The potential use of birds as bioindicators of suspended atmospheric microplastics and artificial fibers

Microplastics (MPs) and artificial fibers (AFs) have been detected suspended tens of meters above ground level in the atmosphere, yet empirical data on them remain scarce. This study aimed to investigate the presence of MPs and AFs in the digestive and respiratory systems of two abundant bird species, the Common House Martin (Delichon urbicum) and the Common Swift (Apus apus), within the Community of Madrid, Spain. Given that these birds spend the majority of their lives airborne, engaging in activities such as mating and sleeping during flight, the research sought to assess the potential of using these bird species as bioindicators for suspended atmospheric MPs and AFs. Samples were obtained from necropsies of birds (N = 24) collected primarily between spring and summer from 2021 to 2023. Only individuals that died within the initial 24-hour period and had not been fed were selected for examination to avoid contamination. MPs and AFS were identified by micro-FTIR, characterized and quantified. Results revealed that 75 % of the sampled birds exhibited at least one MPs in their respiratory and/or digestive system. All identified MPs were fibers, with polyester (PES) being the most predominant (48 %), followed by acrylic fibers (ACR; 28 %), and polyethylene (PE; 18 %). The average concentrations in the respiratory system were 1.12 ± 0.45 MPs/specimen and 2.78 ± 1.04 AFs/specimen for Common Swift and 0.75 ± 0.30 MPs/specimen and 0.75 ± 0.36 AFs/specimen for House Martin. In the digestive system, these were 1.92 ± 0.72 MPs/specimen and 3.42 ± 0.69 AFs/specimen for Common Swift, and 1.34 ± 0.50 MPs/specimen and 1.39 ± 0.47 AFs/specimen for House Martin. Birds collected areas with high population density located in the direction of the prevailing winds showed a concentration of MPs significantly higher in the digestive system. Taken together, these findings confirmed the potential use of these birds as bioindicators for monitoring of suspended atmospheric MPs and AFs.

Scaling approaches and macroecology provide a foundation for assessing ecological resilience in the Anthropocene

In the Anthropocene, intensifying ecological disturbances pose significant challenges to our predictive capabilities for ecosystem responses. Macroecology-which focuses on emergent statistical patterns in ecological systems-unveils consistent regularities in the organization of biodiversity and ecosystems. These regularities appear in terms of abundance, body size, geographical range, species interaction networks, or the flux of matter and energy. This paper argues for moving beyond qualitative resilience metaphors, such as the 'ball and cup', towards a more quantitative macroecological framework. We suggest a conceptual and theoretical basis for ecological resilience that integrates macroecology with a stochastic diffusion approximation constrained by principles of biological symmetry. This approach provides an alternative novel framework for studying ecological resilience in the Anthropocene. We demonstrate how our framework can effectively quantify the impacts of major disturbances and their extensive ecological ramifications. We further show how biological scaling insights can help quantify the consequences of major disturbances, emphasizing their cascading ecological impacts. The nature of these impacts prompts a re-evaluation of our understanding of resilience. Emphasis on regularities of ecological assemblages can help illuminate resilience dynamics and offer a novel basis to predict and manage the impacts of disturbance in the Anthropocene more efficiently. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Exploring the interplay between small and large scales movements in a neotropical small mammal

We record and analyze the movement patterns of the marsupial Didelphis aurita at different temporal scales. Animals trajectories are collected at a daily scale by using spool-and-line techniques and, with the help of radio-tracking devices, animals traveled distances are estimated at intervals of weeks. Small-scale movements are well described by truncated Lévy flight, while large-scale movements produce a distribution of distances which is compatible with a Brownian motion. A model of the movement behavior of these animals, based on a truncated Lévy flight calibrated on the small scale data, converges towards a Brownian behavior after a short time interval of the order of 1 week. These results show that whether Lévy flight or Brownian motion behaviors apply, will depend on the scale of aggregation of the animals paths. In this specific case, as the effect of the rude truncation present in the daily data generates a fast convergence towards Brownian behaviors, Lévy flights become of scarce interest for describing the local dispersion properties of these animals, which result well approximated by a normal diffusion process and not a fast, anomalous one. Interestingly, we are able to describe two movement phases as the consequence of a statistical effect generated by aggregation, without the necessity of introducing ecological constraints or mechanisms operating at different spatio-temporal scales. This result is of general interest, as it can be a key element for describing movement phenomenology at distinct spatio-temporal scales across different taxa and in a variety of systems.

Maximum likelihood outperforms binning methods for detecting differences in abundance size spectra across environmental gradients

Individual body size distributions (ISD) within communities are remarkably consistent across habitats and spatiotemporal scales and can be represented by size spectra, which are described by a power law. The focus of size spectra analysis is to estimate the exponent (λ ) of the power law. A common application of size spectra studies is to detect anthropogenic pressures. Many methods have been proposed for estimatingλ most of which involve binning the data, counting the abundance within bins, and then fitting an ordinary least squares regression in log-log space. However, recent work has shown that binning procedures return biased estimates ofλ compared to procedures that directly estimateλ using maximum likelihood estimation (MLE). While it is clear that MLE produces less biased estimates of site-specific λ's, it is less clear how this bias affects the ability to test for changes in λ across space and time, a common question in the ecological literature. Here, we used simulation to compare the ability of two normalised binning methods (equal logarithmic and log2 bins) and MLE to (1) recapture known values ofλ , and (2) recapture parameters in a linear regression measuring the change inλ across a hypothetical environmental gradient. We also compared the methods using two previously published body size datasets across a natural temperature gradient and an anthropogenic pollution gradient. Maximum likelihood methods always performed better than common binning methods, which demonstrated consistent bias depending on the simulated values ofλ . This bias carried over to the regressions, which were more accurate whenλ was estimated using MLE compared to the binning procedures. Additionally, the variance in estimates using MLE methods is markedly reduced when compared to binning methods. The error induced by binning methods can be of similar magnitudes as the variation previously published in experimental and observational studies, bringing into question the effect sizes of previously published results. However, while the methods produced different regression slope estimates, they were in qualitative agreement on the sign of those slopes (i.e. all negative or all positive). Our results provide further support for the direct estimation ofλ and its relative variation across environmental gradients using MLE over the more common methods of binning.

Extent and risks of microplastic pollution in the Yangtze River. State of the science

The Yangtze River (YR) is the longest river in Asia and the third longest in the world, and is recognized as one of the most microplastic-polluted rivers globally. However, to date, no consistent and systematic risk assessment has been conducted for the YR basin or other rivers in China. Previous assessments of microplastic occurrence, distribution, or risks in the YR basin did not take into account the sometimes-limited quality of the data or compared incomparable data, which can lead to biased assessments. The actual prospective ecological risks of microplastic pollution in the YR basin are therefore unknown. In this review, we analyze data from 21 microplastic studies to assess the occurrence and distribution of microplastics and their characteristics. We also evaluate the quality of the studies using updated quality assurance/quality control (QA/QC) criteria. Although we observe an upward trend in QA/QC scores, indicating improved data quality, no study received positive scores on all critical criteria. The microplastic exposure data from the YR basin is among the highest reported for rivers and other aquatic systems worldwide, exceeding effect thresholds in some parts of the river. We recommend generating comprehensive monitoring data of higher quality, with a greater emphasis on smaller-size microplastics, so that future risk assessments can be performed with less reliance on data alignment.

Increased extinction probability of the Madden-Julian oscillation after about 27 days

The Madden-Julian oscillation (MJO) is a tropical weather system that has an important influence in the tropics and beyond; however, many of its characteristics are poorly understood, including their initiation and termination. Here we define Madden-Julian events as contiguous time periods with an active MJO, and we show that both the durations and the sizes of these events are well described by a double power-law distribution. Thus, small events have no characteristic scale, and the same for large events; nevertheless, both types of events are separated by a characteristic duration of about 27 days (this corresponds to half a cycle, roughly). Thus, after 27 days, there is a sharp increase in the probability that an event becomes extinct. We find that this effect is independent of the starting and ending phases of the events, which seems to point to an internal mechanism of exhaustion rather than to the effect of an external barrier. Our results would imply an important limitation of the MJO as a driver of subseasonal predictability.

Human pressures modulate climate-warming-induced changes in size spectra of stream fish communities

Climate warming can negatively affect the body size of ectothermic organisms and, based on known temperature-size rules, tends to benefit small-bodied organisms. Our understanding of the interactive effects of climate warming and other environmental factors on the temporal changes of body size structure is limited. We quantified the annual trends in size spectra of 583 stream fish communities sampled for more than 20 years across France. The results show that climate warming steepened the slope of the community size spectrum in streams with limited impacts from other human pressures. These changes were caused by increasing abundance of small-bodied individuals and decreasing abundance of large-bodied individuals. However, opposite effects of climate warming on the size spectrum slopes were observed in streams facing high levels of other human pressures. This demonstrates that the effects of temperature on body size structure can depend on other human pressures, disrupting the natural patterns of size spectra in wild communities with potentially strong implications for the fluxes of energy and nutrients in ecosystems.

Scaling of biological rates with body size as a backbone in the assembly of metacommunity biodiversity

The dispersal-body mass association has been highlighted as a main determinant of biodiversity patterns in metacommunities. However, less attention has been devoted to other well-recognized determinants of metacommunity diversity: the scaling in density and regional richness with body size. Among active dispersers, the increase in movement with body size may enhance local richness and decrease β-diversity. Nevertheless, the reduction of population size and regional richness with body mass may determine a negative diversity-body size association. Consequently, metacommunity assembly probably emerges from a balance between the effect of these scalings. We formalize this hypothesis by relating the exponents of size-scaling rules with simulated trends in α-, β- and γ-diversity with body size. Our results highlight that the diversity-body size relationship in metacommunities may be driven by the combined effect of different scaling rules. Given their ubiquity in most terrestrial and aquatic biotas, these scaling rules may represent the basic determinants-backbone-of biodiversity, over which other mechanisms operate determining metacommunity assembly. Further studies are needed, aimed at explaining biodiversity patterns from functional relationships between biological rates and body size, as well as their association with environmental conditions and species interactions.

Correlation dimension in empirical networks

Network correlation dimension governs the distribution of network distance in terms of a power-law model and profoundly impacts both structural properties and dynamical processes. We develop new maximum likelihood methods which allow us robustly and objectively to identify network correlation dimension and a bounded interval of distances over which the model faithfully represents structure. We also compare the traditional practice of estimating correlation dimension by modeling as a power law the fraction of nodes within a distance to a proposed alternative of modeling as a power law the fraction of nodes at a distance. In addition, we illustrate a likelihood ratio technique for comparing the correlation dimension and small-world descriptions of network structure. Improvements from our innovations are demonstrated on a diverse selection of synthetic and empirical networks. We show that the network correlation dimension model accurately captures empirical network structure over neighborhoods of substantial size and span and outperforms the alternative small-world network scaling model. Our improved methods tend to lead to higher estimates of network correlation dimension, implying that prior studies could have produced or utilized systematic underestimates of dimension.

A mixed distribution to fix the threshold for Peak-Over-Threshold wave height estimation

Modelling extreme values distributions, such as wave height time series where the higher waves are much less frequent than the lower ones, has been tackled from the point of view of the Peak-Over-Threshold (POT) methodologies, where modelling is based on those values higher than a threshold. This threshold is usually predefined by the user, while the rest of values are ignored. In this paper, we propose a new method to estimate the distribution of the complete time series, including both extreme and regular values. This methodology assumes that extreme values time series can be modelled by a normal distribution in a combination of a uniform one. The resulting theoretical distribution is then used to fix the threshold for the POT methodology. The methodology is tested in nine real-world time series collected in the Gulf of Alaska, Puerto Rico and Gibraltar (Spain), which are provided by the National Data Buoy Center (USA) and Puertos del Estado (Spain). By using the Kolmogorov-Smirnov statistical test, the results confirm that the time series can be modelled with this type of mixed distribution. Based on this, the return values and the confidence intervals for wave height in different periods of time are also calculated.

Patterns of species richness, abundance and individual-size distributions in native stream-fish assemblages invaded by exotic and translocated fishes

Predicting the impacts of species introductions long has attracted the attention of ecologists yet there still is limited insight into how impacts on native assemblages vary with the degree of shared evolutionary context. Here, we used data from 535 stream-fish surveys from 15 catchments in north-eastern Spain (99,700 km²) to explore whether the relative effects on native fishes differ between fish introductions from two different ecoregions (i.e., evolutionary contexts), namely, catchments within Iberian Peninsula (i.e., 'translocated species') and catchments beyond Iberian Peninsula (i.e., 'exotic fishes'). We used hierarchical Bayesian models to relate taxon richness, abundance, and the individual-size distributions (ISDs) of native fishes to the presence, abundance, and weighted trophic level (TL) of translocated and exotic fishes, conditional on geographic and habitat covariates. Environmental covariates dominated the percentage of explained variance (≥ 65%) for all responses. Translocated fishes accounted for more of the explained variance than did exotic fishes for ISDs and abundance, but not for native fish species richness. The presence of translocated fishes was associated with lower abundance and richness of native fishes, with individuals being smaller in the presence of translocated fishes of higher TL. The presence of exotic fishes was associated with a greater abundance and richness of native fishes, with individuals generally being larger in the presence of exotic fishes. Our study suggests that translocated fishes could be as problematic as exotic fishes when angling and water transfers among catchments to deal with climate change may increase the establishment of translocated fishes. We also discuss the difficulties of using fish body size as species-blind, transferable assemblage-level trait in fish monitoring.

Trophic Transfer Efficiency in Lakes

Trophic transfer efficiency (TTE) is usually calculated as the ratio of production rates between two consecutive trophic levels. Although seemingly simple, TTE estimates from lakes are rare. In our review, we explore the processes and structures that must be understood for a proper lake TTE estimate. We briefly discuss measurements of production rates and trophic positions and mention how ecological efficiencies, nutrients (N, P) and other compounds (fatty acids) affect energy transfer between trophic levels and hence TTE. Furthermore, we elucidate how TTE estimates are linked with size-based approaches according to the Metabolic Theory of Ecology, and how food-web models can be applied to study TTE in lakes. Subsequently, we explore temporal and spatial heterogeneity of production and TTE in lakes, with a particular focus on the links between benthic and pelagic habitats and between the lake and the terrestrial environment. We provide an overview of TTE estimates from lakes found in the published literature. Finally, we present two alternative approaches to estimating TTE. First, TTE can be seen as a mechanistic quantity informing about the energy and matter flow between producer and consumer groups. This approach is informative with respect to food-web structure, but requires enormous amounts of data. The greatest uncertainty comes from the proper consideration of basal production to estimate TTE of omnivorous organisms. An alternative approach is estimating food-chain and food-web efficiencies, by comparing the heterotrophic production of single consumer levels or the total sum of all heterotrophic production including that of heterotrophic bacteria to the total sum of primary production. We close the review by pointing to a few research questions that would benefit from more frequent and standardized estimates of TTE in lakes.

Can size distributions of European lake fish communities be predicted by trophic positions of their fish species?

An organism's body size plays an important role in ecological interactions such as predator-prey relationships. As predators are typically larger than their prey, this often leads to a strong positive relationship between body size and trophic position in aquatic ecosystems. The distribution of body sizes in a community can thus be an indicator of the strengths of predator-prey interactions. The aim of this study was to gain more insight into the relationship between fish body size distribution and trophic position in a wide range of European lakes. We used quantile regression to examine the relationship between fish species' trophic position and their log-transformed maximum body mass for 48 fish species found in 235 European lakes. Subsequently, we examined whether the slopes of the continuous community size distributions, estimated by maximum likelihood, were predicted by trophic position, predator-prey mass ratio (PPMR), or abundance (number per unit effort) of fish communities in these lakes. We found a positive linear relationship between species' maximum body mass and average trophic position in fishes only for the 75% quantile, contrasting our expectation that species' trophic position systematically increases with maximum body mass for fish species in European lakes. Consequently, the size spectrum slope was not related to the average community trophic position, but there were negative effects of community PPMR and total fish abundance on the size spectrum slope. We conclude that predator-prey interactions likely do not contribute strongly to shaping community size distributions in these lakes.

Toward a Unified Analysis of the Brain Criticality Hypothesis: Reviewing Several Available Tools

The study of the brain criticality hypothesis has been going on for about 20 years, various models and methods have been developed for probing this field, together with large amounts of controversial experimental findings. However, no standardized protocol of analysis has been established so far. Therefore, hoping to make some contributions to standardization of such analysis, we review several available tools used for estimating the criticality of the brain in this paper.

Search Foraging Strategies of Migratory Raptors Under Different Environmental Conditions

Several studies have shown in different organisms how their movements can be fitted to different patterns to optimize search of food resources. According to abundance and availability of resources, different strategies will be optimal, such as Lévy and Brownian random search. We analyze the movement patterns of four species of migratory raptors with different degrees of ecological specialization in diet during the breeding and wintering periods to evaluate the differences according to species and season: the Egyptian Vulture, the Short-toed Snake Eagle, the Booted Eagle, and the Red Kite. From GPS locations, we obtained a set of segments and lengths that were analyzed to verify their fitting to the functions of Lévy and Brownian strategies. Egyptian Vulture’s trajectories fitted to both patterns during the breeding period, whereas during the wintering period most trajectories fitted a Brownian pattern. In the case of the Short-toed Eagle, fit was greater to a Lévy strategy throughout the year, while Booted Eagles and Red Kites exhibited a combination of search patterns. These differences could be accounted for different feeding strategies and environmental context over the annual cycle. In species with a specialized diet (i.e., Short-toed Eagle) the Lévy pattern would maximize the encounters with scarce and unpredictable resources, whereas for species with a broad trophic niche (i.e., Booted Eagle and Red Kite), movements could be adapted to exploit different resources according to their abundance. Scavengers like the Egyptian Vulture shift also between search strategies according to the distribution of carrion. Therefore, the analysis of food search patterns can be used as an indirect indicator to track changes in food availability across a broad range of environmental conditions. This is particularly important under the current context of global change which is largely expected to affect migratory species that spend their vital cycle in distant areas.

Modeling Professional Rugby Union Peak Intensity-Duration Relationships Using a Power Law

PURPOSE

Can power law models accurately predict the peak intensities of rugby competition as a function of time?

METHODS

Match movement data were collected from 30 elite and 30 subelite rugby union athletes across competitive seasons, using wearable Global Navigation Satellite Systems and accelerometers. Each athlete's peak rolling mean value of each measure (mean speed, metabolic power, and PlayerLoad™) for 8 durations between 5 seconds and 10 minutes was predicted by the duration with 4 power law (log-log) models, one for forwards and backs in each half of a typical match.

RESULTS

The log of peak exercise intensity and exercise duration (5-600 s) displayed strong linear relationships (R2 = .967-.993) across all 3 measures. Rugby backs had greater predicted intensities for shorter durations than forwards, but their intensities declined at a steeper rate as duration increased. Random prediction errors for mean speed, metabolic power, and PlayerLoad were 5% to 6%, 7% to 9%, and 8% to 10% (moderate to large), respectively, for elite players. Systematic prediction errors across the range of durations were trivial to small for elite players, underestimating intensities for shorter (5-10 s) and longer (300-600 s) durations by 2% to 4% and overestimating 20- to 120-second intensities by 2% to 3%. Random and systematic errors were slightly greater for subelites compared to elites, with ranges of 4% to 12% and 2% to 5%, respectively.

CONCLUSIONS

Peak intensities of professional rugby union matches can be predicted with adequate precision (trivial to small errors) for prescribing training drills of a given duration, irrespective of playing position, match half, level of competition, or measure of exercise intensity. However, practitioners should be aware of the substantial (moderate to large) prediction errors at the level of the individual player.

Lognormals, power laws and double power laws in the distribution of frequencies of harmonic codewords from classical music

Zipf's law is a paradigm describing the importance of different elements in communication systems, especially in linguistics. Despite the complexity of the hierarchical structure of language, music has in some sense an even more complex structure, due to its multidimensional character (melody, harmony, rhythm, timbre, etc.). Thus, the relevance of Zipf's law in music is still an open question. Using discrete codewords representing harmonic content obtained from a large-scale analysis of classical composers, we show that a nearly universal Zipf-like law holds at a qualitative level. However, in an in-depth quantitative analysis, where we introduce the double power-law distribution as a new player in the classical debate between the superiority of Zipf's (power) law and that of the lognormal distribution, we conclude not only that universality does not hold, but also that there is not a unique probability distribution that best describes the usage of the different codewords by each composer.

Individual size distributions across North American streams vary with local temperature

Parameters describing the negative relationship between abundance and body size within ecological communities provide a summary of many important biological processes. While it is considered to be one of the few consistent patterns in ecology, spatiotemporal variation of this relationship across continental scale temperature gradients is unknown. Using a database of stream communities collected across North America (18-68°N latitude, -4 to 25°C mean annual air temperature) over 3 years, we constructed 160 individual size distribution (ISD) relationships (i.e. abundance size spectra). The exponent parameter describing ISD's decreased (became steeper) with increasing mean annual temperature, with median slopes varying by ~0.2 units across the 29°C temperature gradient. In addition, total community biomass increased with increasing temperatures, contrary with theoretical predictions. Our study suggests conservation of ISD relationships in streams across broad natural environmental gradients. This supports the emerging use of size-spectra deviations as indicators of fundamental changes to the structure and function of ecological communities.

Deciphering the fingerprint of disturbance on the three-dimensional structure of the world's forests

Canopy gaps and the processes that generate them play an integral role in shaping the structure and dynamics of forests. However, it is only with recent advances in remote sensing technologies such as airborne laser scanning that studying canopy gaps at scale has become a reality. Consequently, we still lack an understanding of how the size distribution and spatial organization of canopy gaps varies among forests ecosystems, nor have we determined whether these emergent properties can be reconciled with existing theories of forest dynamics. Here, I outline a roadmap for integrating remote sensing with field data and individual-based models to build a comprehensive picture of how environmental constraints and disturbance regimes shape the three-dimensional structure of the world's forests.

Change of Acoustic Emission Characteristics during Temperature Induced Transition from Twinning to Dislocation Slip under Compression in Polycrystalline Sn

In this study, acoustic emission (AE) measurements on polycrystalline tin as a function of temperature at different driving rates under compression were carried out. It is shown that there is a definite difference between the acoustic emission characteristics belonging to twinning (low temperatures) as well as to dislocation slip (high temperatures). The stress averaged values of the exponents of the energy probability density functions decreased from ε = 1.45 ± 0.05 (-60 °C) to ε = 1.20 ± 0.15 (50 °C) at a driving rate of ε=0.15 s-1, and the total acoustic energy decreased by three orders of magnitude with increasing temperature. In addition, the exponent γ in the scaling relation S_AE~D_AE^γ (S_AE is the area and D_AE is the duration) also shows similar temperature dependence (changing from γ = 1.78 ± 0.08 to γ = 1.35 ± 0.05), illustrating that the avalanche statistics belong to two different microscopic deformation mechanisms. The power law scaling relations were also analyzed, taking into account that the detected signal is always the convolution of the source signal and the transfer function of the system. It was obtained that approximate values of the power exponents can be obtained from the parts of the above functions, belonging to large values of parameters. At short duration times, the attenuation effect of the AE detection system dominates the time dependence, from which the characteristic attenuation time, τ_a, was determined as τ_a ≅ 70 μs.

Characterizing the multidimensionality of microplastics across environmental compartments

Understanding the multidimensionality of microplastics is essential for a realistic assessment of the risks these particles pose to the environment and human health. Here, we capture size, shape, area, polymer, volume and mass characteristics of >60,000 individual microplastic particles as continuous distributions. Particles originate from samples taken from different aquatic compartments, including surface water and sediments from the marine and freshwater environment, waste water effluents, and freshwater organisms. Data were obtained using state-of-the-art FTIR-imaging, using the same automated imaging post-processing software. We introduce a workflow with two quality criteria that assure minimum data quality loss due to volumetric and filter area subsampling. We find that probability density functions (PDFs) for particle length follow power law distributions, with median slopes ranging from 2.2 for marine surface water to 3.1 for biota samples, and that these slopes were compartment-specific. Polymer-specific PDFs for particle length demonstrated significant differences in slopes among polymers, hinting at polymer specific sources, removal or fragmentation processes. Furthermore, we provide PDFs for particle width, width to length ratio, area, specific surface area, volume and mass distributions and propose how these can represent the full diversity of toxicologically relevant dose metrics required for the assessment of microplastic risks.

Lévy walk dynamics explain gamma burst patterns in primate cerebral cortex

Lévy walks describe patterns of intermittent motion with variable step sizes. In complex biological systems, Lévy walks (non-Brownian, superdiffusive random walks) are associated with behaviors such as search patterns of animals foraging for food. Here we show that Lévy walks also describe patterns of oscillatory activity in primate cerebral cortex. We used a combination of empirical observation and modeling to investigate high-frequency (gamma band) local field potential activity in visual motion-processing cortical area MT of marmoset monkeys. We found that gamma activity is organized as localized burst patterns that propagate across the cortical surface with Lévy walk dynamics. Lévy walks are fundamentally different from either global synchronization, or regular propagating waves, because they include large steps that enable activity patterns to move rapidly over cortical modules. The presence of Lévy walk dynamics therefore represents a previously undiscovered mode of brain activity, and implies a novel way for the cortex to compute. We apply a biophysically realistic circuit model to explain that the Lévy walk dynamics arise from critical-state transitions between asynchronous and localized propagating wave states, and that these dynamics yield optimal spatial sampling of the cortical sheet. We hypothesise that Lévy walk dynamics could help the cortex to efficiently process variable inputs, and to find links in patterns of activity among sparsely spiking populations of neurons.

Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control

Phylogenetic inference is useful in characterising HIV transmission networks and assessing where prevention is likely to have the greatest impact. However, estimating parameters that influence the network structure is still scarce, but important in evaluating determinants of HIV spread. We analyzed 2017 HIV pol sequences (728 Lake Victoria fisherfolk communities (FFCs), 592 female sex workers (FSWs) and 697 general population (GP)) to identify transmission networks on Maximum Likelihood (ML) phylogenetic trees and refined them using time-resolved phylogenies. Network generative models were fitted to the observed degree distributions and network parameters, and corrected Akaike Information Criteria and Bayesian Information Criteria values were estimated. 347 (17.2%) HIV sequences were linked on ML trees (maximum genetic distance ≤4.5%, ≥95% bootstrap support) and, of these, 303 (86.7%) that consisted of pure A1 (n = 168) and D (n = 135) subtypes were analyzed in BEAST v1.8.4. The majority of networks (at least 40%) were found at a time depth of ≤5 years. The waring and yule models fitted best networks of FFCs and FSWs respectively while the negative binomial model fitted best networks in the GP. The network structure in the HIV-hyperendemic FFCs is likely to be scale-free and shaped by preferential attachment, in contrast to the GP. The findings support the targeting of interventions for FFCs in a timely manner for effective epidemic control. Interventions ought to be tailored according to the dynamics of the HIV epidemic in the target population and understanding the network structure is critical in ensuring the success of HIV prevention programs.

Tail of the distribution of fatalities in epidemics

The final size reached by an epidemic, measured in terms of the total number of fatalities, is an extremely relevant quantity. It has been recently claimed that the size distribution of major epidemics in human history is "strongly fat-tailed," i.e., a power law asymptotically, which has important consequences for risk management. From the point of view of statistical physics and complex-systems modeling this is not an unexpected outcome, nevertheless, strong empirical evidence is also necessary to support such a claim. Reanalyzing previous data, we find that, although the fatality distribution may be compatible with a power-law tail, these results are not conclusive, and other distributions, not fat-tailed, could explain the data equally well. As an example, simulation of a log-normally distributed random variable provides synthetic data whose statistics are undistinguishable from the statistics of the empirical data. Theoretical reasons justifying a power-law tail as well as limitations in the current available data are also discussed.

Distinct flavors of Zipf's law and its maximum likelihood fitting: Rank-size and size-distribution representations

In recent years, researchers have realized the difficulties of fitting power-law distributions properly. These difficulties are higher in Zipfian systems, due to the discreteness of the variables and to the existence of two representations for these systems, i.e., two versions depending on the random variable to fit: rank or size. The discreteness implies that a power law in one of the representations is not a power law in the other, and vice versa. We generate synthetic power laws in both representations and apply a state-of-the-art fitting method to each of the two random variables. The method (based on maximum likelihood plus a goodness-of-fit test) does not fit the whole distribution but the tail, understood as the part of a distribution above a cutoff that separates non-power-law behavior from power-law behavior. We find that, no matter which random variable is power-law distributed, using the rank as the random variable is problematic for fitting, in general (although it may work in some limit cases). One of the difficulties comes from recovering the "hidden" true ranks from the empirical ranks. On the contrary, the representation in terms of the distribution of sizes allows one to recover the true exponent (with some small bias when the underlying size distribution is a power law only asymptotically).

Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing.

Power-law distribution in the number of confirmed COVID-19 cases

COVID-19 is an emerging respiratory infectious disease caused by the coronavirus SARS-CoV-2. It was first reported on in early December 2019 in Wuhan, China and within three months spread as a pandemic around the whole globe. Here, we study macro-epidemiological patterns along the time course of the COVID-19 pandemic. We compute the distribution of confirmed COVID-19 cases and deaths for countries worldwide and for counties in the US and show that both distributions follow a truncated power-law over five orders of magnitude. We are able to explain the origin of this scaling behavior as a dual-scale process: the large-scale spread of the virus between countries and the small-scale accumulation of case numbers within each country. Assuming exponential growth on both scales, the critical exponent of the power-law is determined by the ratio of large-scale to small-scale growth rates. We confirm this theory in numerical simulations in a simple meta-population model, describing the epidemic spread in a network of interconnected countries. Our theory gives a mechanistic explanation why most COVID-19 cases occurred within a few epicenters, at least in the initial phase of the outbreak. By combining real world data, modeling, and numerical simulations, we make the case that the distribution of epidemic prevalence might follow universal rules.

The Differential Importance of Deep and Shallow Seagrass to Nekton Assemblages of the Great Barrier Reef

Seagrass meadows are an important habitat for a variety of animals, including ecologically and socioeconomically important species. Seagrass meadows are recognised as providing species with nursery grounds, and as a migratory pathway to adjacent habitats. Despite their recognised importance, little is known about the species assemblages that occupy seagrass meadows of different depths in the coastal zone. Understanding differences in the distribution of species in seagrass at different depths, and differences in species diversity, abundance, biomass, and size spectra, is important to fully appreciate both the ecological significance and economic importance of these seagrass meadows. Here, we assess differences in the assemblage characteristics of fish, crustacea, and cephalopods (collectively, nekton) between deep (>9 m; Halophila spinulosa dominant) and shallow water (<2 m; Halodule uninervis and/or Zostera muelleri dominant) seagrass meadows of the central Great Barrier Reef coast of Queensland, Australia. Nekton assemblage structure differed between deep and shallow seagrass. Deeper meadows were typified by juvenile emperors (e.g., Lethrinus genivittatus), hairfinned leatherjacket (Paramonacanthus japonicus) and rabbitfish (e.g., Siganus fuscescens) in both biomass per unit effort (BPUE) and catch per unit effort (CPUE), whereas shallow meadows were typified by the green tiger prawn (Penaeus semisulcatus) and pugnose ponyfish (Secutor insidiator) in both BPUE and CPUE. Both meadow depths were distinct in their nekton assemblage, particularly for socioeconomically important species, with 11 species unique to both shallow and deep meadows. However, both meadow depths also included juveniles of socioeconomically important species found in adjacent habitats as adults. The total nekton CPUE was not different between deep and shallow seagrass, but the BPUE and body mass of individual animals were greater in deep than shallow seagrass. Size spectra analysis indicated that in both deep and shallow meadows, smaller animals predominated, even more so than theoretically expected for size spectra. Our findings highlight the unique attributes of both shallow and deeper water seagrass meadows, and identify the distinct and critically important role of deep seagrass meadows within the Great Barrier Reef World Heritage Area (GBRWHA) as a habitat for small and juvenile species, including those of local fisheries value.

Truncated lognormal distributions and scaling in the size of naturally defined population clusters

Using population data of high spatial resolution for a region in the south of Europe, we define cities by aggregating individuals to form connected clusters. The resulting cluster-population distributions show a smooth decreasing behavior covering six orders of magnitude. We perform a detailed study of the distributions, using state-of-the-art statistical tools. By means of scaling analysis we rule out the existence of a power-law regime in the low-population range. The logarithmic-coefficient-of-variation test allows us to establish that the power-law tail for high population, characteristic of Zipf's law, has a rather limited range of applicability. Instead, lognormal fits describe the population distributions in a range covering from a few dozen individuals to more than 1×10^{6} (which corresponds to the population of the largest cluster).

The shaping role of self-organization: linking vegetation patterning, plant traits and ecosystem functioning

Self-organized spatial patterns are increasingly recognized for their contribution to ecosystem functioning, in terms of enhanced productivity, ecosystem stability, and species diversity in terrestrial as well as marine ecosystems. Most studies on the impact of spatial self-organization have focused on systems that exhibit regular patterns. However, there is an abundance of patterns in many ecosystems which are not strictly regular. Understanding of how these patterns are formed and how they affect ecosystem function is crucial for the broad acceptance of self-organization as a keystone process in ecological theory. Here, using transplantation experiments in salt marsh ecosystems dominated by Scirpus mariqueter, we demonstrate that scale-dependent feedback is driving irregular spatial pattern formation of vegetation. Field observations and experiments have revealed that this self-organization process affects a range of plant traits, including shoot-to-root ratio, rhizome orientation, rhizome node number, and rhizome length, and enhances vegetation productivity. Moreover, patchiness in self-organized salt marsh vegetation can support a better microhabitat for macrobenthos, promoting their total abundance and spatial heterogeneity of species richness. Our results extend existing concepts of self-organization and its effects on productivity and biodiversity to the spatial irregular patterns that are observed in many systems. Our work also helps to link between the so-far largely unconnected fields of self-organization theory and trait-based, functional ecology.

Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes

Biomass distribution among size classes follows a power law where the Log-abundance of taxa scales to Log-size with a slope that responds to environmental abiotic and biotic conditions. The interactions between ecological mechanisms controlling the slope of locally realized size-abundance relationships (SAR) are however not well understood. Here we tested how warming, nutrient levels, and grazing affect the slope of phytoplankton community SARs in decadal time-series from eight Swiss lakes of the peri-alpine region, which underwent environmental forcing due to climate change and oligotrophication. We expected rising temperature to have a negative effect on slope (favoring small phytoplankton), and increasing nutrient levels and grazing pressure to have a positive effect (benefiting large phytoplankton). Using a random forest approach to extract robust patterns from the noisy data, we found that the effects of temperature (direct and indirect through water column stability), nutrient availability (phosphorus and total biomass), and large herbivore (copepods and daphnids) grazing and selectivity on slope were non-linear and interactive. Increasing water temperature or total grazing pressure, and decreasing phosphorus levels, had a positive effect on slope (favoring large phytoplankton, which are predominantly mixotrophic in the lake dataset). Our results therefore showed patterns that were opposite to the expected long-term effects of temperature and nutrient levels, and support a paradigm in which (i) small phototrophic phytoplankton appear to be favored under high nutrients levels, low temperature and low grazing, and (ii) large mixotrophic algae are favored under oligotrophic conditions when temperature and grazing pressure are high. The effects of temperature were stronger under nutrient limitation, and the effects of nutrients and grazing were stronger at high temperature. Our study shows that the phytoplankton local SARs in lakes respond to both the independent and the interactive effects of resources, grazing and water temperature in a complex, unexpected way, and observations from long-term studies can deviate significantly from general theoretical expectations.

A regional-scale assessment of using metabolic scaling theory to predict ecosystem properties

Metabolic scaling theory (MST) is one of ecology's most high-profile general models and can be used to link size distributions and productivity in forest systems. Much of MST's foundation is based on size distributions following a power law function with a scaling exponent of -2, a property assumed to be consistent in steady-state ecosystems. We tested the theory's generality by comparing actual size distributions with those predicted using MST parameters assumed to be general. We then used environmental variables and functional traits to explain deviation from theoretical expectations. Finally, we compared values of relative productivity predicted using MST with a remote-sensed measure of productivity. We found that fire-prone heath communities deviated from MST-predicted size distributions, whereas fire-sensitive rainforests largely agreed with the theory. Scaling exponents ranged from -1.4 to -5.3. Deviation from the power law assumption was best explained by specific leaf area, which varies along fire frequency and moisture gradients. While MST may hold in low-disturbance systems, we show that it cannot be applied under many environmental contexts. The theory should remain general, but understanding the factors driving deviation from MST and subsequent refinements is required if it is to be applied robustly across larger scales.

The commonness of rarity: Global and future distribution of rarity across land plants

A key feature of life's diversity is that some species are common but many more are rare. Nonetheless, at global scales, we do not know what fraction of biodiversity consists of rare species. Here, we present the largest compilation of global plant diversity to quantify the fraction of Earth's plant biodiversity that are rare. A large fraction, ~36.5% of Earth's ~435,000 plant species, are exceedingly rare. Sampling biases and prominent models, such as neutral theory and the k-niche model, cannot account for the observed prevalence of rarity. Our results indicate that (i) climatically more stable regions have harbored rare species and hence a large fraction of Earth's plant species via reduced extinction risk but that (ii) climate change and human land use are now disproportionately impacting rare species. Estimates of global species abundance distributions have important implications for risk assessments and conservation planning in this era of rapid global change.

Optimal searching behaviour generated intrinsically by the central pattern generator for locomotion

Efficient searching for resources such as food by animals is key to their survival. It has been proposed that diverse animals from insects to sharks and humans adopt searching patterns that resemble a simple Lévy random walk, which is theoretically optimal for 'blind foragers' to locate sparse, patchy resources. To test if such patterns are generated intrinsically, or arise via environmental interactions, we tracked free-moving Drosophila larvae with (and without) blocked synaptic activity in the brain, suboesophageal ganglion (SOG) and sensory neurons. In brain-blocked larvae, we found that extended substrate exploration emerges as multi-scale movement paths similar to truncated Lévy walks. Strikingly, power-law exponents of brain/SOG/sensory-blocked larvae averaged 1.96, close to a theoretical optimum (µ ≅ 2.0) for locating sparse resources. Thus, efficient spatial exploration can emerge from autonomous patterns in neural activity. Our results provide the strongest evidence so far for the intrinsic generation of Lévy-like movement patterns.