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Bacterial swarming is a collective mode of motion in which cells migrate rapidly over surfaces, forming dynamic patterns of whirls and jets. This review presents a physical point of view of swarming bacteria, with an emphasis on the statistical properties of the swarm dynamics as observed in experiments. The basic physical principles underlying the swarm and their relation to contemporary theories of collective motion and active matter are reviewed and discussed in the context of the biological properties of swarming cells. We suggest a paradigm according to which bacteria have optimized some of their physical properties as a strategy for rapid surface translocation. In other words, cells take advantage of favorable physics, enabling efficient expansion that enhances survival under harsh conditions.

Animal tracking and biologging devices record large amounts of data on individual movement behaviors in natural environments. In these data, movement ecologists often view unexplained variation around the mean as \"noise\"when studying patterns at the population level. In the field of behavioral ecology, however, focus has shifted from population means to the biological underpinnings of variation around means. Specifically, behavioral ecologists use repeated measures of individual behavior to partition behavioral variability into intrinsic among-individual variation and reversible behavioral plasticity and to quantify: a) individual variation in behavioral types (i.e. different average behavioral expression), b) individual variation in behavioral plasticity (i.e. different responsiveness of individuals to environmental gradients), c) individual variation in behavioral predictability (i.e. different residual within-individual variability of behavior around the mean), and d) correlations among these components and correlations in suites of behaviors, called 'behavioral syndromes'. We here suggest that partitioning behavioral variability in animal movements will further the integration of movement ecology with other fields of behavioral ecology. We provide a literature review illustrating that individual differences in movement behaviors are insightful for wildlife and conservation studies and give recommendations regarding the data required for addressing such questions. In the accompanying R tutorial we provide a guide to the statistical approaches quantifying the different aspects of among-individual variation. We use movement data from 35 African elephants and show that elephants differ in a) their average behavior for three common movement behaviors, b) the rate at which they adjusted movement over a temporal gradient, and c) their behavioral predictability (ranging from more to less predictable individuals). Finally, two of the three movement behaviors were correlated into a behavioral syndrome (d), with farther moving individuals having shorter mean residence times. Though not explicitly tested here, individual differences in movement and predictability can affect an individual's risk to be hunted or poached and could therefore open new avenues for conservation biologists to assess population viability. We hope that this review, tutorial, and worked example will encourage movement ecologists to examine the biology of individual variation in animal movements hidden behind the population mean.

Phenology is acknowledged as one of the important traits that contribute significantly to the success of alien species. Differences in the life history traits related to invasive and native vegetation have been shown to influence the success and extent of the plant invasion. Therefore, to obtain this information, we documented and contrasted the duration of key phenological events viz., vegetative, reproductive and senescence between an invasive alien plant species Ageratina adenophora (Sprengel) King and Robinson and associated 29 natives in different habitats viz., Oak, Pine and Cypress forests and grassland. The results indicated vegetative phase and senescence tends to be more synchronised between species than in reproduction. Distinct periodicity in the reproductive phases between A. adenophora and the associated species was observed, with A. adenophora flowering earlier than most of the other species in all habitats. The reproductive cycle of A. adenophora was over before the onset of rainy season which enabled dispersed seeds to optimize the growing condition provided by the rain, giving head start over associated species. Senescence phases in A. adenophora was longest compared to associated species. The observation made in the study may assist in understanding the phenological behavior of A. adenophora and associated species, and may develop a guideline for future mechanistic management of A. adenophora .

Animal movement comes in a variety of ‘types’ including small foraging movements, larger one-way dispersive movements, seasonally-predictable round-trip migratory movements, and erratic nomadic movements. Although most individuals move at some point throughout their lives, movement patterns can vary widely across individuals within the same species: differing within an individual over time (intra-individual), among individuals in the same population (inter-individual), or among populations (inter-population). Yet, studies of movement (theoretical and empirical alike) more often focus on understanding ‘typical’ movement patterns than understanding variation in movement. Here, I synthesize current knowledge of movement variation (drawing parallels across species and movement types), describing the causes (what factors contribute to individual variation), patterns (what movement variation looks like), consequences (why variation matters), maintenance (why variation persists), implications (for management and conservation), and finally gaps (what pieces we are currently missing). By synthesizing across scales of variation, I span across work on plasticity, personality, and geographic variation. Individual movement can be driven by factors that act at the individual, population, community and ecosystem level and have ramifications at each of these levels. Generally the consequences of movement are less well understood than the causes, in part because the effects of movement variation are often nested, with variation manifesting at the population level, which in turn affects communities and ecosystems. Understanding both cause and consequence is particularly important for predicting when variation begets variation in a positive feedback loop, versus when a negative feedback causes variation to be dampened successively. Finally, maintaining standing variation in movement may be important for facilitating species’ ability to respond to future environmental change.

Acoustic telemetry has helped overcome many of the challenges faced when studying the movement ecology of aquatic species, allowing to obtain unprecedented amounts of data. This has made it into one of the most widely used methods nowadays. Many ways to analyse acoustic telemetry data have been made available and deciding on how to analyse the data requires considering the type of research objectives, relevant properties of the data (e.g., resolution, study design, equipment), habits of the study species, researcher experience, among others. To ease this decision process, here we showcase (1) some of the methods used to estimate pseudo-positions and positions from raw acoustic telemetry data, (2) methods to estimate residency and (3) methods to estimate two-dimensional home and occurrence range using geometric or hull-based methods and density-distribution methods, a network-based approach, and three-dimensional methods. We provide examples of some of these were tested using a sample of real data. With this we intend to provide the necessary background for the selection of the method(s) that better fit specific research objectives when using acoustic telemetry.

Principal component analysis (PCA) is a standard technique to summarize the main structures of a data table containing the measurements of several quantitative variables for a number of individuals. Here, we study the case where some of the data values are missing and propose a review of methods which accommodate PCA to missing data. In plant ecology, this statistical challenge relates to the current effort to compile global plant functional trait databases producing matrices with a large amount of missing values. We present several techniques to consider or estimate (impute) missing values in PCA and compare them using theoretical considerations. We carried out a simulation study to evaluate the relative merits of the different approaches in various situations (correlation structure, number of variables and individuals, and percentage of missing values) and also applied them on a real data set. Lastly, we discuss the advantages and drawbacks of these approaches, the potential pitfalls and future challenges that need to be addressed in the future.

Global climate models predict increases in the frequency and severity of drought worldwide, directly affecting most ecosystem types. Consequently, drought legacy effects (drought-induced alterations in ecosystem function postdrought) are expected to become more common in ecosystems varying from deserts to grasslands to forests. Drought legacies in grasslands are usually negative and reduce ecosystem function, particularly after extended drought. Moreover, ecosystems that respond strongly to drought (high sensitivity) might be expected to exhibit the largest legacy effects the next year, but this relationship has not been established. We quantified legacy effects of a severe regional drought in 2012 on postdrought (2013) aboveground net primary productivity (ANPP) in six central US grasslands. We predicted that (1) the magnitude of drought legacy effects measured in 2013 would be positively related to the sensitivity of ANPP to the 2012 drought, and (2) drought legacy effects would be negative (reducing 2013 ANPP relative to that expected given normal precipitation amounts). The magnitude of legacy effects measured in 2013 was strongly related (r² = 0.88) to the sensitivity of ANPP to the 2012 drought across these six grasslands. However, contrary to expectations, positive legacy effects (greater than expected ANPP) were more commonly observed than negative legacy effects. Thus, while the sensitivity of ANPP to drought may be a useful predictor of the magnitude of legacy effects, short-term (1-year) severe droughts may cause legacy effects that are more variable than those observed after multiyear droughts.