Species Abundance Distribution Research Articles

Enhancing model robustness to imbalanced species abundance distributions: Eliminating misclassified records via a model-agnostic approach, exemplified by tuna fisheries datasets

Anomalies in species abundance data can potentially cause classification errors in ecological forecasting models. Accurate estimation of anomalies locations can enhance the predictive capacity of models. This study aims to propose an approach for precisely identifying and correcting anomalies within imbalanced species abundance data, thereby addressing the challenges posed by both anomalous and imbalanced species abundance distributions (SADs). A model-agnostic statistical tool, Confident Learning (CL) theory, is introduced to estimate the probability of each sample being misclassified during the prediction phase. Specifically, the approach targets classification errors from models trained on imbalanced SADs for data-cleansing, identifying these records as anomalies. The approach is applied to tuna fisheries datasets, focusing specifically on bigeye tuna (Thunnus obesus), a targeted species in longline fishing, and albacore tuna (Thunnus alalunga), a non-target species in the tropical Atlantic Ocean. These datasets, spanning from 2016 to 2019, featured a spatial resolution of 0.5° × 0.5° and daily temporal resolution, providing a comprehensive view of imbalanced data scenarios. The results demonstrate that all the predictors: Support Vector Machine (SVM), Logistic Regression (LR), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) were considerably enhanced after training on the cleaned datasets. Notably, SVM and LR achieved overall accuracy rates of over 90% on predicting both low- and high-abundant fishing grounds. The proposed approach reveals that the elimination of anomalies can enhance the robustness of ecological forecasting models to imbalanced SADs, offering new insights and technical support for the delicate prediction and assessment of ecological resources.

Monitoring fish populations using stereo-DOV-based surveys and open-access tools in the Gulf of California

Monitoring fish populations is essential to assess spatial and temporal changes, particularly in an era in which some species are experiencing a rapid decline. Among the great diversity of methodologies for this purpose, stereo-video systems have grown in popularity in the past few decades. However, despite their advantages for surveying fishes, funding for research and monitoring of marine systems is limited. In this context, we aimed to estimate fish relative abundance, species richness, and length-frequency distribution in a Marine Protected Area (MPA) integrating open-source software tools. We recorded fish species using a diver-operated stereo-video system (stereo-DOV) comprised of a pair of GoPro cameras. We conducted five surveys in El Bajo Espiritu Santo seamount (EBES; Gulf of California, Mexico) between September 2021 and October 2022. We registered 90,039 individuals belonging to 26 species, from which 942 fish individuals from 13 species were measured. Species richness remained about the same during the five surveys, whereas fish relative abundance was lower in March 2022. Species composition shifted throughout the year, with 11–13 species found through the year, and the rest found seasonally. The methods presented here offer a low-budget option for institutions to conduct fish monitoring programs.

An Ecoinformatic Model Using Rényi's Entropy Predicts Soil Chemistry Effect on Tree Species Abundance Distributions

ABSTRACTAimAn ecological community consists of species of various abundances that reflect their responses to the environmental conditions. A classic macroecological pattern, the species abundance distribution (SAD), has been studied for diverse taxa and communities and integrated into numerous modelling tools. Despite its widespread use, a mathematical model that can capture variations in the empirical SAD and describe its response to environmental changes is still lacking. By integrating the Maximum Entropy Theory of Ecology (METE) with a generalised entropy called Rényi's entropy, we aim to develop a new ecoinformatic model that can predict the variation of empirical SAD along multiple environmental gradients.LocationPanama.TaxonAngiosperms.MethodsWe extend the METE using the Rényi's entropy as an uncertainty measure. We apply this extended METE, called Rényi model, to the tree abundance data from 49 plots in Panama and predict the SAD within each plot. We estimate Rényi's parameter q by fitting the predicted SAD to the empirical SAD in each plot. We further compile climate and soil data from the Panama plots and analyse their relationships with the estimated q using multiple regressions.ResultsRényi model provides adequate description of the empirical SADs and outperforms lognormal or log‐series models in 40 of the 49 tree plots, according to the Akaike information criterion. Variations in Renyi's q estimates (from 1/2 to 1) reflect shifts in the empirical SADs. Multiple regressions reveal that P, Al and NH4, three soil chemicals that are important for tree growth and species distribution, significantly affect Renyi's q across plots.Main ConclusionsThese findings suggest that the Rényi model and Rényi's q can characterise the SAD of communities under environmental changes. They also indicate the potential of using generalised entropies to predict macroecological patterns in stressed ecosystems.

A potential tool for marine biogeography: eDNA-dominant fish species differ among coastal habitats and by season concordant with gear-based assessments.

Effective ocean management asks for up-to-date knowledge of marine biogeography. Here we compare eDNA and gear-based assessments of marine fish populations using an approach that focuses on the commonest species. The protocol takes advantage of the "hollow curve" of species abundance distributions, with a minority of species comprising the great majority of individuals or biomass. We analyzed new and published teleost eDNA metabarcoding surveys from three neighboring northwest Atlantic coastal locations representing sandy, rocky, or estuary habitat. Fish eDNA followed a hollow curve species abundance distribution at each location-the 10 commonest taxa accounted for more than 90% of eDNA copies. Top ten taxa were designated eDNA-dominant species (eDDS) and categorized as habitat-associated (top 10 in one study) or as shared. eDDS by category were similarly abundant in concurrent bottom trawl and seine surveys. eDDS habitat category profiles correctly classified most (94%-100%) individual eDNA and capture measurements within surveys and recognized estuarine sites in other regional eDNA and seine studies. Using a category metric like that for habitats, eDDS demonstrated strong seasonal turnover concordant with trawl catch weights. eDNA seasonal profiles applied to historical trawl and seine records highlighted known long-term trends in mid-Atlantic fish populations. This study provides evidence that eDNA-abundant fish species differ among coastal habitats and by season consistent with gear-based assessments. Grouping abundant species by category facilitated comparisons among habitats and integration with established surveys. eDNA metabarcoding of dominant fish species potentially offers a useful tool for marine biogeography and ocean monitoring.

GEMimp: An Accurate and Robust Imputation Method for Microbiome Data Using Graph Embedding Neural Network

Microbiome research has increasingly underscored the profound link between microbial compositions and human health, with numerous studies establishing a strong correlation between microbiome characteristics and various diseases. However, the analysis of microbiome data is frequently compromised by inherent sparsity issues, characterized by a substantial presence of observed zeros. These zeros not only skew the abundance distribution of microbial species but also undermine the reliability of scientific conclusions drawn from such data. Addressing this challenge, we introduce GEMimp, an innovative imputation method designed to infuse robustness into microbiome data analysis. GEMimp leverages the node2vec algorithm, which incorporates both Breadth-First Search (BFS) and Depth-First Search (DFS) strategies in its random walks sampling process. This approach enables GEMimp to learn nuanced, low-dimensional representations of each taxonomic unit, facilitating the reconstruction of their similarity networks with unprecedented accuracy.Our comparative analysis pits GEMimp against state-of-the-art imputation methods including SAVER, MAGIC and mbImpute. The results unequivocally demonstrate that GEMimp outperforms its counterparts by achieving the highest Pearson correlation coefficient when compared to the original raw dataset. Furthermore, GEMimp shows notable proficiency in identifying significant taxa, enhancing the detection of disease-related taxa and effectively mitigating the impact of sparsity on both simulated and real-world datasets, such as those pertaining to Type 2 Diabetes (T2D) and Colorectal Cancer (CRC). These findings collectively highlight the strong effectiveness of GEMimp, allowing for better analysis on microbial data. With alleviation of sparsity issues, it could be greatly facilitated in downstream analyses and even in the field of microbiology.

Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in prokaryotic metagenome-assembled genome recovery.

We hypothesize that sample species abundance, sequencing depth, and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG recovery in three in silico microbial communities composed of 42 species with the same richness but different sample species abundance, sequencing depth, and taxonomic distribution profiles using three different pipelines for MAG recovery. The pipeline developed by Parks and colleagues (8K) generated the highest number of MAGs and the lowest number of true positives per community profile. The pipeline by Karst and colleagues (DT) showed the most accurate results (~ 92%), outperforming the 8K and Multi-Metagenome pipeline (MM) developed by Albertsen and collaborators. Sequencing depth influenced the accurate recovery of genomes when using the 8K and MM, even with contrasting patterns: the MM pipeline recovered more MAGs found in the original communities when employing sequencing depths up to 60 million reads, while the 8K recovered more true positives in communities sequenced above 60 million reads. DT showed the best species recovery from the same genus, even though close-related species have a low recovery rate in all pipelines. Our results highlight that more bins do not translate to the actual community composition and that sequencing depth plays a role in MAG recovery and increased community resolution. Even low MAG recovery error rates can significantly impact biological inferences. Our data indicates that the scientific community should curate their findings from MAG recovery, especially when asserting novel species or metabolic traits.

Peculiarities of the Species Composition, Abundance and Biotope Distribution of Mollusks Fam. Sphaeriidae in Rivers with a Many Lakes in the Catchment Area (by the Example of Water Courses of the Onega Lake Basin)

Using the example of the rivers of the Onega Lake basin, the species composition of mollusks of the Sphaeriidae family, their abundance, biomass, and contribution to benthic communities in rivers with many lakes in the catchment areas were analyzed. Patterns of spatial distribution of species across biotopes and gradients of environmental variables were traced. 26 species of mollusks of this family were identified, among them European-Siberian species predominate. Most of the species of mollusks of the family Sphaeriidae found by us have wide ecological ranges and are found in water bodies of various types. It was found that the contribution of Sphaeriidae to the species richness and functioning of aquatic communities in rivers in the conditions of branched lake-river systems (Lake Onega basin) exceeds the values usual for rivers with a small number of lakes in the catchment area. Using the ANOSIM and PERMANOVA methods, it was proved that the species composition of rocky rapids and riffles, sandy rifts and soft bottom soils is different. Using the IndVal test, seven species were shown to have a statistically significant association with one of these biotopes, and four of them, with rocky bottoms of river rapids and riffles. Using the NMDS method, it was shown that the greatest changes in the species composition of representatives of Sphaeriidae are observed when environmental conditions change from small streams with slow current and brown water to large rivers with fast current and clear water.

Trait-based ecology, trait-free ecology, and in between.

Trait-based ecology has become a popular phrase. But all species have traits, and their contributions to ecological processes are governed by those traits. So then, is not all ecology trait-based? Actually, there do exist areas of ecology that are consciously trait-free, such as neutral theory and species abundance distributions. But much of ecology could be considered actually or potentially trait-based. A spectrum is described, from trait-free through trait-implicit and trait-explicit to trait-centric. Trait-centric ecology includes positioning ecological strategies along trait dimensions, with a view to inferring commonalities and to generalizing from species studied in more detail. Trait-explicit includes physiological and functional ecology, and areas of community ecology and ecosystem function that invoke traits. Trait-implicit topics are those where it is important that species are different, but formulations did not initially characterize the differences via traits. Subsequently, strands within these trait-implicit topics have often moved towards making use of species traits, so the boundary with trait-explicit is permeable. Trait-based ecology is productive because of the dialogue between understanding processes in detail, via traits that relate most closely, and generalizing across many species, via traits that can be compared widely. An enduring key question for trait-based ecology is which traits for which processes.

An Ecological Survey of Chiggers (Acariformes: Trombiculidae) Associated with Small Mammals in an Epidemic Focus of Scrub Typhus on the China-Myanmar Border in Southwest China.

Chiggers (chigger mites) are a group of tiny arthropods, and they are the exclusive vector of Orientia tsutsugamushi (Ot), the causative agent of scrub typhus (tsutsugamushi disease). Dehong Prefecture in Yunnan Province of southwest China is located on the China-Myanmar border and is an important focus of scrub typhus. Based on the field surveys in Dehong between 2008 and 2022, the present paper reports the infestation and ecological distribution of chiggers on the body surface of rodents and other sympatric small mammals (shrews, tree shrews, etc.) in the region for the first time. The constituent ratio (Cr), prevalence (PM), mean abundance (MA), and mean intensity (MI) were routinely calculated to reflect the infestation of small-mammal hosts with chiggers. Additionally, the species richness (S), Shannon-Wiener diversity index (H), Simpson dominance index (D), and Pielou's evenness index (E) were calculated to illustrate the chigger community structure. Preston's log-normal model was used to fit the theoretical curve of species abundance distribution, and the Chao 1 formula was used to roughly estimate the expected total species. The "corrplot" package in R software (Version 4.3.1) was used to analyze interspecific relationships, and the online drawing software was used to create a chord diagram to visualize the host-chigger associations. From 1760 small-mammal hosts, a total of 9309 chiggers were identified as belonging to 1 family, 16 genera, and 117 species, with high species diversity. The dominant chigger species were Leptotrombidium deliense, Walchia ewingi, and Gahrliepia longipedalis, with a total Cr = 47.65% (4436/9309), among which L. deliense is the most important vector of Ot in China. The overall infestation indexes (PM, MA, and MI) and community parameters (S, H, and E) of chiggers in the mountainous areas and outdoors were higher than those in the flatland areas and indoors, with an obvious environmental heterogeneity. Leptotrombidium deliense was the dominant species in the flatland and indoors, while G. longipedalis was the prevalent species in the mountainous and outdoor areas. The species abundance distribution of the chigger community conformed to log-normal distribution with the theoretical curve equation: S(R)'=28e-[0.23(R-0)]2, indicating the existence of many rare species and only a few dominant species in the community. The expected total number of chigger species was roughly estimated to be 147 species, 30 more than the 117 species actually collected, suggesting that some uncommon species may have been missed in the sampling survey. The host-parasite association analysis revealed that one host species can harbor different chigger species, and one chigger species can parasitize different host species with low host specificity. A positive or negative correlation existed among different chigger species, indicating a cooperative or competitive interspecific relationship. The species diversity of chiggers is high in Dehong on the China-Myanmar border, and a large host sample is recommended to find more uncommon species. There is an obvious environmental heterogeneity of the chigger community, with different species diversity and dominant species in different environments. The low host specificity of chiggers and the occurrence of a large number of L. deliense in Dehong, especially in flatland areas and indoors, would increase the risk of persistent transmission of scrub typhus in the region.

Generalized Lotka-Volterra Systems with Time Correlated Stochastic Interactions.

In this Letter, we explore the dynamics of species abundances within ecological communities using the generalized Lotka-Volterra (GLV) model. At variance with previous approaches, we present an analysis of GLV dynamics with temporal stochastic fluctuations in interaction strengths between species. We develop a dynamical mean field theory (DMFT) tailored for scenarios with colored noise interactions, which we term annealed disorder, and simple functional responses. Our DMFT framework enables us to show that annealed disorder acts as an effective environmental noise; i.e., every species experiences a time-dependent environment shaped by the collective presence of all other species. We then derive analytical predictions for the species abundance distribution that well match empirical observations. Our results suggest that annealed disorder in interaction strengths favors species coexistence and leads to a large pool of very rare species in the systems, supporting the insurance theory of biodiversity. This Letter offers new insights not only into the modeling of large ecosystem dynamics but also proposes novel methodologies for examining ecological systems.

Species Richness, Abundance, and Vertical Distribution of Epiphytic Bromeliads in Primary Forest and Disturbed Forest.

Epiphytes represent a key component in tropical forests. They are affected by anthropogenic and natural disturbances suffered by forests, since they depend on their hosts and the microclimatic conditions they generate. We analyzed differences in abundance, species richness, and vertical distributions of epiphytic bromeliads in primary and disturbed forests. We found a higher abundance (5316 individuals) and species richness (8 species) of bromeliads in disturbed forest than in primary forest (1360 individuals and 4 species, respectively). Most bromeliads (97%) were found on Taxodium mucronatum, a dominant tree with rough bark in the disturbed forest (gallery forest). Bromeliads were more abundant in the middle of the tree and diminished towards the trunk base and the upper crown. Tillandsia baileyi was the most abundant bromeliad, and the size categories of this species differentially colonize trees in gallery forest according to Johansson zones; seedlings of T. baileyi abundantly colonize the upper canopy, and juveniles colonize the middle canopy or secondary branches. Gallery forest represents an important reservoir for epiphytic bromeliads. Hence, it is important to extend this kind of study to wetland sites to understand the role they play as a habitat for epiphytes, as well as the dynamics and ecological processes that occur in such habitats.

Generalized Dynamical Mean Field Theory for Non-Gaussian Interactions.

We present a generalized dynamical mean field theory for studying the effects of non-Gaussian quenched noise in a general set of dynamical systems. We apply the framework to the generalized Lotka-Volterra equations, a central model in theoretical ecology, where species interactions are fixed over time and heterogeneous. Our results show that the new mean field equations have solutions that depend on all cumulants of the distribution of species interactions. We obtain an analytic solution when the interaction couplings are α-stable distributed and find a relationship between the abundance distribution of species and the statistics of microscopic interactions. In the case of sparse interactions, which we investigate analytically, we establish a simple relationship between the distribution of interactions and the one of population densities.

Phylogenetic relatedness of plant species co‐occurring with an invasive alien plant species varies with elevation

AbstractDarwin's naturalization conundrum posits that the alien species either succeed in the introduced region because they are phylogenetically related to the native species and thus tend to have niches similar to those of native species, or they are phylogenetically dissimilar to native species and thus occupy unfilled niches. This conundrum has received a lot of attention, but the findings regarding these two hypotheses have been contradictory. To reconcile the two seemingly contradictory hypotheses, we assessed the phylogenetic relationship of a highly widespread and invasive species, Anthemis cotula L. (focal species), separately with the native species and all its co‐occurring species (including native and non‐native species) along an elevation gradient. Our results show that the abundance of A. cotula declined continuously with an increase in elevation and with an increase in the number of both all co‐occurring and native species only. The phylogenetic distance between the focal species and the native species based on weighted mean pairwise distance (MPDaw) declined with elevation as well as native species richness. Our study also revealed that soil nutrients strongly influence the abundance of A. cotula; however, their effect on the phylogenetic distance between the focal and other species is negligible. These findings suggest a strong role of microecological factors and spatial heterogeneity in the abundance distributions of invasive species and community assembly. Thus, it could be concluded that the phylogenetic relationship of A. cotula with co‐occurring all or only native species varies with elevation and species richness, adding another layer of complexity to the resolution of Darwin's naturalization conundrum.

Utilizing citizen science data to rapidly assess changing associations between wild birds and avian influenza outbreaks in poultry.

High pathogenicity avian influenza virus (HPAIV) is a rapidly evolving virus causing significant economic and environmental harm. Wild birds are a key viral reservoir and an important source of viral incursions into animal populations, including poultry. However, we lack a thorough understanding of which species drive incursions and whether this changes over time. We explored associations between the abundances of 152 avian species and outbreaks of highly pathogenic avian influenza (HPAI) in poultry premises across Great Britain between October 2021 and January 2023. Spatial generalized additive models were used, with species abundance distributions sourced from eBird. Associations were investigated at the species-specific level and across species aggregations. During autumn/winter, associations were generally strongest with waterbirds such as ducks and geese; however, we also found significant associations in groups such as non-native gamebirds and rapid change in species-specific associations over time. Our results demonstrate the value of citizen science to rapidly explore wild species as potential facilitators of disease incursions into well-monitored populations, especially in regions where viral surveillance in wild species is limited. This can be a critical step towards prioritizing targeted surveillance that could inform species-specific biosecurity measures; particularly for HPAIV, which has undergone sudden shifts in host range and continues to rapidly evolve.

HYBRID NICHE-NEUTRAL MODEL FOR THE GUT MICROBIOTA

The gut microbiota is a complex ecosystem integrated in the human body. Its relevance in the host’s health is by now well established. For this reason, several studies are currently focusing their attention on its composition and ecological properties. Among such properties, biodiversity is indeed a key one, being an indicator of the health of an ecosystem. Despite that, the choice of the optimal metric to measure biodiversity in an effective way is still a matter of debate. Here, we discuss the main approaches proposed for this purpose, considering both classical statistical indices and model-based ones. We describe the relevance of the Relative Species Abundance distribution and the main probabilistic and ecological models that have been used to fit it. Finally, we report results obtained on the gut microbiota of three groups of subjects for which differences are expected: healthy Italian adults, Down Syndrome Italian adults, and Tanzanian Hadza hunter-gathers. Our results show the utility of considering a hybrid niche-neutral model to evaluate the biodiversity of this meaningful ecosystem.

Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT).

Microbiome data exhibit technical and biomedical heterogeneity due to varied processing and experimental designs, which may lead to spurious results if uncorrected. Here, we introduce the Quantile Thresholding (QuanT) method, a comprehensive non-parametric hidden variable inference method that accommodates the complex distributions of microbial read counts and relative abundances. We apply QuanT to synthetic and real data sets and demonstrate its ability to identify unmeasured heterogeneity and improve downstream analysis.

Scale dependence and mechanisms of grazing‐induced biodiversity changes depend on herbivore type in semiarid grasslands

Abstract Grasslands support a variety of herbivores that profoundly impact biodiversity and ecosystem functioning at local and regional scales. Understanding how different herbivores influence plant diversity across multiple scales is crucial for effective biodiversity conservation. However, most studies have focussed on the effects of grazing intensity on plant diversity, neglecting the impacts and associated mechanisms of different herbivores across scales. Based on a 7‐year grazing experiment with 16 plots in Inner Mongolia's typical steppe, we compared the scale‐dependent effects of different herbivores (cattle, sheep and goats) on plant diversity at moderate grazing intensity. We used the species–area relationship (SAR) and linear mixed models to analyse the changes in species richness across multiple scales (1, 16, 32, 64, 128, 256, 512 and 1024 m2). To understand SAR slope changes, we examined four potential mechanisms: overall species richness, total number of individuals, species abundance distribution and species aggregation. We found that grazing had scale effects on plant diversity, as indicated by a significant decrease in the SAR slope. This decrease in slope suggests that the negative impacts of grazing were more pronounced at larger scales. Additionally, the scale effects of different herbivores on plant diversity varied. Cattle had stronger positive impacts on diversity than sheep and goats at both small (1 m2) and large (1024 m2) scales. Conversely, sheep and goats had stronger negative effects on diversity than did the control at the large scale (1024 m2). The decrease in the SAR slope was mainly caused by changes in overall species richness and species abundance distribution. This was primarily because cattle had a stronger impact on dominant species, whereas sheep and goats had a stronger impact on rare species. Synthesis: Our results demonstrate that even at the same intensity, the grazing effects on species diversity vary with the study scale, herbivore type and their interaction. This finding has important implications for the conservation of grassland biodiversity given that grazing typically occurs at larger scales than in studies with quadrats and involves various herbivores with distinct effects on plant diversity.

Grey wolves (Canis lupus) shift selection of anthropogenic landscape features following predator control in the Nearctic boreal forest

Conserving endangered species sometimes involves killing their predators. In the case of Nearctic wolves (Canis lupus), rarely are lethal control measures examined for ancillary effects on predator behaviour or community responses in a before-after design. We examined wolf relative abundance and spatial distribution in a northwestern boreal forest landscape for three years before and after the onset of wolf culling intended to conserve threatened woodland caribou (Rangifer tarandus caribou). We hypothesized that wolf occurrence would increase with density of anthropogenic features created by landscape development before the cull, but that wolves would avoid anthropogenic features after the cull due to associated mortality risk. We used generalized linear models in an information-theoretic framework to weigh evidence for our hypotheses. Post-control, independent wolf detections decreased to 24 % of pre-cull numbers, but wolves maintained 75 % of their distribution. Pre-control, wolves were positively associated with linear features, presumably for hunting efficiency, but post-cull wolves were negatively associated with these features. Thus, wolf control caused not only a numerical reduction of wolf numbers, but also a functional change in wolf behaviour that could further reduce predation pressure on caribou. However, post cull wolf occurrence was more strongly associated with anthropogenic block features which provide forage for alternate prey, potentially subsidizing their fast recovery. Conservation actions involving predator mortality alter landscape-scale distributions and behaviors of surviving predators, with potential indirect effects for the mammal community.

MIDASim: a fast and simple simulator for realistic microbiome data

BackgroundAdvances in sequencing technology has led to the discovery of associations between the human microbiota and many diseases, conditions, and traits. With the increasing availability of microbiome data, many statistical methods have been developed for studying these associations. The growing number of newly developed methods highlights the need for simple, rapid, and reliable methods to simulate realistic microbiome data, which is essential for validating and evaluating the performance of these methods. However, generating realistic microbiome data is challenging due to the complex nature of microbiome data, which feature correlation between taxa, sparsity, overdispersion, and compositionality. Current methods for simulating microbiome data are deficient in their ability to capture these important features of microbiome data, or can require exorbitant computational time.MethodsWe develop MIDASim (MIcrobiome DAta Simulator), a fast and simple approach for simulating realistic microbiome data that reproduces the distributional and correlation structure of a template microbiome dataset. MIDASim is a two-step approach. The first step generates correlated binary indicators that represent the presence-absence status of all taxa, and the second step generates relative abundances and counts for the taxa that are considered to be present in step 1, utilizing a Gaussian copula to account for the taxon-taxon correlations. In the second step, MIDASim can operate in both a nonparametric and parametric mode. In the nonparametric mode, the Gaussian copula uses the empirical distribution of relative abundances for the marginal distributions. In the parametric mode, a generalized gamma distribution is used in place of the empirical distribution.ResultsWe demonstrate improved performance of MIDASim relative to other existing methods using gut and vaginal data. MIDASim showed superior performance by PERMANOVA and in terms of alpha diversity and beta dispersion in either parametric or nonparametric mode. We also show how MIDASim in parametric mode can be used to assess the performance of methods for finding differentially abundant taxa in a compositional model.ConclusionsMIDASim is easy to implement, flexible and suitable for most microbiome data simulation situations. MIDASim has three major advantages. First, MIDASim performs better in reproducing the distributional features of real data compared to other methods, at both the presence-absence level and the relative-abundance level. MIDASim-simulated data are more similar to the template data than competing methods, as quantified using a variety of measures. Second, MIDASim makes few distributional assumptions for the relative abundances, and thus can easily accommodate complex distributional features in real data. Third, MIDASim is computationally efficient and can be used to simulate large microbiome datasets.55nwZAUMVt5VGoVjkFQM1tVideo

Biodiversity in changing environments: An external-driver internal-topology framework to guide intervention.

Accompanying the climate crisis is the more enigmatic biodiversity crisis. Rapid reorganization of biodiversity due to global environmental change has defied prediction and tested the basic tenets of conservation and restoration. Conceptual and practical innovation is needed to support decision making in the face of these unprecedented shifts. Critical questions include: How can we generalize biodiversity change at the community level? When are systems able to reorganize and maintain integrity, and when does abiotic change result in collapse or restructuring? How does this understanding provide a template to guide when and how to intervene in conservation and restoration? To this end, we frame changes in community organization as the modulation of external abiotic drivers on the internal topology of species interactions, using plant-plant interactions in terrestrial communities as a starting point. We then explore how this framing can help translate available data on species abundance and trait distributions to corresponding decisions in management. Given the expectation that community response and reorganization are highly complex, the external-driver internal-topology (EDIT) framework offers a way to capture general patterns of biodiversity that can help guide resilience and adaptation in changing environments.