V. Priputina ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Biomass Production ◽  
Air Temperature ◽  
Forest Ecosystem ◽  
Tree Species ◽  
European Russia ◽  
Production Model ◽  
Tree Age ◽  
Model Parameters ◽  
Wide Range

The proposed model is the version of well-known biomass production model 3-PG (Physiological Principles Predicting Growth), which allows for calculation of biomass production in dependence of consumed soil nitrogen and available solar radiation. The model utilizes the concept of modifiers, i.e. functions describing the effect of tree age and environmental factors (air temperature and humidity, soil moisture, carbon dioxide concentration) on productivity. To make the model applicable to mixed forests of European Russia, the substantial modifications were implemented. In particular, more detailed response functions to air temperature, soil moisture and absorbed nitrogen were introduced. We also implemented new procedure of calculation of light use efficiency taking into account the difference between shade-tolerant and shade-intolerant tree species. The rank distribution equation was used for the description of an increment allocation to different tree biomass compartments. Model parameters were estimated for the 12 most common tree species of European Russia. The model was implemented as sub-routine for calculation of biomass production in forest ecosystem model EFIMOD 2. The model performance was tested against the wide range of environmental conditions.

scholarly journals New version of the 3-PG model of biomass production for the most common tree species in European Russia

2018 ◽  
Author(s):  
V.N. Shanin ◽  
P.Ya. Grabarnik ◽  
S.S. Bykhovets ◽  
O.G. Chertov ◽  
M.P. Shashkov ◽  
...  
Keyword(s):  
Biomass Production ◽  
Tree Species ◽  
European Russia ◽  
Common Tree

Understanding and predicting species- and elevation-dependent dynamics in East Asian temperate forests

2020 ◽  
Author(s):  
Moonil Kim ◽  
Nick Strigul ◽  
Elena Rovenskaya ◽  
Florian Kraxner ◽  
Woo-Kyun Lee
Keyword(s):  
Climate Change ◽  
Tree Species ◽  
Forest Dynamics ◽  
Tree Age ◽  
Mountain Forest ◽  
High Mountain ◽  
Increment Core ◽  
Impact Of Climate Change ◽  
Wide Range ◽  
The Impact

<p>The velocity and impact of climate change on forest appear to be site, environment, and tree species-specific. The primary objective of this research is to assess the changes in productivity of major temperate tree species in South Korea using terrestrial inventory and satellite remote sensing data. The area covered by each tree species was further categorized into either lowland forest (LLF) or high mountain forest (HMF) and investigated. We used the repeated Korean national forest inventory (NFI) data to calculate a stand-level annual increment (SAI). We then compared the SAI, a ground-based productivity measure, to MODIS net primary productivity (NPP) as a measure of productivity based on satellite imagery. In addition, the growth index of each increment core, which eliminated the effect of tree age on radial growth, was derived as an indicator of the variation of productivity by tree species over the past four decades. Based on these steps, we understand the species- and elevation-dependent dynamics. The secondary objective is to predict the forest dynamics under climate change using the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) model. The PPA-SiBGC is an analytically tractable model of forest dynamics, defined in terms of parameters for individual trees, including allometry, growth, and mortality. We estimated these parameters for the major species by using NFI and increment core data. We predicted forest dynamics using the following time-series metrics: Net ecosystem exchange, aboveground biomass, belowground biomass, C, N, soil respiration, and relative abundance. We then focus on comparing the impact of climate change on LLF and HMF. The results of our study can be used to develop climate-smart forest management strategies to ensure that both LLF and HMF continue to be resilient and continue to provide a wide range of ecosystem services in the Eastern Asian region.</p>

scholarly journals Pan-Arctic modelling of net ecosystem exchange of CO 2

2013 ◽  
Vol 368 (1624) ◽  
pp. 20120485 ◽  
Author(s):  
G. R. Shaver ◽  
E. B. Rastetter ◽  
V. Salmon ◽  
L. E. Street ◽  
M. J. van de Weg ◽  
...  
Keyword(s):  
Leaf Area ◽  
Air Temperature ◽  
Net Ecosystem Exchange ◽  
Light Response ◽  
The Arctic ◽  
Model Parameters ◽  
Arctic Ecosystems ◽  
Area Index ◽  
Wide Range ◽  
Single Regression

Net ecosystem exchange (NEE) of C varies greatly among Arctic ecosystems. Here, we show that approximately 75 per cent of this variation can be accounted for in a single regression model that predicts NEE as a function of leaf area index (LAI), air temperature and photosynthetically active radiation (PAR). The model was developed in concert with a survey of the light response of NEE in Arctic and subarctic tundras in Alaska, Greenland, Svalbard and Sweden. Model parametrizations based on data collected in one part of the Arctic can be used to predict NEE in other parts of the Arctic with accuracy similar to that of predictions based on data collected in the same site where NEE is predicted. The principal requirement for the dataset is that it should contain a sufficiently wide range of measurements of NEE at both high and low values of LAI, air temperature and PAR, to properly constrain the estimates of model parameters. Canopy N content can also be substituted for leaf area in predicting NEE, with equal or greater accuracy, but substitution of soil temperature for air temperature does not improve predictions. Overall, the results suggest a remarkable convergence in regulation of NEE in diverse ecosystem types throughout the Arctic.

scholarly journals Assessing effects of drought on tree mortality and productivity in European forests across two decades: a conceptual framework and preliminary results

2021 ◽  
Vol 932 (1) ◽  
pp. 012009
Author(s):  
Jan-Peter George ◽  
Mathias Neumann ◽  
Jürgen Vogt ◽  
Carmelo Cammalleri ◽  
Mait Lang
Keyword(s):  
Soil Moisture ◽  
Conceptual Framework ◽  
Primary Productivity ◽  
Tree Mortality ◽  
Net Primary Productivity ◽  
Forest Growth ◽  
Tree Age ◽  
Ecosystem Functions ◽  
Preliminary Results ◽  
Wide Range

Abstract Forests are currently experiencing an unprecedented period of progressively drier growing conditions around the globe, which is threatening many forest ecosystem functions. Trees as long-living organisms are able to withstand drought periods. Our understanding on critical drought severity resulting in substantial decline in net primary productivity and/or eventually tree mortality is underdeveloped. A wide range of remote sensing products and ground observations, including information on productivity, tree vitality, climate, and soil moisture with high temporal and spatial resolution are now available. Linking these data sources could improve our understanding of the complex relationship between forest growth and drought. We introduce here a conceptual framework using satellite remotely sensed net primary productivity (MOD17A3 and MODIS EURO), ground observations of tree mortality (ICP level I survey data), soil moisture anomaly (Copernicus European Drought Observatory), and spatially-downscaled daily climate data for entire Europe. This unique analysis will enable us to test the influence of biotic and abiotic covariates such as tree age, stand history, and drought legacy using historic droughts for model development. This conceptual framework, as evident from the preliminary results shown here, can help anticipating the effects of future droughts and optimize global climate models considering drought effects.

Remote Sensing and Citizen science supporting irrigation monitoring in the Capitanata Irrigation Consortium (Italy)

2021 ◽  
Author(s):  
Chiara Corbari ◽  
Nicola Paciolla ◽  
Imen Ben Charfi ◽  
Mel Woods
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Citizen Science ◽  
Air Temperature ◽  
Irrigation Water ◽  
Surface Soil ◽  
Management Tool ◽  
Surface Soil Moisture ◽  
Wide Range

<p>In different ways, Citizen Science and Remote Sensing (RS) have been recently developing as innovative and inclusive ways to improve data gathering and the comprehension of many environmental biophysical processes. In this framework, the GROW Observatory has been promoting the individual farmer awareness in agriculture as a counterpart to the ever-developing frequency and accuracy of RS products.</p><p>In this analysis, 456 on-ground sensors from the GROW Observatory have been deployed in the Capitanata Irrigation Consortium (Apulia, Italy), with the aim of measuring the components of the water cycle with a dense, high-resolution pattern. The possibility of channelling these data into a high-resolution, plant-oriented Irrigation Water Need (IWN) parameter has been investigated, as a counterpart of coarser-resolution, spatially distributed monitoring powered by remote sensing and hydrological modelling.</p><p>The instruments have the possibility of measuring three main variables: Surface Soil Moisture (at a maximum depth of 5 cm), Air temperature and Solar Illuminance (measured a few centimetres above ground). The monitoring period is July-October 2019, contemplating a wide range of different cultivation regimes.</p><p>Irrigation water needs estimates has been obtained both in a point-wise (plant-oriented) and field-wise (spatial) format, in order to derive an irrigation water management tool. IWN and Surface Soil Moisture data are also employed in inferring back actual irrigation information from on-ground and RS data. These estimates have then be compared with observed data.</p><p>Intermediate measure of Surface Soil Moisture, Air Temperature and radiation (by the Solar Illuminance proxy) have also been compared both with local measurements (those of and eddy-covariance station in place) and RS products from Sentinel and Landsat. Furthermore, Solar Illuminance data have been processed to extract a Leaf Area Index (LAI) product, also comparable with satellite estimates. These comparisons have been conducted through spatial and temporal correlations between the ground-gathered and remotely-sensed data.</p><p>The potentiality and also the limitations of these low-cost instruments are presented and discussed.</p>

Biscogniauxia (Hypoxylon) Canker or Dieback in Trees

EDIS ◽  
2017 ◽  
Vol 2017 (6) ◽  
Author(s):  
Claudia Paez ◽  
Jason A. Smith
Keyword(s):  
Water Stress ◽  
Tree Species ◽  
Soil Compaction ◽  
Urban Areas ◽  
Root Disease ◽  
Poor Health ◽  
Green Spaces ◽  
Opportunistic Pathogens ◽  
Wide Range ◽  
Quercus Spp

Biscogniauxia canker or dieback (formerly called Hypoxylon canker or dieback) is a common contributor to poor health and decay in a wide range of tree species (Balbalian & Henn 2014). This disease is caused by several species of fungi in the genus Biscogniauxia (formerly Hypoxylon). B. atropunctata or B. mediterranea are usually the species found on Quercus spp. and other hosts in Florida, affecting trees growing in many different habitats, such as forests, parks, green spaces and urban areas (McBride & Appel, 2009).  Typically, species of Biscogniauxia are opportunistic pathogens that do not affect healthy and vigorous trees; some species are more virulent than others. However, once they infect trees under stress (water stress, root disease, soil compaction, construction damage etc.) they can quickly colonize the host. Once a tree is infected and fruiting structures of the fungus are evident, the tree is not likely to survive especially if the infection is in the tree's trunk (Anderson et al., 1995).

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Inferring Linkage Disequilibrium Between a Polymorphic Marker Locus and a Trait Locus in Natural Populations

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 457-467 ◽  
Author(s):  
Z W Luo ◽  
S H Tao ◽  
Z-B Zeng
Keyword(s):  
Linkage Disequilibrium ◽  
Allele Frequency ◽  
Random Mating ◽  
Natural Populations ◽  
Polymorphic Marker ◽  
Marker Locus ◽  
Model Parameters ◽  
Phenotypic Variance ◽  
Wide Range ◽  
Trait Locus

Abstract Three approaches are proposed in this study for detecting or estimating linkage disequilibrium between a polymorphic marker locus and a locus affecting quantitative genetic variation using the sample from random mating populations. It is shown that the disequilibrium over a wide range of circumstances may be detected with a power of 80% by using phenotypic records and marker genotypes of a few hundred individuals. Comparison of ANOVA and regression methods in this article to the transmission disequilibrium test (TDT) shows that, given the genetic variance explained by the trait locus, the power of TDT depends on the trait allele frequency, whereas the power of ANOVA and regression analyses is relatively independent from the allelic frequency. The TDT method is more powerful when the trait allele frequency is low, but much less powerful when it is high. The likelihood analysis provides reliable estimation of the model parameters when the QTL variance is at least 10% of the phenotypic variance and the sample size of a few hundred is used. Potential use of these estimates in mapping the trait locus is also discussed.

scholarly journals Antifungal Activity of Propyl Disulfide from Neem (Azadirachta indica) in Vapor and Agar Diffusion Assays against Anthracnose Pathogens (Colletotrichum gloeosporioides and Colletotrichum acutatum) in Mango Fruit

2021 ◽  
Vol 9 (4) ◽  
pp. 839
Author(s):  
Muhammad Rafiullah Khan ◽  
Vanee Chonhenchob ◽  
Chongxing Huang ◽  
Panitee Suwanamornlert
Keyword(s):  
Antifungal Activity ◽  
Mycelial Growth ◽  
Colletotrichum Gloeosporioides ◽  
Colletotrichum Acutatum ◽  
Model Parameters ◽  
Pathogenicity Test ◽  
Agar Diffusion ◽  
Wide Range ◽  
Significant Difference ◽  
Diffusion Assay

Microorganisms causing anthracnose diseases have a medium to a high level of resistance to the existing fungicides. This study aimed to investigate neem plant extract (propyl disulfide, PD) as an alternative to the current fungicides against mango’s anthracnose. Microorganisms were isolated from decayed mango and identified as Colletotrichum gloeosporioides and Colletotrichum acutatum. Next, a pathogenicity test was conducted and after fulfilling Koch’s postulates, fungi were reisolated from these symptomatic fruits and we thus obtained pure cultures. Then, different concentrations of PD were used against these fungi in vapor and agar diffusion assays. Ethanol and distilled water were served as control treatments. PD significantly (p ≤ 0.05) inhibited more of the mycelial growth of these fungi than both controls. The antifungal activity of PD increased with increasing concentrations. The vapor diffusion assay was more effective in inhibiting the mycelial growth of these fungi than the agar diffusion assay. A good fit (R2, 0.950) of the experimental data in the Gompertz growth model and a significant difference in the model parameters, i.e., lag phase (λ), stationary phase (A) and mycelial growth rate, further showed the antifungal efficacy of PD. Therefore, PD could be the best antimicrobial compound against a wide range of microorganisms.

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