Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.

Modern Applied Statistics With S

The most unique feature of Earth is the existence of life, and the most extraordinary feature of life is its diversity. Approximately 9 million types of plants, animals, protists and fungi inhabit the Earth. So, too, do 7 billion people. Two decades ago, at the first Earth Summit, the vast majority of the world's nations declared that human actions were dismantling the Earth's ecosystems, eliminating genes, species and biological traits at an alarming rate. This observation led to the question of how such loss of biological diversity will alter the functioning of ecosystems and their ability to provide society with the goods and services needed to prosper.

/pdf/biodiversity-loss-and-its-impact-on-humanity-2d0fgc88mg.pdf

Biodiversity loss and its impact on humanity

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

/pdf/species-distribution-models-ecological-explanation-and-1szux2yl31.pdf

Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species’ bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale-dependent

/pdf/predicting-the-impacts-of-climate-change-on-the-distribution-f5pj1nly81.pdf

Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?

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

African shrub distribution emerges via a trade-off between height and sapwood conductivity

Services supplied by South African fynbos ecosystems.

Water Limitation, Fire, and Savanna Persistence

Dynamic global vegetation models (DGVMs) are now central elements in Earth system models, and our ability to understand past and anticipate future changes in the Earth system is intimately linked to the quality of DGVMs (Prentice et al., 2007). There are many ways in which DGVMs need improvement and there are many exciting initiatives under way. In a recent manuscript, Verheijen et al. (2013) describe one pathway. To provide a context for their work, they compare their approach to other initiatives. In this contribution we wish to point out ways in which Verheijen and colleagues misrepresented the aDGVM2 (which they incorrectly call the aDGVM, which is in fact a different model published by Scheiter and Higgins, 2009) as presented in Scheiter et al. (2013). While the aim of this piece is primarily to set the record straight, we additionally point out similarities and differences between the approach described by Verheijen et al. (2013) and that described by Scheiter et al. (2013). Verheijen et al. (2013) motivate their study by stating in reference to the Jena Diversity-DGVM (JeDi-DGVM) (Pavlick et al., 2013) and aDGVM2 (Scheiter et al., 2013) that “none of the approaches so far tried to maximally include trait variation based on observational trait data and capture multiple sources of this variation by relating trait data to environmental variables”. Although we appreciate that this statement was designed to illustrate the uniqueness of Verheijen et al. (2013) and the statistical approach they adopt, it does have the side-effect of suggesting that these two papers ignored variations in traits and the relationships between traits and the environment. We would like to point out that Fig. 5 of our paper plots the positions of modelled individuals in multivariate trait space and relates the axes of this trait space to environmental variables. In the same paragraph, the authors go on to suggest that DGVM modellers need to apply assembly theory to understand and model relationships between traits and the environment better, when they state that “such relationships between environmental conditions and traits can potentially be understood via ecological assembly theory”. This is exactly what we propose in Scheiter et al. (2013), where the introduction explicitly proposes that DGVM modelling could benefit from two branches of community ecology, namely coexistence theory and community assembly theory. Moreover, the title of Scheiter et al. (2013) includes the words “learning from community ecology”. Our impression from reading Pavlick et al. (2013) is that the traits that JeDi-DGVM predicts at a site are, as is the case with aDGVM2, a function of how environmental attributes select for trait combinations. This is an interpretation that Verheijen et al. (2013) appear, in apparent contradiction to their statement we cite above, to share in their discussion, when they state that “some DGVMs also implement the concept of environmental filtering, like the JeDi-DGVM (Pavlick et al., 2013)”.

/pdf/progress-in-dgvms-a-comment-on-impacts-of-trait-variation-3uj8sevvg9.pdf

Progress in DGVMs: a comment on "Impacts of trait variation through observed trait–climate relationships on performance of an Earth system model: a conceptual analysis" by Verheijen et al. (2013)

Steven I. Higgins

Papers

African shrub distribution emerges via a trade-off between height and sapwood conductivity

Services supplied by South African fynbos ecosystems.

Water Limitation, Fire, and Savanna Persistence

Progress in DGVMs: a comment on "Impacts of trait variation through observed trait–climate relationships on performance of an Earth system model: a conceptual analysis" by Verheijen et al. (2013)

Diversification is decoupled from biome fidelity: Acacia – a case study