Null Model Research Articles

Synthesis of recovery patterns in microbial communities across environments

BackgroundDisturbances alter the diversity and composition of microbial communities. Yet a generalized empirical assessment of microbiome responses to disturbance across different environments is needed to understand the factors driving microbiome recovery, and the role of the environment in driving these patterns.ResultsTo this end, we combined null models with Bayesian generalized linear models to examine 86 time series of disturbed mammalian, aquatic, and soil microbiomes up to 50 days following disturbance. Overall, disturbances had the strongest effect on mammalian microbiomes, which lost taxa and later recovered their richness, but not their composition. In contrast, following disturbance, aquatic microbiomes tended away from their pre-disturbance composition over time. Surprisingly, across all environments, we found no evidence of increased compositional dispersion (i.e., variance) following disturbance, in contrast to the expectations of the Anna Karenina Principle.ConclusionsThis is the first study to systematically compare secondary successional dynamics across disturbed microbiomes, using a consistent temporal scale and modeling approach. Our findings show that the recovery of microbiomes is environment-specific, and helps to reconcile existing, environment-specific research into a unified perspective.FPVwQXB65-N4idcpgwJZ6_Video

Proper network randomization is key to assessing social balance.

Social ties, either positive or negative, lead to signed network patterns, the subject of balance theory. For example, strong balance introduces cycles with even numbers of negative edges. The statistical significance of such patterns is routinely assessed by comparisons to null models. Yet, results in signed networks remain controversial. Here, we show that even if a network exhibits strong balance by construction, current null models can fail to identify it. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose the STP null model, which integrates both constraints within a maximum entropy framework. STP randomization leads to qualitatively different results, with most social networks consistently demonstrating strong balance in three- and four-node patterns. On the basis our results, we present a potential wiring mechanism behind the observed signed patterns and outline further applications of STP randomization.

Phenomic landscape and pharmacogenomic implications for HLA region in a Taiwan Han Chinese population

BackgroundThe human leukocyte antigen (HLA) genes, exhibiting significant genetic diversity, are associated with susceptibility to various clinical diseases and diverse in drug responses. High costs of HLA sequencing and the population-specific architecture of this genetic region necessitate the establishment of a population-specific HLA imputation reference panel. Moreover, there is a lack of understanding about the genetic and phenotypic landscape of HLA variations within the Taiwanese population.MethodsWe created models for a Taiwanese-specific HLA imputation reference panel. These models were trained with the array genotype data and HLA sequencing data from 845 Taiwanese subjects. HLA imputation was applied for 59,448 Taiwanese subjects to characterize the HLA allele and haplotype frequencies. Additionally, a phenome-wide association study (PheWAS) was conducted to identify the phenotypes associated with HLA variations. The association of the biallelic HLA variants with the binary and quantitative traits were evaluated with additive logistic and linear regression models, respectively. Furthermore, an omnibus test with likelihood-ratio test was applied for each HLA amino acid position in the multiallelic HLA amino acid polymorphisms to compare the difference between a fitted model and a null model following a χ2 distribution of n-1 degree of freedom at a position with n residues. Finally, we estimated the prevalence of adverse drug reactions (ADR)-related HLA alleles in the Taiwanese population.ResultsIn this study, the reference panel models displayed remarkable accuracy, with averages of 99.3%, 98.9%, and 99.1% for 2-, 4-, 6-digit alleles of the eight classical HLA genes, respectively. For PheWAS, a total of 18,136 significant associations with HLA variants across 26 phenotypes are identified (p < 5×10-8), highlighting the pleiotropy feature of the HLA region. Among the independent signals, 15 are novel, including the association of HLA-B pos 138 variation with ankylosing spondylitis (AS), and rs9266290 and rs9266292 with allergy. Through an analysis spanning the entire HLA region, we identified clusters of phenotype correlations. Finally, the carriers of pharmacogenomic related HLA alleles, including HLA-C*01:02 (35.86%), HLA-B*58:01 (20.9%), and HLA-B*15:02 (8.38%), were characterized in the Taiwanese general population.ConclusionsWe successfully delivered the HLA imputation for 59,448 Taiwanese subjects and characterized the genetic and phenotypic landscapes of the HLA variations. In addition, we quantified the estimated prevalence of the ADR-related HLA alleles in the Taiwanese population. The developed HLA imputation reference panel could be used for estimation of population HLA allele frequencies, which can facilitate further studies in the role of HLA variants in a wider range of phenotypes in the population.

Why does the number of antenatal care visits in Ethiopia remain low?: A Bayesian multilevel approach.

Antenatal care (ANC) visit is a proxy for maternal and neonatal health. The ANC is a key indicator of access and utilization of health care for pregnant women. Recently, eight times ANC visits have been recommended during the pregnancy period. However, nearly 57% of women received less than four ANC visits in Ethiopia. Therefore, the objective of this study is to identify factors associated withthe number of ANC visits in Ethiopia. A community-based cross-sectional study design was conducted from March 21 to June 28/2019. Data were collected using interviewer-administered questionnaires from reproductive age groups. A stratified cluster sampling was used to select enumeration areas, households, and women from selected households. A Bayesian multilevel negative binomial model was applied for the analysis of this study. There is an intra-class correlation (ICC) = 23.42% and 25.51% for the null and final model, respectively. Data were analyzed using the STATA version 17.0. The adjusted incidence risk ratio (IRR) with 95% credible intervals (CrI) was used to declare the association. A total of 3915 pregnant women were included in this study. The mean(SD) age of the participants was 28.7 (.11) years. Nearly one-fourth (26.5%) of pregnant women did not have ANC visits, and 3% had eight-time ANC visits in Ethiopia. In the adjusted model, the age of the women 25-28 years (IRR:1.13; 95% CrI: 1.11, 1.16), 29-33 years (IRR: 1.15; 95% CrI: 1.15, 1.16), ≥34 years (IRR:1.14; 95% CrI: 1.12, 1.17), being a primary school (IRR: 1.22, 95% CrI: 1.21, 1.22), secondary school and above (IRR: 1.26, 95% CrI: 1.26, 1.26), delivered in health facility (IRR: 1.93; 95% CrI: 1.92, 1.93), delivered with cesarian section (IRR: 1.18; 95% CrI: 1.18, 1.19), multiple (twin) pregnancy (IRR: 1.11; 95% CrI: 1.10, 1.12), richest (IRR:1.23; 95% CrI: 1.23, 1.24), rich family (IRR: 1.34, 95% CrI: 1.30, 1.37), middle income (IRR: 1.29, 95% CrI: 1.28, 1.31), and poor family (IRR = 1.28, 95% CrI:1.28, 1.29) were shown to have significant association with higher number of ANC vists, while, households with total family size of ≥ 5 (IRR: 0.92; 95% CrI: 0.91, 0.92), and being a rural resident (IRR: 0.92, 95% CrI: 0.92, 0.94) were shown to have a significant association with the lower number of ANC visits. Overall, 26.5% of pregnant women do not have ANC visits during their pregnancy, and 3% of women have eight-time ANC visits. This result is much lower as compared to WHO's recommendation, which states that all pregnant women should have at least eight ANC visits. In this study, the ages of the women 25-28, 29-33, and ≥34 years, being a primary school, secondary school, and above, delivered in a health facility, delivered with caesarian section, multiple pregnancies, rich, middle and poor wealth index, were significantly associated with the higher number of ANC visits, while households with large family size and rural residence were significantly associated with a lower number of ANC visits in Ethiopia.

Abstract PO5-16-09: Associations of age, body mass index, diabetes and hypertension with relative dose intensity among women receiving anthracycline- and/or taxane-based chemotherapy for invasive breast cancer

Abstract Research suggests that medical comorbidity is associated with reduced relative dose intensity (RDI) in patients receiving chemotherapy for treatment of breast cancer. We aimed to determine factors associated with RDI of specific chemotherapy drugs in anthracycline- and/or taxane-based regimens. Women with a diagnosis of lymph node positive, invasive breast cancer who received neoadjuvant or adjuvant chemotherapy with either doxorubicin (DOX) + paclitaxel (PAC) or docetaxel (DOCE)-containing regimens at a single centerfrom2012 to2019 were included in this retrospective study. Age, race, comorbidities, height, weight, and chemotherapy regimen and dose were abstracted from the electronic health record. BMI was calculated using height and weight measured in clinic at the time initial chemotherapy orders were written and categorized according to US national guidelines (18.5 to &amp;lt; 25, 25 to &amp;lt; 30, and &amp;gt;30kg/m2). Presence of diabetes (DM) and hypertension (HTN) at time of diagnosis were determined by chart review. The oncologists’ initial chemotherapy orders (dose and duration) were used as the basis for RDI calculations. Cumulative actual dose received and duration of treatment were compared to planned dose and duration for individual chemotherapy drug and by regimen (DOX+PAC vs. DOCE). Associations between patient characteristics and RDI were evaluated in bivariate and multivariable analyses. A total of 230 women met the inclusion criteria, with 125 receiving a DOX+PAC regimen and 105 receiving a DOCE-based regimen. A higher percentage of women on DOX+PAC received &amp;gt;85% RDI compared to women receiving DOCE (80.8% vs 67.6%). Women aged 65+ years had lower median RDI [IQR] for DOCE than younger women (0.83 [0.67 - 0.98] vs 0.94 [0.84 - 1.00], p = 0.04), and a lower percentage in the older group received &amp;gt;85% RDI (35.3% vs 73.9%, p&amp;lt; 0.01). Median RDIs of PAC and DOX+PAC were lower for Black women than for White and Asian women (PAC: 0.91 [0.75 - 1.00] vs 1.00 [0.89 - 1.00], p = 0.02, DOX+PAC: 0.92[0.87-0.99] vs. 0.97 [0.91-1.00], p = 0.03), and the percent of Black women receiving &amp;gt;85% RDI of PAC was also lower (55.3% vs 77.6%, p = 0.01). BMI category was associated with median RDI for PAC (p= 0.03) and DOX+PAC (p = 0.01), with women in the &amp;gt;30kg/m2 category having the lowest median RDIs and PAC RDI &amp;gt;85% (p &amp;lt; 0.01). Median RDI of DOX+PAC was lower for women with DM (0.89 [0.84-0.92] vs. 0.96 [0.88-1:00], p = 0.03), and a lower percentage of women with DM received DOX RDI &amp;gt;85% compared to women without DM (66.7% vs 91.6%, p &amp;lt; 0.01). Median RDI was lower among women with HTN compared to women without for PAC (0.85 [0.73 - 1.00] vs 0.98 [0.83 - 1.00], p = 0.01) and DOX+PAC (0.91 [0.84 - 0.97] vs 0.97 [0.90 - 1.00], p = 0.01), and a lower percentage of women with HTN received &amp;gt;85% RDI for PAC (50.0% vs 73.3%, p = 0.01). Multiple logistic regression models adjusted for age (continuous), race, BMI (continuous), DM, and HTN had modestly improved predictive value compared to null models (McFadden’s R2 = 0.10 to 0.19); each year of increased age was associated with lower odds of RDI &amp;gt; 85% for DOX (OR [95% confidence interval] 0.91 [0.84-0.97], p = &amp;lt; 0.01), DOX+PAC (0.95 [0.90-0.99], p = 0.02), and DOCE (0.93 [0.88-0.97], p &amp;lt; 0.01), and DM with lower odds for DOX (0.26 [0.07-0.95], p = 0.04). Our findings of associations between increasing age, Black race, BMI, DM, and HTN and reduced RDI are consistent with prior research and present new data regarding associations between medical comorbidities and individual components of chemotherapy regimens. Citation Format: Heather Wopat, Annette Aldous, Adam Ciarleglio, Kendall Anderson, Kim Robien. Associations of age, body mass index, diabetes and hypertension with relative dose intensity among women receiving anthracycline- and/or taxane-based chemotherapy for invasive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-16-09.

Microbial perspective on restoration of degraded urban soil using ornamental plants

The urban soil where abandoned buildings are demolished is barren and structurally poor, and this degraded soil requires restoration. Ornamental plants enhance the urban environment, increase biodiversity, and affect soil physicochemical properties, microbial diversity; however, their effects remain unclear. Thus, in this study, a mixed-planting meadow consisting of 14 perennial ornamental flower species, including Iris tectorum, Iris lacteal, and Patrinia scabiosaefolia, etc. Was planted at a demolition site with sewage-contaminated soil in Beijing. Simultaneously, a single-planting lawn of I. tectorum was established in a nearby park. We aimed to examine soil physicochemical properties, sequence soil bacterial 16S rRNA and fungal ITS amplicons, and analyze soil microbial diversity and community structure at both sites at five time points in the year after planting, To explore the effect of herbaceous ornamental plants on degraded urban soil, we used FAPROTAX and FUNGuild to predict bacterial and fungal functions, the bin-based null model to evaluate the soil microbial community, and random matrix theory to construct soil microbial molecular networks. The mixed-planting meadow produced a visually appealing landscape and dynamic seasonal enrichment, significantly increasing soil total nitrogen (TN) and organic matter (SOM) contents by 1.99 and 1.21 times, respectively. TN had a positive correlation with soil microbial α diversity and community structure. Dominant phyla at both sites included Proteobacteria, Actinobacteria, and Ascomycota. Although soil microorganisms were primarily influenced by stochastic processes, stochasticity was notably higher in the mixed-planting meadow than in the single-planting lawn. The mixed-planting meadow significantly increased the relative abundance of beneficial microorganisms, improving nitrification and aerobic ammonium oxidation of soil bacteria, as well as symbiotroph of fungi. No significant changes were observed in the single-planting lawn. The mixed-planting meadow established a complex soil microbial molecular network, enhancing the correlation between bacteria and fungi and increasing the number of key microorganisms. Our findings suggest the potential of mixed-planting meadow in restoring degraded urban soils by influencing the soil microbial community and enhancing the ecological service function. Our study provides theoretical support for applying mixed-planting meadow communities to improve the soil environment of urban green spaces.

Satellite hyperspectral imagery reveals scale dependence of functional diversity patterns in a Qinghai-Tibetan alpine meadow

Knowing how functional diversity varies across environmental gradients is crucial in investigating biodiversity formation and community assembly processes. The majority of studies concerning functional diversity are based on one fixed plot size, resulting in weakened sensitivity of the spatial patterns to sampling resolution. This weakness may obscure the true mechanisms behind community assemblage. In this study, we utilized hyperspectral imagery collected by Orbita hyperspectral satellites (OHS) to explore the spatial distribution patterns of functional traits and diversity in alpine meadows at four sampling radii (25 m, 50 m, 100 m, 200 m). We examined how functional traits and their diversity responded to variations in climatic and soil conditions at different sampling resolutions. Our findings revealed a significant influence of solar radiation and soil depth on the patterns of functional traits in alpine meadows. Scale dependency was evident in the functional richness (FRic), functional evenness (FEve), and functional dispersion (FDis) of different traits, indicated by a notable difference from the null model. In the presence of stronger soil nutrient and water limitation, chlorophyll a content (CAC), specific leaf area (SLA), leaf thickness (LT), and plant height (Height) exhibited increased clustering and convergence at larger sampling resolutions, illustrating environmental filtering in habitat patch assembly in the alpine meadow. For the first time, this study explores scale dependency in functional diversity patterns in alpine meadows using satellite-based hyperspectral imagery. It unveils the environmental filtering effect of soil nutrient limitation on shaping vegetation patch structures at a landscape level from a remote sensing perspective. The study underscores the significance of accounting for sampling resolution in future related research.

Can coaching bridge the gap for incoming Latinx graduate students?

ABSTRACT Attrition rates in the first two years of graduate school are high and ∼50% higher for underrepresented students. Here we evaluate an online group coaching intervention using a controlled and an experimental group to determine its impact on skills that are likely predictors of student success, namely the Hallmark of Success indices developed by the National Institutes of Health (NIH). Our intervention includes eight online coaching sessions (four prior to the first year of graduate school and four during the first year of graduate school). Coaching group sessions, led by certified coaches, address Resilience, Risk and Protective Factors, Accountability, Leadership, Teamwork, Professional Networking, and Self-Assessment. We evaluated the change in Hallmark of Success indices with a pre- and post-questionnaire of 44 items used to assess students’ self-evaluation in nine areas. Students in the experimental coaching group showed a significant increase in the Hallmark of Success indices in seven out of nine areas of student performance between the pre- and post-questionnaires compared to the null model of no changes between time periods. In addition, in all of the nine indices of success, we noted significant differences between the pre- and post-surveys for the experimental group. Our experimental design shows that our cost-effective coaching model improves student performance as perceived by the students.

Comparison of two statistical measures of complexity applied to ecological bipartite networks

Networks are a convenient way to represent many interactions among different entities as they provide an efficient and clear methodology to evaluate and organize relevant data. While there are many features for characterizing networks, a quantity seems rather elusive: Complexity. The quantification of the complexity of networks is nowadays a fundamental problem. Here, we present a novel tool for identifying the complexity of ecological networks. We compare the behavior of two relevant indices of complexity: K-complexity and Single Value Decomposition (SVD) entropy. For that, we use real data and two types of null models. Both null models consist of randomized networks built by swapping a controlled number of links of the original ones. We analyze 23 plant–pollinator and 19 host-parasite networks as case studies. Our results show that (a) it is necessary to calculate, not only the original network K-complexity and SVD entropy but also to calculate the corresponding indices of the randomized networks (b) the density and degree distribution are essential in the characterization of a network and the randomized networks are a suitable tool to detect the network complexity, and (c) plant–pollinator networks are more complex than host-parasite networks. We found that, for the first null model, K-complexity and SVD did not change with link swapping in both pollinator-plant and host-parasite networks. For the second null model, K-complexity for pollinator-plant networks generally decreased with an increasing number of links swapped (i.e. negative slope), showing that plant–pollinator networks lose complexity with increasing link swapping. In contrast, there was a positive slope between K-complexity and link swapping for host-parasite networks, showing that these networks are less complex than plant–pollinator networks. For both types of networks, in general, the slope between K-complexity and the number of links swapped became more positive with network density. Overall, SVD entropy was less responsive to link swapping. Our analyses show that although SVD entropy has been widely used to characterize network complexity, K-complexity is a more reliable tool. Additionally, they show that degree distribution and density are important drivers of network complexity and should be accounted for in future studies.

Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

Assessing intracellular and extracellular distribution of antibiotic resistance genes in the commercial organic fertilizers

Compost-based organic fertilizers often contain high levels of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs). Previous studies focused on quantification of total ARGs and MGEs. For a more accurate risk assessment of the dissemination risk of antibiotic resistance, it is necessary to quantify the intracellular and extracellular distribution of ARGs and MGEs. In the present study, extracellular ARGs and MGEs (eARGs and eMGEs) and intracellular ARGs and MGEs (iARGs and iMGEs) were separately analyzed in 51 commercial composts derived from different raw materials by quantitative polymerase chain reaction (qPCR) and metagenomic sequencing. Results showed that eARGs and eMGEs accounted for 11–56% and 4–45% of the total absolute abundance of ARGs and MGEs, respectively. Comparable diversity, host composition and association with MGEs were observed between eARGs and iARGs. Contents of high-risk ARGs were similar between eARGs and iARGs, with high-risk ARGs in the two forms accounting for 6.7% and 8.2% of the total abundances, respectively. Twenty-four percent of the overall ARGs were present in plasmids, while 56.7% of potentially mobile ARGs were found to be associated with plasmids. Variation partitioning analysis, null model and neutral community model indicated that the compositions of both eARGs and iARGs were largely driven by deterministic mechanisms. These results provide important insights into the cellular distribution of ARGs in manure composts that should be paid with specific attention in risk assessment and management.

Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations

BackgroundThe co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug–drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems.MethodsThis study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated.ResultsA large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women — with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis.ConclusionsDDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records’ analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.

Inferring the allometric growth coefficient of juvenile African mud catfish, &lt;i&gt;Clarias gariepinus&lt;/i&gt; (Burchell, 1822), using Bayesian and Frequentist regression models

Statistics is essential in biological and ecological scientific research. However, the default Frequentist statistics based on p-value and null hypothesis testing is often misused and misinterpreted, hence causing reproducible crises. The p-value concept deserved further examination because it has been irretrievably lost. Therefore, there is dire need for reform in the default Frequentist statistics as practiced by researchers because of the perils of p-values. Bayesian statistics, using the tools of Bayes Factors and posterior distributions derived from priors and likelihood function; rooted in Bayes’ Theorem is one of the suggested alternatives. Frequentist (least square) and Bayesian (specifying uniform Jeffreys-Zellner-Siow prior, r-scale =0.35) regression models, a standard statistical protocol in fisheries were applied to determine the allometric growth coefficient based on length (mm) and weight (g) measurements of juvenile African mud catfish, Clarias gariepinus from Epe Lagoon. The growth coefficient, b=3.20, 95% Confidence Interval [3.07, 3.34], t(96)=47.55, p&lt;0.001 was significant with 96% explanatory power (R2=0.96).While Bayesian method, with 96% explanatory power (R2=0.96), also estimated, b=3.20, (with Credible Interval between 3.06 and 3.32). The Bayes Factor (&gt;100) suggested the data is more plausible under the alternative model than the null model, but p-value cannot quantify evidence in support of alternative hypothesis, since p-value can only reject or fail to reject a null hypothesis. These findings suggested that juvenile C. gariepinus thrived in Epe Lagoon. Therefore, Bayesian inference is a robust substitute for Frequentist regression model in fisheries.

Incorporating uncertainty within dynamic interoceptive learning.

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty. Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures. Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty. These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

Spatial distribution of woody plants in relation to mistletoe-infected Vachellia karroo trees in a semi-arid African savanna

By increasing resource heterogeneity, mistletoe-infected trees can restructure plant community processes and distribution patterns. No information is available on how mistletoe-infected Vachellia (Acacia) karroo trees within V. karroo dominated stands are spatially distributed, and on how they influence the spatial patterns of their surrounding conspecifics and heterospecifics. Each woody plant was stem mapped using a cartesian plane (x, y) within three 50×50 m plots located in V. karroo dominated stands in a semi-arid savanna, South West Zimbabwe. Pair correlations g(r) were used for the univariate analysis and Poisson process null models were applied to quantify and detect overall departure from randomness. For the bivariate analysis, pair correlations g12(r) under the null model of independence were used, whilst the mark correlation function (kmm(r)) was used to analyse the correlation of tree canopy area and mistletoe infection intensity. For each plot, size class distributions, based on tree height and basal stem diameter displayed negative J curves, with steep negative regression slopes across the size classes, clearly indicating a strongly recruiting population of V. karroo. The univariate patterns of all trees (infected and non-infected) were consistent with a random pattern, which is attributed to unsystematic mistletoe seed dispersal by birds. The univariate analysis of all woody plants (adults and juveniles) exhibited aggregation at small spatial scales due to the high abundance of clustered seedlings and saplings. At small spatial scales, understory woody plants (both conspecifics and non-conspecifics) were positively associated with mistletoe-infected trees due to mistletoephily which is the facilitation (or nurse protégé interactions) within the more resource-rich mistletoe-infected tree subcanopies. These results provide strong evidence suggesting that the variations in spatial pattern modification by mistletoe-infected trees could further increase spatial heterogeneity in this semi-arid savanna. As such, by increasing heterogeneity, mistletoe-infected trees can increase the resilience of semi-arid savannas in the face of perturbations and stochastic events.

Persisting in a transformed landscape: occupancy and habitat association of the range-restricted Agulhas Long-billed Lark Certhilauda brevirostris

ABSTRACT We investigated how the presence of the highly range-restricted Agulhas Long-billed Lark Certhilauda brevirostris (ALBL) varies with vegetation type and landscape features. The lark’s distribution is nearly completely outside formally protected areas and overlaps with a region where agriculture has transformed the landscape, with very little of the original vegetation type remaining. Establishing important habitat requirements is needed to better manage conservation efforts for the species. We conducted 10-minute point counts at 51 sites along small rural roads quarterly between October 2020 and January 2022, and recorded ALBL in 58% of the counts. A subset of 45 sites was used to model occupancy. According to a null constant parameter model, occupancy at sample sites was high (0.91) with a detection probability of 0.67 for each site where the species was present. Modelling of habitat features and vegetation type found that ALBL occupancy was best explained by the presence of renosterveld, the shrubby indigenous vegetation type. Occupancy increased at a site up to 25% renosterveld cover, after which there was no effect. Occupancy was also positively associated with renosterveld vegetation presence along fence lines and a combination of renosterveld and decreasing riverine vegetation cover. Presence was little modified by seasonal changes in agricultural land use. Overall, our results suggest that although the ALBL is locally abundant, certain habitat features need to be preserved to ensure the species’ continued presence in this agricultural region.

Enhanced AHL-mediated quorum sensing accelerates the start-up of biofilm reactors by elevating the fitness of fast-growing bacteria in sludge and biofilm communities

Quorum sensing (QS)-based manipulations emerge as a promising solution for biofilm reactors to overcome challenges from inefficient biofilm formation and lengthy start-ups. However, the ecological mechanisms underlying how QS regulates microbial behaviors and community assembly remain elusive. Herein, by introducing different levels of N-acyl-homoserine lactones, we manipulated the strength of QS during the start-up of moving bed biofilm reactors and compared the dynamics of bacterial communities. We found that enhanced QS elevated the fitness of fast-growing bacteria with high ribosomal RNA operon (rrn) copy numbers in their genomes in both the sludge and biofilm communities. This led to notably increased extracellular substance production, as evidenced by strong positive correlations between community-level rrn copy numbers and extracellular proteins and polysaccharides (Pearson's r = 0.529−0.830, P < 0.001). Network analyses demonstrated that enhanced QS significantly promoted the ecological interactions among taxa, particularly cooperative interactions. Bacterial taxa with higher network degrees were more strongly correlated with extracellular substances, suggesting their crucial roles as public goods in regulating bacterial interactions and shaping network structures. However, the assembly of more cooperative communities in QS-enhanced reactors came at the cost of decreased network stability and modularity. Null model and dissimilarity-overlap curve analysis revealed that enhanced QS strengthened stochastic processes in community assembly and rendered the universal population dynamics more convergent. Additionally, these shaping effects were consistent for both the sludge and biofilm communities, underpinning the planktonic-to-biofilm transition. This work highlights that QS manipulations efficiently drive community assembly and confer specialized functional traits to communities by recruiting taxa with specific life strategies and regulating interspecific interactions. These ecological insights deepen our understanding of the rules governing microbial societies and provide guidance for managing engineering ecosystems.

Beyond fitness: The information imparted in population states by selection throughout lifecycles

We approach the questions, what part of evolutionary change results from selection, and what is the adaptive information flow into a population undergoing selection, as a problem of quantifying the divergence of typical trajectories realized under selection from the expected dynamics of their counterparts under a null stochastic-process model representing the absence of selection. This approach starts with a formulation of adaptation in terms of information and from that identifies selection from the genetic parameters that generate information flow; it is the reverse of a historical approach that defines selection in terms of fitness, and then identifies adaptive characters as those amplified in relative frequency by fitness. Adaptive information is a relative entropy on distributions of histories computed directly from the generators of stochastic evolutionary population processes, which in large population limits can be approximated by its leading exponential dependence as a large-deviation function. We study a particular class of generators that represent the genetic dependence of explicit transitions around reproductive cycles in terms of stoichiometry, familiar from chemical reaction networks. Following Smith (2023), which showed that partitioning evolutionary events among genetically distinct realizations of lifecycles yields a more consistent causal analysis through the Price equation than the construction from units of selection and fitness, here we show that it likewise yields more complete evolutionary information measures.

New insights into substrates shaped nutrients removal, species interactions and community assembly mechanisms in tidal flow constructed wetlands treating low carbon-to-nitrogen rural wastewater

A limited understanding of microbial interactions and community assembly mechanisms in constructed wetlands (CWs), particularly with different substrates, has hampered the establishment of ecological connections between micro-level interactions and macro-level wetland performance. In this study, CWs with distinct substrates (zeolite, CW_A; manganese ore, CW_B) were constructed to investigate the nutrient removal efficiency, microbial interactions, metabolic mechanisms, and ecological assembly for treating rural sewage with a low carbon-to-nitrogen ratio. CW_B showed higher removal of ammonia nitrogen and total nitrogen by about 1.75–6.75% and 3.42–5.18%, respectively, compared to CW_A. Candidatus_Competibacter (denitrifying glycogen-accumulating bacteria) was the dominant microbial genus in CW_A, whereas unclassified_f_Blastocatellaceae (involved in carbon and nitrogen transformation) dominated in CW_B. The null model revealed that stochastic processes (drift) dominated community assembly in both CWs; however, deterministic selection accounted for a higher proportion in CW_B. Compared to those in CW_A, the interactions between microbes in CW_B were more complex, with more key microbes involved in carbon, nitrogen, and phosphorus conversion; the synergistic cooperation of functional bacteria facilitated simultaneous nitrification-denitrification. Manganese ores favour biofilm formation, increase the activity of the electron transport system, and enhance ammonia oxidation and nitrate reduction. These results elucidated the ecological patterns exhibited by microbes under different substrate conditions thereby contributing to our understanding of how substrates shape distinct microcosms in CW systems. This study provides valuable insights for guiding the future construction and management of CWs.

Quantification of microbial community assembly processes during degradation on diverse plastispheres based on physicochemical characters and phylogenetic bin-based null model analysis

To understand the differences in degradation processes depending on the chemical properties of polymers, it is necessary to both quantify the microbiome composition and evaluate the process of microbial turnover (i.e., community assembly processes) in a variety of polymer materials. In this study, using a phylogenetic bin-based null model analysis (i.e., iCAMP), we evaluated community assembly processes from original estuary water to 37 types of polymers, which provide overwhelmingly diverse niches for microbes, in 14-day incubation experiments. First, we evaluated the polymer properties related to degradation rates. Polymers with higher adipic acid (AdA) monomer exhibited higher motility, hydrophilicity, and degradation rates, whereas those with higher aromatic monomer exhibited the opposite trends. Second, microbiome composition analysis was performed, and the microbiomes were significantly changed by the AdA or aromatic content. This was consistent with the polymer properties, suggesting that polymer motility and hydrophilicity attributable to the first-order structure modify the accessibility of the enzyme to the reaction site and hence the degradation rate, resulting in differences in microbiome community composition. Finally, we determined community assembly processes from estuary water to plastics using a phylogenetic bin-based null model analysis. The importance of heterogeneous selection was higher in mobile, hydrophilic, and fast-degrading polymers, while that of homogeneous selection was lower. This suggests that the environmental difference between before and after incubation becomes significant under rapid degradation, which select microbes adapted to biofilm environments. In addition, the more stochastic turnover prevailed, the more variation in the communities (i.e., β-diversity) increased. This suggests that turnover processes not dictated by the environment lead to instability in community compositions.