Dispersion Metrics Research Articles

White Matter Microstructural Changes in Psychotic Spectrum Disorder are Associated With Cognitive Function and Symptoms

White matter (WM) microstructural abnormalities in Psychotic Spectrum Disorders (PSD) can now be assessed using novel diffusion MRI (dMRI) techniques. We applied Diffusion Kurtosis Imaging (DKI) and Neurite Orientation Density and Dispersion Imaging Bingham (NODDI-B) alongside Diffusion Tensor Imaging (DTI) to obtain metrics of WM density, orientation dispersion, and complexity and better describe WM microstructural changes in PSD. We additionally tested whether dMRI metrics are associated with cognition and schizotypal symptoms in patients and controls.

Genetic Analysis Reveals Rare Variants in T-Cell Response Gene MR1 Associated with Poor Overall Survival after Urothelial Cancer Diagnosis.

Simple SummaryThe objectives of this study were to identify rare germline variant associations in urothelial carcinoma of the bladder (UC) incidence and to determine its association with clinical outcomes. Our analysis reveals that individuals with MR1 rare germline variants had significantly worse OS than those without any, and those with ADGRL2 variants were slightly more likely to have UC compared to a control cohort matched for age, sex, and smoking status. These associations, using models incorporating known environmental covariates and using well-defined clinical phenotypes, highlight several candidates for prognostic indicator genes for the differential presentation of UC. These associations highlight several candidate genes that have the potential to explain clinical disparities in UC and predict UC outcomes.Urothelial carcinoma of the bladder (UC) is the fifth most common cancer in the United States. Germline variants, especially rare germline variants, may account for a portion of the disparity seen among patients in terms of UC incidence, presentation, and outcomes. The objectives of this study were to identify rare germline variant associations in UC incidence and to determine its association with clinical outcomes. Using exome sequencing data from the DiscovEHR UC cohort (n = 446), a European-ancestry, North American population, the complex influence of germline variants on known clinical phenotypes were analyzed using dispersion and burden metrics with regression tests. Outcomes measured were derived from the electronic health record (EHR) and included UC incidence, age at diagnosis, and overall survival (OS). Consequently, key rare variant association genes were implicated in MR1 and ADGRL2. The Kaplan–Meier survival analysis reveals that individuals with MR1 germline variants had significantly worse OS than those without any (log-rank p-value = 3.46 × 10−7). Those with ADGRL2 variants were found to be slightly more likely to have UC compared to a matched control cohort (FDR q-value = 0.116). These associations highlight several candidate genes that have the potential to explain clinical disparities in UC and predict UC outcomes.

Ground-based validation of aerosol optical depth from CAMS reanalysis project: An uncertainty input on direct normal irradiance under cloud-free conditions

Under cloud-free skies, aerosols are mainly responsible for controlling the observed Direct Normal Irradiance (DNI) variability for studying and designing Solar Concentrating Technologies. In this study, the induced uncertainties on cloud-free DNI due to the use of aerosol optical properties from the CAMS reanalysis project (AODCAMS) are investigated. DNI is calculated using a radiative transfer model while a pre-comparison and calibration of aerosol reanalysis products are performed using as skill reference the observations from AERONET. The site-specific validation showed an overestimation of the AOD in locations affected by volcanic eruptions while underestimation and higher dispersion metrics are revealed in regions dominated by coarse aerosols from mineral dust and biomass burning. A pre-calibration process of AODCAMS significantly improved the error metrics in all AERONET locations providing significant reductions in all ‘hot-spot error’ regions. In terms of DNI, using the initial AODCAMS, rMBE extends between −28% and 30%, exhibiting higher magnitudes in South and South-Eastern Asia. Using the calibrated AODCAMS, the pre-described error metrics are significantly improved in site-specific and regional scales.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

BackgroundClassical ST‐T waveform changes on standard 12‐lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear.Methods and ResultsThis was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal‐spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology‐driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data‐driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data‐ and physiology‐driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance.ConclusionsWe identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications.RegistrationURL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.

Assessing the phylogenetic host breadth of millet pathogens and its implication for disease spillover

Abstract1. Increasing agriculture intensification has led to dramatically improved crop yields; however, this shift in agricultural practice has been accompanied by increasing threats from new and emerging plant pathogens. While the pathogens associated with crop species are often well studied, especially within North America and Europe, less is known about pathogen pressures on crops elsewhere, and our ability to predict the emergence of novel pathogens is limited. Here, we model phylogenetic constraints on the distribution of pathogens of millet – one of the most important crops in Africa.2. We conducted a literature review to compile a database of common millet pathogens and the non‐millet host crops associated with each. We then characterized the phylogenetic host range for each pathogen using measures of mean pairwise distance (MPD) and mean nearest taxon distance (MNTD) separating crop hosts.3. We detected robust phylogenetic clustering for both metrics of phylogenetic dispersion (MPD and MNTD). Evidence for phylogenetic clustering tended to be stronger (more negative standard effect sizes) and more variable for MPD than for MNTD.4. Although patterns for individual pathogens were variable, we did not find significant differences in phylogenetic dispersion of hosts among pathogen types (bacteria, viruses and fungi). However, in several cases, we observed evidence of phylogenetic clustering in evolutionarily distant host clades, a possible signal of occasional large phylogenetic host jumps.5. We show that pathogens cluster on closely related hosts, and it is thus likely that closely related millets also share similar pathogen communities. On average, the probability of a pathogen host shift may, therefore, be predicted by the phylogenetic relatedness between host species. However, host shifts between distantly related hosts are not infrequent. This finding has relevance not only for the design of agronomic systems to reduce disease spillover but also for biological control agents risk analysis, quarantine regulations in international trade and our understanding of the distribution and abundance of plants in natural systems.

Comparing multiband and singleband EPI in NODDI at 3\xa0T: what are the implications for reproducibility and study sample sizes?

ObjectiveThe reproducibility of Neurite orientation dispersion and density imaging (NODDI) metrics from time-saving multiband (MB) EPI compared with singleband (SB) has not been considered. This study aims to evaluate the reproducibility of NODDI parameters from SB and MB acquisitions, determine the agreement between acquisitions and estimate the sample sizes required to detect between-group change.MethodsBrain diffusion MRI data were acquired using SB and MB (acceleration factors 2 (MB2) and 3 (MB3)) on 8 healthy subjects on 2 separate visits. NODDI maps of isotropic volume fraction (FISO), neurite density (NDI) and orientation dispersion index (ODI) were estimated. Region-of-interest analysis was performed; variability across subjects and visits was measured using coefficients of variation (CoV). Intraclass correlation coefficient and Bland–Altman analysis were performed to assess reproducibility and detect any systematic bias between SB, MB2 and MB3. Power calculations were used to determine sample sizes required to detect group differences.ResultsBoth NDI and ODI were reproducible between visits; however, FISO was variable. All parameters were not reproducible across methods; a systematic bias was observed with the derived values decreasing as the MB factor increases. The number of subjects needed to detect a between-group change is not significantly different between methods; however, ODI needs considerably higher sample sizes than NDI.ConclusionsBoth SB and MB yield highly reproducible NDI and ODI measures, but direct comparison of these parameters between methods is complicated by systematic differences that exist between the two approaches.

Cognitive dispersion and ApoEe4 genotype predict dementia diagnosis in 8-year follow-up of the oldest-old.

Cognitive dispersion, or inconsistencies in performance across cognitive domains, has been posited as a cost-effective tool to predict conversion to dementia in older adults. However, there is a dearth of studies exploring cognitive dispersion in the oldest-old (>80years) and its relationship to dementia incidence. The main aim of this study was to examine whether higher cognitive dispersion at baseline was associated with dementia incidence within an 8-year follow-up of very old adults, while controlling for established risk factors and suggested protective factors for dementia. Participants (n = 468) were from the Origins of Variance in the Old-Old: Octogenarian Twins study, based on the Swedish Twin Registry. Cox regression analyses were performed to assess the association between baseline cognitive dispersion scores and dementia incidence, while controlling for sociodemographic variables, ApoEe4 carrier status, co-morbidities, zygosity and lifestyle engagement scores. An additional model included a composite of average cognitive performance. Cognitive dispersion and ApoEe4 were significantly associated with dementia diagnosis. These variables remained statistically significant when global cognitive performance was entered into the model. Likelihood ratio tests revealed that cognitive dispersion and cognitive composite scores entered together in the same model was superior to either predictor alone in the full model. The study underscores the usefulness of cognitive dispersion metrics for dementia prediction in the oldest-old and highlights the influence of ApoEe4 on cognition in very late age. Our findings concur with others suggesting that health and lifestyle factors pose little impact upon cognition in very advanced age.

Diffusion magnetic resonance imaging assessment of regional white matter maturation in preterm neonates

PurposeDiffusion magnetic resonance imaging (dMRI) studies report altered white matter (WM) development in preterm infants. Neurite orientation dispersion and density imaging (NODDI) metrics provide more realistic estimations of neurite architecture in vivo compared with standard diffusion tensor imaging (DTI) metrics. This study investigated microstructural maturation of WM in preterm neonates scanned between 25 and 45 weeks postmenstrual age (PMA) with normal neurodevelopmental outcomes at 2 years using DTI and NODDI metrics.MethodsThirty-one neonates (n = 17 male) with median (range) gestational age (GA) 32+1 weeks (24+2–36+4) underwent 3 T brain MRI at median (range) post menstrual age (PMA) 35+2 weeks (25+3–43+1). WM tracts (cingulum, fornix, corticospinal tract (CST), inferior longitudinal fasciculus (ILF), optic radiations) were delineated using constrained spherical deconvolution and probabilistic tractography in MRtrix3. DTI and NODDI metrics were extracted for the whole tract and cross-sections along each tract to assess regional development.ResultsPMA at scan positively correlated with fractional anisotropy (FA) in the CST, fornix and optic radiations and neurite density index (NDI) in the cingulum, CST and fornix and negatively correlated with mean diffusivity (MD) in all tracts. A multilinear regression model demonstrated PMA at scan influenced all diffusion measures, GA and GAxPMA at scan influenced FA, MD and NDI and gender affected NDI. Cross-sectional analyses revealed asynchronous WM maturation within and between WM tracts.).ConclusionWe describe normal WM maturation in preterm neonates with normal neurodevelopmental outcomes. NODDI can enhance our understanding of WM maturation compared with standard DTI metrics alone.

Healthcare-Associated Pneumonia in a Mexican Tertiary Care Center Micro to Systemic Analysis: A 2017–2019 Case Series Study

Objectives: The aim of this study was to identify the biological, microbiological, and healthcare factors related to the occurrence of nosocomial pneumonia in our confirmed cases during 2017–2019. Methods: We conducted a case series study. For the selection of the cases we used the CDC criteria for hospital-acquired pneumonia, we collected cases from the data set for healthcare-associated infections from a tertiary-care hospital in Mexico City. For the quantitative analysis, we used Stata v14 software, and we obtained frequencies, proportions, accumulated incidence rate, lethality rate, central tendency, and dispersion metrics. This study was a secondary data set analysis; we obtained signed authorization for the use of the data from the Epidemiological Surveillance Unit. Results: During our analysis period (January 2017 to June 2019), we identified 107 cases that fulfilled the CDC criteria: 47 cases (43.93%) from 2017, 38 cases (35.51%) from 2018, and 20.56% from 2019. The month that reported the highest frequency was February, with 17 cases (15.89%). The median age was 63 years (range, 0–97 years; IQR, 36). The most affected age group was 65 years (48.60%), and the most affected 5-year age group was 75–79 years (13.08%). Moreover, 60 cases (56.07%) were men and 47 (43.93%) were women. Regarding the reason for discharge, 71% were discharged due to improvement, 27% died, and 2% were transferred to another healthcare facility. Also, 17 patients (15.89%) required readmission due to respiratory illness within 72 hours of previous discharge. The most common diagnosis was a solid malignant neoplasm (20.19%), followed by heart or vascular malformation or anomaly (12.50%). The mean inpatient hospital stay was 39.95 days (46.40; median, 27 days, range, 2–317 days; IQR 35 days). The median time elapsed until detection was 14 days. The hospitalization area with the most cases was the intensive care unit, with 24 cases (22.43%); the service with most cases was oncology with 21 cases (20.56%). The most isolated pathogen was Pseudomonas aeruginosa (14%). Moreover, 59% were gram-negative, 36% were gram-positive, 19.67% were viruses, and 14.75% were fungi. Our accumulated-incidence-rate was 0.58 cases per 1,000 patient days and our case-fatality-rate was 25.23%. Furthermore, 41% of cases required invasive mechanical ventilation, 52.34% required noninvasive mechanical ventilation, 5% cases had an endo-pleural tube, 9.35% had a nasogastric tube, and 41.12% had a central venous catheter. The most-prescribed antimicrobial was meropenem (33.33%), and meropenem-resistance was 61.54%. Conclusions: Infection prevention efforts should target oncological patients, critical-care units, and the elderly. We must reinforce our antimicrobial policy due to our overprescription of carbapenems. Early detection is needed to reduce mortality.Funding: NoneDisclosures: None

Finite Blocklength Analysis of Multiple Access Channels With/Without Cooperation

Motivated by the demand of reliable and finite blocklength communications, we employ tools from information theory, stochastic processes and queueing theory, in order to provide a comprehensive framework regarding the analysis of a Time Division Multiple Access (TDMA) network with bursty traffic, in the finite blocklength regime. Specifically, we reexamine the stability conditions of a non-cooperative TDMA multiple access channel, evaluate the optimal throughput, and identify the optimal data packet size, k, for fixed codeword of blocklength, n. The evaluation is performed both numerically and via the proposed approximations, which result in closed form expressions and provide insight on how the optimal data size, k*, relates to the information metrics of channel capacity and channel dispersion in the finite blocklength regime. Then, we examine the stability conditions and the performance of the Multiple Access Relay Channel with TDMA scheduling, subject to finite blocklength constraints, by applying a cognitive cooperation protocol that assumes relaying is enabled when sources are idle. Finally, we propose the novel Batch-And-Forward (BAF) strategy, a mechanism that allows terminals to send batches of data packets instead on individual data packets, and evaluate the stability conditions and the optimal throughput. Numerical evaluation of the proposed strategy indicates that the performance of the cooperative network in the finite blocklength regime, in terms of throughput, can be significantly enhanced. Moreover, it reduces the requirement in control signals (metadata) [3], since, it avoids the unnecessary repetition of metadata (e.g. address of the source terminal and the destination). The BAF strategy is quite versatile, thus, it can be embedded in existing cooperative protocols, without imposing additional complexity on the overall scheme.

Estimating inter-patient variability of dispersion in dry powder inhalers using CFD-DEM simulations

Drug delivery from a capsule-based dry powder inhaler depends on the inhaler's design, the drug's formulation, and the inhalation maneuver. The latter affects both the air flow and the capsule motion in the inhaler. It is well known that patient-to-patient variability is a major challenge in the design of new inhaler types. Modeling and simulation are important tools for understanding such systems, yet quite complex. Simulation studies of capsule-based dry powder inhalers have disregarded the transient nature of the inhalation process, adopting a constant flow rate through the inhaler instead. In addition, either no capsules, a capsule in a fixed position, or a capsule rotating at a constant rate have been considered.In this work, literature data for three inhalation flow profiles were incorporated into coupled simulations of the air flow and carrier particle motion through an Aerolizer® dry powder inhaler with a rotating capsule and compared to simulations at constant air flow rates. The results for the profile simulations indicated that the carrier powder experienced larger velocity fluctuations. Acceleration events were tracked as a measure of collision- and flow-induced dispersion. The majority of fast particle accelerations occurred when the particles collided with the swirl chamber walls. Of the two common particle dispersion metrics, only the peak particle force distribution appeared to be sensitive to the inhalation profiles, while the effect of the profiles on the cumulative impulse was small.

Functional diversity of habitat formers declines scale-dependently across an environmental stress gradient

Marine habitat formers such as seaweeds and corals are lynchpins of coastal ecosystems, but their functional diversity and how it varies with scale and context remains poorly studied. Here, we investigate the functional diversity of seaweed assemblages across the rocky intertidal stress gradient at large (zones) and small (quadrat) scales. We quantified complementary metrics of emergent group richness, functional richness (functional space occupied) and functional dispersion (trait complementarity of dominant species). With increasing shore height, under species loss and turnover, responses of functional diversity were scale- and metric-dependent. At the large scale, functional richness contracted while—notwithstanding a decline in redundancy—emergent group richness and functional dispersion were both invariant. At the small scale, all measures declined, with the strongest responses evident for functional and emergent group richness. Comparisons of observed versus expected values based on null models revealed that functional richness and dispersion were greater than expected in the low shore but converged with expected values higher on the shore. These results show that functional diversity of assemblages of marine habitat formers can be especially responsive to environmental stress gradients at small scales and for richness measures. Furthermore, niche-based processes at the small—neighbourhood—scale can favour co-occurrence of functionally distinctive species under low, but not high, stress, magnifying differences in functional diversity across environmental gradients. As assemblages of marine habitat formers face accelerating environmental change, further studies examining multiple aspects of functional diversity are needed to elucidate patterns, processes, and ecosystem consequences of community (dis-)assembly across diverse groups.

Imaging microstructure with diffusion and susceptibility MR: neuronal density correlation in Disrupted-in-Schizophrenia-1 mutant mice.

To probe cerebral microstructural abnormalities and assess changes of neuronal density in Disrupted-in-Schizophrenia-1 (DISC1) mice using non-Gaussian diffusion and quantitative susceptibility mapping (QSM). Brain specimens of transgenic DISC1 mice (n = 8) and control mice (n = 7) were scanned. Metrics of neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging (DKI), as well as QSM, were acquired. Cell counting was performed on Nissl-stained sections. Group differences of imaging metrics and cell density were assessed. Pearson correlations between imaging metrics and cell densities were also examined. Significant increases of neuronal density were observed in the hippocampus of DISC1 mice. DKI metrics such as mean kurtosis exhibited significant group differences in the caudate putamen (P = 0.015), cerebral cortex (P = 0.021), and hippocampus (P = 0.011). However, DKI metrics did not correlate with cell density. In contrast, significant positive correlation between density of neurons and the neurite density index of NODDI in the hippocampus was observed (r = 0.783, P = 0.007). Significant correlation between density of neurons and susceptibility (r = 0.657, P = 0.039), as well as between density of neuroglia and susceptibility (r = 0.750, P = 0.013), was also observed in the hippocampus. The imaging metrics derived from DKI were not sensitive specifically to cell density, while NODDI could provide diffusion metrics sensitive to density of neurons. The magnetic susceptibility values derived from the QSM method can serve as a sensitive biomarker for quantifying neuronal density.

Distance to Headquarter and Real Estate Equity Performance

We study the effect of geographic portfolio diversification of real estate firms on their investment performance before and after the global financial crisis (GFC). In addition to previously used dispersion metrics, we also account for the distance of the properties to the corporate headquarters. We document a notable shift in the non-market performance of real estate companies after the crisis. Pre-GFC, we do not find a difference in non-market performance across equities based on geographic diversification. Post-GFC, equities with high geographic dispersion significantly outperform the market, while firms with concentrated property holdings do not deliver a significant alpha. Increased real estate equity market sophistication and strong institutional presence can explain why this effect is only observed for dispersed small firms, those invested outside gateway metro areas, or companies with low institutional ownership.

The Application of Bidirectional Reflectance Distribution Function Data to Recognize the Spatial Heterogeneity of Mixed Pixels in Vegetation Remote Sensing: A Simulation Study

Spectral decomposition of mixed pixels can provide information about the abundance of end members but fails to indicate the spatial distribution of end members in vegetation remote sensing. This work is a significant attempt to use the bidirectional reflectance distribution function (<small>BRDF</small>) characteristics of mixed pixels in the prediction of spatial-heterogeneity metrics. Data sets from this function with different spatial distributions were constructed by the discrete anisotropic radiative transfer model, and three spatial aggregation and dispersion metrics were calculated: percentage of like adjacencies, spatial division index, and aggregation index. A simple linear regression method was used to construct the prediction model of spatial aggregation and dispersion metrics. The potential of multiangle remote sensing model for identifying spatial patterns well was demonstrated, and its importance was found to differ for different spatial aggregation and dispersion metrics. Specifically, the precision of the model based on multiangle reflectance used for predicting the spatial division index could meet a minimum root mean square of 5.95%. The reflectance features from backward observation on the principal plane play the leading role in recognizing the spatial heterogeneity of mixed pixels. The prediction model is sufficiently robust to distinguish the same vegetation with different growth trends, but also performs well when the ground objects have a smaller reflectance difference in the mixed pixels in a certain band. This study is expected to offer a new thought for spatial-heterogeneity identification of ground objects and thus promote the development of remote sensing technology in assessing spatial distribution.

Integrated framework of system dynamics and meta-heuristic for multi-objective land use planning problem

Land use optimization as a resource allocation problem can be defined as the process of assigning different land uses to a region. Sustainable development also involves the exploitation of environmental resources, investment orientation, technology development, and industrial changes in a coordinated form. This paper studies the multi-objective sustainable land use planning problem and proposes an integrated framework, including simulation, forecasting, and optimization approaches for this problem. Land use optimization, a multifaceted process, requires complex decisions, including selection of land uses, forecasting land use allocation percentage, and assigning locations to land uses. The land use allocation percentage in the selected horizons is simulated and predicted by designing a System Dynamics (SD) model based on socio-economic variables. Furthermore, land use assignment is accomplished with a multi-objective integer programming model that is solved using augmented e-constraint and non-dominated sorting genetic algorithm II (NSGA-II) methods. According to the results of the SD model, land use changes depend on population growth rate and labor productivity variables. Among the possible scenarios, a scenario focusing more on sustainable planning is chosen and the forecasting results of this scenario are used for optimal land use allocation. The computational results show that the augmented e-constraint method cannot solve this problem even for medium sizes. The NSGA-II method not only solves the problem at large sizes over a reasonable time, but also generates good-quality solutions. NSGA-II showed better performance in metrics, including number of non-dominated Pareto solutions (NNPS), mean ideal distance (MID), and dispersion metric (DM). Integrated framework is implemented to allocate four types of land uses consisting of residential, commercial, industrial, and agricultural to a given region with 900 cells.

The influence of ecological infrastructures adjacent to crops on their carabid assemblages in intensive agroecosystems.

BackgroundConserving biodiversity and enhancing ecosystem services of interest in intensive agroecosystems is a major challenge. Perennial ecological infrastructures (EIs), such as hedges and grassy strips, and annual EI under Agri-Environment Schemes appear to be good candidates to promote both. Our study focused on carabids, an indicator group responding both at the species and functional trait level to disturbances and supporting pest control and weed seed consumption services.MethodsWe compared carabid assemblages at the species and functional traits levels, sampled via pitfall trapping, in three types of EIs (hedges, grassy strips and annual flower strips) and crops. We also tested via GLMs the effect of (1) the type of EI at the crops’ border and (2) the distance from the crops’ border (two meters or 30 meters) on carabid assemblages of crops. Tested variables comprised: activity-density, species richness, functional dispersion metrics (FDis) and proportions of carabids by functional categories (Diet: generalist predators/specialist predators/seed-eaters; Size: small/medium/large/very large; Breeding period: spring/autumn).Results and DiscussionCarabid assemblages on the Principal Coordinate Analysis split in two groups: crops and EIs. Assemblages from all sampled EIs were dominated by mobile generalist predator species from open-land, reproducing in spring. Assemblages of hedges were poor in activity-density and species richness, contrarily to grassy and annual flower strips. Differences in carabid assemblages in crops were mainly driven by the presence of hedges. The presence of hedges diminished the Community Weighted Mean size of carabids in crops, due to an increased proportion of small (<5 mm) individuals, while distance from crops’ border favoured large (between 10–15 mm) carabids. Moreover, even if they were attracted by EIs, granivorous carabid species were rare in crops. Our results underlie the importance of local heterogeneity when adapting crops’ borders to enhance carabid diversity and question the relevance of hedge implantation in intensive agrolandscapes, disconnected from any coherent ecological network. Moreover, this study emphasizes the difficulty to modify functional assemblages of crops for purposes of ecosystem services development, especially for weed seed consumption, as well as the role of distance from the crops’ border in the shaping of crop carabid assemblages.

Anisotropy and Inhomogeneity in Drifter Dispersion

AbstractOcean flows are known to be locally anisotropic and inhomogeneous. Nonetheless, the ocean's statistical dispersion properties are traditionally assumed to be isotropic and homogeneous. Here, we investigate the effect of local anisotropy and inhomogeneity on dispersion statistics, using a unique data set of roughly 300 near‐surface drifters that were launched within 10 days in the summer of 2012. The unique launch strategy based on nested triplets resulted in an unusually large number of nearly colocated drifter pairs. Thus, this data set is ideally suited for an estimate of the directional bias and inhomogeneity effects inherent in drifter pair statistics.Several metrics are proposed to assess the time evolution of anisotropy and inhomogeneity effects at multiple initial separation scales (100 m, 200 m, 500 m, 1 km, and 10 km). Locally, statistically significant anisotropy and inhomogeneity are observed at all scales, although anisotropy is noticeably less at 10 km, suggesting that the energetic processes driving anisotropic dispersion operate primarily at smaller scales. Moreover, averaged over a sufficient variety of different flow regimes, the signature of both anisotropy and inhomogeneity in dispersion metrics lessens. These trends hold generally across all scales, with longer time scales associated with larger spatial scales. The results indicate that oceanic dispersion is statistically isotropic and homogeneous over large swaths, but for an application in a specific location, local anisotropy and inhomogeneity matter. What size swath is large enough is situation dependent: For this specific data set, statistics had to be evaluated over multiple deployments, giving a required area greater than 150 km2.

Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation.

Ecologists often use dispersion metrics and statistical hypothesis testing to infer processes of community formation such as environmental filtering, competitive exclusion, and neutral species assembly. These metrics have limited power in inferring assembly models because they rely on often‐violated assumptions. Here, we adapt a model of phenotypic similarity and repulsion to simulate the process of community assembly via environmental filtering and competitive exclusion, all while parameterizing the strength of the respective ecological processes. We then use random forests and approximate Bayesian computation to distinguish between these models given the simulated data. We find that our approach is more accurate than using dispersion metrics and accounts for uncertainty in model selection. We also demonstrate that the parameter determining the strength of the assembly processes can be accurately estimated. This approach is available in the R package CAMI; Community Assembly Model Inference. We demonstrate the effectiveness of CAMI using an example of plant communities living on lava flow islands.

Phylogenetic dispersion and diversity in regional assemblages of seed plants in China

Species assemble into communities through ecological and evolutionary processes. Phylogenetic niche conservatism-the tendency of species to retain ancestral ecological distributions-is thought to influence which species from a regional species pool can persist in a particular environment. We analyzed data for seed plants in China to test hypotheses about the distribution of species within regional floras. Of 16 environmental variables, actual evapotranspiration, minimum temperature of the coldest month, and annual precipitation most strongly influenced regional species richness, phylogenetic dispersion, and phylogenetic diversity for both gymnosperms (cone-bearing plants) and angiosperms (flowering plants). For most evolutionary clades at, and above, the family level, the relationships between metrics of phylogenetic dispersion (i.e., average phylogenetic distance among species), or phylogenetic diversity, and the 3 environmental variables were consistent with the tropical niche conservatism hypothesis, which predicts closer phylogenetic relatedness and reduced phylogenetic diversity with increasing environmental stress. The slopes of the relationships between phylogenetic relatedness and the 3 environmental drivers identified in this analysis were steeper for primarily tropical clades, implying greater niche conservatism, than for primarily temperate clades. These observations suggest that the distributions of seed plants across large-scale environmental gradients in China are constrained by conserved adaptations to the physical environment, i.e., phylogenetic niche conservatism.