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Abstract Floral fire ecology incorporates a feedback loop in which plants influence fire behaviour and fire behaviour influences the flora. Recent advances in fire behaviour modelling have quantified many plant‐based drivers of fire behaviour, but the consequent ecological effects of this have not yet been adequately modelled mechanistically. Here, I introduce the Fire Research and Modelling Environment (FRaME) as the open‐source R package frame on GitHub. FRaME calculates the influence of plants on fire behaviour using a biophysical, mechanistic model of fire behaviour, building this into complex simulations. From these, it models heat transfer from flames into surrounding surfaces, calculating its ecological effects on plants and soils. I demonstrate the application of the central analysis functions using a detailed case study, in which I validate predictions of fire behaviour and ecological effects, and derive quantitative measures for the efficacy of different management treatments to mitigate fire risk to a vulnerable ecosystem. FRaME modelling predicted ecological effects such as the breaking of seed dormancy, scorch and the girdling of different tree strata, consistent with observed effects and providing insights into treatment efficacy that were not captured by existing assumptions. FRaME analyses were able to identify treatments that both increased the likelihood of success in containing fires and minimised fire impacts on a fire‐sensitive ecosystem. FRaME provides a platform to examine the full role of fire in an ecosystem, from the ways that biota drive flammability to the influence of that flammability on the ecosystem. By mechanistically incorporating the effect of biophysical drivers throughout this feedback, FRaME can provide novel insights and solutions for complex problems, quantify risk and guide effective mitigation measures. The model is extensible, providing a conceptual framework into which emerging work on flammability and fire effects can be incorporated.

Floral fire ecology incorporates a feedback loop in which plants influence fire behaviour and fire behaviour influences the flora. Recent advances in fire behaviour modelling have quantified many plant‐based drivers of fire behaviour, but the consequent ecological effects of this have not yet been adequately modelled mechanistically.Here, I introduce the Fire Research and Modelling Environment (FRaME) as the open‐source R package frame on GitHub. FRaME calculates the influence of plants on fire behaviour using a biophysical, mechanistic model of fire behaviour, building this into complex simulations. From these, it models heat transfer from flames into surrounding surfaces, calculating its ecological effects on plants and soils. I demonstrate the application of the central analysis functions using a detailed case study, in which I validate predictions of fire behaviour and ecological effects, and derive quantitative measures for the efficacy of different management treatments to mitigate fire risk to a vulnerable ecosystem.FRaME modelling predicted ecological effects such as the breaking of seed dormancy, scorch and the girdling of different tree strata, consistent with observed effects and providing insights into treatment efficacy that were not captured by existing assumptions. FRaME analyses were able to identify treatments that both increased the likelihood of success in containing fires and minimised fire impacts on a fire‐sensitive ecosystem.FRaME provides a platform to examine the full role of fire in an ecosystem, from the ways that biota drive flammability to the influence of that flammability on the ecosystem. By mechanistically incorporating the effect of biophysical drivers throughout this feedback, FRaME can provide novel insights and solutions for complex problems, quantify risk and guide effective mitigation measures. The model is extensible, providing a conceptual framework into which emerging work on flammability and fire effects can be incorporated. [ABSTRACT FROM AUTHOR]

Featured Application: Modulus of elasticity for grain-supported carbonate rocks is successfully estimated by using simple estimation models. The methodology and models described in this paper can help engineers in the early design stages when they need a quick and easy estimate. The determination and estimation of elastic behaviour are essential in engineering practise, especially in quarrying, mining, construction, and all engineering professions that perform operations dealing with rock materials. Young's modulus, or modulus of elasticity, is the most important property describing the deformability of rock material. In this paper, grain-supported carbonates from Croatia are described and their elastic modulus and significant physical and mechanical properties are determined. The analysis of the collected data was performed in the R statistical environment. Estimation models based on multiple linear regression and the regression tree model were created. The methodology of model development and evaluation in R environment is described in detail. According to the more stringent coefficients (RMSECV and adjusted R2) used to evaluate the success of the estimation, simple regression tree models were found to perform well for the preliminary estimation, while more complex models based on Bagging performed very well. [ABSTRACT FROM AUTHOR]

The Terrestrial Ecosystem Research Network (TERN), Australia's national land ecosystem monitoring programme, measures critical environmental attributes from local to continental scale and generates quality data for research and land management. Since 2011, TERN has performed standardised field surveys and sampling across a national plot network. At each plot, TERN records vegetation structure, composition and diversity, soil characteristics, and collects plant and soil samples for analysis. At the time of submission, TERN has established over 750 plots and performed over 1,000 plot surveys across Australia. Here we present ausplotsR, an R package for the R statistical computing environment that provides a user‐friendly interface to rapidly import, visualise, and analyse TERN plot data. Easy‐to‐use functions extract the data and compile data tables that can be incorporated into a variety of statistical analysis, most notably multivariate applications requiring plant community data with standardised relative abundances. ausplotsR includes functions to calculate useful vegetation metrics, such as species presence/absence, cover, and basal area. The package also provides information on TERN’s extensive soil and plant sample collection. We expect ausplotsR will help facilitate and advance ecological research and management throughout Australia and provide useful data for vegetation modellers globally.The Terrestrial Ecosystem Research Network (TERN), Australia's national land ecosystem monitoring programme, has launched ausplotsR — an R package for live extraction, preparation, visualisation and analysis of TERN ecosystem monitoring data. Through ausplotsR, users can directly access plot‐based data on vegetation and soils across Australia from over 600 TERN monitoring sites, such as cover by species and physical sample barcode numbers. [ABSTRACT FROM AUTHOR]

The determination and estimation of elastic behaviour are essential in engineering practise, especially in quarrying, mining, construction, and all engineering professions that perform operations dealing with rock materials. Young’s modulus, or modulus of elasticity, is the most important property describing the deformability of rock material. In this paper, grain-supported carbonates from Croatia are described and their elastic modulus and significant physical and mechanical properties are determined. The analysis of the collected data was performed in the R statistical environment. Estimation models based on multiple linear regression and the regression tree model were created. The methodology of model development and evaluation in R environment is described in detail. According to the more stringent coefficients (RMSECV and adjusted R2) used to evaluate the success of the estimation, simple regression tree models were found to perform well for the preliminary estimation, while more complex models based on Bagging performed very well.

Our proposal is that the process of validating the construct of a competency inferred from an observation grid raises a multivariate and multi-dimensional latent classification problem. The analysis models a given competency based on the identification of discrete classes using latent classification (LCA) and latent transition (LTA) models. Then, the approach is illustrated in the context of the growth of the morphosyntax competency of learners of French as a second language. Advantages and limits of the analysis are outlined.

Event interval analysis had historical antecedents in the past century, but the analysis of rates of events has been largely performed using counts of events. When the information content of intervals and counts of the same events are compared, it is clear that the information content of counts is sensitive to the number of events in a counting interval. The reduced information content of counts where the number of events in a counting interval is small may affect the analysis of event rates. Both simulated and historical data are used to illustrate such effects. It is concluded that event interval analysis may be more appropriate for the analysis of event rates when the events in question are few in the counting intervals.

The addition of error bars in graphs can greatly assist readers in determining where differences lie between groups or conditions. The use of error bars to represent the standard error of the mean or confidence intervals is relatively straightforward for between-subject (independent group) designs. However, confusion still abounds as to how best to graph these same measures for within-subject (repeated-measures) designs. This paper serves both to consolidate previous recommendations regarding error bars for within-subject studies and to provide clear instructions as to how to implement these recommendations using typical statistical packages.

Meta-analysis is an indispensable tool used to synthesize research findings in the social, educational, medical, management, and behavioral sciences. Most meta-analytic models assume independence among effect sizes. However, effect sizes can be dependent for various reasons. For example, studies might report multiple effect sizes on the same construct, and effect sizes reported by participants from the same cultural group are likely to be more similar than those reported by other cultural groups. This article reviews the problems and common methods to handle dependent effect sizes. The objective of this article is to demonstrate how 3-level meta-analyses can be used to model dependent effect sizes. The advantages of the structural equation modeling approach over the multilevel approach with regard to conducting a 3-level meta-analysis are discussed. This article also seeks to extend the key concepts of Q statistics, I², and R² from 2-level meta-analyses to 3-level meta-analyses. The proposed procedures are implemented using the open source metaSEM package for the R statistical environment. Two real data sets are used to illustrate these procedures. New research directions related to 3-level meta-analyses are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Rarefaction curves represent a powerful method for comparing species richness among habitats on an equal-effort basis. Three assumptions are required to correctly perform rarefaction analysis: (i) data collection should be a representative sample of the community under study, (ii) individuals are randomly dispersed, and (iii) species are independently dispersed. However, the community structure is spatially organized, and these criteria cannot be guaranteed. Recently, Chiarucci et al. (2009) proposed a new type of rarefaction, named Spatially Constrained Rarefaction (SCR), which allows to include the autocorrelated structure of the samples in the construction of a rarefaction curve. Here we present an easy-to-use procedure to calculate Spatially Constrained Rarefaction curve in the R environment. [Copyright &y& Elsevier]