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Chapter 11 illustrates different strategies to obtain reliable and robust trait data from both field sampling and experiments. An exercise is provided to familiarize readers with different alternatives for sampling traits, and their implications for sampling effort, providing advice on defining a realistic trait sampling campaign. Examples show that a feasible sampling strategy needs to sacrifice aspects of trait variability of lower importance for the ecological questions being asked and how researchers should attempt to compromise between the most accurate and most precise estimations of trait values. Special attention is given to the expected effect of species turnover vs intraspecific trait variability adjustments across gradients, depending on the extent of the studied environmental gradient. The choice of a given sampling scheme is framed into simple trade-offs between two extreme cases: sampling several individuals for each species from only a single population, or sampling one individual per species in each population in which the species occur along a gradient. A flowchart guide for choosing among different sampling combinations along this trade-off is provided.
Chapter 12 provides some key examples of how to use trait-based methods as a tool for biodiversity monitoring, one that often shows more sensitivity to environmental changes as compared to taxonomic-based metrics. The examples show how trait-based approaches can help to broaden the scope of applied environmental sciences, using ecological theory to solve different types of environmental issues of concern. Focusing on response and effect traits, a discussion is provided on how it is possible to restore or create new ecological communities that are more resilient to environmental changes, or that enhance desirable ecosystem services. Finally, it is argued that poor literacy in functional ecology might act as a barrier to communicating with decision makers, and incorporating trait-based approaches in environmental policies.
Chapter 6 analyses the ecological mechanisms, and implications, of intraspecific trait variability (ITV) and some key approaches to take ITV properly into account in modern trait-based analyses. The different sources of ITV, genetic variation, epigenetic effects and phenotypic plasticity, are discussed and put in the context of species evolution, adaptation to environmental conditions, species distribution potential (including invasive species) and the effects of species on multiple ecosystem properties and trophic interactions. Different tools are provided to quantify how strong ITV affect ecological patterns. A comparison of within- vs between-species trait variability in a community is discussed. Tools showing how strong the effect of changes in species composition (turnover) compared to ITV along environmental gradients are provided. Finally, methods considering ITV to quantify trait differences between species, via trait overlap in trait probability distributions, are discussed in the light of modern tools measuring functional diversity across different scales
Chapter 8 illustrates the importance of considering the phylogeny of species when investigating different ecological questions related to species traits. First, the concept of phylogenetic trees is provided with the notion that, in some cases, species that share a common ancestor share some common traits, while in others distantly related species have evolved similar adaptations independently. Models of evolution, in particular the Brownian motion model, are introduced to set a reference for comparing the extent of trait conservatism. The importance of phylogeny is first discussed with respect to ‘species level’ analyses (Chapter 4) relating traits, species environmental preferences and species fitness. Tests such as Phylogenetic Independent Contrasts (PICs) are discussed in terms of whether they provide, or not, a way to ‘correct’ for the phylogenetic non-independence between species. Then the concept of phylogenetic relatedness between species is discussed in the context of Phylogenetic Diversity (PD) indices and combined with functional diversity measurements. Different R tools are described to support these types of analyses in the material accompanying this book.
Chapter 3 provides an overview of the concepts and approaches needed to assess ecological and phenotypic differentiation between organisms. First, an historical perspective on earlier systems ‘classifying’ species in terms of their traits into different ‘types’ is provided. Second, other schemes such as the r/K continuum, the C-S-R scheme and the leaf economic spectrum are introduced. These approaches, aimed at defining different ‘types’ of organisms, are discussed in terms of their importance for interpreting ecological patterns and for communication with non-experts. A further distinction between response and effect functional groups is provided, with a guideline on how to define these groups with ‘a priori’ ecological hypotheses or ‘a posteriori’ data-driven approaches. The Gower distance is introduced as a useful way to characterize the differences between organisms in terms of multiple types of traits. At the same time, a number of often overlooked problems with this distance metric are discussed. The R material for this chapter illustrates these issues with practical examples.
Chapter 4 focuses on different approaches to studying the relationship between environmental conditions and trait variability, both within and between species. First, an historical perspective on species distribution and adaptations along environmental gradients is provided. The concept of environmental gradients is then discussed in depth, with distinctions between different types of gradients. This leads to a description of the widely applied trait-filtering metaphor, describing how environmental conditions filter out species with traits less adapted to a given habitat. The distinction between different types of analyses relating traits to environmental conditions is discussed (species- vs community-level analyses). Examples of these analyses are provided in the accompanying R material for this chapter. The importance of species-level analyses is highlighted, particularly in terms of species’ trait-fitness relationships and the parameterization of species distribution models.
Chapter 10 synthesizes the concepts already introduced, regarding response and effect traits, into the so-called response--effect trait framework. It shows how such a framework can be expanded and tested across different trophic levels, thus assessing how functional traits control species interactions and the consequences of these interactions for ecosystem functioning. The concepts of ‘trophic effect' and 'response traits’ are introduced to assess how traits within a trophic level affect other trophic levels. A further discussion is provided on interesting perspectives incorporating trait-based concepts into plant--animal interaction networks, to identify both niche and neutral mechanisms driving interactions networks and the resulting ecosystem services. Finally, the importance of intraspecific trait variability in the context of species interactions, and ecosystem processes resulting from them, is discussed. The R material accompanying this chapter provides one approach on how to calculate species interaction niche.
Chapter 9 builds on the concepts of effect traits to provide a tool for connecting biodiversity effects to multiple ecosystem processes and services, through species traits. First, an overview of the multiple effects of different traits, and organism types, on different ecosystem processes is provided. Then, two main hypotheses are proposed to explain how traits influence ecosystem processes: the mass ratio hypothesis (the dominant trait in the community, mainly associated to CWM) and the complementarity hypothesis (the variation in trait values in the community, mainly associated to FD). A detailed discussion is provided on how to disentangle the roles of CWM and FD in affecting ecosystem functions, for which specifically designed experiments are often needed (particularly to tease apart the mathematical non-independence between CWM and FD). These tools are further discussed in the light of classical approaches decomposing biodiversity effects into different components (net diversity effect, selection effect and niche complementarity).
Chapter 5 provides concepts and tools to characterize the functional trait structure of communities. Various indices are introduced, mainly community weighted mean (CWM) and various functional diversity (FD) indices. The power and the limitations of CWM are discussed. Various indices of FD, which expresses the extent of trait differences between organisms, are introduced to simplify their use and interpretation. The broad classification into ‘families’ of indices, i.e. functional richness, functional evenness and functional divergence, is discussed. A selection of indices, with their ability to provide a measure of FD at different scales (alpha, beta and gamma diversity), is discussed, together with other emerging components such as functional redundancy and functional rarity. A discussion on existing R tools, with their potential tricks and problems, is provided, also with examples available in the R material accompanying this chapter.
Chapter 7 expands on the ideas already introduced in Chapters 4 and 6 on community assembly rules, understood as any constraint restricting the number and identity of the species observed in an assemblage. The different ecological processes behind such rules are discussed, together with the expected effects of these rules on trait patterns (trait convergence vs trait divergence) at different ecological scales. The importance of defining a proper reference species pool for assessing these mechanisms is explained. A further discussion is provided on the difficulty of ascertaining the specific ecological processes leading to observed patterns of trait variation without experimental approaches. This leads to introducing how null models and data randomizations can provide valuable insight into different assembly rules mechanisms, when proper care is given to considering the effect of scale and an adequate reference species pool. The R examples accompanying this chapter provide different tools to implement a variety of null models in combinations with functional diversity indices.
Chapter 2 provides general answers to some of the most frequently asked questions by researchers and practitioners aiming to apply trait-based methods: How to select the right trait(s) and how many traits should be selected? Where to find reliable trait values? Are the trait values provided in the literature or databases appropriate, and sufficient, for a given study system, or should traits be measured in the field? The need for standardization in trait measurements is discussed, particularly in terms of the importance of building reliable and useful trait databases. Different types of traits (quantitative, categorical, circular etc.) are introduced, as multiple types of traits are often needed to answer most ecological questions. A list is provided of existing trait databases from which trait information for different taxonomic groups can be obtained. The R material accompanying the book provides tools to extract trait data from some of these databases and combine it with other available species and community data.
Chapter 1 summarizes the main concepts representing the pillars of trait-based ecology. Key definitions from the literature, and widely used in the book, are synthetized and clarified. This includes an in-depth discussion of which traits are to be considered more functional, dissecting the relationship between species traits and species fitness and how this can change across different habitats. The classic distinction between response and effect traits is introduced, together with some broad open challenges for future research in trait-based ecology.
Functional ecology is the branch of ecology that focuses on various functions that species play in the community or ecosystem in which they occur. This accessible guide offers the main concepts and tools in trait-based ecology, and their tricks, covering different trophic levels and organism types. It is designed for students, researchers and practitioners who wish to get a handy synthesis of existing concepts, tools and trends in trait-based ecology, and wish to apply it to their own field of interest. Where relevant, exercises specifically designed to be run in R are included, along with accompanying on-line resources including solutions for exercises and R functions, and updates reflecting current developments in this fast-changing field. Based on more than a decade of teaching experience, the authors developed and improved the way theoretical aspects and analytical tools of trait-based ecology are introduced and explained to readers.
The “schizophrenia spectrum” concept allowed better identifying the psychopathology underpinning disorders including schizophrenia, schizoaffective disorder (SZA) and cluster A personality disorders (PD).
To compare the clinical portrait of the schizophrenia spectrum disorders, focusing on the impact of the affective dimension.
Inpatients at the acute psychiatric ward of Perugia (Umbria-Italy) were evaluated with the structured clinical interview for DSM-IV Axis I and Axis II disorders and diagnosed with a “schizophrenia spectrum” disorder according to DSM-IV-TR. The clinical evaluation was conducted using the positive and negative syndrome scale (PANSS). Pearson correlations of the different subscales in the three groups and between the negative scales with the affective symptom “depression” were conducted.
The sample consisted of 72 inpatients (schizophrenia 55.6%, SZA 20% and cluster A PD 19.4%). The negative and the general psychopathology scales directly correlated at different degrees in the three groups (schizophrenia: r = 0.750; P < 0.001; SZA: r = 0.625, P = 0.006; cluster A PD: r = 0.541, P = 0.046). The symptom “depression” directly correlated with 5 out of 7 negative symptoms: blunted affect (r = 0.616, P < 0.001), emotional withdrawal (r = 0.643, P < 0.001), poor rapport (r = 0.389, P = 0.001), passive/apathetic social withdrawal (r = 0.538, P < 0.001), lack of spontaneity & flow of conversation (r = 0.399, P = 0.001).
Our study confirmed the existence of the “schizophrenia spectrum” with combined different disorders lying on a continuum in which negative symptoms mainly correlated with the psychopathological functioning. Noteworthy, the symptoms of the negative scale strongly correlated with the “depression” symptom, underlying the impact of the affective symptoms on the severity of the “schizophrenia spectrum” disorders.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Aim of the current study is to investigate the associations between daily levels of air pollutants (particulate matter, ozone, carbon monoxide, nitrogen dioxide) and daily admissions for mental disorders to the emergency department of two general hospitals in Umbria region (Italy).
We collected data about daily admissions to psychiatric emergency services of two general hospitals, air pollutants' levels and meteorological data for the time period 1 January 2015 until 31 December 2016. We assessed the impact of an increase in air pollutants on the number of daily admissions using a time-series econometric framework.
A total of 1860 emergency department admissions for mental disorders were identified. We observed a statistically significant impact of ozone levels on daily admissions. The estimated coefficient of O3 is statistically significant at the 1% level. All other pollutants were not significantly associated with the number of daily admissions.
Short-term exposure to ozone may be associated with increased psychiatric emergency services admissions. Findings add to previous literature on existing evidence for air pollution to have an impact on mental health. Ozone may be considered a potential environmental risk factor for impaired mental health.