Depth Distribution and Biomass of Submersed Aquatic Macrophyte Communities in Relation to Secchi Depth

1985 ◽  
Vol 42 (4) ◽  
pp. 701-709 ◽  
Author(s):  
Patricia A. Chambers ◽  
Jacob Kaiff
Keyword(s):  
Regression Models ◽  
Depth Distribution ◽  
Aquatic Macrophyte ◽  
Secchi Depth ◽  
Environmental Parameters ◽  
Growing Season ◽  
Original Data ◽  
Maximum Depth ◽  
Macrophyte Communities ◽  
Surface Changes

Using original data from eight lakes in southern Quebec and literature values from other fakes throughout the world, regression models were developed that allow prediction of the maximum depth of macrophyte colonization (zc) for angiosperms ((zc)0.5 = 1.33 log (D) + 1.40), bryophytes (zc)−0.5 = −0.48 log (D) + 0.81), and charophytes (log (zc) = 0.87 log (D) + 0.31) and the depth of maximum angiosperm biomass (zb)(zb0.5 = 0.54 log (D) + 1.15) from mean summer Secchi depth (D). Irradiance over the growing season at the maximum depth of colonization was about 1800 J/cm2 (1 cal/cm2 = 0.239 J/cm2) for angiosperms and bryophytes and 1200 J/cm2 for charophytes. These values represent, on average, 21 and 11% of the photo-synthetically available radiation incident on the water surface. Changes in maximum angiosperm biomass were, however, not correlated with Secchi depth. This suggests that while the depth distribution of aquatic macrophyte communities is primarily controlled by irradiance, environmental parameters other than irradiance and nutrients are also important in determining maximum angiosperm biomass in individual lakes.

How local personal vote-earning attributes affect the aggregate party vote share: Evidence from the Belgian flexible-list PR system (2003–2014)

Politics ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 464-479
Author(s):  
Gert-Jan Put ◽  
Jef Smulders ◽  
Bart Maddens
Keyword(s):  
Regression Models ◽  
Empirical Test ◽  
Original Data ◽  
Ordinary Least Squares ◽  
Vote Share ◽  
District Level ◽  
National Party ◽  
Data Set ◽  
Geographically Distributed ◽  
Party Vote Shares

This article investigates the effect of candidates exhibiting local personal vote-earning attributes (PVEA) on the aggregate party vote share at the district level. Previous research has often assumed that packing ballot lists with localized candidates increases the aggregate party vote and seat shares. We present a strict empirical test of this argument by analysing the relative electoral swing of ballot lists at the district level, a measure of change in party vote shares which controls for the national party trend and previous party results in the district. The analysis is based on data of 7527 candidacies during six Belgian regional and federal election cycles between 2003 and 2014, which is aggregated to an original data set of 223 ballot lists. The ordinary least squares (OLS) regression models do not show a significant effect of candidates exhibiting local PVEA on relative electoral swing of ballot lists. However, the results suggest that ballot lists do benefit electorally if candidates with local PVEA are geographically distributed over different municipalities in the district.

scholarly journals Методы оценки продуктивности представителей рода эхинацея (Echinacea Moench) прегенеративного периода онтогенеза

2013 ◽  
pp. 24-30
Author(s):  
С. В. Поспелов
Keyword(s):  
Regression Models ◽  
Input Data ◽  
Growing Season ◽  
Echinacea Purpurea ◽  
System Productivity ◽  
Aerial Parts ◽  
Purple Coneflower ◽  
Echinacea Pallida ◽  
First Time

За багаторічними дослідженнями ехінацеї пурпурової (Echinacea purpurea (L.) Moench,) сорту Зірка Миколи Вавилова та ехінацеї блідої (Echinacea pallida (Nutt.) Nutt.) сорту Красуня Прерій вперше розроблені й запатентовані методи визначення продуктивності рослин прегенеративного періоду онтогенезу. Методики засновані на регресійних моделях із високими коефіцієнтами детермінації, на підставі яких можна без пошкодження рослин провести оцінку продуктивності надземної частини та кореневої системи протягом веґетаційного періоду. Вихідними даними для розрахунків слугують показники довжини і ширини листковоїпластинки, їх кількість, а також сума температур вище 5 0С і кількість діб від сівби. On the basis of long-term researches of Purple Coneflower (Echinacea purpurea (L.) Moench) variety «Zirka Mykoly Vavylova» and Pale Coneflower (Echinacea pallida (Nutt.) variety «Krasunja Preriy» for the first time ever there were developed and patented the methods for determining the efficiency of plants in pregenesic period of ontogeny. The foundation of methodology was made on the studies of regression models with high coefficients of determination which allow to make the estimation of aerial parts and root system productivity for the whole growing season without damaging the plants. The input data for the calculation are the indicators of the length and width of the leaf blade, the amount and the sum of temperatures above + 5 ºC and the number of days from sowing.

scholarly journals The Biodiversity–Biomass Relationship of Aquatic Macrophytes Is Regulated by Water Depth: A Case Study of a Shallow Mesotrophic Lake in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Fei Ma ◽  
Lei Yang ◽  
Tian Lv ◽  
Zhenjun Zuo ◽  
Haocun Zhao ◽  
...  
Keyword(s):  
Water Depth ◽  
Aquatic Macrophyte ◽  
Interspecific Interactions ◽  
Species Identity ◽  
Community Biomass ◽  
Macrophyte Communities ◽  
Macrophyte Community ◽  
Evenness Indices ◽  
Aquatic Macrophyte Community ◽  
The Relationship

The relationship between biodiversity and productivity (or biomass production) (BPR) has been a popular topic in macroecology and debated for decades. However, this relationship is poorly understood in macrophyte communities, and the mechanism of the BPR pattern of the aquatic macrophyte community is not clear. We investigated 78 aquatic macrophyte communities in a shallow mesotrophic freshwater lake in the middle and lower reaches of the Yangtze River in China. We analyzed the relationship between biodiversity (species richness, diversity, and evenness indices) and community biomass, and the effects of water environments and interspecific interactions on biodiversity–biomass patterns. Unimodal patterns between community biomass and diversity indices instead of evenness indices are shown, and these indicate the importance of both the number and abundance of species when studying biodiversity–biomass patterns under mesotrophic conditions. These patterns were moderated by species identity biologically and water depth environmentally. However, water depth determined the distribution and growth of species with different life-forms as well as species identities through environmental filtering. These results demonstrate that water depth regulates the biodiversity–biomass pattern of the aquatic macrophyte community as a result of its effect on species identity and species distribution. Our study may provide useful information for conservation and restoration of macrophyte vegetation in shallow lakes through matching water depth and species or life-form combinations properly to reach high ecosystem functions and services.

scholarly journals Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2782 ◽  
Author(s):  
Amith Khandakar ◽  
Muhammad E. H. Chowdhury ◽  
Monzure- Khoda Kazi ◽  
Kamel Benhmed ◽  
Farid Touati ◽  
...  
Keyword(s):  
Feature Selection ◽  
Output Power ◽  
Regression Models ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Environmental Parameters ◽  
Ann Model ◽  
Power Prediction ◽  
Pv System ◽  
Energy Evaluation

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

Assessing the Environmental Characteristics of the Margaret River Wine Region, Australia

2019 ◽  
Vol 10 (3) ◽  
pp. 1-24
Author(s):  
Mathieu Lacorde
Keyword(s):  
Environmental Parameters ◽  
Growing Season ◽  
Environmental Data ◽  
Fault System ◽  
Soil Parameters ◽  
Significant Control ◽  
Climate Patterns ◽  
Growing Conditions ◽  
Wine Region ◽  
Soil Distribution

Six sub-regions of the Margaret River Geographical Indication were proposed in 1999 in an attempt to characterize local variations in grape-growing conditions. Detailed environmental data has since been produced and this article aims at reassessing the proposed sub-regions by means of a GIS-based spatial analysis of the new datasets. Topography, climate, and a variety of soil parameters were reviewed, and a relevant set submitted to an unsupervised isocluster classification to determine the natural clustering of environmental parameters. The analysis shows that the initial sub-regions do not consistently respect climate patterns and soil type distribution. It is proposed to distinguish twelve natural units by considering temperature and rainfall gradients as well as the presence of the Dunsborough Fault system which appears to have a significant control on soil distribution. This study also shows that average growing season temperatures have gained +0.2°C across the peninsula from 1961–2000 to 2001–2015.

scholarly journals Deterministic Imputation in Multienvironment Trials

ISRN Agronomy ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Sergio Arciniegas-Alarcón ◽  
Marisol García-Peña ◽  
Wojtek Janusz Krzanowski ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Missing Values ◽  
Interaction Model ◽  
Environmental Parameters ◽  
Real Data ◽  
Original Data ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Data Set ◽  
Value Decomposition ◽  
Main Effects

This paper proposes five new imputation methods for unbalanced experiments with genotype by-environment interaction (G×E). The methods use cross-validation by eigenvector, based on an iterative scheme with the singular value decomposition (SVD) of a matrix. To test the methods, we performed a simulation study using three complete matrices of real data, obtained from G×E interaction trials of peas, cotton, and beans, and introducing lack of balance by randomly deleting in turn 10%, 20%, and 40% of the values in each matrix. The quality of the imputations was evaluated with the additive main effects and multiplicative interaction model (AMMI), using the root mean squared predictive difference (RMSPD) between the genotypes and environmental parameters of the original data set and the set completed by imputation. The proposed methodology does not make any distributional or structural assumptions and does not have any restrictions regarding the pattern or mechanism of missing values.

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