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Flowering Probability (flowering + probability)

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Life Sciences100%

Selected Abstracts

Climate, size and flowering history determine flowering pattern of an orchid

BOTANICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 4 2006
MARION PFEIFER
The flowering pattern of plant species, including orchid species, may fluctuate irregularly. Several explanations are given in the literature to explain that pattern, including: costs associated with reproduction, herbivory effects, intrinsically triggered unpredictable variation of the system, and external conditions (i.e. weather). The influence of age is discussed, but is difficult to determine because relevant long-term field observations are generally absent in the literature. The influence of age, size, reproductive effort and climatic conditions on flowering variability of Himantoglossum hircinum are examined using data collected in a long-term project (1976,2001) in Germany. PCA and multiple regression analysis were used to analyse variability in flowering pattern over the years as a function of size and weather variability. We studied future size after flowering to quantify costs of reproduction. Flowering probability was strongly determined by plant size, while there was no significant influence of age class on flowering probability of the population. Costs associated with reproduction resulted in a decrease in plant size, causing reduced flowering probability of the plants in the following year. The weather explained about 50% of the yearly variation in the proportion of large plants and thus had an indirect, strong influence on the flowering percentage. We conclude that variability in flowering is caused mainly by the variability of weather conditions in the previous and current year, whereby reproductive effort causes further variability in flowering at the individual and, consequently, the population levels. © 2006 The Linnean Society of London, Botanical Journal of the Linnean Society, 2006, 151, 511,526. [source]

Scales of association: hierarchical linear models and the measurement of ecological systems

ECOLOGY LETTERS, Issue 6 2007
Sean M. McMahon
Abstract A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance,covariance parameters in hierarchically structured data. Although hierarchical models have occasionally been used in the analysis of ecological data, their full potential to describe scales of association, diagnose variance explained, and to partition uncertainty has not been employed. In this paper we argue that the use of the HLM framework can enable significantly improved inference about ecological processes across levels of organization. After briefly describing the principals behind HLM, we give two examples that demonstrate a protocol for building hierarchical models and answering questions about the relationships between variables at multiple scales. The first example employs maximum likelihood methods to construct a two-level linear model predicting herbivore damage to a perennial plant at the individual- and patch-scale; the second example uses Bayesian estimation techniques to develop a three-level logistic model of plant flowering probability across individual plants, microsites and populations. HLM model development and diagnostics illustrate the importance of incorporating scale when modelling associations in ecological systems and offer a sophisticated yet accessible method for studies of populations, communities and ecosystems. We suggest that a greater coupling of hierarchical study designs and hierarchical analysis will yield significant insights on how ecological processes operate across scales. [source]

Climate, size and flowering history determine flowering pattern of an orchid

BOTANICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 4 2006
MARION PFEIFER
The flowering pattern of plant species, including orchid species, may fluctuate irregularly. Several explanations are given in the literature to explain that pattern, including: costs associated with reproduction, herbivory effects, intrinsically triggered unpredictable variation of the system, and external conditions (i.e. weather). The influence of age is discussed, but is difficult to determine because relevant long-term field observations are generally absent in the literature. The influence of age, size, reproductive effort and climatic conditions on flowering variability of Himantoglossum hircinum are examined using data collected in a long-term project (1976,2001) in Germany. PCA and multiple regression analysis were used to analyse variability in flowering pattern over the years as a function of size and weather variability. We studied future size after flowering to quantify costs of reproduction. Flowering probability was strongly determined by plant size, while there was no significant influence of age class on flowering probability of the population. Costs associated with reproduction resulted in a decrease in plant size, causing reduced flowering probability of the plants in the following year. The weather explained about 50% of the yearly variation in the proportion of large plants and thus had an indirect, strong influence on the flowering percentage. We conclude that variability in flowering is caused mainly by the variability of weather conditions in the previous and current year, whereby reproductive effort causes further variability in flowering at the individual and, consequently, the population levels. © 2006 The Linnean Society of London, Botanical Journal of the Linnean Society, 2006, 151, 511,526. [source]

Growth rules based on the modularity of the Canarian Aeoniwm (Crassulaceae) and their phylogenetic value

BOTANICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 3 2000
TOVE H. JORGENSEN
Growth forms of 22 species of Aeonium (Crassulaceae) were quantified. Since all species are simple in their modular construction, models were developed to predict module length, branching mode and flowering probability using linear and logistic regression. When combined, the parameters of these models are species specific. A discriminant analysis generates a statistically significant separation of species at the level of phylogenetic sections. The results therefore demonstrate the phylogenetic value of growth rules in plants. This dynamic approach strongly contrasts with the traditional static view on forms in systematics and morphology. It also leaves scope for predicting the evolutionary pathways of morphological change which have caused the great diversity of growth forms in the genus Aeonium. [source]