scholarly journals Evidence that gestation duration and lactation duration are coupled traits in primates

2012 ◽  
Vol 8 (6) ◽  
pp. 998-1001 ◽  
Author(s):  
Evgenia Dubman ◽  
Mark Collard ◽  
Arne Ø. Mooers
Keyword(s):  
Body Size ◽  
Linear Regression ◽  
Metabolic Rate ◽  
Least Squares ◽  
Generalized Least Squares ◽  
Related Factor ◽  
Scaling Exponent ◽  
Metabolic Theory ◽  
Lactation Duration ◽  
Duration Of Gestation

Gestation duration and lactation duration are usually treated as independently evolving traits in primates, but the metabolic theory of ecology (MTE) suggests both durations should be determined by metabolic rate. We used phylogenetic generalized least-squares linear regression to test these different perspectives. We found that the allometries of the durations are divergent from each other and different from the scaling exponent predicted by the MTE (0.25). Gestation duration increases much more slowly (0.06 < m < 0.12), and lactation duration much more quickly (0.36 < m < 0.52) with body mass than the MTE predicts. By contrast, we found that the combined duration of gestation and lactation is consistent with the MTE's predictions (0.22 < m < 0.35). These results suggest that gestation duration and lactation duration might best be viewed as distinct but coupled adaptations. When transferring energy to their offspring, primate mothers must meet metabolically dictated physiological requirements while optimizing the timing of the switch from gestation to lactation in relation to some as-yet-unidentified body-size-related factor.

scholarly journals Changes in body temperature influence the scaling of and aerobic scope in mammals

2006 ◽  
Vol 3 (1) ◽  
pp. 100-103 ◽  
Author(s):  
James F Gillooly ◽  
Andrew P Allen
Keyword(s):  
Body Size ◽  
Metabolic Rate ◽  
Size Dependence ◽  
Maximal Activity ◽  
Scaling Exponent ◽  
Temperature Influence ◽  
Aerobic Scope ◽  
Metabolic Scope ◽  
Single Power ◽  
Maximum Metabolic Rate

Debate on the mechanism(s) responsible for the scaling of metabolic rate with body size in mammals has focused on why the maximum metabolic rate ( ) appears to scale more steeply with body size than the basal metabolic rate (BMR). Consequently, metabolic scope, defined as /BMR, systematically increases with body size. These observations have led some to suggest that and BMR are controlled by fundamentally different processes, and to discount the generality of models that predict a single power-law scaling exponent for the size dependence of the metabolic rate. We present a model that predicts a steeper size dependence for than BMR based on the observation that changes in muscle temperature from rest to maximal activity are greater in larger mammals. Empirical data support the model's prediction. This model thus provides a potential theoretical and mechanistic link between BMR and .

scholarly journals Habitat productivity is a poor predictor of body size in rodents

2019 ◽  
Vol 66 (2) ◽  
pp. 135-143 ◽  
Author(s):  
Bader H Alhajeri ◽  
Lucas M V Porto ◽  
Renan Maestri
Keyword(s):  
Body Size ◽  
Least Squares ◽  
Body Mass ◽  
Resource Availability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Positive Association ◽  
Generalized Least Squares ◽  
Habitat Productivity ◽  
Resource Availability Hypothesis

Abstract The “resource availability hypothesis” predicts occurrence of larger rodents in more productive habitats. This prediction was tested in a dataset of 1,301 rodent species. We used adult body mass as a measure of body size and normalized difference vegetation index (NDVI) as a measure of habitat productivity. We utilized a cross-species approach to investigate the association between these variables. This was done at both the order level (Rodentia) and at narrower taxonomic scales. We applied phylogenetic generalized least squares (PGLS) to correct for phylogenetic relationships. The relationship between body mas and NDVI was also investigated across rodent assemblages. We controlled for spatial autocorrelation using generalized least squares (GLS) analysis. The cross-species approach found extremely low support for the resource availability hypothesis. This was reflected by a weak positive association between body mass and NDVI at the order level. We find a positive association in only a minority of rodent subtaxa. The best fit GLS model detected no significant association between body mass and NDVI across assemblages. Thus, our results do not support the view that resource availability plays a major role in explaining geographic variation in rodent body size.

Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances

1997 ◽  
Vol 13 (3) ◽  
pp. 406-429 ◽  
Author(s):  
Anoop Chaturvedi ◽  
Hikaru Hasegawa ◽  
Ajit Chaturvedi ◽  
Govind Shukla
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Generalized Least Squares ◽  
Least Squares Estimator ◽  
Regression Coefficients ◽  
Confidence Sets ◽  
Generalized Least Squares Estimator ◽  
Coverage Probabilities

In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.

scholarly journals On Least Squares Estimation in a Simple Linear Regression Model with Periodically Correlated Errors: A Cautionary Note

2016 ◽  
Vol 41 (3) ◽  
Author(s):  
Abdullah A. Smadi ◽  
Nour H. Abu-Afouna
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Autoregressive Model ◽  
Relative Efficiency ◽  
Generalized Least Squares ◽  
Correlated Errors ◽  
Simple Linear Regression ◽  
Simple Regression Model ◽  
Autocorrelated Errors

In this research the simple linear regression (SLR) model with autocorrelated errors is considered. Traditionally, correlated errors are assumed to follow the autoregressive model of order one (AR(1)). Beside this model we will also study the SLR model with errors following the periodic autoregressive model of order one (PAR(1)). The later model is useful for modeling periodically autocorrelated errors. In particular, it is expected to beuseful when the data are seasonal. We investigate the properties of the least squares estimators of the parameters of the simple regression model when the errors are autocorrelated and for various models. In particular, the relative efficiency of those estimates are obtained and compared for the white noise, AR(1) and PAR(1) models. Also, the generalized least squares estimates for the SLR with PAR(1) errors are derived. The relative efficiency of the intercept and slope estimates based on both methods is investigated via Monte-Carlo simulation. An application on real data set is also provided.It should be emphasized that once there are sufficient evidences that errors are autocorrelated then the type of this autocorrelation should be uncovered. Then estimates of model’s parameters should be obtained accordingly, using some method like the generalized least squares but not the ordinary least squares.

scholarly journals Extreme return, extreme volatility and investor sentiment

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 3949-3961 ◽  
Author(s):  
Xu Gong ◽  
Fenghua Wen ◽  
Zhifang He ◽  
Jia Yang ◽  
Xiaoguang Yang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Investor Sentiment ◽  
Generalized Least Squares ◽  
Regression Methods ◽  
Leading Role ◽  
The Impact

The extreme return and extreme volatility have great influences on the investor sentiment in stock market. However, few researchers have taken the phenomenon into consideration. In this paper, we first distinguish the extreme situations from non-extreme situations. Then we use the ordinary generalized least squares and quantile regression methods to estimate a linear regression model by applying the standardized AAII, the return and volatility of SP 500. The results indicate that, except for extremely negative return, other return sequences can cause great changes in investor sentiment, and non-extreme return plays a leading role in affecting the overall American investor sentiment. Extremely positive (negative) return can rapidly improve (further reduce) the level of investor sentiment when investors encounter extremely pessimistic situations. The impact gradually decreases with improvement of the sentiment until the situation turns optimistic. In addition, we find that extreme and non-extreme volatility cannot a_ect the overall investor sentiment.

scholarly journals Benthic biomass size spectra in shelf and deep-sea sediments

2014 ◽  
Vol 11 (22) ◽  
pp. 6401-6416 ◽  
Author(s):  
B. A. Kelly-Gerreyn ◽  
A. P. Martin ◽  
B. J. Bett ◽  
T. R. Anderson ◽  
J. I. Kaariainen ◽  
...  
Keyword(s):  
Body Size ◽  
Atlantic Water ◽  
Scaling Exponent ◽  
Metabolic Theory ◽  
Size Spectra ◽  
Size Classes ◽  
The North ◽  
The North Sea ◽  
Wet Weight ◽  
Biomass Spectra

Abstract. The biomass distributions of marine benthic metazoans (meio- to macro-fauna, 1 μg–32 mg wet weight) across three contrasting sites were investigated to test the hypothesis that allometry can consistently explain observed trends in biomass spectra. Biomass (and abundance) size spectra were determined from observations made at the Faroe–Shetland Channel (FSC) in the Northeast Atlantic (water depth 1600 m), the Fladen Ground (FG) in the North Sea (150 m), and the hypoxic Oman Margin (OM) in the Arabian Sea (500 m). Observed biomass increased with body size as a power law at FG (scaling exponent, b = 0.16) and FSC (b = 0.32), but less convincingly at OM (b = 0.12 but not significantly different from 0). A simple model was constructed to represent the same 16 metazoan size classes used for the observed spectra, all reliant on a common detrital food pool, and allowing the three key processes of ingestion, respiration and mortality to scale with body size. A micro-genetic algorithm was used to fit the model to observations at the sites. The model accurately reproduces the observed scaling without needing to include the effects of local influences such as hypoxia. Our results suggest that the size-scaling of mortality and ingestion are dominant factors determining the distribution of biomass across the meio- to macrofaunal size range in contrasting marine sediment communities. Both the observations and the model results are broadly in agreement with the "metabolic theory of ecology" in predicting a quarter power scaling of biomass across geometric body size classes.

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