A physiological approach to dendroclimatic modeling of oak radial growth in the midwestern United States

1993 ◽  
Vol 23 (5) ◽  
pp. 783-798 ◽  
Author(s):  
Jeffrey R. Foster ◽  
David C. LeBlanc
Keyword(s):  
Physiological Response ◽  
Radial Growth ◽  
Regression Models ◽  
Predictor Variables ◽  
Tissue Respiration ◽  
Climate Variables ◽  
White Oak ◽  
Physiological Indices ◽  
Daily Weather Data ◽  
Quercus Species

This paper describes the development of OAKWBAL, a physiologically based model that integrates daily weather data with site and species-specific ecophysiological data to estimate climate effects on physiology and radial growth of oak (Quercus) species. This model generates relative physiological response indices for cumulative canopy net photosynthesis and woody tissue respiration during the season of radial growth and the season of carbohydrate storage. These indices are entered as predictor variables in regression models, with detrended annual basal area increment as the response variable. Separate analyses were performed for seven similar sites located from northwest Arkansas to eastern Ohio. The analyses showed that (i) individual physiological response indices produced by the OAKWBAL model were better correlated with radial growth of black oak (Quercusvelutina Lam.) and white oak (Quercusalba L.) than were monthly climate variables; (ii) coefficients of determination for dendroclimatic regression models based on monthly weather variables were slightly higher than those for models based on physiological indices, but the monthly weather models included an average of three more predictor variables; and (iii) dendroclimatic regression models using physiological indices exhibited greater consistency across sites and were more amenable to biological interpretation than models using monthly climate variables.

Temporal and spatial variation of oak growth–climate relationships along a pollution gradient in the midwestern United States

1993 ◽  
Vol 23 (5) ◽  
pp. 772-782 ◽  
Author(s):  
David C. LeBlanc
Keyword(s):  
Physiological Response ◽  
Radial Growth ◽  
Acidic Deposition ◽  
Net Photosynthesis ◽  
Growing Season ◽  
Ohio River ◽  
River Region ◽  
Daily Weather Data ◽  
Deposition Gradient ◽  
Response Variables

The Kalman filter procedure was used to evaluate temporal variation in associations between physiologically based climate indices and radial growth of black oak (Quercusvelutina Lam.) and white oak (Quercusalba L.) at seven similar sites along the Ohio River corridor acidic-deposition gradient. Physiological response variables were derived by a model that used daily weather data to estimate effects of climate on growing season net photosynthesis and woody respiration. Correlations between oak radial growth indices and physiological response variables deteriorated over the period of record (1900–1987) at all seven study sites; there was no spatial association between the deterioration and the acidic-deposition gradient. This deterioration of growth–climate correlations was temporally associated with decreased growing season temperature at all seven sites; no consistent temporal trend was found for growing season precipitation. The effects of decreasing temperature on modeled physiological response variables included increased net photosynthesis and decreased woody respiration. These results suggest that recent assessments of relationships between acidic deposition and forest condition in the Ohio River region have been done during a time period of relaxed climatic stress and may have underestimated pollution–climate stress interactions.

Predicting effects of global warming on growth and mortality of upland oak species in the midwestern United States: a physiologically based dendroecological approach

1992 ◽  
Vol 22 (11) ◽  
pp. 1739-1752 ◽  
Author(s):  
David C. LeBlanc ◽  
Jeffrey R. Foster
Keyword(s):  
Global Warming ◽  
Physiological Response ◽  
Radial Growth ◽  
Growing Season ◽  
Daily Temperature ◽  
Severe Drought ◽  
White Oak ◽  
Growth Indices ◽  
River Region ◽  
Black Oak

This study combined an ecophysiological model and dendroecological analyses to evaluate potential effects of global warming on the physiology, growth, and mortality of white oak (Quercusalba L.) and black oak (Quercusvelutina Lam.) in the Ohio River region. The model integrated data for ecophysiology of oak species, site attributes, and daily temperature and precipitation to model nonlinear responses of stomatal conductance (gs), net photosynthesis (Pnet), and woody respiration (Rw) to variations in temperature and soil water content. Relationships between modeled physiological response indices and actual white and black oak annual radial growth indices were evaluated by regression analyses, using growth and weather data for the period 1900–1987 for seven upland oak–hickory forests. Modeled physiological response indices explained 40–60% of variation in radial growth indices. To evaluate the effects of global warming, daily temperature values for the period 1900–1987 were increased by 2 or 5 °C, without changing precipitation values, and physiological response indices were computed. Model indices generated in warming simulations were entered into dendroclimatic regression models calibrated under conditions without any warming to predict radial growth under warming scenarios. Under the warming scenarios, OAKWBAL predicted a substantial increase in growing season Rw, but little change in growing season Pnet. Warming merely shifted the period of near-maximal Pnet earlier in the growing season, without changing its duration. However, this result was somewhat dependent upon the ability of leaf-out phenology to track changes in temperature regime. The net effect of increased Rw, with little change in Pnet, was a reduction in radial growth and a higher frequency of years with climatic conditions stressful to oaks on upland sites. A historical association between severe drought and increased incidence of oak growth decline and mortality indicated that global warming could increase the incidence of decline and mortality in oak populations on upland sites similar to those in this study.

scholarly journals Evaluation of Climate Change Impacts on Cotton Yield using Cropsyst and Regression Models

2018 ◽  
Vol 8 ◽  
pp. 1433-1451 ◽  
Author(s):  
Pantazis Georgiou ◽  
Panagiota Koukouli
Keyword(s):  
Climate Change ◽  
Regression Models ◽  
General Circulation ◽  
Management Practices ◽  
Potential Evapotranspiration ◽  
Circulation Model ◽  
Planting Date ◽  
Climate Variables ◽  
Cotton Yield ◽  
Cotton Production

The regional as well as the international crop production is expected to be influenced by climate change. This study describes an assessment of simulated potential cotton yield using CropSyst, a cropping systems simulation model, in Northern Greece. CropSyst was used under the General Circulation Model CGCM3.1/T63 of the climate change scenario SRES B1 for time periods of climate change 2020-2050 and 2070-2100 for two planting dates. Additionally, an appraisal of the relationship between climate variables, potential evapotranspiration and cotton yield was done based on regression models. Multiple linear regression models based on climate variables and potential evapotranspiration could be used as a simple tool for the prediction of crop yield changes in response to climate change in the future. The CropSyst simulation under SRES B1, resulted in an increase by 6% for the period 2020-2050 and a decrease by about 15% in cotton yield for 2070-2100. For the earlier planting date a higher increase and a slighter reduction was observed in cotton yield for 2020-2050 and 2070-2100, respectively. The results indicate that alteration of crop management practices, such as changing the planting date could be used as potential adaptation measures to address the impacts of climate change on cotton production.

scholarly journals Risk Assessment Models for Wheat Fusarium Head Blight Epidemics Based on Within-Season Weather Data

2003 ◽  
Vol 93 (4) ◽  
pp. 428-435 ◽  
Author(s):  
E. D. De Wolf ◽  
L. V. Madden ◽  
P. E. Lipps
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Weather Conditions ◽  
Predictor Variables ◽  
Stepwise Logistic Regression ◽  
Head Blight ◽  
Logistic Regression Models

Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30°C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30°C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.

scholarly journals Lodgepole pine and interior spruce radial growth response to climate and topography in the Canadian Rocky Mountains, Alberta

2021 ◽  
pp. 1-16
Author(s):  
Frances Ackerman ◽  
David Goldblum
Keyword(s):  
Climate Change ◽  
Rocky Mountains ◽  
Radial Growth ◽  
Growth Response ◽  
Lodgepole Pine ◽  
Slope Aspect ◽  
Climate Variables ◽  
Canadian Rocky Mountains ◽  
Growth Responses ◽  
Interior Spruce

Climate change may have spatially variable impacts on growth of trees in topographically diverse environments, making generalizing across broad spatial and temporal extents inappropriate. Therefore, topography must be considered when analyzing growth response to climate. We address these topo-climatic relationships in the Canadian Rocky Mountains, focusing on lodgepole pine (Pinus contorta Douglas ex Louden) and interior spruce (Picea glauca (Moench) Voss × Picea engelmannii hybrid Parry) growth response to climate, Palmer drought severity index (PDSI), aspect, and slope angle. Climate variables correlate with older lodgepole pine growth on south- and west-facing slopes, including previous August temperature, winter and spring precipitation, and previous late-summer and current spring PDSI, but younger lodgepole pine were generally less sensitive to climate. Climate variables correlate with interior spruce growth on all slope aspects, with winter temperature and PDSI important for young and old individuals. Numerous monthly growth–climate correlations are not temporally stable, with shifts over the past century, and response differs by slope aspect and angle. Both species are likely to be negatively affected by moisture stress in the future in some, but not all, topographic environments. Results suggest species-specific and site-specific spatiotemporally diverse climate–growth responses, indicating that climate change is likely to have spatially variable impacts on radial growth response in mountainous environments.

Use of sodium hydroxide DNA extraction methods for nested PCR detection of Bretziella fagacearum in the sapwood of oak species in Minnesota

2021 ◽  
Author(s):  
Melanie Joy Moore ◽  
Jennifer Juzwik ◽  
Olga Saiapina ◽  
Snober Ahmed ◽  
Anna Yang ◽  
...  
Keyword(s):  
Sodium Hydroxide ◽  
Dna Extraction ◽  
Nested Pcr ◽  
Pathogen Detection ◽  
Abiotic Factors ◽  
Laboratory Diagnosis ◽  
Extraction Methods ◽  
White Oak ◽  
Detection Rates ◽  
Quercus Species

Oak wilt caused by Bretziella fagacearum is an important disease of Quercus species, but its diagnosis may be confused with damage resulting from other diseases, insects, or abiotic factors. Laboratory diagnosis is important in such situations and when disease control action is desired. Polymerase chain reaction (PCR) tests can provide accurate lab diagnosis within two days. Two variations of a simple DNA extraction protocol using sodium hydroxide (NaOH) were compared to that of the proprietary protocol of a commercially available kit (CK) for nested PCR to detect the pathogen in oak sapwood. High frequencies of pathogen detection (98 to 100% of 48 branch segments assayed) were found for northern pin oak using the two NaOH-based and the CK methods. Detection rates were similar but lower for bur oak (ranged from 58 to 79%) and white oak (ranged from 54 to 71%) regardless of DNA extraction method. Using our alternative DNA extraction protocols may reduce total time and cost of B. fagacearum detection in PCR-based diagnosis and other downstream applications.

Variable Selection Method for Regression Models Using Computational Intelligence Techniques

2021 ◽  
pp. 742-761
Author(s):  
Dhamodharavadhani S. ◽  
Rathipriya R.
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Computational Intelligence ◽  
Regression Models ◽  
Selection Method ◽  
Predictor Variables ◽  
Filter Method ◽  
Selection Methods ◽  
Forward Selection ◽  
Variable Selection Method

Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.

Statistical prediction of 20th century European summer temperatures based on ERA20c reanalysis data

2020 ◽  
Author(s):  
Maria Pyrina ◽  
Sebastian Wagner ◽  
Eduardo Zorita
Keyword(s):  
Statistical Modeling ◽  
North Atlantic ◽  
Regression Models ◽  
Reanalysis Data ◽  
Statistical Prediction ◽  
Predictor Variables ◽  
Ensemble Prediction ◽  
Linear Regression Models ◽  
Seasonal Forecasts ◽  
Study Region

<p>An alternative to dynamical seasonal prediction of European climate is statistical modeling. Statistical modeling is an appealing and computationally effective approach for producing seasonal forecasts by exploiting the physical connections between the predictand variable and the predictors. We assess the seasonal predictability of summer European 2m temperature (T2m) using canonical correlation analysis. Seasonal means of spring Soil Moisture (SM), Sea Level Pressure (SLP) and Sea Surface Temperature (SST) are used as predictors of mean summer T2m. For SSTs, we test the potential predictability of T2m using three different regions. These regions include what we define as: Extratropical North Atlantic (ENA), Tropical North Atlantic (TNA), and North Atlantic (NA). The predictability is explored in the ERA20c reanalysis and in comprehensive Earth System Model (ESM) fields. The results are provided for the European domain on a horizontal grid of 1°x1° degrees.</p><p>In order to identify the local T2m predictability related to the different predictor variables, we first built Univariate Linear Regression models, one for every predictor. The regression models are calibrated and validated during 1902-1950 and a prediction is provided for the periods 1951-1998, 1951-2004, and 1951-2008, respectively. The resulting correlation maps between the original and the predicted T2m anomalies showed that for the predictor variables SLP, SM, and SST<sub>ENA</sub> the results of the experiments using ESM data share similar T2m predictability patterns with the results of the experiments using reanalysis data. Most prominent disagreements between the predictability patterns resulting from ESMs and from ERA20c refers to the T2m prediction that utilizes tropical SSTs. SM is identified as the most important predictor for the summer European temperature predictability.</p><p>The ERA20c data show that the SM predictor field can be used for the T2m prediction over most of our study region west of 15° E and that the ENA SSTs can be used for the prediction over Europe east of 15° E. The resulting gridded correlation coefficients vary between 0.3 and 0.5. These results are not sensitive to the prediction period and to the number of Canonical Coefficients used in the regression model. Our approach complements existing numerical seasonal forecast frameworks and can be implemented for ensemble prediction studies.</p>

Sign in / Sign up

Export Citation Format

Share Document