scholarly journals The impact of temporal patterns of temperature and precipitation on silver Prussian carp (Carassius gibelio) spawning events

2019 ◽  
Vol 27 (2) ◽  
pp. 106-117 ◽  
Author(s):  
D. L. Bondarev ◽  
O. M. Kunah ◽  
M. P. Fedushko ◽  
N. L. Gubanova

It was revealed that Gibel carp, Carassius gibelio (Bloch, 1782) spawning phenology is determined by regular patterns of variability in temperature and precipitation over the period of time after spawning in the previous year. The sensitivity to temperature of water as a stimulus to spawning was shown as being dependent on the characteristics of the environment and fish development trajectories over the previous year. Information about fish spawning was collected in the Dnipro-Orylskiy Nature Reserve in the following locations: Nikolayev system of water bodies, River Protoch system and the Obukhov floodplain, the channel of the River Dnipro, water bodies of the Taromske ledge. The materials that formed the basis of this research were collected from the waters of the reserve in the years 1997–2018. There are dependencies between the air and water temperatures that have their own characteristics depending on the type of water body. These dependencies are described by a logistic equation. The temperature regime in water bodies of the Taromske ledge is very similar to the temperature regime of Nikolayev system of water bodies. The difference is that the spring period of the temperature increase in water bodies of the Taromske ledge comes earlier – in late February – early March. The air temperature dynamics over the year is described by the fourth degree polynomial. The residuals of the polynomial regression for each year were subjected to principal component analysis. The principal component analysis allowed us to identify 8 principal components of the temperature regimes, which together describe 59.2% of the variation of the residual of the polynomial trend. Deviations from the linear trend of the precipitation are systematic throughout the year, reflecting the uneven nature of rainfall during the year. The residuals of linear trend regression models of cumulative precipitation can be used as indicators of the dynamics characteristic of precipitation during the year. The residuals were used for principal component analysis, which revealed that the dynamics of the precipitation can be divided into five orthogonal principal components. The first five principal components explain 82.6% of the feature space variation. The timing of the onset of C. gibelio spawning in 95% of cases occurs on the 113–139th days of the year (on average, it takes place on the 125th day). Regression analysis revealed that the climate principal components and types of the lake systems can explain 79% of the variation in the timing of the onset of C. gibelio spawning. The water temperature at the time of C. gibelio spawning was exposed to natural dynamics in year aspect. The local minimum of the onset of spawning temperatures occured in the mid 2000s. Total of 69% of the water temperature variation at which spawning begins can be described by climatic predictors and water biotope types. The variation explained by some temperature principal components may be increased considerably after including as covariates such variables as the precipitation principal component and the biotope type. This principal component which represents the high frequency variation of the air temperature regime with a characteristic period 20 and 45 days has the highest explanation ability of all the components and is a constant explanatory predictor for all considered spawning characteristics.

2021 ◽  
Vol 12 (22) ◽  
pp. 41-55
Author(s):  
Lidija Tadić ◽  
◽  
Tamara Brleković ◽  
Kristina Potočki ◽  
Marija Leko-Kos ◽  
...  

Drought has become a very frequent hydrological event globally, including in Croatia. It can generally be explained by air temperature and precipitation changes on an annual and seasonal basis, owing to climate change. To contribute to the knowledge on drought phenomena in Croatia, the changes in air temperature and precipitation over a relatively long period between 1951 and 2018 were analyzed. The meteorological stations included in the research were Osijek, Zagreb, and Split, which represented the climate of the entire country. Drought was estimated using the standardized precipitation evapotranspiration index, which is one of the most comprehensive drought indices. Furthermore, the drought severity and duration were calculated using run theory. These parameters were tested for homogeneity using the standard normal homogeneity test. Only the air temperature exhibited inhomogeneity, with a break year in 1991 (Zagreb and Split) and 1998 (Osijek). The existence of significant temporal trends was tested using the non-parametric Mann–Kendall trend test. The probability of drought occurrence with a certain duration and severity was calculated using the copula function. Finally, principal component analysis was applied to the computed standardized Mann–Kendall test statistic (ZMK ) to define the relevance of each parameter change and their combination in drought occurrence on a seasonal basis. Drought occurrence was less recognizable from 1951 to 1991 (1998). In the second sub-period, the impact of an increasing air temperature was the most significant variable, particularly in Zagreb.


2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.


2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.


2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.


2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.


2015 ◽  
Vol 36 (6) ◽  
pp. 3909
Author(s):  
Michelle Santos da Silva ◽  
Luciana Shiotsuki ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.


2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.


Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1321
Author(s):  
Wenjing Quan ◽  
Huiyu Zhou ◽  
Datao Xu ◽  
Shudong Li ◽  
Julien S. Baker ◽  
...  

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.


2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Guangqi Ying ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Yuxin Liu ◽  
Shengyong Zhang

For the traditional multi-process capability construction method based on principal component analysis, the process variables are mainly considered, but not the process capability, which leads to the deviation of the contribution rate of principal component. In response to the question, this paper first clarifies the problem from two aspects: theoretical analysis and example proof. Secondly, aiming at the rationality of principal components degree, an evaluation method for pre-processing data before constructing MPCI using PCA is proposed. The pre-processing of data is mainly to standardize the specification interval of quality characteristics making the principal components degree more reasonable and optimizes the process capability evaluation method. Finally, the effectiveness and feasibility of the method are proved by an application example.


Author(s):  
A.K. Mishra ◽  
Anand Jain ◽  
S. Singh ◽  
R.K. Pundir

Background: The principal component analysis is applied to identify minimum number of combined variables that account for maximum portion of the variance existing in all variables studied. Chitarangi is a lesser known carpet type wool sheep distributed in Fazilka and Muktsar districts of Punjab, Sri Ganganagar district of Rajasthan and the adjoining areas. The information on body biometry is a prerequisite to characterize the lesser known sheep population available in the country. Hence, it is important to describe the body conformation by recording minimum number of biometric traits. Methods: Body biometry traits of Chitarangi sheep, a lesser known carpet quality wool producing sheep population were studied using Principal Component Analysis. The traits studied were body length (BL), height at wither (HW), chest girth (CG), paunch girth (PG), ear length (EL), face length (FL), face width (FW), tail length (TL) and adult body weight (BW). The data were collected on 297 ewes in the breeding tract of Chitarangi sheep. The descriptive statistics were determined for all the traits. The phenotypic correlations between different body biometric traits were estimated using partial correlations. Principal components were estimated using correlation matrix. Principal component analysis (PCA), a multivariate approach, is used when the recorded traits are highly correlated. Rotation of principal components was through the transformation of the components to approximate a simple structure. Factor analysis using oblique (promax) rotation was used. All the analysis was carried out using the SPSS statistical package. Result: The averages for body weight and biometry traits confirmed large size of Chitarangi animals. Most of the phenotypic correlations amongst the studied traits were positive and significant (p less than 0.01). The three components extracted from nine principal components accounted for 69.06% of the total variance. The first component, which described body size of ewes, accounted for 43.68% of the total variation with high loading for BW, CG, PG, HW, BL and FL. The components two and three explained 13.54 and 11.83% of total variance, respectively. The communalities ranged from 0.490 (FL) to 0.888 (PG). The lower communalities for face length indicated lower contribution of the trait to explain the total variation than others. The study indicates that principal components provided a means of reduction in number of biometric traits to explain body confirmation of adult female Chitarangi sheep.


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