Spatial analysis of ponderosa pine trees infected with dwarf mistletoe

1991 ◽  
Vol 21 (12) ◽  
pp. 1808-1815 ◽  
Author(s):  
Robin M. Reich ◽  
Paul W. Mielke Jr. ◽  
Frank G. Hawksworth
Keyword(s):  
Ponderosa Pine ◽  
Distance Sampling ◽  
Specific Model ◽  
Point Pattern ◽  
Data Sets ◽  
Sampling Errors ◽  
Dwarf Mistletoe ◽  
Pine Trees ◽  
Computationally Intensive ◽  
Using Data

Distance-based multiresponse permutation procedures are presented as an alternative method of characterizing the spatial point pattern of mapped data sets. Methods are described for studying the spatial relationships and patterns in populations consisting of two or more groups. The ability of multiresponse permutation procedures to detect both nonrandom spatial patterns and segregation is compared with Hopkins and Skellam's coefficient of aggregation and Pielou's index of segregation, respectively, using data on the size and severity of dwarf mistletoe (Arceuthobiumvaginatum ssp. cryptopodum (Engelm.) Hawksw. & Wiens) infection of ponderosa pine (Pinusponderosa Laws.) trees. One advantage of multiresponse permutation procedures is that it uses all of the data in the analysis instead of a random sample that is subject to edge effect and sampling errors. Unlike distance sampling, multiresponse permutation procedures are not linked to specific model assumption and hence are more widely applicable as a descriptive measure. However, this method is computationally intensive and has certain limitations, which are highlighted in the discussion.

scholarly journals A Comparison of Wildlife Use in Broomed and Unbroomed Ponderosa Pine Trees in Northern Arizona

2004 ◽  
Vol 19 (1) ◽  
pp. 42-46 ◽  
Author(s):  
Robert L. Mathiasen ◽  
Gregg N. Garnett ◽  
Carol L. Chambers
Keyword(s):  
Ponderosa Pine ◽  
Wildlife Habitat ◽  
Passerine Birds ◽  
Dwarf Mistletoe ◽  
Erethizon Dorsatum ◽  
Resting Sites ◽  
Northern Arizona ◽  
Wildlife Use ◽  
Pine Trees

Abstract Dwarf mistletoe infections often induce structures known as witches' brooms that may provide an important wildlife habitat element. We compared evidence of wildlife use in broomed and unbroomed ponderosa pine trees at 12 mistletoe-infested sites in northern Arizona. We systematically sampled 12 broomed and unbroomed trees on each site (n = 144 broomed and 144 unbroomed trees) by climbing and inspecting each tree to document evidence of wildlife use. Broomed trees were used more frequently than unbroomed trees for wildlife activities including foraging/caching, nesting, and roosting/resting sites. We observed evidence of use by Abert squirrel (Sciurus aberti), porcupine (Erethizon dorsatum), and passerine birds in witches' brooms. Of the 226 brooms we examined, 23% (n = 52) contained evidence of wildlife use. Mammal use was found in 80% (n = 42) of the brooms and of these, 39 were used by Abert squirrel. We recommend that management agencies consider retaining some of these broomed trees to provide habitat for wildlife. West. J. Appl. For. 19(1):42–46.

Polar Magnetic Field Reversals of the Sun in Maunder Minimum

2000 ◽  
Vol 179 ◽  
pp. 193-196
Author(s):  
V. I. Makarov ◽  
A. G. Tlatov
Keyword(s):  
Magnetic Field ◽  
Maunder Minimum ◽  
The Sun ◽  
Annual Rings ◽  
Field Reversal ◽  
Polar Magnetic Field ◽  
Pine Trees ◽  
Using Data ◽  
Magnetic Field Reversal

AbstractA possible scenario of polar magnetic field reversal of the Sun during the Maunder Minimum (1645–1715) is discussed using data of magnetic field reversals of the Sun for 1880–1991 and the14Ccontent variations in the bi-annual rings of the pine-trees in 1600–1730 yrs.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Variable Selection and Regularization in Quantile Regression via Minimum Covariance Determinant Based Weights

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 33
Author(s):  
Edmore Ranganai ◽  
Innocent Mudhombo
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Data Sets ◽  
Least Absolute Deviation ◽  
Minimum Covariance Determinant ◽  
Absolute Deviation ◽  
Leverage Points ◽  
Influential Points ◽  
Computationally Intensive ◽  
Space Data

The importance of variable selection and regularization procedures in multiple regression analysis cannot be overemphasized. These procedures are adversely affected by predictor space data aberrations as well as outliers in the response space. To counter the latter, robust statistical procedures such as quantile regression which generalizes the well-known least absolute deviation procedure to all quantile levels have been proposed in the literature. Quantile regression is robust to response variable outliers but very susceptible to outliers in the predictor space (high leverage points) which may alter the eigen-structure of the predictor matrix. High leverage points that alter the eigen-structure of the predictor matrix by creating or hiding collinearity are referred to as collinearity influential points. In this paper, we suggest generalizing the penalized weighted least absolute deviation to all quantile levels, i.e., to penalized weighted quantile regression using the RIDGE, LASSO, and elastic net penalties as a remedy against collinearity influential points and high leverage points in general. To maintain robustness, we make use of very robust weights based on the computationally intensive high breakdown minimum covariance determinant. Simulations and applications to well-known data sets from the literature show an improvement in variable selection and regularization due to the robust weighting formulation.

POSTPARTUM AMENORRHOEA IN RURAL EASTERN UTTAR PRADESH, INDIA

1998 ◽  
Vol 30 (2) ◽  
pp. 227-243
Author(s):  
K. N. S. YADAVA ◽  
S. K. JAIN
Keyword(s):  
Higher Education ◽  
Uttar Pradesh ◽  
Current Status Data ◽  
North India ◽  
Current Status ◽  
Data Sets ◽  
Survival Status ◽  
The Mean ◽  
The Difference ◽  
Using Data

This paper calculates the mean duration of the postpartum amenorrhoea (PPA) and examines its demographic, and socioeconomic correlates in rural north India, using data collected through 'retrospective' (last but one child) as well as 'current status' (last child) reporting of the duration of PPA.The mean duration of PPA was higher in the current status than in the retrospective data;n the difference being statistically significant. However, for the same mothers who gave PPA information in both the data sets, the difference in mean duration of PPA was not statistically significant. The correlates were identical in both the data sets. The current status data were more complete in terms of the coverage, and perhaps less distorted by reporting errors caused by recall lapse.A positive relationship of the mean duration of PPA was found with longer breast-feeding, higher parity and age of mother at the birth of the child, and the survival status of the child. An inverse relationship was found with higher education of a woman, higher education of her husband and higher socioeconomic status of her household, these variables possibly acting as proxies for women's better nutritional status.

scholarly journals Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

2018 ◽  
Vol 7 (2.28) ◽  
pp. 312
Author(s):  
Manu Kohli
Keyword(s):  
Data Extraction ◽  
Indirect Costs ◽  
True Positive Rate ◽  
Spare Parts ◽  
Machine Learning Algorithms ◽  
Machine Language ◽  
Support Vector ◽  
Data Sets ◽  
Equipment Failure ◽  
Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations. 

Relationship Between Environmental Temperature and Yields of Subarctic and Temperate Zone Fish Species

1983 ◽  
Vol 40 (10) ◽  
pp. 1829-1837 ◽  
Author(s):  
David A. Schlesinger ◽  
Henry A. Regier
Keyword(s):  
Optimal Growth ◽  
Temperate Zone ◽  
Data Sets ◽  
Coregonus Clupeaformis ◽  
Influence Of Temperature On ◽  
Northern United States ◽  
Using Data ◽  
Curvilinear Relationships ◽  
Fish Yields

Fishes inhabiting subarctic and temperate zone lakes exhibit distinct optimal growth temperatures and temperature preferenda. However, within regional data sets, attempts to correlate fish yields with temperature variables have generally been unsuccessful. In our study, curvilinear relationships between "long-term mean annual air temperature" (TEMP) and sustained yields of three species were fitted using data from 23 intensively fished lakes in Canada and the northern United States. Optimum TEMP values for sustained yield were approximately −1.0, 1.5, and 2 °C, respectively, for lake whitefish (Coregonus clupeaformis), northern pike (Esox lucius), and walleye (Stizostedion vitreum vitreum). These differences suggest that the influence of temperature on sustained fish yields from subarctic and temperate zone lakes may, in the past, have been underestimated.

Three-centre C—H...O hydrogen bonds in the DNA minor groove: analysis of oligonucleotide crystal structures

1999 ◽  
Vol 55 (12) ◽  
pp. 2005-2012 ◽  
Author(s):  
Anirban Ghosh ◽  
Manju Bansal
Keyword(s):  
Hydrogen Bonds ◽  
Crystal Structures ◽  
Base Pair ◽  
Minor Groove ◽  
Local Geometry ◽  
Data Sets ◽  
Base Pairs ◽  
Dna Minor Groove ◽  
Close Proximity ◽  
Using Data

AA·TT and GA·TC dinucleotide steps in B-DNA-type oligomeric crystal structures and in protein-bound DNA fragments (solved using data with resolution <2.6 Å) show very small variations in their local dinucleotide geometries. A detailed analysis of these crystal structures reveals that in AA·TT and GA·TC steps the electropositive C2—H2 group of adenine is in very close proximity to the keto O atoms of both the pyrimidine bases in the antiparallel strand of the duplex structure, suggesting the possibility of intra-base pair as well as cross-strand inter-base pair C—H...O hydrogen bonds in the DNA minor groove. The C2—H2...O2 hydrogen bonds in the A·T base pairs could be a natural consequence of Watson–Crick pairing. However, the cross-strand interactions between the bases at the 3′-end of the AA·TT and GA·TC steps obviously arise owing to specific local geometry of these steps, since a majority of the H2...O2 distances in both data sets are considerably shorter than their values in the uniform fibre model (3.3 Å) and many are even smaller than the sum of the van der Waals radii. The analysis suggests that in addition to already documented features such as the large propeller twist of A·T base pairs and the hydration of the minor groove, these C2—H2...O2 cross-strand interactions may also play a role in the narrowing of the minor groove in A-tract regions of DNA and help explain the high structural rigidity and stability observed for poly(dA)·poly(dT).

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