Taxonomic Dimensions Research Articles

Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis

AbstractImaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.

Scale-dependent influences of environmental, historical, and spatial processes on taxonomic and functional beta diversity of Japanese bat assemblages.

This study investigated the relative influences of environmental, spatial, and historical factors, including the island-specific history of land connectivity, on bat assemblages in the Japanese Archipelago. We collected bat distribution data from 1408 studies and assigned them to Japan's First Standard Grid (approximately 6400 km2). Japanese bat assemblages were analyzed at two scales: the entire Japanese Archipelago comprised 16 islands and exclusively the four main islands. At first, we calculated taxonomic and functional total beta diversity (β total) by Jaccard pairwise dissimilarity and then divided this into turnover (β repl) and richness-difference (β rich) components. We conducted hierarchical clustering of taxonomic beta diversity to examine the influence of the two representative sea straits, Tsugaru and Tokara, which are considered biogeographical borders. Variation partitioning was conducted to evaluate the relative effects of the three factors on the beta diversity. Clustering revealed that the Tokara Strait bordered the two major clades; however, the Tsugaru Strait did not act as a biogeographical border for bats. In the variation partitioning, shared fraction between spatial and historical factors significantly explained taxonomic and functional β total and taxonomic β repl at the entire archipelago scale, but not at the four main islands scale extending only Tsugaru Strait but not Tokara Strait. Pure environmental factors significantly explained functional β total at both scales and taxonomic β total only at the four main islands scale. These results suggest that spatial and historical factors are more pronounced in biogeographical borders, primarily structuring assemblage composition at the entire archipelago scale, especially in taxonomic dimension. However, current environmental factors primarily shape the assemblage composition of Japanese bats at the main island scale. The difference in results between the two scales highlights that the primary processes governing assemblages of both dimensions depend on the quality of the dispersal barriers between terrestrial and aquatic barriers for bats.

Taxonomic, phylogenetic, and functional nestedness of mammal assemblages in the Zhoushan Archipelago, China

Abstract Nestedness is an important part of the theoretical framework of island biogeography and community ecology. However, most previous studies focused on taxonomic dimension and overlooked functional and phylogenetic nestedness. Here, we simultaneously investigated taxonomic, functional, and phylogenetic nestedness of terrestrial mammals on 39 land-bridge islands in the Zhoushan Archipelago, China. As mammals’ response to the environment may depend on their body size, we performed analyses for three mammal assemblages separately: overall species, large and medium-sized species, and small species. The taxonomic nestedness was quantified by organizing the species incidence matrix, while the functional and phylogenetic nestedness were estimated by combining the similarity of their ecological traits and phylogeny. Island characteristics (island area, three isolation indices, land use intensity, and habitat diversity) and species traits (body size, litter size, habitat specificity, geographic range size, and minimum area requirement) were used as predictors of nestedness. Overall and small species were significantly nested in 3 facets of nestedness, and results supported the selective extinction, selective colonization, and habitat nestedness hypotheses. Large and medium-sized species were functionally and phylogenetically nested when matrices were ordered by increasing distance to mainland, supporting the selective colonization hypothesis. Overall, differences in nestedness and its underlying mechanisms were detected not only in 3 facets of nestedness but also in the 3 mammal assemblages. Therefore, frameworks that incorporate taxonomic, phylogenetic, and functional nestedness can contribute to a more comprehensive understanding of nestedness processes. Additionally, it also improves our ability to understand the divergent responses of mammal assemblages to the insular environment.

B-Cubed: Leveraging Analysis-Ready Biodiversity Datasets and Cloud Computing for Timely and Actionable Biodiversity Monitoring

Effective biodiversity management and policy decisions require timely access to accurate and reliable information on biodiversity status, trends, and threats. However, the process of data cleaning, aggregation, and analysis is often time-consuming, convoluted, laborious, and irreproducible. Biodiversity monitoring across large areas faces challenges in evaluating data completeness and quantifying sampling effort. Despite these obstacles, unprecedented amounts of biodiversity data are being accumulated from diverse sources, aided by emerging technologies such as automatic sensors, eDNA, and satellite tracking. To address these challenges, the development of tools and infrastructure is crucial for meaningful interpretations and deeper understanding of biodiversity data (Kissling et al. 2017). Furthermore, a significant delay exists in converting biodiversity data into actionable knowledge. Efforts have been made to reduce this lag through rapid mobilisation of biodiversity observations, digitization of collections (Nelson and Ellis 2018), and streamlined workflows for data publication (Reyserhove et al. 2020). However, delays still occur in the analysis, publication, and dissemination of data. The B-Cubed project (Biodiversity Building Blocks for Policy)*1 proposes solutions to overcome these challenges. It implements the concept of Occurrence Cubes (Oldoni et al. 2020), which aggregate occurrence data along spatial, temporal and taxonomic dimensions. Cube generation will be available as a new service provided by the Global Biodiversity Information Facility (GBIF). By leveraging aggregated occupancy cubes as analysis-ready biodiversity datasets, we aim to enhance comprehension and reduce barriers to accessing and interpreting biodiversity data. Automation of workflows will provide regular and reproducible indicators and models that are open and useful to users. Additionally, the use of cloud computing offers scalability, flexibility, and collaborative opportunities for applying advanced data science techniques anywhere. Finally, close collaboration with stakeholders will inform us of the requirements for tools, increase impact, and facilitate the flow of information from primary data to the decision-making processes.

Explainable neural networks for trait‐based multispecies distribution modelling—A case study with butterflies and moths

AbstractSpecies response traits mediate environmental effects on species distribution. Traits are used in joint and multispecies distribution models (JSDMs and MSDMs) to enable community‐wide shared parameters that characterise niche filtering along environmental gradients. Multispecies machine learning SDMs, however, do not use traits as their inclusion requires an additional taxonomic dimension that is incompatible with their usual tabular inputs. This has confined trait mediation in SDMs to hierarchical Bayesian models. Here we provide a novel artificial neural network (ANN) architecture that solves this dimensionality problem.Our ANN includes species traits (via a time distributed layer) and is therefore able to identify not only species‐specific responses to the environment, but also shared responses across the community that are mediated by species traits. Model performance evaluated at the species level not only quantifies the reliability of species predictions, but also their departure from an average response dictated by traits only.We apply our model to two unique long‐term spatio‐temporal of butterfly and moth datasets collected across the United Kingdom between 1990 and 2019. In addition to species traits, predictors include numerous metrics derived from weather, land‐cover and topology data. For butterflies and moths we show convincing model performance for classifying species occupancy. We use SHAP (Shapley Additive exPlanations) to explain the ANN and show how trait‐mediated and species‐specific responses can be approximated, hence yielding ecological insights on the key drivers of species distribution. We highlight a range of drivers of change that determine occupancy, including wind, temperature as well as habitat type.We demonstrate that a trait‐based approach can be encoded as an ANN by using a time distributed layer. This brings ANNs unmatched predictive capabilities to the field of MSDMs, at the same time of lifting their reputed drawback of poor explainability.

Gaussian Process Regression as a Replicable, Streamlined Approach to Inventory and Uncertainty Analysis in Life Cycle Assessment.

Life cycle assessment plays a critical role in quantifying environmental impacts, but its credibility remains challenged when data and uncertainty analysis are lacking. In this study, we propose a data compilation framework to address these two issues. The framework first quantifies the correlations of production activities among existing data in temporal, geographical, and taxonomic dimensions. The framework then introduces covariance functions to convert these correlations to a similarity matrix, and the Gaussian process regression model is adopted to predict new data based on these covariance functions. The associated uncertainty is automatically characterized using the posterior distribution of predictions. The framework is demonstrated on the nitrogen fertilizer application rate for food production─an activity recognized for its environmental burden─with results capable of reflecting temporal and geographical variations. By introducing the concept of phylogenetic distance as a correlation of taxonomy, the framework provides a quantitative basis for predictions in a proxy data usage scenario. The framework can be used in developing temporally and regionally representative life cycle inventories and databases and can facilitate consistent uncertainty quantification in future life cycle assessment methodologies.

Forest area predicts all dimensions of small mammal and lizard diversity in Amazonian insular forest fragments

ContextAlthough hydropower development is one of the primary drivers of habitat loss and insular fragmentation, its impacts on species identity and their functional and phylogenetic roles have often been overlooked.ObjectivesHere we use an integrative approach, considering taxonomic, functional and phylogenetic dimensions at multiple scales, to understand the processes underlying species (dis)assembly of two taxa exhibiting relatively low dispersal ability: small mammals and lizards.MethodsWe surveyed 26 islands within the Balbina Hydroelectric Reservoir, and adjacent continuous forest, in Central Amazonia. Each dimension of diversity was related to spatial and habitat variables. We also examined functional composition using community-weighted mean trait values, and community redundancy using functional uniqueness. β-diversity was partitioned into their richness (βrich) and replacement (βrepl) components.ResultsFunctional and phylogenetic α-diversities of both taxa mirrored the taxonomic dimension, all of which increased with forest area. Individual small mammal (body mass and matrix tolerance), and lizard traits (body length, heliothermic mode and habitat type) were also predicted by forest area. For both groups, functional uniqueness decreased with forest area, and all dimensions of β-diversity were predominantly partitioned in βrich.ConclusionsThe environmental filter created by forest area resulted in the low conservation value associated with small forest islands, only occupied by a small set of species comprised by generalist lizards and matrix-tolerant small mammals. On the other side, large forest sites ensured ecosystem resilience to disturbance. To maintain ecosystem integrity, creating myriad small islands over large expanses of floodwaters should be avoided in future hydropower development.

A multi-faceted comparative perspective on elevational beta-diversity: the patterns and their causes.

The observed patterns and underlying mechanisms of elevational beta-diversity have been explored intensively, but multi-dimensional comparative studies remain scarce. Herein, across distinct beta-diversity components, dimensions and species groups, we designed a multi-faceted comparative framework aiming to reveal the general rules in the observed patterns and underlying causes of elevational beta-diversity. We have found that: first, the turnover process dominated altitudinal patterns of species beta-diversity (βsim > βsne), whereas the nestedness process appeared relatively more important for elevational trait dissimilarity (βfuncsim < βfuncsne); second, the taxonomic turnover was relative higher than its phylogenetic and functional analogues (βsim > βphylosim/βfuncsim), conversely, nestedness-resultant trait dissimilarity tended to be higher than the taxonomic and phylogenetic measures (βfuncsne > βsne/βphylosne); and third, as elevational distance increased, the contradicting dynamics of environmental filtering and limiting similarity have jointly led the elevational patterns of beta-diversity, especially at taxonomic dimension. Based on these findings, we infer that the species turnover among phylogenetic relatives sharing similar functional attributes appears to be the main cause of shaping the altitudinal patterns of multi-dimensional beta-diversity. Owing to the methodological limitation in the randomization approach, currently, it remains extremely challenging to distinguish the influence of the neutral process from the offset between opposing niche-based processes. Despite the complexities and uncertainties during species assembling, with a multi-dimensional comparative perspective, this work offers us several important commonalities of elevational beta-diversity dynamics.

Peirce on Proper Names

This paper offers a developmental account of Peirce's theory of proper names. It identifies two main dimensions of Peirce's thinking on proper names, a "taxonomic dimension," concerning the place of proper names within the taxonomy of signs, and a "maturational dimension," concerning the different "stages of maturity" a proper name goes through when interpreted. These two dimensions also constitute distinct phases of Peirce's (continuously evolving) theory of proper names. The chronological reconstruction offered in the paper is also shown to solve some apparent inconsistencies in what Peirce wrote about proper names.

Adding Taxonomic Dimensions to the Scientific Names Index in the Biodiversity Heritage Library via Integration with the Catalogue of Life

The most significant specialized and open resource for biodiversity literature is the Biodiversity Heritage Library (BHL). BHL contains more than 200,000 volumes that cover hundreds of years of biological publications. The Catalogue of Life (CoL) is the largest aggregator of global taxonomic information. The Global Names project collaborates with BHL and CoL to create a BHL scientific names index that is continuously improved. The index allows researchers to conveniently find relevant information about any name. Building a scientific names index is challenging because scientific names are quite dynamic and can dramatically change in taxonomic meaning over time. According to our estimates, on average, there are three scientific names per species. Also, a significant number of scientific names have fallen into disuse. For example, approximately 25% of names used in Zoology between 1750–1850 and collected by Charles Sherborn into Index Animalium disappeared from current nomenclatural and taxonomic databases. It is important, therefore, to add "taxonomic intelligence" to BHL names index search and present not only data about a specific name-string but also about all known synonyms so that all relevant information in BHL can be consolidated and conveniently accessed from taxonomic information aggregators like CoL. We developed an open source program "bhlnames", which creates a two-way bridge between the Catalogue of Life and Biodiversity Heritage Library. It provides the location of information in BHL about a taxon, using all names associated with the taxon according to Catalogue of Life synonymy data. Using that information, it attempts to provide CoL with a BHL link to the first descriptions of scientific names in the literature.

Occurrence Cubes: A new way of aggregating heterogeneous species occurrence data

The digital era has brought about an impressive increase in the volume of published species occurrence data. Research infrastructures such as the Global Biodiversity Information Facility (GBIF), the digitization of legacy data, and the use of mobile applications have all played a role in this transition. More data implies, unavoidably, more heterogeneity at multiple levels as a result of the different methods and standards used to collect data. Data standardization and aggregation help to reduce this heterogeneity. Furthermore, intermediate data products that can be used for activities such as mapping, modeling and monitoring improve the repeatability and reproducibility of biodiversity research (Kissling et al. 2017). Occurrences can be defined as events in a three-dimensional space where the dimensions are taxonomic (what), temporal (when) and spatial (where). They are then aggregated into what we coined occurrence cube (Fig. 1). The taxonomic dimension is categorical. Research infrastructures like GBIF use a taxonomic backbone, thus making data aggregation at species level or higher rank relatively easy. The temporal dimension is a continuum and the temporal uncertainty is usually lower than the typical aggregation span, typically a year. Regarding the spatial dimension, occurrences are typically filtered to remove those with too large an uncertainty to fit the grid scheme being used. Meaning that the spatial uncertainty is largely unused. We developed a method to take into account this spatial uncertainty while aggregating data. In particular, we state that an occurrence is spatially representable as a closed plane figure such as a circle, hexagon or square, never as the geometric centre (centroid) of it. As for GBIF occurrence data, the coordinateUncertaintyInMeters is defined as the radius describing the smallest circle containing the whole of the location (see Darwin Core standard). So, spatially speaking, we refer to occurrences as circles, even if the method described below is general. After harvesting the occurrence data and providing a data quality assessment (e.g. removing occurrences without coordinates or with suspicious coordinates) we can assign occurrences to a reference grid such as the European reference grid of the European Environment Agency (EEA) at 1 km scale. In this spatial aggregation we randomly choose a point within the occurrence circle and assign it to the grid cell in which it is contained. We can aggregate further by time (e.g. by year) and taxonomy (e.g. by species), where aggregating means counting how many occurrences are in each specific taxonomic-spatial-temporal unit. The analogy with geometry goes further: the occurrence cube can, as any cube, be projected on an orthogonal plane by aggregating along one of the three dimensions. In particular, projecting the cube on the taxonomic and temporal dimensions can be done by adding up the number of occurrences, or counting the number of occupied cells, thus estimating the area of occupancy. The occurrence cube paradigm has been developed within the Tracking Invasive Alien Species (TrIAS) project (Vanderhoeven et al. 2017) following Open Science and FAIR principles. We created and published occurrence cubes at the species level for Belgium and Italy (Oldoni et al. 2020b) and the occurrence cubes for non-native taxa in Belgium and Europe (Oldoni et al. 2020a).

Discriminating the effects of local stressors from climatic factors and dispersal processes on multiple biodiversity dimensions of macroinvertebrate communities across subtropical drainage basins

Metacommunity ecology emphasizes that community structure and diversity are not only determined by local environmental conditions through environmental filtering, but also by dispersal-related processes, such as mass effects, dispersal limitation and patch dynamics. However, the roles of dispersal processes are typically ignored in bioassessment approaches. Here, we simultaneously explored the potential influences of four groups of factors: local stressors, climatic factors, within-basin spatial factors and basin identity in explaining variation in diversity indices of macroinvertebrate assemblages from seven subtropical tributary rivers. A total of 12 biodiversity indices based on species identities, functional traits and taxonomic relatedness were calculated and used in the subsequent statistical analysis. Our results showed that, although differing in their relative importance, the four explanatory factor groups all played important roles in explaining variation in biodiversity indices. Of the pure fractions, index variation was best explained by local environmental stressors, whereas the other three explanatory factor groups appeared less influential. Furthermore, diversity indices from species, functional and taxonomic dimensions responded distinctly to the focal ecological factors, and differed in their abilities to portray the effects of human disturbances on macroinvertebrate communities. Taxonomic distinctness indices performed best, with the highest amount of variation associated to local stressors and hardly any variation explained by other factors, implying that these indices are robust in portraying human disturbances in streams. However, species diversity and functional diversity indices were also affected by spatial processes and climatic factors, suggesting that these indices should be used with caution in bioassessment. We hence conclude that environmental assessment of riverine ecosystems should not rely entirely on the perspective of species sorting. In contrast, both roles of spatial processes and environmental variables related to human disturbances and climatic variation should be incorporated in management and conservation of riverine ecosystems.

Taxonomic shortfalls in digitised collections of Australia’s flora

Rapid growth in the digitisation of the world’s natural history collections substantially simplifies scientific access to taxonomic and biogeographic information. Despite recent efforts to collate more than two centuries of biodiversity inventories into comprehensive databases, these collections suffer limitations across spatial, temporal and taxonomic dimensions. We assessed taxonomic shortfalls in preserved specimens from 296 plant families native to Australia, for which records have been collated into the Australasian Virtual Herbarium (AVH), specifically addressing the following questions: (1) Based on the number of specimen records per species, which Australian native plant families are under- or over-represented in the collection of preserved specimens digitised in the AVH? (2) To what extent does the distribution of collectors among plant families, or the area occupied by plant families, explain patterns of taxonomic representativeness? We found that the number of preserved specimens per family is not proportional to the family’s known species richness. For 29% of Australia’s plant families (i.e. 86), the number of digitised records constitutes < 50% of the number expected given species richness within those families. Further, only 34% of families (100) have at least 20 specimens digitised for each species recorded in the AVH. Families occupying small areas (< 200 grid cells) are more likely to be under-represented taxonomically, while there is a strong positive correlation between the number of unique collectors and the extent of taxonomic over-representation. A sound understanding of biodiversity is critical for megadiverse countries such as Australia, and identifying biases in digital inventories may help with establishing future sampling and digitisation strategies to enhance taxonomic representation.

Testing Darwin’s naturalization conundrum based on taxonomic, phylogenetic and functional dimensions of vascular plant diversity

Testing Darwin’s naturalization conundrum based on taxonomic, phylogenetic and functional dimensions of vascular plant diversity

The Concept of Purposeful Prototyping: Towards a New Kind of Taxonomic Classification

AbstractA prototype can be generally defined as a preliminary version of a final product and it can represent both aesthetic and functional features. Prototyping, the process of building a prototype, constitutes an indispensable part of product development processes. Many classifications of prototypes which use a different range of criteria have already been developed and are known as Prototype Taxonomies. This paper proposes an agenda towards the development of a new taxonomic classification which will be based on a prototype's attributes and its intended purpose. This work is justified from the results of an analysis of 8 existing prototype taxonomies which indicate that there is not a taxonomy that explicitly uses the prototypes’ purposes as its basic taxonomic dimension. A definition of the term ‘Prototype Purpose’ is proposed through discussing the differences and relations to the term ‘Prototype Role’. This work results in a list of 23 explicit prototype purposes which arise from 7 roles of prototyping found in relevant literature: Learning, Communication, Demonstration, Integration, Refinement, Exploration and Requirement Elicitation.

Comparative responses of termite functional and taxonomic diversity to land‐use change

1. While it is clear that land‐use change significantly impacts the taxonomic dimension of soil biodiversity, how the functional dimension responds to land‐use change is less well understood.2. This study examined how the transformation of primary forests into rubber tree monocultures impacts individual termite species and how this change is reflected in termite taxonomic and functional α‐diversity (within site) and β‐diversity (among sites).3. Overall, individual species responded strongly to land‐use change, whereby only 11 of the 27 species found were able to tolerate both habitats. These differences caused a 27% reduction in termite taxonomic richness and reduced taxonomic β‐diversity in rubber plantations compared with primary forests. The study also revealed that the forest conversion led to a shift in some termite species with smaller body size, shorter legs and smaller mandibular traits. Primary forests exhibited higher functional richness and functional β‐diversity of termite species, indicating that functional traits of termite species in rubber plantations are more evenly distributed.4. The present study suggests that forest conversion does not merely decrease taxonomic diversity of termites, but also exerts functional trait filtering within some termite species. The results affirm the need for biodiversity assessments that combine taxonomic and functional indicators when monitoring the impact of land‐use change.

Improved scheme of membership function optimisation for fuzzy air‐fuel ratio control of GDI engines

This study researches an improved scheme of membership function optimisation (MFO) for fuzzy air–fuel ratio (AFR) control of gasoline direct injection (GDI) engines based on correspondence analysis (CA). This proportional–integral-like fuzzy knowledge-based controller (FKBC) optimised by the proposed scheme can further optimise AFR control performance while maximising conversion efficiency of the three-way catalyst to eliminate the exhaust emissions in real time. Different from the conventional experience-based membership function (MF) design method for an FKBC, the proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines. Within this context: (i) specialised MFs for self-adaptive AFR control system of a GDI engine are designed with weight distribution; (ii) based on designed scalar parameters, the CA model with taxonomic dimensions is built for acquiring a customised MF to counter transient scenario changes more effectively; (iii) the engine controller with the proposed scheme is real time validated in a production V6 GDI engine, and its advantage in terms of engine transient control performance is further demonstrated by comparing with a benchmark controller designed based on experience.

Doctor, Teacher, and Stethoscope: Neural Representation of Different Types of Semantic Relations.

Concepts can be related in many ways. They can belong to the same taxonomic category (e.g., "doctor" and "teacher," both in the category of people) or be associated with the same event context (e.g., "doctor" and "stethoscope," both associated with medical scenarios). How are these two major types of semantic relations coded in the brain? We constructed stimuli from three taxonomic categories (people, manmade objects, and locations) and three thematic categories (school, medicine, and sports) and investigated the neural representations of these two dimensions using representational similarity analyses in human participants (10 men and nine women). In specific regions of interest, the left anterior temporal lobe (ATL) and the left temporoparietal junction (TPJ), we found that, whereas both areas had significant effects of taxonomic information, the taxonomic relations had stronger effects in the ATL than in the TPJ ("doctor" and "teacher" closer in ATL neural activity), with the reverse being true for thematic relations ("doctor" and "stethoscope" closer in TPJ neural activity). A whole-brain searchlight analysis revealed that widely distributed regions, mainly in the left hemisphere, represented the taxonomic dimension. Interestingly, the significant effects of the thematic relations were only observed after the taxonomic differences were controlled for in the left TPJ, the right superior lateral occipital cortex, and other frontal, temporal, and parietal regions. In summary, taxonomic grouping is a primary organizational dimension across distributed brain regions, with thematic grouping further embedded within such taxonomic structures.SIGNIFICANCE STATEMENT How are concepts organized in the brain? It is well established that concepts belonging to the same taxonomic categories (e.g., "doctor" and "teacher") share neural representations in specific brain regions. How concepts are associated in other manners (e.g., "doctor" and "stethoscope," which are thematically related) remains poorly understood. We used representational similarity analyses to unravel the neural representations of these different types of semantic relations by testing the same set of words that could be differently grouped by taxonomic categories or by thematic categories. We found that widely distributed brain areas primarily represented taxonomic categories, with the thematic categories further embedded within the taxonomic structure.

Functional and phylogenetic dimensions are more important than the taxonomic dimension for capturing variation in stream fish communities

AbstractBiodiversity is inherently multidimensional in nature, differences in evolutionary history, attributes of species, taxonomic composition constitutes a small fraction of whole variation present in this multidimensional space. Despite its multidimensional characteristic, biodiversity has been traditionally measured by assessing its dimensions separately through metrics of diversity. However, assessing multiple dimensions in a common framework opens the possibility of answering interesting questions that, until now, are poorly understood, such as: What dimensions capture most of the variation present in biodiversity among communities? We assess this question by extending the framework of Importance Values (IVs) to three dimensions of variation in biodiversity, functional, taxonomic and phylogenetic, and evaluate which of these captures the most variation in biodiversity space. To address this question we used data from stream fish communities of the Ivinhema River Basin in Brazil. We found that functional and phylogenetic dimensions are more important than the taxonomic dimension (represented by richness) in capturing variation in the biodiversity space formed by these three dimensions together. Furthermore, the IVs of these three dimensions were similar along an altitudinal gradient, indicating similar contributions by a given dimension in different environmental conditions. We highlight the importance of adopting a multidimensional approach when describing biodiversity, since richness (the proxy for taxonomic dimension), despite being the most commonly used, is an incomplete surrogate to capture the variation present in the biodiversity space of stream fish communities.

Riding the Fifth Wave: Organizational Justice as Dependent Variable

This chapter calls attention to a paradigmatic shift in the organizational justice literature, in which fairness serves as the dependent rather than independent variable. Drawing on two taxonomic dimensions, we structure approaches to studying fairness as a consequence rather than as a cause. One dimension refers to the focal party whose reactions are being examined (the actor, the recipient, and the observer) whereas the other consists of the nature of the reaction itself (behavior, desire, and perception). We sample selectively from the nine cells emanating from the 3×3 classification scheme, emphasizing conceptual and empirical works that advance our understanding of fairness or connect fairness with other literatures in organizational and social psychology, such as ethics, social hierarchy, trust, self-handicapping, and construal level theory. Thus, we illustrate how the study of fairness as a dependent variable enriches not only theory and research in organizational justice, but also how it may contribute to other literatures. Additionally, we consider some of the practical implications and future research possibilities related to studying fairness as a dependent variable.