Chinook salmon (Oncorhynchus tshawytscha) — seabird covariation off central California and possible forecasting applications

2007 ◽  
Vol 64 (8) ◽  
pp. 1080-1090 ◽  
Author(s):  
Jennifer E Roth ◽  
Kyra L Mills ◽  
William J Sydeman
Keyword(s):  
Chinook Salmon ◽  
Breeding Success ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Oncorhynchus Tshawytscha ◽  
Central California ◽  
Time Periods ◽  
Ocean Conditions

We evaluated covariation between Chinook salmon (Oncorhynchus tshawytscha) abundance and seabird breeding success in central California, USA, and compared potential forecasts to predictive models based on jack (2-year-old male) returns in the previous year. Stepwise regression models based on seabird breeding success in the previous year were comparable to or stronger than jack-based models. Including seabird breeding success in the current year improved the strength of the relationships. Combined approaches that included seabird and jack data further improved the models in some cases. The relationships based on seabird breeding success remained relatively strong over both shorter (1990–2004) and longer (1976–2004) time periods. Regression models based on multivariate seabird or combined seabird–jack indices were not as strong as stepwise regression models. Our results indicate that there is significant covariation in the responses of salmon and seabirds to variability in ocean conditions and that seabird data may offer an alternate way of forecasting salmon abundance in central California.

COMPARISON OF OBSERVATIONS AND MACROCLIMATIC MODEL ESTIMATES OF MONTHLY WINTER SOIL TEMPERATURES AT OTTAWA

1985 ◽  
Vol 65 (1) ◽  
pp. 109-122 ◽  
Author(s):  
L. M. DWYER ◽  
H. N. HAYHOE
Keyword(s):  
Snow Cover ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Regression Coefficients ◽  
Data Sets ◽  
Regression Equations ◽  
Grass Cover ◽  
Partial Regression ◽  
Soil Temperatures

Estimates of monthly soil temperatures under short-grass cover across Canada using a macroclimatic model (Ouellet 1973a) were compared to monthly averages of soil temperatures monitored over winter at Ottawa between November 1959 and April 1981. Although the fit between monthly estimates and Ottawa observations was generally good (R for all months and depths 0.10, 0.20, 0.50, 1.00 and 1.50 m was 0.90), it was noted that midwinter estimates were generally below observed temperatures at all soil depths. Data sets used in the development of the original Ouellet (1973a) multiple regression equations were collected from stations across Canada, many of which have reduced snow cover. It was found that the buffering capability of the snow cover accumulated at Ottawa during the winter months was underestimated by the pertinent partial regression coefficients in these equations. The coefficients were therefore modified for the Ottawa station during the winter months. The resultant regression models were used to estimate soil temperature during the winters of 1981–1982 and 1982–1983. Although the Ottawa-based models included fewer variables because of the smaller data base available from a single site, comparisons of model estimates and observations were good (R = 0.84 and 0.91) and midwinter estimates were not consistently underestimated as they were using the original Ouellet (1973a) model. Reliable monthly estimates of soil temperatures are important since they are a necessary input to more detailed predictive models of daily soil temperatures. Key words: Regression model, snowcover, stepwise regression, variable selection

scholarly journals Influences of ocean conditions and feeding ecology on the survival of juvenile Chinook Salmon ( Oncorhynchus tshawytscha )

2016 ◽  
Vol 25 (4) ◽  
pp. 407-419 ◽  
Author(s):  
E. Hertz ◽  
M. Trudel ◽  
S. Tucker ◽  
T.D. Beacham ◽  
C. Parken ◽  
...  
Keyword(s):  
Feeding Ecology ◽  
Chinook Salmon ◽  
Oncorhynchus Tshawytscha ◽  
Juvenile Chinook Salmon ◽  
Juvenile Chinook ◽  
Ocean Conditions

Relationships between heterozygosity, allelic distance (d2), and reproductive traits in chinook salmon, Oncorhynchus tshawytscha

2002 ◽  
Vol 59 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Daniel D Heath ◽  
Colleen A Bryden ◽  
J Mark Shrimpton ◽  
George K Iwama ◽  
Joanne Kelly ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Chinook Salmon ◽  
Regression Models ◽  
Oncorhynchus Tshawytscha ◽  
Gonadosomatic Index ◽  
Reproductive Traits ◽  
Reproductive Fitness ◽  
Egg Survival ◽  
Allele Size ◽  
Multivariate Relationships

Correlations of various measures of individual genetic variation with fitness have been reported in a number of taxa; however, the genetic nature of such correlations remains uncertain. To explore this, we mated 100 male and 100 female chinook salmon (Oncorhynchus tshawytscha) in a one-to-one breeding design and quantified reproductive fitness and allocation (male gonadosomatic index, GSI; female fecundity; egg size; egg survival). Each fish was scored for allele size at seven microsatellite loci. We applied univariate and multivariate regression models incorporating two genetic variation statistics (microsatellite heterozygosity and squared allelic distance, d2) with reproductive parameters. The majority of the relationships were found to be nonsignificant; however, we found significant, positive, univariate relationships for fecundity and GSI (25% of tests) and significant, multivariate relationships at individual loci for all four traits (13% of tests). One microsatellite locus, Omy207, appeared to be closely associated with reproductive fitness in female chinook salmon (but not male), based on the multivariate analysis. Although direct tests for overdominance versus inbreeding effects proved inconclusive, our data are consistent with the presence of both inbreeding (general) and overdominance (local) effects on reproductive traits in chinook salmon.

Systematic hexamitid (Protozoa: Diplomonadida) Infection in seawater pen-reared Chinook salmon Oncorhynchus tshawytscha

1992 ◽  
Vol 14 ◽  
pp. 81-89 ◽  
Author(s):  
ML Kent ◽  
J Ellis ◽  
JW Fournie ◽  
SC Dawe ◽  
JW Bagshaw ◽  
...  
Keyword(s):  
Chinook Salmon ◽  
Oncorhynchus Tshawytscha

Do New Chiefs Mean Fresh Perspectives? The Institutionalization of the Police Executive Environment

2021 ◽  
pp. 009385482110067
Author(s):  
Matthew C. Matusiak
Keyword(s):  
Law Enforcement ◽  
Regression Models ◽  
Ordinary Least Squares ◽  
Limited Attention ◽  
Control Group ◽  
Multivariate Methods ◽  
Environmental Perceptions ◽  
Police Organizations ◽  
Law Enforcement Agency ◽  
Time Periods

Research suggests policing is a highly institutionalized field. Limited attention has been paid, however, to the institutionalization of leaders’ views. Assessing turnover in 71 Texas police organizations between October, 2011, and July, 2015, this research evaluates whether there is consistency (i.e., institutional homogenization) after turnover in chiefs’ perceptions of their environments and agency priorities. The research is unique in that it assesses two chiefs’ perceptions that have both led the same law enforcement agency in successive time periods. Assessments of environment and priorities from former chiefs and those replacing them are evaluated utilizing descriptive, bivariate, and multivariate methods. These assessments are also compared with a control group of chiefs from agencies not experiencing turnover. Bivariate results suggest little variation across current and former chiefs, whereas ordinary least squares (OLS) regression models suggest differing relationships across chiefs groups between environmental perceptions and agency priorities. Discussion of the findings is framed by institutional theory.

scholarly journals CANTARE: finding and visualizing network-based multi-omic predictive models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Janet C. Siebert ◽  
Martine Saint-Cyr ◽  
Sarah J. Borengasser ◽  
Brandie D. Wagner ◽  
Catherine A. Lozupone ◽  
...  
Keyword(s):  
Random Forests ◽  
Predictive Models ◽  
Regression Models ◽  
Dynamic Range ◽  
P Value ◽  
Published Data ◽  
Logistic Regression Models ◽  
Pairwise Models ◽  
Biological Insight ◽  
Analytical Approaches

Abstract Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

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