scholarly journals Does more maternal investment mean a larger brain? Evolutionary relationships between reproductive mode and brain size in chondrichthyans

2011 ◽  
Vol 62 (6) ◽  
pp. 567 ◽  
Author(s):  
Christopher G. Mull ◽  
Kara E. Yopak ◽  
Nicholas K. Dulvy
Keyword(s):  
Least Squares ◽  
Body Mass ◽  
Brain Size ◽  
Maternal Investment ◽  
Branch Length ◽  
Reproductive Mode ◽  
Ordinary Least Squares ◽  
Egg Laying ◽  
Wide Range ◽  
Relative Brain Size

Chondrichthyans have the most diverse array of reproductive strategies of any vertebrate group, ranging from egg-laying to live-bearing with placental matrotrophy. Matrotrophy is defined as additional maternal provisioning beyond the yolk to the developing neonate; in chondrichthyans, this occurs through a range of mechanisms including uterine milk, oophagy, uterine cannibalism and placentotrophy. Chondrichthyans also exhibit a wide range of relative brain sizes and highly diverse patterns of brain organisation. Brains are energetically expensive to produce and maintain, and represent a major energetic constraint during early life in vertebrates. In mammals, more direct maternal–fetal placental connections have been associated with larger brains (steeper brain–body allometric scaling relationships). We test for a relationship between reproductive mode and relative brain size across 85 species from six major orders of chondrichthyans by using several phylogenetic comparative analyses. Ordinary least-squares (OLS) and reduced major axis (RMA) regression of body mass versus brain mass suggest that increased maternal investment results in a larger relative brain size. Our findings were supported by phylogenetic generalised least-squares models (pGLS), which also highlighted that these results vary with evolutionary tempo, as described by different branch-length assumptions. Across all analyses, maximum body size had a significant influence on the relative brain size, with large-bodied species (body mass >100 kg) having relatively smaller brains. The present study suggests that there may be a link between reproductive investment and relative brain size in chondrichthyans; however, a more definitive test requires a better-resolved phylogeny and a more nuanced categorisation of the level of maternal investment in chondrichthyans.

scholarly journals Maternal investment evolves with larger body size and higher diversification rate in sharks and rays

2022 ◽  
Author(s):  
Christopher G Mull ◽  
Matthew W Pennell ◽  
Kara E Yopak ◽  
Nicholas K Dulvy
Keyword(s):  
Body Size ◽  
Maternal Investment ◽  
Reproductive Mode ◽  
Egg Laying ◽  
Diversification Rate ◽  
Ancestral State ◽  
Ecological Diversity ◽  
Wide Range ◽  
Shelf Seas ◽  
The Tropics

Across vertebrates, live-bearing has evolved at least 150 times from the ancestral state of egg-laying into a diverse array of forms and degrees of prepartum maternal investment. A key question is how this diversity of reproductive modes arose and whether reproductive diversification underlies species diversification? To test these questions, we evaluate the most basal jawed vertebrates, Chondrichthyans, which have one of the greatest ranges of reproductive and ecological diversity among vertebrates. We reconstructed the sequence of reproductive mode evolution across a time-calibrated molecular phylogeny of 610 chondrichthyans. We find that egg-laying is ancestral, and that live-bearing evolved at least seven times. Matrotrophy (i.e. additional maternal contributions) evolved at least 15 times, with evidence of one reversal. In sharks, transitions to live-bearing and matrotrophy are more prevalent in larger-bodied species in the tropics. Further, the evolution of live-bearing is associated with a near-doubling of the diversification rate, but, there is only a small increase in diversification associated with the appearance of matrotrophy. The chondrichthyan diversification and radiation, particularly throughout the shallow tropical shelf seas and oceanic pelagic habitats, appears to be associated with the evolution of live-bearing and the proliferation of a wide range of maternal investment in their developing offspring.

scholarly journals Do Different Methods Yield Equivalent Estimations of Brain Size in Birds?

2020 ◽  
Vol 95 (2) ◽  
pp. 113-122
Author(s):  
Diego Ocampo ◽  
César Sánchez ◽  
Gilbert Barrantes
Keyword(s):  
Body Size ◽  
Brain Size ◽  
Brain Mass ◽  
Wide Range ◽  
Evolutionary Studies ◽  
Relative Brain Size ◽  
Using Data ◽  
Endocranial Volume ◽  
The Brain ◽  
Size Estimates

The ratio of brain size to body size (relative brain size) is often used as a measure of relative investment in the brain in ecological and evolutionary studies on a wide range of animal groups. In birds, a variety of methods have been used to measure the brain size part of this ratio, including endocranial volume, fixed brain mass, and fresh brain mass. It is still unclear, however, whether these methods yield the same results. Using data obtained from fresh corpses and from published sources, this study shows that endocranial volume, mass of fixed brain tissue, and fresh mass provide equivalent estimations of brain size for 48 bird families, in 19 orders. We found, however, that the various methods yield significantly different brain size estimates for hummingbirds (Trochilidae). For hummingbirds, fixed brain mass tends to underestimate brain size due to reduced tissue density, whereas endocranial volume overestimates brain size because it includes a larger volume than that occupied by the brain.

scholarly journals The Development of an Ordinary Least Squares Parametric Model to Estimate the Cost Per Flying Hour of ‘Unknown’ Aircraft Types and a Comparative Application †

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 104 ◽  
Author(s):  
Ilias Lappas ◽  
Michail Bozoudis
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Ordinary Least Squares ◽  
Parametric Model ◽  
Development Programs ◽  
Cost Estimating ◽  
Life Cycle Stages ◽  
Wide Range ◽  
High Level ◽  
The Cost

The development of a parametric model for the variable portion of the Cost Per Flying Hour (CPFH) of an ‘unknown’ aircraft platform and its application to diverse types of fixed and rotary wing aircraft development programs (F-35A, Su-57, Dassault Rafale, T-X candidates, AW189, Airbus RACER among others) is presented. The novelty of this paper lies in the utilization of a diverse sample of aircraft types, aiming to obtain a ‘universal’ Cost Estimating Relationship (CER) applicable to a wide range of platforms. Moreover, the model does not produce absolute cost figures but rather analogy ratios versus the F-16’s CPFH, broadening the model’s applicability. The model will enable an analyst to carry out timely and reliable Operational and Support (O&S) cost estimates for a wide range of ‘unknown’ aircraft platforms at their early stages of conceptual design, despite the lack of actual data from the utilization and support life cycle stages. The statistical analysis is based on Ordinary Least Squares (OLS) regression, conducted with R software (v5.3.1, released on 2 July 2018). The model’s output is validated against officially published CPFH data of several existing ‘mature’ aircraft platforms, including one of the most prolific fighter jet types all over the world, the F-16C/D, which is also used as a reference to compare CPFH estimates of various next generation aircraft platforms. Actual CPFH data of the Hellenic Air Force (HAF) have been used to develop the parametric model, the application of which is expected to significantly inform high level decision making regarding aircraft procurement, budgeting and future force structure planning, including decisions related to large scale aircraft modifications and upgrades.

scholarly journals Using relative brain size to better understand trophic interactions and phenotypic plasticity of invasive lionfish (Pterois volitans)

2020 ◽  
Author(s):  
McKenna Becker
Keyword(s):  
Body Mass ◽  
Brain Size ◽  
Predation Pressure ◽  
Mass Ratio ◽  
Pterois Volitans ◽  
Predator Prey ◽  
Brain Mass ◽  
Body Mass Ratio ◽  
Relative Brain Size ◽  
The Brain

AbstractPredator-prey dynamics provide critical insight into overall coral reef health. It has been shown that predator-prey relationships link the relative brain size of predators to their prey. Predation pressure forces prey to use decision-making skills that require higher cognition by inspecting and identifying predators and then adjusting their behavior to achieve the highest chance for survival. However, the predation pressure that prey face outweighs the pressure predators face to find prey, resulting in prey having larger relative brain sizes than their predators. There is little data on the relative brain size of fishes with few natural predators such as Pterois volitans. This study compared the brain mass to body mass ratio of Pterois volitans, which have very few natural predators and thus very little predation pressure, to the brain mass to body mass ratio of their prey, possible predators, competitors, and taxonomically similar fish. Lionfish had a significantly smaller relative brain size than their predators, prey, and competitors, but was not significantly smaller than taxonomically similar fish. These results demonstrate that the morphological anti-predator adaptation of venomous spines causes little predation pressure. Thus, lionfish do not use the same cognitive skills as other prey or predators and, in turn, have smaller relative brain sizes.

Improving hydrological forecasts through temporal hierarchal reconciliation

2021 ◽  
Author(s):  
Mohammad Sina Jahangir ◽  
John Quilty
Keyword(s):  
Least Squares ◽  
Performance Metrics ◽  
Moving Average ◽  
Weighted Least Squares ◽  
Absolute Error ◽  
Efficiency Index ◽  
Ordinary Least Squares ◽  
Data Driven ◽  
Wide Range ◽  
Different Levels

<p>Hydrological forecasts at different horizons are often made using different models. These forecasts are usually temporally inconsistent (e.g., monthly forecasts may not sum to yearly forecasts), which may lead to misaligned or conflicting decisions. Temporal hierarchal reconciliation (or simply, hierarchical reconciliation) methods can be used for obtaining consistent forecasts at different horizons. However, their effectiveness in the field of hydrology has not yet been investigated. Thus, this research assesses hierarchal reconciliation for precipitation forecasting due to its high importance in hydrological applications (e.g., reservoir operations, irrigation, drought and flood forecasting). Original precipitation forecasts (ORF) were produced using three different models, including ‘automatic’ Exponential Time-Series Smoothing (ETS), Artificial Neural Networks (ANN), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). The forecasts were produced at six timescales, namely, monthly, 2-monthly, quarterly, 4-monthly, bi-annual, and annual, for 84 basins selected from the Canadian model parameter experiment (CANOPEX) dataset. Hierarchical reconciliation methods including Hierarchical Least Squares (HLS), Weighted Least Squares (WLS), and Ordinary Least Squares (OLS) along with the Bottom-Up (BU) method were applied to obtain consistent forecasts at all timescales.</p><p>Generally, ETS and ANN showed the best and worst performance, respectively, according to a wide range of performance metrics (root mean square error (RMSE), normalized RMSE (nRMSE), mean absolute error (MAE), normalized MAE (nMAE), and Nash-Sutcliffe Efficiency index (NSE)). The results indicated that hierarchal reconciliation has a dissimilar impact on the ORFs’ accuracy in different basins and timescales, improving the RMSE in some cases while decreasing it in others. Also, it was highlighted that for different forecast models, hierarchical reconciliation methods showed different levels of performance. According to the RMSE and MAE, the BU method outperformed the hierarchical methods for ETS forecasts, while for ANN and SARIMA forecasts, HLS and OLS improved the forecasts more substantially, respectively. The sensitivity of ORF to hierarchical reconciliation was assessed using the RMSE. It was shown that both accurate and inaccurate ORF could be improved through hierarchical reconciliation; in particular, the effectiveness of hierarchical reconciliation appears to be more dependent on the ORF accuracy than it is on the type of hierarchical reconciliation method.</p><p>While in the present work, the effectiveness of hierarchical reconciliation for hydrological forecasting was assessed via data-driven models, the methodology can easily be extended to process-based or hybrid (process-based data-driven) models. Further, since hydrological forecasts at different timescales may have different levels of importance to water resources managers and/or policymakers, hierarchical reconciliation can be used to weight the different timescales according to the user’s preference/desired goals.</p>

scholarly journals Absolute, not relative brain size correlates with sociality in ground squirrels

2016 ◽  
Vol 283 (1827) ◽  
pp. 20152725 ◽  
Author(s):  
Jan Matějů ◽  
Lukáš Kratochvíl ◽  
Zuzana Pavelková ◽  
Věra Pavelková Řičánková ◽  
Vladimír Vohralík ◽  
...  
Keyword(s):  
Body Mass ◽  
Life History Traits ◽  
Brain Size ◽  
Empirical Support ◽  
Ground Squirrels ◽  
Single Family ◽  
Social Brain ◽  
The Social ◽  
Tightly Coupled ◽  
Relative Brain Size

The social brain hypothesis (SBH) contends that cognitive demands associated with living in cohesive social groups favour the evolution of large brains. Although the correlation between relative brain size and sociality reported in various groups of birds and mammals provides broad empirical support for this hypothesis, it has never been tested in rodents, the largest mammalian order. Here, we test the predictions of the SBH in the ground squirrels from the tribe Marmotini. These rodents exhibit levels of sociality ranging from solitary and single-family female kin groups to egalitarian polygynous harems but feature similar ecologies and life-history traits. We found little support for the association between increase in sociality and increase in relative brain size. Thus, sociality does not drive the evolution of encephalization in this group of rodents, a finding inconsistent with the SBH. However, body mass and absolute brain size increase with sociality. These findings suggest that increased social complexity in the ground squirrels goes hand in hand with larger body mass and brain size, which are tightly coupled to each other.

scholarly journals LEAST SQUARES AND IVX LIMIT THEORY IN SYSTEMS OF PREDICTIVE REGRESSIONS WITH GARCH INNOVATIONS

2021 ◽  
pp. 1-38
Author(s):  
Tassos Magdalinos
Keyword(s):  
Least Squares ◽  
Wald Test ◽  
Ordinary Least Squares ◽  
Conditional Heteroskedasticity ◽  
Exact Order ◽  
Test Statistics ◽  
Inference Procedure ◽  
Working Paper ◽  
Predictive Regressions ◽  
Wide Range

The paper examines the effect of conditional heteroskedasticity on least squares inference in stochastic regression models of unknown integration order and proposes an inference procedure that is robust to models within the (near) I(0)–(near) I(1) range with GARCH innovations. We show that a regressor signal of exact order $O_{p}\left ( n\kappa _{n}\right ) $ for arbitrary $\,\kappa _{n}\rightarrow \infty $ is sufficient to eliminate stationary GARCH effects from the limit distributions of least squares based estimators and self-normalized test statistics. The above order dominates the $O_{p}\left ( n\right ) $ signal of stationary regressors but may be dominated by the $O_{p}\left ( n^{2}\right ) $ signal of I(1) regressors, thereby showing that least squares invariance to GARCH effects is not an exclusively I(1) phenomenon but extends to processes with persistence degree arbitrarily close to stationarity. The theory validates standard inference for self normalized test statistics based on the ordinary least squares estimator when $\kappa _{n}\rightarrow \infty $ and $\kappa _{n}/n\rightarrow 0$ and the IVX estimator (Phillips and Magdalinos (2009a), Econometric Inference in the Vicinity of Unity. Working paper, Singapore Management University; Kostakis, Magdalinos, and Stamatogiannis, 2015a, Review of Financial Studies 28(5), 1506–1553.) when $\kappa _{n}\rightarrow \infty $ and the innovation sequence of the system is a covariance stationary vec-GARCH process. An adjusted version of the IVX–Wald test is shown to also accommodate GARCH effects in purely stationary regressors, thereby extending the procedure’s validity over the entire (near) I(0)–(near) I(1) range of regressors under conditional heteroskedasticity in the innovations. It is hoped that the wide range of applicability of this adjusted IVX–Wald test, established in Theorem 4.4, presents an advantage for the procedure’s suitability as a tool for applied research.

Using limited dependent variable estimators for estimating percent decay

1993 ◽  
Vol 23 (2) ◽  
pp. 266-274 ◽  
Author(s):  
Valerie M. Lemay ◽  
Antal Kozak ◽  
Peter L. Marshall
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Unexpected Result ◽  
Data Set ◽  
A Value ◽  
Wide Range ◽  
Truncated Normal ◽  
Tobit Estimator

The data used for the estimation of percent decay are bounded by zero and 100. Because a value of 100% indicates that the tree is completely decayed, this value is not observable in nature. However, a value of zero percent is often observed over a wide range of the independent variables. The distribution of percent decay is a combination of a truncated continuous distribution for percent decay greater than zero and a discrete component for the zero percents. The use of ordinary least squares with this type of data results in biased and inconsistent estimates of the coefficients of a percent decay equation. An alternative is the tobit estimator (a combined regression and probit estimator based on a maximum likelihood equation), which results in consistent estimates of the coefficients if the error terms of the model are independent and identically distributed as the truncated normal distribution. A Monte Carlo simulation using data for three species with different proportions of zero percents was performed to compare the ordinary least squares and tobit estimators. As expected, the tobit estimator resulted in quite different estimates of the coefficients of the equations than did ordinary least squares. An unexpected result was that the estimated expected percent decay was slightly more biased for the tobit estimator than for the ordinary least squares estimator, even with a large number of zero percents in the data set. Possible explanations for the Monte Carlo simulation results and recommendations for fitting percent decay equations are given in the paper.

scholarly journals The Evolution of Relative Brain Size in Marsupials Is Energetically Constrained but Not Driven by Behavioral Complexity

2015 ◽  
Vol 85 (2) ◽  
pp. 125-135 ◽  
Author(s):  
Vera Weisbecker ◽  
Simon Blomberg ◽  
Anne W. Goldizen ◽  
Meredeth Brown ◽  
Diana Fisher
Keyword(s):  
Social Complexity ◽  
Brain Size ◽  
Maternal Investment ◽  
Generalized Least Squares ◽  
Brain Morphology ◽  
Mammalian Brain ◽  
Data Set ◽  
Behavioral Complexity ◽  
Size Evolution ◽  
Relative Brain Size

Evolutionary increases in mammalian brain size relative to body size are energetically costly but are also thought to confer selective advantages by permitting the evolution of cognitively complex behaviors. However, many suggested associations between brain size and specific behaviors - particularly related to social complexity - are possibly confounded by the reproductive diversity of placental mammals, whose brain size evolution is the most frequently studied. Based on a phylogenetic generalized least squares analysis of a data set on the reproductively homogenous clade of marsupials, we provide the first quantitative comparison of two hypotheses based on energetic constraints (maternal investment and seasonality) with two hypotheses that posit behavioral selection on relative brain size (social complexity and environmental interactions). We show that the two behavioral hypotheses have far less support than the constraint hypotheses. The only unambiguous associates of brain size are the constraint variables of litter size and seasonality. We also found no association between brain size and specific behavioral complexity categories within kangaroos, dasyurids, and possums. The largest-brained marsupials after phylogenetic correction are from low-seasonality New Guinea, supporting the notion that low seasonality represents greater nutrition safety for brain maintenance. Alternatively, low seasonality might improve the maternal support of offspring brain growth. The lack of behavioral brain size associates, found here and elsewhere, supports the general ‘cognitive buffer hypothesis' as the best explanatory framework of mammalian brain size evolution. However, it is possible that brain size alone simply does not provide sufficient resolution on the question of how brain morphology and cognitive capacities coevolve.

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