scholarly journals Estimating flowering transition dates from status-based phenological observations: a test of methods

2019 ◽  
Author(s):  
Shawn D Taylor
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Population Level ◽  
Monte Carlo Analysis ◽  
Sampling Frequency ◽  
Herbarium Specimens ◽  
Echinacea Angustifolia ◽  
Final Sample ◽  
Growth And Reproduction

The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb *Echinacea angustifolia*. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available.

scholarly journals Estimating flowering transition dates from status-based phenological observations: a test of methods

2019 ◽  
Author(s):  
Shawn D Taylor
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Population Level ◽  
Monte Carlo Analysis ◽  
Sampling Frequency ◽  
Herbarium Specimens ◽  
Echinacea Angustifolia ◽  
Final Sample ◽  
Growth And Reproduction

The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb *Echinacea angustifolia*. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available.

scholarly journals Estimating flowering transition dates from status-based phenological observations: a test of methods

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7720 ◽  
Author(s):  
Shawn D. Taylor
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Population Level ◽  
Monte Carlo Analysis ◽  
Sampling Frequency ◽  
Herbarium Specimens ◽  
Echinacea Angustifolia ◽  
Final Sample ◽  
Growth And Reproduction

The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb Echinacea angustifolia. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall, a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise, the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available.

scholarly journals Estimating transition dates from status-based phenology observations: a test of methods

2019 ◽  
Author(s):  
Shawn D Taylor
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Population Level ◽  
Monte Carlo Analysis ◽  
Sampling Frequency ◽  
Statistical Moments ◽  
Herbarium Specimens ◽  
High Sampling ◽  
Final Sample ◽  
Growth And Reproduction

The scale of phenology research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the Julian day to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of flowering observations where, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known. I used a Monte Carlo analysis to test estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I tested estimates of onset, peak, and end of flowing at the population level, and estimates of onset and end for individual plants. Overall a recently introduced method using a Weibull curve performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise the Weibull method is adequate for individual estimates as well.

In situ morphometric study of the Diuris punctata species complex (Orchidaceae), with implications for conservation

2008 ◽  
Vol 21 (4) ◽  
pp. 289 ◽  
Author(s):  
Zoë F. Smith ◽  
Elizabeth A. James ◽  
Cassandra B. McLean
Keyword(s):  
Species Complex ◽  
Flower Colour ◽  
Population Level ◽  
Morphological Characters ◽  
Population Variation ◽  
Morphometric Study ◽  
Herbarium Specimens ◽  
Taxonomic Groups ◽  
Floral Characters

Taxa within the Diuris punctata species complex exhibit high levels of variation at both species and population level. Morphometric data collected in situ were used to investigate species boundaries of four Victorian Diuris species within the Diuris punctata species complex. Morphological characters and taxonomic groups identified in the present study were compared to those described under the current taxonomic treatment. Sixty-five multistate and continuous characters, including seven vegetative and 58 floral characters, were measured in situ across the range of each species within Victoria. The importance of flower colour in distinguishing taxa was highlighted but characters used were generally indiscrete. Certain characters used in current taxonomic descriptions, e.g. floral fragrance, were found to be uninformative. D. fragrantissima was confirmed as a separate taxon within the D. punctata group, justifying its recognition as a unique entity for conservation. Clustering of D. daltonii within D. punctata suggests that the recent elevation of the D. punctata var. daltonii to species level is not justified. The in situ measurement of morphological characters made it possible to incorporate sufficient sampling to encompass intra-specific and intra-population variation and is a feasible method to overcome sampling limitations encountered when herbarium specimens and limited destructive sampling are used.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Monte Carlo Analysis of Nonequilibrium Ionized Flows.

1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis
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