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Juvenile Albacore tuna (Thunnus alalunga) foraging ecology varies with environmental conditions in the California Current Large Marine Ecosystem

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FISHERIES OCEANOGRAPHY 2023年第5期32卷 431-447页

作者： Nickels, Catherine F. Portner, Elan J. Snodgrass, Owyn Muhling, Barbara Dewar, Heidi NOAA Fisheries Resources Div Southwest Fisheries Sci Ctr Natl Marine Fisheries Serv 8901 La Jolla Shores Dr La Jolla CA 92037 USA Univ Calif San Diego Scripps Inst Oceanog 9500 Gilman Dr La Jolla CA 92093 USA Univ Calif Santa Cruz Inst Marine Sci 1156 High St Santa Cruz CA 95064 USA

Juvenile North Pacific Albacore tuna (Thunnus alalunga) support commercial and recreational fisheries in the California Current Large Marine Ecosystem (CCLME), where they forage during summer and fall. The distributions of the commercial and recreational fisheries and estimates of forage availability have varied substantially over the past century. Time-series quantifying Albacore diet can help link forage composition to variability in Albacore abundance and distribution and, consequently, their availability to fishers. Previous diet studies in the CCLME are of relatively short duration, and long-term variability in Albacore diet remains poorly understood. We describe the diets of juvenile Albacore from three regions in the CCLME from 2007 to 2019 and use classification and regression tree analysis to explore environmental drivers of variability. Important prey include Northern Anchovy (Engraulis mordax), rockfishes (Sebastes spp.), Boreal Clubhook Squid (Onychoteuthis borealijaponica), euphausiids (Order: Euphausiidae), and amphipods (Order: Amphipoda), each contributing >5% mean proportional abundance. Most prey items were short lived species or young-of-the-year smaller than 10 cm. Diet variability was related to environmental conditions over the first 6 months of the year (PDO, sea surface temperature, and NPGO) and conditions concurrent with Albacore capture (region and surface nitrate flux). We describe foraging flexibility over regional and annual scales associated with these environmental influences. Continuous, long-term studies offer the opportunity to identify flexibility in Albacore foraging behavior and begin to make a predictive link between environmental conditions early in the year and Albacore foraging during summer and fall.

关键词： classification and regression tree diet environmental drivers fisheries interactions foraging strategy prey stomach contents

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Assessing Transmission of Crop Diseases by Insect Vectors in a Landscape Context

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JOURNAL OF ECONOMIC ENTOMOLOGY 2014年第1期107卷 1-10页

作者： Carriere, Y. Degain, B. Hartfield, K. A. Nolte, K. D. Marsh, S. E. Ellers-Kirk, C. Van Leeuwen, W. J. D. Liesner, L. Dutilleul, P. Palumbo, J. C. Univ Arizona Dept Entomol Tucson AZ 85721 USA Univ Arizona Sch Nat Resources & Environm Arizona Remote Sensing Ctr Tucson AZ 85721 USA Univ Arizona Yuma Co Cooperat Extens Yuma AZ 85364 USA Arizona Cotton Res & Protect Council Phoenix AZ 85040 USA McGill Univ Dept Plant Sci Quebec City PQ H9X 3V9 Canada

Theory indicates that landscape composition affects transmission of vector-borne crop diseases, but few empirical studies have investigated how landscape composition affects plant disease epidemiology. Since 2006, Bemisia tabaci (Gennadius) has vectored the cucurbit yellow stunting disorder virus (CYSDV) to cantaloupe and honeydew melons (Cucumis melo L.) in the southwestern United States and northern Mexico, causing significant reductions in yield of fall melons and increased use of insecticides. Here, we show that a landscape-based approach allowing simultaneous assessment of impacts of local (i.e., planting date) and regional (i.e., landscape composition) factors provides valuable insights on how to reduce crop disease risks. Specifically, we found that planting fall melon fields early in the growing season, eliminating plants germinating from seeds produced by spring melons after harvest, and planting fall melon fields away from cotton and spring melon fields may significantly reduce the incidence of CYSDV infection in fall melons. Because the largest scale of significance of the positive association between abundance of cotton and spring melon fields and CYSDV incidence was 1,750 and 3,000 m, respectively, reducing areas of cotton and spring melon fields within these distances from fall melon fields may decrease CYSDV incidence. Our results indicate that landscape-based studies will be fruitful to alleviate limitations imposed on crop production by vector-borne diseases.

关键词： Bemisia tabaci classification and regression tree cucurbit yellow stunting disorder virus integrated pest management landscape ecology

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The Derivation of Nutrient Criteria for the Adjacent Waters of Yellow River Estuary in China

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Journal of Ocean University of China 2022年第5期21卷 1227-1236页

作者： LOU Qi ZHANG Xueqing ZHAO Bei CAO Jing LI Zhengyan College of Environmental Science and Engineering Ocean University of ChinaQingdao 266100China North China Sea Environmental Monitoring Center State Oceanic AdministrationQingdao 266033China Key Laboratory of Marine Environmental Science and Ecology Ministry of EducationQingdao 266100China

Ecological protection and high-quality development of the Yellow River basin are becoming part of the national strategy in recent *** Yellow River Estuary has been seriously affected by human ***,it has been severely polluted by the nitrogen and phosphorus from land sources,which have caused serious eutrophication and harmful algal *** criteria,however,was not developed for the Yellow River Estuary,which hindered nutrient management measures and eutrophication risk assessment in this key ecological function zone of *** on field data during 2004-2019,we adopted the frequency distribution method,correlation analysis,Linear regression Model(LRM),classification and regression tree(CART)and Nonparametric Changepoint Analysis(nCPA)methods to establish the nutrient criteria for the adjacent waters of Yellow River *** water quality criteria of dissolved inorganic nitrogen(DIN)and soluble reactive phosphorus(SRP)are recommended as 244.0μg L^(−1) and 22.4μg L^(−1),*** is hoped that the results will provide scientific basis for the formulation of nutrient standards in this important estuary of China.

关键词： water quality criteria nutrient Yellow River Estuary frequency distribution classification and regression tree eutro-phication

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Spatiotemporal Invasion Severity of Chinese Tallow (Triadica sebifera) and Invasibility of Forest Types in Southern US Forestlands

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FOREST SCIENCE 2021年第5期67卷 491-500页

作者： Nepal, Sunil Moser, W. Keith Fan, Zhaofei Auburn Univ Sch Forestry & Wildlife Sci 602 Duncan Dr Auburn AL 36849 USA US Forest Serv USDA Rocky Mt Res Stn 2500 S Pine Knoll Dr Flagstaff AZ 86001 USA

Quantifying invasion severity of nonnative invasive plant species is vital for the development of appropriate mitigation and control measures. We examined more than 23,250 Forest Inventory and Analysis (FIA) plots from the southern coastal states of the United States to develop an alternative method to classify and map the invasion severity of Chinese tallow (Triadica sebifera). Remeasured FIA plot-level data were used to examine the spatiotemporal changes in the presence probability and cover percentage of tallow. Four invasion severity classes were identified by using the product of presence probability and cover percentage. Chinese tallow invasion severity increased over time with 90 and 123 counties being classified into the highest severity class for the first and second measurement, respectively. Further, the invasibility of major forest-type groups by severity class was examined using the product of the county-level mean presence probability and mean cover percentage of Chinese tallow as a proxy of invasibility. Longleaf/slash pine (Pinus palustris/P. elliottii) forests were highly resilient to the Chinese tallow invasion. In contrast, elm/ash/cottonwood (Ulmus spp./Fraxinus spp./Populus deltoides) and oak/gum/cypress (Quercus spp./Nyssa spp./Taxodium spp.) forest-type groups were vulnerable to invasion. Study Implications: In the southern United States forestland, differences in invasion severity and vulnerability of forest types to Chinese tallow invasion have been observed across time and space. Our findings provide insight into spatial variations in the severity of Chinese tallow invasion and the relative susceptibility of different forest-type groups in the region to inform monitoring and management of this invasive species. High invasion severity occurs in the lower Gulf of Mexico coastal region of Texas, Louisiana, and Mississippi and the Atlantic coastal region of South Carolina and Georgia, with the longleaf/slash pine and oak/gum/cypress forest-type

关键词： Chinese tallow invasion severity invasibility forest inventory and analysis (FIA) classification and regression tree

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Matrix decomposition based feature extraction for murmur classification

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MEDICAL ENGINEERING & PHYSICS 2012年第6期34卷 756-761页

作者： Chen, Yuerong Wang, Shengyong Shen, Chia-Hsuan Choy, Fred K. Univ Akron Dept Mech Engn Akron OH 44325 USA

Heart murmurs often indicate heart valvular disorders. However, not all heart murmurs are organic. For example, musical murmurs detected in children are mostly innocent. Because of the challenges of mastering auscultation skills and reducing healthcare expenses, this study aims to discover new features for distinguishing innocent murmurs from organic murmurs, with the ultimate objective of designing an intelligent diagnostic system that could be used at home. Phonocardiographic signals that were recorded in an auscultation training CD were used for analysis. Instead of the discrete wavelet transform that has been used often in previous work, a continuous wavelet transform was applied on the heart sound data. The matrix that was derived from the continuous wavelet transform was then processed via singular value decomposition and QR decomposition, for feature extraction. Shannon entropy and the Gini index were adopted to generate features. To reduce the number of features that were extracted, the feature selection algorithm of sequential forward floating selection (SFFS) was utilized to select the most significant features, with the selection criterion being the maximization of the average accuracy from a 10-fold cross-validation of a classification algorithm called classification and regression trees (CART). An average sensitivity of 94%, a specificity of 83%, and a classification accuracy of 90% were achieved. These favorable results substantiate the effectiveness of the feature extraction methods based on the proposed matrix decomposition method. Published by Elsevier Ltd on behalf of IPEM.

关键词： Murmur differentiation Wavelet transform Singular value decomposition QR decomposition Shannon entropy Gini index classification and regression tree

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Methodology for assessing determinants of manure use in urban areas of Africa

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WASTE MANAGEMENT & RESEARCH 2010年第12期28卷 1076-1086页

作者： Suman, Paul Thys, Eric Mfoukou-Ntsakala, Andre Ali, Laouali Ouedraogo, Mamadou Van den Bossche, Peter Van Huylenbroeck, Guido Berkvens, Dirk Speybroeck, Niko Inst Trop Med Natl Str 150 B-2000 Antwerp Belgium Sylhet Agr Univ Sylhet Bangladesh Ctr Rech Vet & Zootech Brazzaville Rep Congo Direct Reg Ressources Anim Maradi Niger Projet PCZLD PATTEC BF Bobo Dioulasso Burkina Faso Fac Biosci Ghent Belgium

The objectives of this study were to identify the major determinants (e.g. zootechnical and socio-economic parameters) of manure management practised by urban livestock keepers. Data were collected from four single-visit, multi-object surveys in three cities of Africa. The livestock keepers were classified either as 'good user' or 'bad user' of manure depending on how they use the manure. The results of this study indicate that the habit of manure utilization shows geographical differences. In general, livestock keepers engaged in agricultural activities are good manure users. Furthermore, manual workers, traders or the self-employed are better manure users than other professional groups. The manure utilization varies also in function of the livestock numbers. In Brazzaville, the habit of selling animals influenced manure management positively. Experience of keeping livestock before the civil war in Brazzaville was also a determining factor of manure use in the post-war urban livestock keeping in 2001. When designing policies to reduce the environmental effects of urban livestock keeping, the highlighted factors can assist city planners to understand how urban livestock keepers use manure. Using the non-parametric classification tree method CART, two approaches were used to select an optimal classification of 'bad' manure users: the 'Cook and Goldman rule' and the 'classification with different misclassification cost ratios' method. The optimal tree was selected by using receiver operating characteristics curve information. This method can be useful in assessing other aspects of waste management.

关键词： classification and regression tree cost ratio misclassification receiver operating characteristic curve manure socio-economic status

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Modeling Vehicle Merging Behavior in Work Zone Merging Areas During the Merging Implementation Period

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2016年第4期17卷 917-925页

作者： Weng, Jinxian Xue, Shan Yan, Xuedong Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China Beijing Jiaotong Univ MOE Key Lab Urban Transportat Complex Syst Theory Beijing 100044 Peoples R China

Using the classification and regression tree (CART) method, this study aims to model the vehicle merging behavior at work zone merging areas during the merging implementation period. Hereafter, themerging implementation period is defined as the period from the starting time to the completion time of a merging maneuver. From the safety perspective, the times to collision (TTC) between the merging vehicle and its neighboring vehicles are regarded as the factors affecting vehicle merging decisions. The results show that a larger delay, a shorter remaining distance to the work zone, a smaller TTC to the merging lead vehicle, and a higher merging vehicle speed may encourage drivers to complete merging maneuvers early. It is also found that the merging vehicle tends to continue merging when the TTCs between the merging vehicle and its neighboring vehicles in the through lane are too small. In addition, another CART model without the use of TTC is built for comparison. The finding indicates that the use of TTC can not only contribute to a perfect result but also highlight the merging safety situation more clearly. The merging rules of this study are ready to be incorporated into the merging assistance system for guiding a safety merging at work zone merging areas.

关键词： Merging classification and regression tree time-to-collision merging implementation period

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The effect of species response form on species distribution model prediction and inference

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ECOLOGICAL MODELLING 2009年第19期220卷 2365-2379页

作者： Santika, Truly Hutchinson, Michael F. Australian Natl Univ Fenner Sch Environm & Soc Canberra ACT 0200 Australia

Ecological theory and current evidence support the validity of various species response curves according to a variety of environmental gradients. Various methods have been developed for building species distribution models but it is not well known how these methods perform under various assumptions about the form of the underlying species response. It is also not well known how spatial correlation in species occurrence affects model performance. These effects were investigated by applying an environmental envelope method (BIOCLIM) and three regression-based methods: logistic regression (I-R), generalized additive modelling (GAM), and classification and regression tree (CART) to simulated species occurrence data. Each simulated species was constructed as a sum of responses with varying weights. Three basic species response curves were assumed: Gaussian (bell-shaped), Beta (skew) and linear. The two non-linear responses conform to standard ecological niche theory. All three responses were applied in turn to three simulated environmental variables, each with varying degrees of spatial autocorrelation. GAM produced the most consistent model performance over all forms of simulated species response. BIOCLIM and CART were inclined to underrate the performance of variables with a linear response. BIOCLIM was less sensitive to data density. LR was susceptible to model misspecification. The use of a linear function in LR underestimated the performance of variables with non-linear species response and contributed to increased spatial autocorrelation in model residuals. Omission of important environmental variables with non-linear species response also contributed to increased spatial autocorrelation in model residuals. Adding a spatial autocovariate term to the LR model (autologistic model) reduced the spatial autocorrelation and improved model performance, but did not correct the misidentification of the dominant environmental determinant. This is to be expected since the aut

关键词： Autocovariate logistic BIOCLIM classification and regression tree Generalized additive modelling Logistic regression Species response curve Spatial autocorrelation

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Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM-CART Model

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2012年第1期23卷 97-108页

作者： Seera, Manjeevan Lim, Chee Peng Ishak, Dahaman Singh, Harapajan Univ Sci Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Malaysia Univ Sci Malaysia Sch Comp Sci George Town 11800 Malaysia Univ Teknol MARA Fac Elect Engn Permatang Pauh 13500 Malaysia

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

关键词： classification and regression tree fault detection and diagnosis fuzzy min-max neural network induction motor motor current signature analysis

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Automatic Microstructural Characterization and classification Using Higher-Order Spectra on Ultrasound Signals

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JOURNAL OF NONDESTRUCTIVE EVALUATION 2016年第1期35卷 1-14页

作者： Vejdannik, Masoud Sadr, Ali IUST Sch Elect Engn Tehran 16844 Iran

During the gas tungsten arc welding of nickel based superalloys, the secondary phases such as Laves and carbides are formed in final stage of solidification. But, other phases such as gamma '' and delta phases can precipitate in the microstructure, during aging at high temperatures. Nevertheless, choosing the appropriate conditions of welding can minimize the formation of the Nb-rich Laves phases and thus reduce the susceptibility to solidification cracking. This study aims at the automatic microstructurally characterizing the kinetics of phase transformations on an Nb-base alloy, thermally aged at 650 and 950 degrees C for 10, 100 and 200 h, through backscattered ultrasound signals at frequency of 4 MHz. For this, an automated processing system was designed using the spectrum representation of higher order statistics. The ultrasound signals are inherently non-linear and thus the conventional linear time and frequency domain methods can not reveal the complexity of these signals clearly. Bispectrum (the spectral representation of third order correlation) is a non-linear method which is highly robust to noise. In the proposed system, the bispectrum coefficients are subjected to linear discriminant analysis (LDA) technique to reduce the statistical redundancy and reveal discriminating features. These dimensionality reduced features are fed to the classification and regression tree, random forest and k-nearest neighbor (k-NN) classifiers to automatic microstructural characterization. Bispectrum coupled with LDA and k-NN yielded the highest average accuracy of 95.0 and 78.0 %, respectively for thermal aging at 650 and 950 degrees C. Thus, the proposed processing system provides high reliability to be used for microstructure characterization through ultrasound signals.

关键词： Bispectrum classification and regression tree k-Nearest neighbor Linear discriminant analysis Microstructural characterization Non-destructive inspection Random forest Ultrasound signals

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