Estimating hunting harvest from partial reporting: a Bayesian approach

AbstractQuantifying hunting harvest is essential for numerous ecological topics, necessitating reliable estimates. We here propose novel analytical tools for this purpose. Using a hierarchical Bayesian framework, we introduce models for hunting reports that accounts for different structures of the data. Focusing on Swedish harvest reports of red fox (Vulpes vulpes), wild boar (Sus scrofa), European pine marten (Martes martes), and Eurasian beaver (Castor fiber), we evaluated predictive performance through training and validation sets as well as Leave One Out Cross Validation. The analyses revealed that to provide reliable harvest estimates, analyses must account for both random variability among hunting teams and the effect of hunting area per team on the harvest rate. Disregarding the former underestimated the uncertainty, especially at finer spatial resolutions (county and hunting management precincts). Disregarding the latter imposed a bias that overestimated total harvest. We also found support for association between average harvest rate and variability, yet the direction of the association varied among species. However, this feature proved less important for predictive purposes. Importantly, the hierarchical Bayesian framework improved previously used point estimates by reducing sensitivity to low reporting and presenting inherent uncertainties.

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A hierarchical Bayesian framework for calibrating micro-level models with macro-level data

Journal of Building Performance Simulation ◽

10.1080/19401493.2012.723750 ◽

2013 ◽

Vol 6 (4) ◽

pp. 293-318 ◽

Cited By ~ 42

Author(s):

A.T. Booth ◽

R. Choudhary ◽

D.J. Spiegelhalter

Keyword(s):

Bayesian Framework ◽

Macro Level ◽

Hierarchical Bayesian ◽

Level Data ◽

Micro Level

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Mining gravitational-wave catalogs to understand binary stellar evolution: A new hierarchical Bayesian framework

Physical Review D ◽

10.1103/physrevd.98.083017 ◽

2018 ◽

Vol 98 (8) ◽

Cited By ~ 23

Author(s):

Stephen R. Taylor ◽

Davide Gerosa

Keyword(s):

Gravitational Wave ◽

Stellar Evolution ◽

Bayesian Framework ◽

Hierarchical Bayesian

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A hierarchical Bayesian framework for force field selection in molecular dynamics simulations

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2015.0032 ◽

2016 ◽

Vol 374 (2060) ◽

pp. 20150032 ◽

Cited By ~ 20

Author(s):

S. Wu ◽

P. Angelikopoulos ◽

C. Papadimitriou ◽

R. Moser ◽

P. Koumoutsakos

Keyword(s):

Experimental Data ◽

Molecular Dynamics ◽

Force Field ◽

Computational Cost ◽

Md Simulations ◽

Bayesian Framework ◽

Underlying Structure ◽

Hierarchical Bayesian ◽

Wide Range ◽

Selection Of

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.

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A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

Sensors ◽

10.3390/s19214610 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4610 ◽

Cited By ~ 3

Author(s):

Adolfo Molada-Tebar ◽

Gabriel Riutort-Mayol ◽

Ángel Marqués-Mateu ◽

José Luis Lerma

Keyword(s):

Gaussian Process ◽

Process Model ◽

Rock Art ◽

Predictive Performance ◽

Practical Applications ◽

Nonparametric Models ◽

Novel Approach ◽

Characterization Procedure ◽

Leave One Out ◽

Gp Model

In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and Δ E a b * color differences. Values of less than 3 CIELAB units were achieved for Δ E a b * . The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and Δ E a b * . We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.

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SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

Bioinformatics ◽

10.1093/bioinformatics/btz734 ◽

2019 ◽

Vol 36 (4) ◽

pp. 1074-1081 ◽

Cited By ~ 25

Author(s):

Bin Yu ◽

Wenying Qiu ◽

Cheng Chen ◽

Anjun Ma ◽

Jing Jiang ◽

...

Keyword(s):

Drug Design ◽

Protein Function ◽

Predictive Performance ◽

Supplementary Information ◽

Gradient Boosting ◽

Source Codes ◽

Extreme Gradient Boosting ◽

Auto Correlation Function ◽

Abnormal Mitochondria ◽

Leave One Out

Abstract Motivation Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design. Results We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases. Availability and implementation The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/. Supplementary information Supplementary data are available at Bioinformatics online.

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LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

International Journal of Molecular Sciences ◽

10.3390/ijms21041508 ◽

2020 ◽

Vol 21 (4) ◽

pp. 1508

Author(s):

Yi Zhang ◽

Min Chen ◽

Ang Li ◽

Xiaohui Cheng ◽

Hong Jin ◽

...

Keyword(s):

Computational Methods ◽

Area Under The Curve ◽

Predictive Performance ◽

Weighted Networks ◽

Disease Associations ◽

Space Projection ◽

Auc Value ◽

Leave One Out ◽

Key Steps

Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA–disease associations. In this research, we proposed a lncRNA–disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA–disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA–disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA–disease associations and isolated diseases.

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