There is growing recognition that classifying terrestrial plant species on the basis of their function (into 'functional types') rather than their higher taxonomic identity, is a promising way forward for tackling important ecological questions at the scale of ecosystems, landscapes or biomes. These questions include those on vegetation responses to and vegetation effects on, environmental changes (e.g. changes in climate, atmospheric chemistry, land use or other disturbances). There is also growing consensus about a shortlist of plant traits that should underlie such functional plant classifications, because they have strong predictive power of important ecosystem responses to environmental change and/or they themselves have strong impacts on ecosystem processes. The most favoured traits are those that are also relatively easy and inexpensive to measure for large numbers of plant species. Large international research efforts, promoted by the IGBP–GCTE Programme, are underway to screen predominant plant species in various ecosystems and biomes worldwide for such traits. This paper provides an international methodological protocol aimed at standardising this research effort, based on consensus among a broad group of scientists in this field. It features a practical handbook with step-by-step recipes, with relatively brief information about the ecological context, for 28 functional traits recognised as critical for tackling large-scale ecological questions.

/pdf/a-handbook-of-protocols-for-standardised-and-easy-2uo4112zhh.pdf

A handbook of protocols for standardised and easy measurement of plant functional traits worldwide

Fitting a line to a bivariate dataset can be a deceptively complex problem, and there has been much debate on this issue in the literature. In this review, we describe for the practitioner the essential features of line-fitting methods for estimating the relationship between two variables: what methods are commonly used, which method should be used when, and how to make inferences from these lines to answer common research questions. A particularly important point for line-fitting in allometry is that usually, two sources of error are present (which we call measurement and equation error), and these have quite different implications for choice of line-fitting method. As a consequence, the approach in this review and the methods presented have subtle but important differences from previous reviews in the biology literature. Linear regression, major axis and standardised major axis are alternative methods that can be appropriate when there is no measurement error. When there is measurement error, this often needs to be estimated and used to adjust the variance terms in formulae for line-fitting. We also review line-fitting methods for phylogenetic analyses. Methods of inference are described for the line-fitting techniques discussed in this paper. The types of inference considered here are testing if the slope or elevation equals a given value, constructing confidence intervals for the slope or elevation, comparing several slopes or elevations, and testing for shift along the axis amongst several groups. In some cases several methods have been proposed in the literature. These are discussed and compared. In other cases there is little or no previous guidance available in the literature. Simulations were conducted to check whether the methods of inference proposed have the intended coverage probability or Type I error. We identified the methods of inference that perform well and recommend the techniques that should be adopted in future work.

/pdf/bivariate-line-fitting-methods-for-allometry-3qyicyo7mh.pdf

Bivariate line-fitting methods for allometry.

INTRODUCTION 419 BASIC CONCEPTS 421 Structural Equations and Path Diagrams 421 Model Specification and Research Design 425 Model Testing 427 Model Modification and Reconfirmation 429 Example: Models of Attitude-Behavior Relations 429

Multivariate analysis with latent variables: causal modeling

Biological N2 fixation represents the major source of N input in agricultural soils including those in arid regions. The major N2-fixing systems are the symbiotic systems, which can play a significant role in improving the fertility and productivity of low-N soils. The Rhizobium-legume symbioses have received most attention and have been examined extensively. The behavior of some N2-fixing systems under severe environmental conditions such as salt stress, drought stress, acidity, alkalinity, nutrient deficiency, fertilizers, heavy metals, and pesticides is reviewed. These major stress factors suppress the growth and symbiotic characteristics of most rhizobia; however, several strains, distributed among various species of rhizobia, are tolerant to stress effects. Some strains of rhizobia form effective (N2-fixing) symbioses with their host legumes under salt, heat, and acid stresses, and can sometimes do so under the effect of heavy metals. Reclamation and improvement of the fertility of arid lands by application of organic (manure and sewage sludge) and inorganic (synthetic) fertilizers are expensive and can be a source of pollution. The Rhizobium-legume (herb or tree) symbiosis is suggested to be the ideal solution to the improvement of soil fertility and the rehabilitation of arid lands and is an important direction for future research.

Rhizobium-Legume Symbiosis and Nitrogen Fixation under Severe Conditions and in an Arid Climate

BACKGROUND AND PURPOSE: In subjects performing no specific cognitive task (“resting state”), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients (“functional connectivity”). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. METHODS: In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the “resting state” with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). RESULTS: For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies ( CONCLUSION: Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.

http://www.ajnr.org/content/ajnr/22/7/1326.full.pdf

Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data

Nitrogen Fixing Organisms

1 Organisms that fix nitrogen.- 1.1 Introduction.- 1.2 The range of nitrogen-fixing organisms.- 1.3 Cyanobacterial symbioses.- 2 Legume and non-legume nodules.- 2.1 Introduction.- 2.2 Legume-rhizobial symbioses.- 2.3 The 'classical' picture of nodule development.- 2.4 Stages of nodulation.- 2.5 Stems and nodules.- 2.6 Non-legumes (actinorhizal plants).- 2.7 Comparisons between legume and non-legume nodules.- 3 Metabolism of nitrogen fixation.- 3.1 The basic problem.- 3.2 The nitrogenase enzyme.- 3.3 The oxygen problem.- 3.4 Ammonia assimilation.- 4 Nitrogen fixation in agriculture.- 4.1 Scope and problems.- 4.2 Legumes.- 4.3 Forage and pasture legumes.- 4.4 Grain legumes.- 4.5 Factors affecting legume nitrogen fixation in the field.- 4.6 Free-living bacteria.- 4.7 Associative symbiosis (bacteria-angiosperm).- 4.8 Cyanobacteria.- 4.9 Non-legume symbioses.- 5 Nitrogen fixation and forestry.- 5.1 Nitrogen requirements in forests.- 5.2 Direct use of nitrogen-fixing legume species.- 5.3 Direct use of actinorhizal plants.- 5.4 Indirect use of symbiotic nitrogen-fixing plants.- 5.5 Nitrogen fixation by free-living micro-organisms in forests.- 5.6 Cyanobacteria.- 6 Nitrogen fixation in aquatic ecosystems.- 6.1 Fixation and nitrogen budgets.- 6.2 Freshwater aquatic systems.- 6.3 Saline aquatic environments.- 7 Nitrogen fixation in terrestrial ecosystems.- 7.1 Possible classifications.- 7.2 Soils.- 7.3 Some systems involving symbiotic higher plants.- 7.4 The phyllosphere.- 7.5 Three-membered symbioses.- 7.6 Colonization and pioneer associations.- 7.7 Climax situations.- 8 Evolution - and the future.- 8.1 The origins of biological fixation.- 8.2 The evolution of symbiotic systems.- 8.3 Current developments and the future.- References.

Nitrogen Fixing Organisms: Pure and Applied Aspects

Nitrogen fixing organisms: pure and applied aspects.

Models in Regression and Related Topics.

Data-Driven Inference. The Bootstrap. Outliers, Contamination, and Robustness. Location Tests for Two Independent Samples. More One- and Two-Sample Tests. Three or More Independent Samples. Designed Experiments. Correlation and Concordance. Bivariate Regression. Other Regression Models and Diagnostics. Categorical Data Analysis. Further Categorical Data Analysis. Data-Driven or Model-Driven? References. Index.

Data driven statistical methods

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