Community Datasets Research Articles

Bird communities in the Dry Chaco of South America: vegetation structure and climate effects

Species lead to a complex and dynamic environment affected by external processes. Better understanding the importance of these factors is particularly urgent for the world’s tropical dry forest, which is understudied, highly threatened and rapidly disappearing. Building on a unique, field-based bird community dataset, we used multivariate analysis and generalized linear models to test the effects of climate and vegetation structure on bird composition and richness in forest corridors. Our analyses revealed the importance of forest corridors that not only connect the landscape but may facilitate the movement of species, having a high potential for management and connectivity planning. We found significant differences in bird communities to environmental changes when focusing on all birds or when analyzing dry-forest birds only. For all birds, composition revealed preferences of habitat. Birds of open habitats were positively associated with canopy openness, temperature, and relative humidity, while birds to avoid open habitats were positively associated with higher canopy density. The most important variables explaining variations of dry-forest birds were understory and canopy density. Richness increases with temperature for the entire community, yet higher temperatures during the day decrease bird activity. Overall, we showed that bird composition differences were associated with canopy changes, yet richness increased with understory cover. Likewise, our study highlights the importance of maintaining a microenvironment based on local requirements for composition or richness. Moreover, the conservation strategies should be consistent to those requirements to promote the viability of corridors uses that potentially connect the landscape.

Introduction to deep learning methods for multi‐species predictions

Abstract Predicting species distributions and entire communities is crucial for ecologists, to enhance our understanding of the drivers behind species distributions and community assembly and to provide quantitative data for conservation efforts. Popular species distribution models use statistical and machine learning methods but face limitations with multi‐species predictions at the community level, hindered by scalability and data imbalance sensitivity. This paper explores the potential of deep learning methods to overcome these challenges and provide more accurate multi‐species predictions. Specifically, we introduced four distinct deep learning models that use site × species community data but differ in their internal structure or on the input environmental data structure: (1) a multi‐layer perceptron (MLP) model for tabular data (e.g. in‐situ/raster climate or soil data), (2) a convolutional neural network (CNN) and (3) a vision transformer (ViT) models tailored for image data (e.g. aerial ortho‐photographs, satellite imagery), and a multimodal model that integrates both tabular and image data. We also show how adapted loss functions can address imbalance issues. We applied these deep learning models to a plant community dataset comprising 130,582 vegetation surveys encompassing 2522 species located in the French Alps. The tabular environmental data consisted of climate, terrain and soil information, while the images were derived from aerial photographs. All models achieved approximately 70% true skill statistics on hold‐out data, demonstrating high predictive capacity for community data, the multimodal model being the best performing one. Additionally, we showcased how interpretability tools can illuminate community structure as seen by deep learning models. Deep learning models offer a broad array of features for predicting entire species communities. They handle imbalance issues and accommodate various data types, from tabular datasets to images, while also being equipped with insightful interpretation tools. The versatility extends to tabular datasets and images, with no clear superiority between the two. The last hidden layers can provide valuable features for modelling other species, and the trained models can be used to support transfer learning to related tasks. The field of ecology now possesses an additional, potent tool in its arsenal that can foster basic and fundamental research.

Multidrug resistance in urinary E. coli higher in males compared to females

BackgroundUrinary tract infections (UTIs) are common however the widespread use of antibiotics has led to a rise in antimicrobial resistance (AMR) amongst uropathogens, rendering a significant proportion of infections resistant to first line treatment. AMR in UTIs may differentially affect men and women, younger and older patients. The purpose of this study was to investigate MDR (multi-drug resistance) and AMR in males and females in an Australian health district.MethodsThere were 85,844 E. coli urinary isolates (2007–2020) analysed from adult patients. An E. coli isolate with MDR was defined as resistant to at least 1 agent in ≥ 3 antimicrobial classes. Chi-square tests and relative risk were calculated by comparing resistance in males and females and by age for antibiotics commonly used to treat UTIs in hospital and community collected samples.ResultsThere was a higher proportion of MDR E. coli in males compared to females in both the community (6.4% vs. 5.2%, P < 0.001) and hospital datasets (16.5% vs. 12.8%, P < 0.001). The proportions of MDR for both males and females were significantly higher in the hospital setting. Resistance rates were higher in males compared to females for amoxicillin, amoxicillin/clavulanate, cephalexin and norfloxacin (p < 0.005), though not for trimethoprim. Antibiotic resistance was seen to increase over time.ConclusionsA higher proportion of MDR E. coli were noted in urine samples from males compared with females, possibly due to the increased likelihood of prior treatment for UTIs in men. Antimicrobial stewardship interventions could be targeted towards this cohort to address increasing rates of AMR.

Semantic Web Approaches in Stack Overflow

StackOverflow (SO), a prominent question-answering site for programming, has amassed a vast repository of user-generated content since its inception in 2008. This paper conducts a thorough analysis of research trends on SO, examining 170 publications from 2008 to 2019. Utilizing qualitative and quantitative methods, the study categorizes papers using literature review and Latent Dirichlet Allocation (LDA), identifying 62 topics grouped into 8 main categories. Additionally, it highlights tools developed by researchers using SO data sets, showcasing their practical applications. The analysis also identifies research gaps and proposes future directions for each research area. This study serves as a valuable resource for practitioners and researchers interested in utilizing community data sets, offering insights into existing work, essential tools and techniques, and potential avenues for future research.

Macrophage heterogeneity in cardiometabolic disease

Abstract Metabolic diseases such as obesity and atherosclerosis are characterised by chronic inflammation that leads to the accumulation of immune cells in affected organs. Macrophages in the obese adipose tissue and the atherosclerotic plaque show transcriptional, phenotypic and functional heterogeneity. The accumulation of CD11c+ macrophages in atherosclerosis and obesity suggests the presence of conserved macrophage phenotypes in metabolic tissues. We aimed to characterise shared and distinct features of monocytes and macrophages in the aorta and adipose tissue in the context of metabolic disease using single-cell techniques. Single-cell RNA sequencing of the blood, aorta and visceral adipose tissue during a time course of metabolic disease progression (steady state, 12w western diet, 18w western diet) and data integration with murine aortic and adipose tissue community datasets revealed conserved and tissue specific features of metabolic disease. Resident macrophages in the aorta and adipose tissue displayed tissue specific transcriptional programs, highlighting that cell maintenance and function of tissue resident macrophages differ between metabolic tissues. Further, macrophages in the aorta and adipose tissue undergo disease associated compositional changes. For example, we uncovered distinct CD11c+ macrophage subsets that are conserved in the aorta and adipose tissue across multiple metabolic disease models and share core transcriptional signatures between both metabolic tissues. However, CD11c+ macrophage subsets showed tissue specific dynamics during metabolic disease progression. Overall, our analysis underscores that tissue niche affects macrophage composition as well as activation and transcriptional regulation patterns. Understanding similarities and differences between the two tissues will be important to inform identification of new therapeutic targets for cardiometabolic diseases.

Coping With Interstitial Cystitis/Bladder Pain Syndrome.

Compensatory coping, or maladaptive alterations in behavior with the intention of preventing or managing symptoms, is increasingly being explored as a key factor in how people respond to bladder conditions. Preliminary investigations have identified relations between coping behaviors and psychological distress in urologic conditions, including interstitial cystitis/bladder pain syndrome (IC/BPS). However, previous explorations of coping have not accounted for heterogeneity in coping behaviors or addressed the likelihood that some coping behaviors may be more adaptive than others. This study sought to examine how two specific types of coping behaviors, primary control coping and disengaged coping, are related to distress and symptoms in IC/BPS, and to explore the potential role of pain phenotype in this relationship. A secondary data analysis was conducted with a large community data set (N = 677 women with IC/BPS) and employed descriptive and inferential statistics to characterize coping patterns and explore novel predictors of distress. Results indicated that almost all participants engaged in at least one compensatory coping behavior within the last week. Both types of coping behaviors correlated with psychological symptoms, and when controlling for relevant clinical variables (i.e., age and severity of urinary symptoms), disengaged coping behaviors were significantly associated with psychological distress. Further, the addition of pain phenotype to multiple regression models resulted in a more effective predictive model when considering the relation between coping behaviors and depression. By investigating more deeply the relationship between coping and distress, understanding of potential risk factors and mechanisms is increased, offering valuable insights for intervention strategies for IC/BPS patients.

Complete pipeline for Oxford Nanopore Technology amplicon sequencing (ONT-AmpSeq): from pre-processing to creating an operational taxonomic unit table.

Amplicon sequencing has long served as a robust method for characterising microbial communities, and despite inherent resolution limitations, it remains a preferred technique, offering cost- and time-effective insights into bacterial compositions. Here, we introduce ONT-AmpSeq, a user-friendly pipeline designed for processing amplicon sequencing data generated from Oxford Nanopore Technology (ONT) devices. Our pipeline enables efficient creation of taxonomically annotated operational taxonomic unit (OTU) tables from ONT sequencing data, with the flexibility to multiplex amplicons on the same barcode. The pipeline encompasses six main steps-statistics, quality filtering, alignment, clustering, polishing, and taxonomic classification-integrating various state-of-the-art software tools. We provide a detailed description of each step, along with performance tests and robustness evaluations using both test data and a ZymoBIOMICS® Microbial Community Standard mock community dataset. Our results demonstrate the ability of ONT-AmpSeq to effectively process ONT amplicon data, offering valuable insights into microbial community composition. Additionally, we discuss the influence of polishing tools on taxonomic insight and the impact of taxonomic annotation methods on the derived microbial composition. Overall, ONT-AmpSeq represents a comprehensive solution for analysing ONT amplicon sequencing data, facilitating streamlined and reliable microbial community analysis. The pipeline, along with test data, is freely available for public use.

Do community changes persist after irruptive population dynamics? A case study from an invasive species boom and bust.

Irruptive or boom-and-bust population dynamics, also known as 'outbreaks', are an important phenomenon that has been noted in biological invasions at least since Charles Elton's classic book was published in 1958. Community-level consequences of irruptive dynamics are poorly documented and invasive species provide excellent systems for their study. African Jewelfish (Rubricatochromis letourneuxi, "jewelfish") are omnivores that demonstrate opportunistic carnivory, first reported in Florida in the 1960s and in Everglades National Park (ENP) in 2000. Twelve years after invasion in ENP, jewelfish underwent a 25-fold increase in density in one year. By 2016, jewelfish represented 25-50% of fish biomass. Using a 43-year fish community dataset at two sites (1978-2021), and a 25-year dataset of fish and invertebrate communities from the same drainage (1996-2021), with additional spatial coverage, we quantified differences in fish and invertebrate communities during different phases of invasion. During jewelfish boom, abundant, native cyprinodontiform fishes decreased in density and drove changes in community structure as measured by similarity of relativized abundance. Density of two species declined by > 70%, while four declined by 50-62%. Following the jewelfish bust, some species recovered to pre-boom densities while others did not. Diversity of recovery times produced altered community structure that lagged for at least four years after the jewelfish population declined. Community structure is an index of ecological functions such as resilience, productivity, and species interaction webs; therefore, these results demonstrate that irruptive population dynamics can alter ecological functions of ecosystems mediated by community structure for years following that population's decline.

Climate change vulnerability of Arctic char across Scandinavia.

Climate change is anticipated to cause species to shift their ranges upward and poleward, yet space for tracking suitable habitat conditions may be limited for range-restricted species at the highest elevations and latitudes of the globe. Consequently, range-restricted species inhabiting Arctic freshwater ecosystems, where global warming is most pronounced, face the challenge of coping with changing abiotic and biotic conditions or risk extinction. Here, we use an extensive fish community and environmental dataset for 1762 lakes sampled across Scandinavia (mid-1990s) to evaluate the climate vulnerability of Arctic char (Salvelinus alpinus), the world's most cold-adapted and northernly distributed freshwater fish. Machine learning models show that abiotic and biotic factors strongly predict the occurrence of Arctic char across the region with an overall accuracy of 89 percent. Arctic char is less likely to occur in lakes with warm summer temperatures, high dissolved organic carbon levels (i.e., browning), and presence of northern pike (Esox lucius). Importantly, climate warming impacts are moderated by habitat (i.e., lake area) and amplified by the presence of competitors and/or predators (i.e., northern pike). Climate warming projections under the RCP8.5 emission scenario indicate that 81% of extant populations are at high risk of extirpation by 2080. Highly vulnerable populations occur across their range, particularly near the southern range limit and at lower elevations, with potential refugia found in some mountainous and coastal regions. Our findings highlight that range shifts may give way to range contractions for this cold-water specialist, indicating the need for pro-active conservation and mitigation efforts to avoid the loss of Arctic freshwater biodiversity.

Extending the CWM approach to intraspecific trait variation: how to deal with overly optimistic standard tests?

Community weighted means (CWMs) are widely used to study the relationship between community-level functional traits and environment. For certain null hypotheses, CWM-environment relationships assessed by linear regression or ANOVA and tested by standard parametric tests are prone to inflated Type I error rates. Previous research has found that this problem can be solved by permutation tests (i.e., the max test). A recent extension of the CWM approach allows the inclusion of intraspecific trait variation (ITV) by the separate calculation of fixed, site-specific, and intraspecific CWMs. The question is whether the same Type I error rate inflation exists for the relationship between environment and site-specific or intraspecific CWM. Using simulated and real-world community datasets, we show that site-specific CWM-environment relationships have also inflated Type I error rate, and this rate is negatively related to the relative ITV magnitude. In contrast, for intraspecific CWM-environment relationships, standard parametric tests have the correct Type I error rate, although somewhat reduced statistical power. We introduce an ITV-extended version of the max test, which can solve the inflation problem for site-specific CWM-environment relationships and, without considering ITV, becomes equivalent to the "original" max test used for the CWM approach. We show that this new ITV-extended max test works well across the full possible magnitude of ITV on both simulated and real-world data. Most real datasets probably do not have intraspecific trait variation large enough to alleviate the problem of inflated Type I error rate, and published studies possibly report overly optimistic significance results.

Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity.

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

LighterFace Model for Community Face Detection and Recognition

This research proposes a face detection algorithm named LighterFace, which is aimed at enhancing detection speed to meet the demands of real-time community applications. Two pre-trained convolutional neural networks are combined, namely Cross Stage Partial Network (CSPNet), and ShuffleNetv2. Connecting the optimized network with Global Attention Mechanism (GAMAttention) extends the model to compensate for the accuracy loss caused by optimizing the network structure. Additionally, the learning rate of the detection model is dynamically updated using the cosine annealing method, which enhances the convergence speed of the model during training. This paper analyzes the training of the LighterFace model on the WiderFace dataset and a custom community dataset, aiming to classify faces in real-life community settings. Compared to the mainstream YOLOv5 model, LighterFace demonstrates a significant reduction in computational demands by 85.4% while achieving a 66.3% increase in detection speed and attaining a 90.6% accuracy in face detection. It is worth noting that LighterFace generates high-quality cropped face images, providing valuable inputs for subsequent face recognition models such as DeepID. Additionally, the LighterFace model is specifically designed to run on edge devices with lower computational capabilities. Its real-time performance on a Raspberry Pi 3B+ validates the results.

Neural Greenery: Advancement in Plant Leaf Diseases Recognition Using Deep Learning

Agriculture is the backbone of our country. Large section in our country is highly dependent on agriculture. High production of crop provides global food security. High production largely depends on health of plant. Plant health is greatly affected by pest, climatic condition etc. Thus, early detection of these disease is very necessary to stop further spread of the disease. In this paper, we have proposed a real-time plant leaf disease detection system exploring deep learning techniques. This model is capable of recognizing several types of plant disease using real time data of leave. Our approach utilizes convolutional neural networks (CNNs) to automatically extract relevant features from leaf images, enabling accurate classification of healthy and diseased leaves. Firstly, all enlargement is applied on dataset to increase the sample size. Convolution Neural Network (CNN) is used with several convolution and pooling layers. Botanical community dataset is used to train the model. After training the model, it is examined properly to validate the results. Keywords: Plant Disease, Convolution Neural Network (CNN), Deep Learning, Agriculture, and Botanical community.

MCSS: microbial community simulator based on structure.

De novo assembly plays a pivotal role in metagenomic analysis, and the incorporation of third-generation sequencing technology can significantly improve the integrity and accuracy of assembly results. Recently, with advancements in sequencing technology (Hi-Fi, ultra-long), several long-read-based bioinformatic tools have been developed. However, the validation of the performance and reliability of these tools is a crucial concern. To address this gap, we present MCSS (microbial community simulator based on structure), which has the capability to generate simulated microbial community and sequencing datasets based on the structure attributes of real microbiome communities. The evaluation results indicate that it can generate simulated communities that exhibit both diversity and similarity to actual community structures. Additionally, MCSS generates synthetic PacBio Hi-Fi and Oxford Nanopore Technologies (ONT) long reads for the species within the simulated community. This innovative tool provides a valuable resource for benchmarking and refining metagenomic analysis methods. Code available at: https://github.com/panlab-bio/mcss.

Interaction network rewiring and species' contributions to community-scale flexibility.

The architecture of species interaction networks is a key factor determining the stability of ecological communities. However, the fact that ecological network architecture can change through time is often overlooked in discussions on community-level processes, despite its theoretical importance. By compiling a time-series community dataset involving 50 spider species and 974 Hexapoda prey species/strains, we quantified the extent to which the architecture of predator-prey interaction networks could shift across time points. We then developed a framework for finding species that could increase the flexibility of the interaction network architecture. Those "network coordinator" species are expected to promote the persistence of species-rich ecological communities by buffering perturbations in communities. Although spiders are often considered as generalist predators, their contributions to network flexibility vary greatly among species. We also found that detritivorous prey species can be cores of interaction rewiring, dynamically interlinking below-ground and above-ground community dynamics. We further found that the predator-prey interactions between those network coordinators differed from those highlighted in the standard network-analytical framework assuming static topology. Analyses of network coordinators will add a new dimension to our understanding of species coexistence mechanisms and provide platforms for systematically prioritizing species in terms of their potential contributions in ecosystem conservation and restoration.

A New Species Richness Measure Improved From Margalef And Menhinick Indices

In the present study, a new estimator is proposed to measure species richness. Its’ essential inputs are alfa scale parameters of Margalef and Menhinick indices. To evaluate the performance of the new estimator, a hypothetical community dataset was used. The computations were performed using a spreadsheet program created for the new estimator. The new proposed estimator is also integrated into the BİÇEB software. According to estimation results, the new estimator seems to be a better form rather than Margalef and Menhinick indices. Therefore it may be employed for comparing species richness of the natural communities. To better understand the performance of the new estimator, further studies should be however generated using various types of real ecological data.

A Community Data Set for Comparing Automated Coronal Hole Detection Schemes

Automated detection schemes are nowadays the standard approach for locating coronal holes in extreme-UV images from the Solar Dynamics Observatory (SDO). However, factors such as the noisy nature of solar imagery, instrumental effects, and others make it challenging to identify coronal holes using these automated schemes. While discrepancies between detection schemes have been noted in the literature, a comprehensive assessment of these discrepancies is still lacking. The contribution of the Coronal Hole Boundary Working Team in the COSPAR ISWAT initiative to close this gap is threefold. First, we present the first community data set for comparing automated coronal hole detection schemes. This data set consists of 29 SDO images, all of which were selected by experienced observers to challenge automated schemes. Second, we use this community data set as input to 14 widely applied automated schemes to study coronal holes and collect their detection results. Third, we study three SDO images from the data set that exemplify the most important lessons learned from this effort. Our findings show that the choice of the automated detection scheme can have a significant effect on the physical properties of coronal holes, and we discuss the implications of these findings for open questions in solar and heliospheric physics. We envision that this community data set will serve the scientific community as a benchmark data set for future developments in the field.

Predicting niche overlap with model‐based ordination

The ecological niche is a fundamental concept in ecology that can be used in order better understand species relationships. The overlap in species niches provides a measure of the likelihood for species to co‐occur. Most approaches that quantify niche overlap have been based on distance and similarity indices, for pairwise combinations of species. In this paper, we suggest that niche overlap can be calculated from the predictions of a model. Using a statistical model to predict niche overlap provides various benefits, includes the possibility to adjust the model to properties of the data. We demonstrate this using an example dataset of an ecological community of Foraminifera species, to which we fit a generalized linear latent variable model (GLLVM). GLLVMs are a flexible class of models that allow to estimate the distribution of species using both measured environmental predictors and residual covariation between species. We demonstrate how to calculate niche overlap from GLLVMs for any combination of species, and separately for different environments. Predicting niche overlap from a model further expands the toolset available to ecologists for the exploration of species co‐occurrence patterns.

ARAapp: filling gaps in the ecological knowledge of spiders using an automated and dynamic approach to analyze systematically collected community data.

The ARAMOB data repository compiles meticulously curated spider community datasets from systematical collections, ensuring a high standard of data quality. These datasets are enriched with crucial methodological data that enable the datasets to be aligned in time and space, facilitating data synthesis across studies, respectively, collections. To streamline the analysis of these datasets in a species-specific context, a suite of tailored ecological analysis tools named ARAapp has been developed. By harnessing the capabilities of ARAapp, users can systematically evaluate the spider species data housed within the ARAMOB repository, elucidating intricate relationships with a range of parameters such as vertical stratification, habitat occurrence, ecological niche parameters (moisture and shading) and phenological patterns. Database URL: ARAapp is available at www.aramob.de/en.

LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome.

Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of linked-read technologies are well known and have been demonstrated in many human genomic and metagenomic studies. However, existing linked-read analysis pipelines (e.g., Long Ranger) were primarily developed to process sequencing data from the human genome and are not suited for analyzing metagenomic sequencing data. Moreover, linked-read analysis pipelines are typically limited to 1 specific sequencing platform. To address these limitations, we present the Linked-Read ToolKit (LRTK), a unified and versatile toolkit for platform agnostic processing of linked-read sequencing data from both human genome and metagenome. LRTK provides functions to perform linked-read simulation, barcode sequencing error correction, barcode-aware read alignment and metagenome assembly, reconstruction of long DNA fragments, taxonomic classification and quantification, and barcode-assisted genomic variant calling and phasing. LRTK has the ability to process multiple samples automatically and provides users with the option to generate reproducible reports during processing of raw sequencing data and at multiple checkpoints throughout downstream analysis. We applied LRTK on linked reads from simulation, mock community, and real datasets for both human genome and metagenome. We showcased LRTK's ability to generate comparative performance results from preceding benchmark studies and to report these results in publication-ready HTML document plots. LRTK provides comprehensive and flexible modules along with an easy-to-use Python-based workflow for processing linked-read sequencing datasets, thereby filling the current gap in the field caused by platform-centric genome-specific linked-read data analysis tools.