Elevational patterns and hierarchical determinants of biodiversity across microbial taxonomic scales

Microbial biogeography is gaining increasing attention due to recent molecular methodological advance. However, the diversity patterns and their environmental determinants across taxonomic scales are still poorly studied. By sampling along an extensive elevational gradient in subarctic ponds of Finland and Norway, we examined the diversity patterns of aquatic bacteria and fungi from whole community to individual taxa across taxonomic coverage and taxonomic resolutions. We further quantified cross‐phylum congruence in multiple biodiversity metrics and evaluated the relative importance of climate, catchment and local pond variables as the hierarchical drivers of biodiversity across taxonomic scales. Bacterial community showed significantly decreasing elevational patterns in species richness and evenness, and U‐shaped patterns in local contribution to beta diversity (LCBD). Conversely, no significant species richness and evenness patterns were found for fungal community. Elevational patterns in species richness and LCBD, but not in evenness, were congruent across bacterial phyla. When narrowing down the taxonomic scope towards higher resolutions, bacterial diversity showed weaker and more complex elevational patterns. Taxonomic downscaling also indicated a notable change in the relative importance of biodiversity determinants with stronger local environmental filtering, but decreased importance of climatic variables. This suggested that niche conservatism of temperature preference was phylogenetically deeper than that of water chemistry variables. Our results provide novel perspectives for microbial biogeography and highlight the importance of taxonomic scale dependency and hierarchical drivers when modelling biodiversity and species distribution responses to future climatic scenarios.     

A national approach for mapping and quantifying habitat-based biodiversity metrics across multiple spatial scales

Ecosystem services, i.e., services provided to humans from ecological systems have become a key issue of this century in resource management, conservation planning, and environmental decision analysis. Mapping and quantifying ecosystem services have become strategic national interests for integrating ecology with economics to help understand the effects of human policies and actions and their subsequent impacts on both ecosystem function and human well-being. Some aspects of biodiversity are valued by humans in varied ways, and thus are important to include in any assessment that seeks to identify and quantify the benefits of ecosystems to humans. Some biodiversity metrics clearly reflect ecosystem services (e.g., abundance and diversity of harvestable species), whereas others may reflect indirect and difficult to quantify relationships to services (e.g., relevance of species diversity to ecosystem resilience, cultural value of native species). Wildlife habitat has been modeled at broad spatial scales and can be used to map a number of biodiversity metrics. In the present study, we present an approach that (1) identifies mappable biodiversity metrics that are related to ecosystem services or other stakeholder concerns, (2) maps these metrics throughout a large multi-state region, and (3) compares the metric values obtained for selected watersheds within the regional context. The broader focus is to design a flexible approach for mapping metrics to produce a national-scale product. We map 20 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for all vertebrate species except fish. Metrics include species richness for all vertebrates, specific taxon groups, harvestable species (i.e., upland game, waterfowl, furbearers, small game, and big game), threatened and endangered species, and state-designated species of greatest conservation need, and also a metric for ecosystem (i.e., land cover) diversity. The project is being conducted at multiple scales in a phased approach, starting with place-based studies, then multi-state regional areas, culminating into a national-level atlas. As an example of this incremental approach, we provide results for the southwestern United States (i.e., states of Arizona, New Mexico, Nevada, Utah, and Colorado) and portions of two watersheds within this region: the San Pedro River (Arizona) and Rio Grande River (New Mexico). Geographic patterns differed considerably among metrics across the southwestern study area, but metric values for the two watershed study areas were generally greater than those for the southwestern region as a whole.     

Ecological complexity for unifying ecological theory across scales: A field ecologist's perspective

The evening session in ecological complexity at the last Joint Meeting of the International Association for Ecology (INTECOL) and the Ecological Society of America (ESA) held in Montreal was an occasion to evaluate the pertinence and upcoming challenges of the complex systems approach (CSA) applied to ecology. Through concepts such as the interaction topology among biological objects, the phenotypic integration of individual traits, the meaning of biological objects and complexity measures in space and time, the management of human dominated ecosystems, and non-equilibrium thermodynamics as a paradigm for the development of ecosystems, the panel members covered some of the most active areas of research in ecological complexity. However, for many ecologists, and particularly field ecologists, a comprehensive framework clearly emphasizing how and why the CSA provides a unique corpus for studying ecosystem functions is missing. The purpose of this article is thus to provide an overview of the different themes visited during the evening session and to emphasize the distinctiveness of the CSA as an alternative to contemporary ecological issues. Examples from functional ecology and food webs are given to support the discussion.     

Experimental-neuromodeling framework for understanding auditory object processing: integrating data across multiple scales.

In this article, we review a combined experimental-neuromodeling framework for understanding brain function with a specific application to auditory object processing. Within this framework, a model is constructed using the best available experimental data and is used to make predictions. The predictions are verified by conducting specific or directed experiments and the resulting data are matched with the simulated data. The model is refined or tested on new data and generates new predictions. The predictions in turn lead to better-focused experiments. The auditory object processing model was constructed using available neurophysiological and neuroanatomical data from mammalian studies of auditory object processing in the cortex. Auditory objects are brief sounds such as syllables, words, melodic fragments, etc. The model can simultaneously simulate neuronal activity at a columnar level and neuroimaging activity at a systems level while processing frequency-modulated tones in a delayed-match-to-sample task. The simulated neuroimaging activity was quantitatively matched with neuroimaging data obtained from experiments; both the simulations and the experiments used similar tasks, sounds, and other experimental parameters. We then used the model to investigate the neural bases of the auditory continuity illusion, a type of perceptual grouping phenomenon, without changing any of its parameters. Perceptual grouping enables the auditory system to integrate brief, disparate sounds into cohesive perceptual units. The neural mechanisms underlying auditory continuity illusion have not been studied extensively with conventional neuroimaging or electrophysiological techniques. Our modeling results agree with behavioral studies in humans and an electrophysiological study in cats. The results predict a particular set of bottom-up cortical processing mechanisms that implement perceptual grouping, and also attest to the robustness of our model.     

A unifying framework for metapopulation dynamics

Many biologically important processes, such as genetic differentiation, the spread of disease, and population stability, are affected by the (natural or enforced) subdivision of populations into networks of smaller, partly isolated, subunits. Such "metapopulations" can have extremely complex dynamics. We present a new general model that uses only two functions to capture, at the metapopulation scale, the main behavior of metapopulations. We show how complex, structured metapopulation models can be translated into our generalized framework. The metapopulation dynamics arising from some important biological processes are illustrated: the rescue effect, the Allee effect, and what we term the "antirescue effect." The antirescue effect captures instances where high migration rates are deleterious to population persistence, a phenomenon that has been largely ignored in metapopulation conservation theory. Management regimes that ignore a significant antirescue effect will be inadequate and may actually increase extinction risk. Further, consequences of territoriality and conspecific attraction on metapopulation-level dynamics are investigated. The new, simplified framework can incorporate knowledge from epidemiology, genetics, and population biology in a phenomenological way. It opens up new possibilities to identify and analyze the factors that are important for the evolution and persistence of the many spatially subdivided species.     

A unifying gravity framework for dispersal

Theoretical Ecology - Most organisms disperse at some life-history stage, but different research traditions to study dispersal have evolved in botany, zoology, and epidemiology. In this paper, we...     

A comparison of the strength of biodiversity effects across multiple functions

In order to predict which ecosystem functions are most at risk from biodiversity loss, meta-analyses have generalised results from biodiversity experiments over different sites and ecosystem types. In contrast, comparing the strength of biodiversity effects across a large number of ecosystem processes measured in a single experiment permits more direct comparisons. Here, we present an analysis of 418 separate measures of 38 ecosystem processes. Overall, 45 % of processes were significantly affected by plant species richness, suggesting that, while diversity affects a large number of processes not all respond to biodiversity. We therefore compared the strength of plant diversity effects between different categories of ecosystem processes, grouping processes according to the year of measurement, their biogeochemical cycle, trophic level and compartment (above- or belowground) and according to whether they were measures of biodiversity or other ecosystem processes, biotic or abiotic and static or dynamic. Overall, and for several individual processes, we found that biodiversity effects became stronger over time. Measures of the carbon cycle were also affected more strongly by plant species richness than were the measures associated with the nitrogen cycle. Further, we found greater plant species richness effects on measures of biodiversity than on other processes. The differential effects of plant diversity on the various types of ecosystem processes indicate that future research and political effort should shift from a general debate about whether biodiversity loss impairs ecosystem functions to focussing on the specific functions of interest and ways to preserve them individually or in combination.     

Regional management units for marine turtles: a novel framework for prioritizing conservation and research across multiple scales

Background

Resolving threats to widely distributed marine megafauna requires definition of the geographic distributions of both the threats as well as the population unit(s) of interest. In turn, because individual threats can operate on varying spatial scales, their impacts can affect different segments of a population of the same species. Therefore, integration of multiple tools and techniques — including site-based monitoring, genetic analyses, mark-recapture studies and telemetry — can facilitate robust definitions of population segments at multiple biological and spatial scales to address different management and research challenges.

Methodology/Principal Findings

To address these issues for marine turtles, we collated all available studies on marine turtle biogeography, including nesting sites, population abundances and trends, population genetics, and satellite telemetry. We georeferenced this information to generate separate layers for nesting sites, genetic stocks, and core distributions of population segments of all marine turtle species. We then spatially integrated this information from fine- to coarse-spatial scales to develop nested envelope models, or Regional Management Units (RMUs), for marine turtles globally.

Conclusions/Significance

The RMU framework is a solution to the challenge of how to organize marine turtles into units of protection above the level of nesting populations, but below the level of species, within regional entities that might be on independent evolutionary trajectories. Among many potential applications, RMUs provide a framework for identifying data gaps, assessing high diversity areas for multiple species and genetic stocks, and evaluating conservation status of marine turtles. Furthermore, RMUs allow for identification of geographic barriers to gene flow, and can provide valuable guidance to marine spatial planning initiatives that integrate spatial distributions of protected species and human activities. In addition, the RMU framework — including maps and supporting metadata — will be an iterative, user-driven tool made publicly available in an online application for comments, improvements, download and analysis.     

Consensus framework for exploring microarray data using multiple clustering methods

The large variety of clustering algorithms and their variants can be daunting to researchers wishing to explore patterns within their microarray datasets. Furthermore, each clustering method has distinct biases in finding patterns within the data, and clusterings may not be reproducible across different algorithms. A consensus approach utilizing multiple algorithms can show where the various methods agree and expose robust patterns within the data. In this paper, we present a software package - Consense, written for R/Bioconductor - that utilizes such an approach to explore microarray datasets. Consense produces clustering results for each of the clustering methods and produces a report of metrics comparing the individual clusterings. A feature of Consense is identification of genes that cluster consistently with an index gene across methods. Utilizing simulated microarray data, sensitivity of the metrics to the biases of the different clustering algorithms is explored. The framework is easily extensible, allowing this tool to be used by other functional genomic data types, as well as other high-throughput OMICS data types generated from metabolomic and proteomic experiments. It also provides a flexible environment to benchmark new clustering algorithms. Consense is currently available as an installable R/Bioconductor package (http://www.ohsucancer.com/isrdev/consense/).     

A combined field survey and molecular identification protocol for comparing forest arthropod biodiversity across spatial scales

Obtaining fundamental biodiversity metrics such as alpha, beta and gamma diversity for arthropods is often complicated by a lack of prior taxonomic information and/or taxonomic expertise, which can result in unreliable morphologically based estimates. We provide a set of standardized ecological and molecular sampling protocols that can be employed by researchers whose taxonomic skills may be limited, and where there may be a lack of robust a priori information regarding the regional pool of species. These protocols combine mass sampling of arthropods, classification of samples into parataxonomic units (PUs) and selective sampling of individuals for mtDNA sequencing to infer biological species. We sampled ten lowland rainforest plots located on the volcanic oceanic island of Réunion (Mascarene archipelago) for spiders, a group with limited taxonomic and distributional data for this region. We classified adults and juveniles into PUs and then demonstrated the reconciliation of these units with presumed biological species using mtDNA sequence data, ecological data and distributional data. Because our species assignment protocol is not reliant upon prior taxonomic information, or taxonomic expertise, it minimizes the problem of the Linnean shortfall to yield diversity estimates that can be directly compared across independent studies. Field sampling can be extended to other arthropod groups and habitats by adapting our field sampling protocol accordingly.     

A unifying probabilistic framework for analyzing residual dipolar couplings

Residual dipolar couplings provide complementary information to the nuclear Overhauser effect measurements that are traditionally used in biomolecular structure determination by NMR. In a de novo structure determination, however, lack of knowledge about the degree and orientation of molecular alignment complicates the analysis of dipolar coupling data. We present a probabilistic framework for analyzing residual dipolar couplings and demonstrate that it is possible to estimate the atomic coordinates, the complete molecular alignment tensor, and the error of the couplings simultaneously. As a by-product, we also obtain estimates of the uncertainty in the coordinates and the alignment tensor. We show that our approach encompasses existing methods for determining the alignment tensor as special cases, including least squares estimation, histogram fitting, and elimination of an explicit alignment tensor in the restraint energy.     

A unifying explanation for diverse metabolic scaling in animals and plants

The scaling of metabolic rate with body mass has long been a controversial topic. Some workers have claimed that the slope of log-log metabolic scaling relationships typically obeys a universal 3/4-power law resulting from the geometry of resource-transport networks. Others have attempted to explain the broad diversity of metabolic scaling relationships. Although several potentially useful models have been proposed, at present none successfully predicts the entire range of scaling relationships seen among both physiological states and taxonomic groups of animals and plants. Here I argue that our understanding may be aided by three shifts in focus: from explaining average tendencies to explaining variation between extreme boundary limits, from explaining the slope and elevation (metabolic level) of scaling relationships separately to showing how and why they are interrelated, and from focusing primarily on internal factors (e.g. body design) to a more balanced consideration of both internal and external (ecological) factors. By incorporating all of these shifts in focus, the recently proposed metabolic-level boundaries hypothesis appears to provide a useful way of explaining both taxonomic and physiological variation in metabolic scaling relationships. This hypothesis correctly predicts that the scaling slope should vary mostly between 2/3 and 1 and that it should be related to metabolic (activity) level according to an approximately U-shaped function. It also implies that the scaling of other energy-dependent biological processes should be related to the metabolic level of the organisms being examined. Some data are presented that support this implication, but further research is needed.     

Planning connectivity at multiple scales for large mammals in a human-dominated biodiversity hotspot

Connectivity for large mammals across human-altered landscapes results from movement by individuals that can be described via nested spatial scales as linkages (or zones or areas) with compatible land use types, constrictions that repeatedly funnel movement (as corridors) or impede it (as barriers), and the specific paths (or routes) across completely anthropogenic features (such as highways). Mitigation to facilitate animal movement through such landscapes requires similar attention to spatial scale, particularly when they involve complex topography, diverse types of human land use, and transportation infrastructure. We modeled connectivity for Asian elephant (Elephas maximus) and gaur (Bos gaurus) in the Shencottah Gap, a multiple-use region separating two tiger reserves in the Western Ghats, India. Using 840 km of surveys for animal signs within a region of 621 km², we modeled landscape linkages via resource selection functions integrated across two spatial resolutions, and then potential dispersal corridors within these linkages using circuit theoretical models. Within these corridors, we further identified potential small-scale movement paths across a busy transportation route via least-cost paths and evaluated their viability. Both elephants and gaur avoided human-dominated habitat, resulting in broken connectivity across the Shencottah Gap. Predicted corridor locations were sensitive to analysis resolution, and corridors derived from scale-integrated habitat models correlated best with habitat quality. Less than 1% of elephant and gaur detections occurred in habitat that was poorer in quality than the lowest-quality component of the movement path across the transportation route, suggesting that connectivity will require habitat improvement. Only 28% of dispersal corridor area and 5% of movement path length overlapped with the upper 50% quantile of the landscape linkage; thus, jointly modeling these three components enabled a more nuanced evaluation of connectivity than any of them in isolation.     

Addressable self-immobilization of lactate dehydrogenase across multiple length scales

Successful nanobiotechnology implementation largely depends on control over the interfaces between inorganic materials and biological molecules. Controlling the orientations of biomolecules and their spatial arrangements on the surface may transform many technologies including sensors, to energy. Here, we demonstrate the self-organization of L -lactate dehydrogenase (LDH), which exhibits enhanced enzymatic activity and stability on a variety of gold surfaces ranging from nanoparticles to electrodes, by incorporating a gold-binding peptide tag (AuBP2) as the fusion partner for Bacillus stearothermophilus LDH (bsLDH). Binding kinetics and enzymatic assays verified orientation control of the enzyme on the gold surface through the genetically incorporated peptide tag. Finally, redox catalysis efficiency of the immobilized enzyme was detected using cyclic voltammetry analysis in enzyme-based biosensors for lactate detection as well as in biofuel cell energy systems as the anodic counterpart. Our results demonstrate that the LDH enzyme can be self-immobilized onto different gold substrates using the short peptide tag under a biologically friendly environment. Depending on the desired inorganic surface, the proposed peptide-mediated path could be extended to any surface to achieve single-step oriented enzyme immobilization for a wide range of applications.     

The multiple decrement life table: a unifying framework for cause-of-death analysis in ecology

Summary The multiple decrement life table is used widely in the human actuarial literature and provides statistical expressions for mortality in three different forms: i) the life table from all causes-of-death combined; ii) the life table disaggregated into selected cause-of-death categories; and iii) the life table with particular causes and combinations of causes eliminated. The purpose of this paper is to introduce the multiple decrement life table to the ecological literature by applying the methods to published death-by-cause information on Rhagoletis pomonella. Interrelations between the current approach and conventional tools used in basic and applied ecology are discussed including the conventional life table, Key Factor Analysis and Abbott's Correction used in toxicological bioassay.     

A unifying computational framework for motor control and social interaction

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.