Selection of Factors Affecting the Presence of Brook Trout (Salvelinus fontinalis) in Adirondack Lakes: A Case Study

1992 ◽  
Vol 49 (3) ◽  
pp. 597-608 ◽  
Author(s):  
J. J. Beauchamp ◽  
S. W. Christensen ◽  
E. P. Smith
Keyword(s):  
Logistic Regression ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Model Development ◽  
Predictive Ability ◽  
Absolute Magnitude ◽  
Regression Diagnostics ◽  
Data Set ◽  
Factors Affecting ◽  
Adirondack Lakes

We used multiple logistic regression techniques to develop models for estimating the probability of brook trout (Salvelinus fontinalis) presence/absence as a function of observable water chemistry variables and watershed characteristics. The data set consists of the Adirondack Lakes Survey Corporation data collected on 1469 lakes during 1984–87. Two models fitted to a randomly selected development subset of lakes, using two sets of candidate explanatory/predictor variables of particular interest, were compared on the basis of coefficient consistency and predictive ability. In addition to the usual maximum likelihood logistic regression results, we also applied collinearity and other associated diagnostics and variable-selection procedures designed specifically for the logistic regression model to arrive at parsimonious models. Both models correctly predicted fish presence in more than 85% of the model development set and more than 80% of the lakes in the verification data. For those variables appearing in both models, the signs of the estimated coefficients were the same and in agreement with expectation. The removal of influential observations, as indicated by the logistic regression diagnostics, caused all of the estimated coefficients to increase in absolute magnitude. This results in a model which is more sensitive to changes in the explanatory variables.

Differences in sexual maturity and fall emigration between diploid and triploid brook trout (Salvelinus fontinalis) in an Adirondack lake

1997 ◽  
Vol 54 (8) ◽  
pp. 1808-1812 ◽  
Author(s):  
J A Warrillow ◽  
D C Josephson ◽  
W D Youngs ◽  
C C Krueger
Keyword(s):  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Gonadal Development ◽  
Spawning Habitat ◽  
Diploid Males ◽  
Reproductive Risk ◽  
Spawning Areas ◽  
In Captivity ◽  
Adirondack Lakes ◽  
Triploid Fish

High levels of emigration coincident with maturity and spawning have been reported from brook trout (Salvelinus fontinalis) populations in Adirondack lakes. These lakes typically had few spawning areas and required stocking to maintain populations. We compared diploid and triploid brook trout to identify differences in gonadal development and emigration. Age 1 + and 2 + diploid and triploid brook trout held in captivity were examined internally for gonadal development. More diploid trout were mature than triploid fish (p < 0.01). Of triploid brook trout that matured, all were males. Yearling diploid and triploid brook trout were also stocked into a lake that had an outlet but no spawning areas. During the fall spawning season, only mature yearling triploid males, diploid males, and diploid females were caught in an outlet trap. No triploid females were caught. A greater proportion of diploids emigrated than triploids (p < 0.01). Triploidy in females arrested emigration by preventing sexual maturation. Triploid male brook trout should not be stocked because they can pose a reproductive risk to wild brook trout downstream from lakes. Stocking triploid females could reducefall emigration and thus reduce the loss of catchable brook trout from Adirondack lakes with outlets and little spawning habitat.

Models of Ammonia Excretion for Brook Trout (Salvelinus fontinalis) and Rainbow Trout (Salmo gairdneri)

1980 ◽  
Vol 37 (9) ◽  
pp. 1421-1425 ◽  
Author(s):  
Larry J. Paulson
Keyword(s):  
Body Weight ◽  
Rainbow Trout ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Ammonia Excretion ◽  
Salmo Gairdneri ◽  
Feeding Rates ◽  
Data Set ◽  
Nitrogen Consumption ◽  
Good Agreement

Ammonia excretion by brook trout (Salvelinus fontinalis) and rainbow trout (Salmo gairdneri) was measured in relation to nitrogen consumption, body weight (15–154 g for rainbow trout and 50–360 g for brook trout), and temperature (11.2–21.0 °C) under laboratory conditions. Four natural diets, collected from Castle Lake, California, and a commercial pellet diet were fed to the trout in gelatin capsules at feeding rates from 2.5 to 5% body weight∙d−1. Nitrogen consumption was the most important factor influencing ammonia excretion, followed by body weight and temperature. Testing the models with an independent data set revealed good agreement between measured and predicted rates of excretion. The models seem to estimate adequately ammonia excretion by trout in both natural and artificial aquatic systems.Key words: models, ammonia excretion, nitrogen consumption, body weight, temperature, multiple regression, rainbow trout, brook trout

Inorganic Monomeric Aluminum and pH as Predictors of Acidic Water Toxicity to Brook Trout (Salvelinus fontinalis)

1990 ◽  
Vol 47 (8) ◽  
pp. 1631-1640 ◽  
Author(s):  
Benjamin R. Parkhurst ◽  
Harold L. Bergman ◽  
Joseph Fernandez ◽  
David D. Gulley ◽  
J. Russell Hockett ◽  
...  
Keyword(s):  
Water Quality ◽  
Logistic Regression ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Important Variable ◽  
Stepwise Logistic Regression ◽  
Acidic Water ◽  
Water Quality Variable ◽  
Primary Determinant ◽  
Trout Feeding

Inorganic monomeric aluminum concentrations were found to be the primary determinant of survival of brook trout feeding fry exposed for 21 d to various combinations of total aluminum and pH, plus several concentrations of fluoride or DOC, or several temperatures. Total aluminum concentrations in the 4 × 4 × 4 experimental matrices ranged from 3–1276 ppb, pH from 4.4–6.7, fluoride from <10–328 ppb, DOC from <1–9.2 ppm, and temperatures from 5.4–12.0 °C. Stepwise logistic regression was used to determine the significance of the effect of each water quality variable on survival. Inorganic monomeric aluminum accounted for 78–98% of the variation in survival. Except in the fluoride bioassay where pH was not significant, pH was the second-most important variable, accounting for up to 16% of the variation in survival. Fluoride or temperature accounted for 1–2% of the variation in survival, while DOC accounted for 6% of the variation in survival.

scholarly journals Social and Cognitive Aspects of Women Entrepreneurs: Evidence from India

2020 ◽  
Vol 45 (4) ◽  
pp. 223-239
Author(s):  
Mohd Yasir Arafat ◽  
Javed Ali ◽  
Amit Kumar Dwivedi ◽  
Imran Saleem
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Risk Perception ◽  
Adult Population ◽  
Predictive Ability ◽  
Binary Logistic Regression ◽  
Executive Summary ◽  
Data Set ◽  
Women Entrepreneurship ◽  
Factors Affecting

Executive Summary In the present era, the role of women entrepreneurship has been recognized in the process of economic development worldwide; hence, it must be promoted. Before designing any policy intervention to boost women entrepreneurship, it is important to understand the factors driving women to become entrepreneurs. The previous research on women entrepreneurship was preoccupied with performance of businesses run by women. This research aimed at answering the question: ‘What motivates or discourages the women of a society or an economy from becoming an entrepreneur?’ More specifically, this research investigates factors affecting the entrepreneurial propensity of Indian women through the lenses of cognitive and social capital perspectives. The present study is steered to enhance the understanding of women entrepreneurship at a niche level. Scholars have tried to explain factors affecting women entrepreneurship using myriad of approaches. However, these approaches have been criticized on methodological, conceptual and predictive ability weaknesses. Recently, cognitive and social capital perspectives have gained currency in explaining entrepreneurship. The purpose of this study was to examine the influence of cognitive factors—opportunity perception (Hypothesis 1), risk perception (Hypothesis 2) and perceived capabilities (Hypothesis 3)—and social capital factors—social networks (Hypothesis4) and informal investment (Hypothesis 5)—on women’s entrepreneurial propensity in India, a developing country. A data set of Global Entrepreneurship Monitor Adult Population Survey including a sample of 1305 Indians was used and binary logistic regression technique was employed to analyse the data. The finding shows that the entrepreneurial opportunities have no significant influence on women entrepreneurship; risk perception discourages women from becoming entrepreneurs, and perceived capabilities influence the decision of women to engage in entrepreneurship; social network motivates women to be entrepreneurial, and being an informal investor encourages them to start their venture. Surprisingly, we do not find support for opportunity perception. Therefore, policymakers should pay more attention to these factors of perception and social networks so that, the propensity of a woman to become entrepreneur would be increased.

Weighted Multicollinearity in Logistic Regression: Diagnostics and Biased Estimation Techniques with an Example from Lake Acidification

1990 ◽  
Vol 47 (6) ◽  
pp. 1128-1135 ◽  
Author(s):  
Brian D. Marx ◽  
Eric P. Smith
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Water Chemistry ◽  
Acid Precipitation ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Regression Diagnostics ◽  
Data Set ◽  
Biased Estimation

An historical data set from the Adirondack region of New York is revisited to study the relationship between water chemistry variables associated with acid precipitation and the presence/absence of brook trout (Salvelinus fontinalis) and lake trout (Salvelinus namaycush). For the trout species data sets, water chemistry variables associated with acid precipitation, for example pH and alkalinity, are highly correlated. Regression models to assess their effects on the probability of the presence of fish species are therefore affected by multicollinearity. Because the appropriate regressions are logistic, correction techniques based on least squares do not work. Maximum likelihood parameter estimation is highly unstable for the trout presence/absence data. Developments in weighted multicollinearity diagnostics are used to evaluate maximum likelihood logistic regression parameter estimates. Further, an application of biased parameter estimation is presented as an option to the traditional maximum likelihood logistic regression. Biased estimation methods, like ridge, principal component, or Stein estimation can substantially reduce the variance of the parameter estimates and prediction variance for certain future observations. In many cases, only a slight modification to the converged maximum likelihood estimator is necessary.

scholarly journals Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution

2015 ◽  
Vol 18 (3) ◽  
pp. 515 ◽  
Author(s):  
Zvetanka Dobreva Zhivkova ◽  
Tsvetelina Mandova ◽  
Irini Doytchinova
Keyword(s):  
External Validation ◽  
Model Development ◽  
Predictive Ability ◽  
Structural Features ◽  
Volume Of Distribution ◽  
Pharmacokinetic Parameters ◽  
Basic Drugs ◽  
Quantitative Structure ◽  
Data Set ◽  
Rapid Discrimination

Purpose. The early prediction of pharmacokinetic behavior is of paramount importance for saving time and resources and for increasing the success of new drug candidates. The steady-state volume of distribution (VDss) is one of the key pharmacokinetic parameters required for the design of a suitable dosage regimen. The aim of the study is to propose a quantitative structure – pharmacokinetics relationships (QSPkR) for VDss of basic drugs. Methods: The data set consists of 216 basic drugs, divided to a modeling (n = 180) and external validation set (n = 36). 179 structural and physicochemical descriptors are calculated using validated commercial software. Genetic algorithm, stepwise regression and multiple linear regression are applied for variable selection and model development. The models are validated by internal and external test sets. Results: A number of significant QSPkRs are developed. The most frequently emerged descriptors are used to derive the final consensus model for VDss with good explanatory (r2 0.663) and predictive ability (q2LOO-CV 0.606 and r2pred 0.593). The model reveals clear structural features determining VDss of basic drugs which are summarized in a short list of criteria for rapid discrimination between drugs with a large and small VDss. Conclusions: Descriptors like lipophilicity, fraction ionized as a base at pH 7.4, number of cycles and fused aromatic rings, presence of Cl and F atoms contribute positively to VDss, while polarity and presence of strong electrophiles have a negative effect. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

Predicting Timber Harvests on Private Forests in Southwest Wisconsin

1987 ◽  
Vol 4 (3) ◽  
pp. 152-154 ◽  
Author(s):  
John C. Bliss ◽  
Mark J. Grassl
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Model Development ◽  
Timber Harvest ◽  
Private Forests ◽  
Data Set ◽  
River Watershed ◽  
Harvest Data ◽  
Timber Harvests

Abstract A procedure for predicting timber harvest occurrence on nonindustrial private forests correctly predicted harvests on 87% of the properties used for model development, and 71% of the properties in a separately collected data set. The procedure requires limited, easily obtained data and little computation, yet it approximates logistic regression analysis. The study utilized historical timber harvest data on 84 properties in the Kickapoo River watershed of Vernon County, Wisconsin. Tract size and farm status was significantly related to timber harvest occurrence on the properties studied; owner residency was not. The procedure can help field foresters identify potential users of timber harvest assistance. North. J. Appl. For. 4:152-154, Sept. 1987.

scholarly journals Finding of the factors affecting the severity of COVID-19 based on mathematical models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiahao Qu ◽  
Brian Sumali ◽  
Ho Lee ◽  
Hideki Terai ◽  
Makoto Ishii ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Test ◽  
Recognition Rate ◽  
Predictive Ability ◽  
Training Group ◽  
Acute Onset ◽  
Prognostic Indicators ◽  
Test Results ◽  
Data Set ◽  
Factors Affecting

AbstractSince 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

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