scholarly journals Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

2021 ◽  
Author(s):  
Christopher Weiss-Lehman ◽  
Chhaya M Werner ◽  
Catherine H Bowler ◽  
Lauren M Hallett ◽  
Margaret M Mayfield ◽  
...  
Keyword(s):  
Species Interactions ◽  
Predictive Accuracy ◽  
Bayesian Variable Selection ◽  
Model Parameters ◽  
Interaction Coefficients ◽  
Direct Role ◽  
Sparse Modeling ◽  
Modeling Approach ◽  
Key Species ◽  
Diverse Communities

Modeling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species-interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species' intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighboring species from the diverse community that had non-generic interactions with our focal species. This sparse modeling approach facilitates exploration of species-interactions in diverse communities while maintaining a manageable number of parameters.

scholarly journals Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1265 ◽  
Author(s):  
Johanna Geis-Schroer ◽  
Sebastian Hubschneider ◽  
Lukas Held ◽  
Frederik Gielnik ◽  
Michael Armbruster ◽  
...  
Keyword(s):  
Low Voltage ◽  
Measurement Data ◽  
Analytical Calculation ◽  
Laboratory Measurement ◽  
Model Parameters ◽  
Calculation Methods ◽  
Modeling Approach ◽  
Measurement Results ◽  
Simulation Results ◽  
Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

The hierarchy-of-effects model and prelaunch forecasting

2019 ◽  
pp. 147078531987800
Author(s):  
Jonathan Lee ◽  
Heungsun Hwang
Keyword(s):  
Indirect Effects ◽  
Predictive Accuracy ◽  
Model Parameters ◽  
Direct And Indirect Effects ◽  
Mediation Model ◽  
Box Office ◽  
Marketing Planning ◽  
Proposed Model ◽  
Serial Mediation ◽  
Hierarchy Of Effects

Pre-release forecasting of the opening box office revenue allows a studio to prepare a more effective marketing campaign and budget allocation. The purpose of this study is to forecast the opening box office revenue using attitudinal tracking measures. The proposed model aims to establish a relationship between the opening box office and the tracking data of the hierarchy-of-effects (HOE) constructs, which managers can use as the target of marketing planning. To test the causal relationships between the HOE constructs and opening box office revenue, we estimate a serial mediation model that incorporates direct and indirect effects of advertising to the HOE constructs with the covariates including marketing efforts, movie characteristics, and viewer demographics. Based on the posterior predictive distributions of the model parameters, we obtain the forecasts of opening box office revenue as new data become available. The validation results show highly encouraging predictive accuracy, indicating the benefit of utilizing attitudinal tracking.

Simple Linear Modeling Approach for Linking Hydrological Model Parameters to the Physical Features of a River Basin

2015 ◽  
Vol 29 (9) ◽  
pp. 3265-3289 ◽  
Author(s):  
Chengcheng Huang ◽  
Guoqiang Wang ◽  
Xiaogu Zheng ◽  
Jingshan Yu ◽  
Xinyi Xu
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Model Parameters ◽  
Linear Modeling ◽  
Modeling Approach ◽  
Physical Features

Development of an advanced turbocharger simulation method for cycle simulation of turbocharged internal combustion engines

2009 ◽  
Vol 223 (5) ◽  
pp. 661-672 ◽  
Author(s):  
W Zhuge ◽  
Y Zhang ◽  
X Zheng ◽  
M Yang ◽  
Y He
Keyword(s):  
Internal Combustion Engines ◽  
Flow Model ◽  
Predictive Accuracy ◽  
Integrated Design ◽  
Simulation Method ◽  
Low Flow ◽  
Model Parameters ◽  
Cycle Simulation ◽  
Cent Error ◽  
Engine Cycle

An advanced turbocharger simulation method for engine cycle simulation was developed on the basis of the compressor two-zone flow model and the turbine mean-line flow model. The method can be used for turbocharger and engine integrated design without turbocharger test maps. The sensitivities of the simulation model parameters on turbocharger simulation were analysed to determine the key modelling parameters. The simulation method was validated against turbocharger test data. Results show that the methods can predict the turbocharger performance with a good accuracy, less than 5 per cent error in general for both the compressor and the turbine. In comparison with the map-based extrapolation methods commonly used in engine cycle simulation tools such as GT-POWER®, the turbocharger simulation method showed significant improvement in predictive accuracy to simulate the turbocharger performance, especially in low-flow and low-operating-speed conditions.

scholarly journals A comparison of regionalisation methods for catchment model parameters

2005 ◽  
Vol 2 (2) ◽  
pp. 509-542 ◽  
Author(s):  
J. Parajka ◽  
R. Merz ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Predictive Accuracy ◽  
Model Parameters ◽  
Stream Network ◽  
Ungauged Catchments ◽  
Verification Period ◽  
Model Efficiency ◽  
Complete Set ◽  
Rainfall Runoff Model ◽  
Catchment Model

Abstract. In this study we examine the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments. We calibrate 11 parameters of a semi-distributed conceptual rainfall-runoff model to daily runoff and snow cover data of 320 Austrian catchments in the period 1987-1997 and verify the model for the period 1976-1986. We evaluate the predictive accuracy of the regionalisation methods by jack-knife cross-validation against daily runoff and snow cover data. The results indicate that two methods perform best. The first is a kriging approach where the model parameters are regionalised independently from each other based on their spatial correlation. The second is a similarity approach where the complete set of model parameters is transposed from a donor catchment that is most similar in terms of its physiographic attributes (mean catchment elevation, stream network density, lake index, areal proportion of porous aquifers, land use, soils and geology). For the calibration period, the median Nash-Sutcliffe model efficiency ME of daily runoff is 0.67 for both methods as compared to ME=0.72 for the at-site simulations. For the verification period, the corresponding efficiencies are 0.62 and 0.66. All regionalisation methods perform similar in terms of simulating snow cover.

Sensitivity to expected negative outcomes during approach-avoidance conflict in a trans-diagnostic patient sample: a computational (active inference) modeling approach

2019 ◽  
Author(s):  
Ryan Smith ◽  
Namik Kirlic ◽  
Jennifer L. Stewart ◽  
James Touthang ◽  
Rayus Kuplicki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Reaction Times ◽  
Model Parameters ◽  
Active Inference ◽  
Negative Outcomes ◽  
Decision Uncertainty ◽  
Modeling Approach ◽  
Affective Stimuli ◽  
Approach Avoidance

Background: Sacrificing rewarding aspects of one’s life due to potential aversive outcomes is an important characteristic of multiple psychiatric disorders. Such decisions occur during approach-avoidance conflict (AAC), which has become the topic of a growing number of behavioral and neuroimaging studies. Here we describe a novel computational modeling approach to studying AAC.Methods: A previously-validated AAC task was completed by 479 participants including healthy controls (HCs), and individuals with depression, anxiety, and/or substance use disorders (SUDs), as part of the Tulsa 1000 study. An active inference model was utilized to identify parameters corresponding to the subjective aversiveness of affective stimuli (VNegative), the subjective value of points that could be won (VPoints), and decision uncertainty (β). We used correlational analyses to examine relationships to self-reported experiences during the task, analyses of variance to examine diagnostic group differences (depression/anxiety, substance use, HCs), and exploratory machine learning analyses to examine the contribution of dimensional clinical and neuropsychological measures.Results: Model parameters correlated with self-reported experience and reaction times during the task in expected directions. Relatve to HCs, both clinical groups showed higher VNegative values, and the SUD group exhibited less decision uncertainty (lower β values). Machine learning analyses highlighted several clinical domains (i.e., alcohol use, personality, working memory) potentially contributing to task parameters.Conclusions: Our results suggest that avoidance behavior in individuals with depression, anxiety, and SUDs may be driven by increased sensitivity to predicted negative outcomes and that insufficient decision uncertainty (overconfidence) may also further contribute to avoidance in substance use disorder.

Ecological Leverage Points: Species Interactions Amplify the Physiological Effects of Global Environmental Change in the Ocean

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kristy J. Kroeker ◽  
Eric Sanford
Keyword(s):  
Environmental Change ◽  
Species Interactions ◽  
Environmental Changes ◽  
Global Environmental Change ◽  
Marine Ecosystems ◽  
Annual Review ◽  
Publication Date ◽  
Leverage Points ◽  
Global Environmental ◽  
Key Species

Marine ecosystems are increasingly impacted by global environmental changes, including warming temperatures, deoxygenation, and ocean acidification. Marine scientists recognize intuitively that these environmental changes are translated into community changes via organismal physiology. However, physiology remains a black box in many ecological studies, and coexisting species in a community are often assumed to respond similarly to environmental stressors. Here, we emphasize how greater attention to physiology can improve our ability to predict the emergent effects of ocean change. In particular, understanding shifts in the intensity and outcome of species interactions such as competition and predation requires a sharpened focus on physiological variation among community members and the energetic demands and trophic mismatches generated by environmental changes. Our review also highlights how key species interactions that are sensitive to environmental change can operate as ecological leverage points through which small changes in abiotic conditions are amplified into large changes in marine ecosystems. Expected final online publication date for the Annual Review of Marine Science, Volume 14 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Saturating effects of species diversity on life-history evolution in bacteria

2015 ◽  
Vol 282 (1815) ◽  
pp. 20151794 ◽  
Author(s):  
Francesca Fiegna ◽  
Thomas Scheuerl ◽  
Alejandra Moreno-Letelier ◽  
Thomas Bell ◽  
Timothy G. Barraclough
Keyword(s):  
Life History ◽  
Species Richness ◽  
Species Interactions ◽  
Evolutionary Dynamics ◽  
Life History Evolution ◽  
Relative Strength ◽  
Recent Theory ◽  
Mechanistic Models ◽  
As Species ◽  
Diverse Communities

Species interactions can play a major role in shaping evolution in new environments. In theory, species interactions can either stimulate evolution by promoting coevolution or inhibit evolution by constraining ecological opportunity. The relative strength of these effects should vary as species richness increases, and yet there has been little evidence for evolution of component species in communities. We evolved bacterial microcosms containing between 1 and 12 species in three different environments. Growth rates and yields of isolates that evolved in communities were lower than those that evolved in monocultures, consistent with recent theory that competition constrains species to specialize on narrower sets of resources. This effect saturated or reversed at higher levels of richness, consistent with theory that directional effects of species interactions should weaken in more diverse communities. Species varied considerably, however, in their responses to both environment and richness levels. Mechanistic models and experiments are now needed to understand and predict joint evolutionary dynamics of species in diverse communities.

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