Fundamental contradictions among observational and experimental estimates of non-trophic species interactions

Rhizobiome diversity of field-collected hyperaccumulating Noccaea sp

Hyperaccumulating plants are able to (hyper)accumulate high concentrations of metal(loid)s in their above-ground tissues without any signs of toxicity. Studies on the root-associated microbiome have been previously conducted in relation to hyperaccumulators, yet much remains unknown about the interactions between hyperaccumulating hosts and their microbiomes, as well as the dynamics within these microbial communities. Here, we assess the impact of the plant host on shaping microbial communities of three naturally occurring populations of Noccaea species in Slovenia: Noccaea praecox and co-occurring N. caerulescens from the non-metalliferous site and N. praecox from the metalliferous site. We investigated the effect of metal enrichment on microbial communities and explored the interactions within microbial groups and their environment. The abundance of bacterial phyla was more homogeneous than fungal classes across all three Noccaea populations and across the three root-associated compartments (roots, rhizosphere, and bulk soil). While most fungal and bacterial Operational Taxonomic Units (OTUs) were found at both sites, the metalliferous site comprised more unique OTUs in the root and rhizosphere compartments than the non-metalliferous site. In contrast to fungi, bacteria exhibited differentially significant abundance between the metalliferous and non-metalliferous sites as well as statistically significant correlations with most of the soil parameters. Results revealed N. caerulescens had the highest number of negative correlations between the bacterial phyla, whereas the population from the metalliferous site had the fewest. This decrease was accompanied by a big perturbation in the bacterial community at the metalliferous site, indicating increased selection between the bacterial taxa and the formation of potentially less stable rhizobiomes. These findings provide fundamentals for future research on the dynamics between hyperaccumulators and their associated microbiome.

Effects of Pre-Dehydration Treatments on Physicochemical Properties, Non-Volatile Flavor Characteristics, and Microbial Communities during Paocai Fermentation

The paocai industry faces challenges related to the production of large volumes of high-salinity and acidic brine by-products. Maintaining paocai quality while reducing brine production is crucial. This study utilized high-throughput sequencing technology to analyze microbial changes throughout the fermentation process, along with the non-volatile flavor compounds and physicochemical properties, to assess the impact of hot-air and salt-pressing pre-dehydration treatments on paocai quality. The findings indicate that pre-dehydration of raw material slowed the fermentation process but enhanced the concentration of non-volatile flavor substances, including free amino acids and organic acids. Hot-air pre-dehydration effectively reduced initial salinity to levels comparable to those in high-salinity fermentation of fresh vegetables. Furthermore, pre-dehydration altered microbial community structures and simplified inter-microbial relationships during fermentation. However, the key microorganisms such as Lactobacillus, Weissella, Enterobacter, Wallemia, Aspergillus, and Kazachstania remained consistent across all groups. Additionally, this study found that biomarkers influenced non-volatile flavor formation differently depending on the treatment, but these substances had minimal impact on the biomarkers and showed no clear correlation with high-abundance microorganisms. Overall, fermenting pre-dehydrated raw materials presents an environmentally friendly alternative to traditional paocai production.

Asymmetric Biotic Interactions Cannot Be Inferred Without Accounting for Priority Effects

Understanding biotic interactions is a crucial goal in community ecology and species distribution modelling, and large strides have been made towards improving multivariate computational methods with the aim of quantifying biotic interactions and improving predictions of species occurrence. Yet, while considerable attention has been given to computational approaches and the interpretation of these quantitative tools, the importance of sampling design to reveal these biotic interactions has received little consideration. This study explores the influential role of priority effects, that is, the order of habitat colonisation, in shaping our ability to detect biotic interactions. Using a simple set of simulations, we demonstrate that commonly used cross-sectional co-occurrence data alone cannot be used to make reliable inferences on asymmetric biotic interactions, even if they perform well in predicting the occurrence of species. We then show how sampling designs that consider priority effects can recover the asymmetric effects that are lost when priority effects are ignored. Based on these findings, we urge for caution when drawing inferences on biotic interactions from cross-sectional binary co-occurrence data, and provide guidance on sampling designs that may provide the necessary data to tackle this longstanding challenge.

Microbial communities assembly in wastewater treatment plants in China

Community assembly processes determine community structure. Deterministic processes are essential for optimizing activated sludge (AS) bioreactor performance. However, the debate regarding the relative importance of determinism versus stochasticity remains contentious, and the influencing factors are indistinct. This study used large-scale 16S rRNA gene data derived from 252 AS samples collected from 28 cities across China to explore the mechanism of AS community assembly. Results showed that the northern communities possessed lower spatial turnover and more significant dispersal limitation than those in the south, whereas the latter had more substantial deterministic processes than the former (14.46 % v.s. 9.12 %). Meanwhile, the communities in the south exhibited lower network complexity and stability. We utilized a structural equation model to explore the drivers of deterministic processes. Results revealed that low network complexity (r = -0.56, P < 0.05) and high quorum sensing bacteria abundance (r = 0.25, P < 0.001) promoted deterministic assembly, which clarifies why determinism was stronger in southern communities than northern ones. Furthermore, total phosphorus and hydraulic retention time were found to be the primary abiotic drivers. These findings provide evidence to understand the community deterministic assembly, which is crucial for resolving community structure and improving bioreactor performance.

Microeukaryotic plankton community dynamics under ecological water replenishment: Insights from eDNA metabarcoding

Ecological water replenishment (EWR) is an important strategy for river restoration globally, but timely evaluation of its ecological effects at a large spatiotemporal scale to further adjust the EWR schemes is of great challenge. Here, we examine the impact of EWR on microeukaryotic plankton communities in three distinct river ecosystems through environmental DNA (eDNA) metabarcoding. The three ecosystems include a long-term cut-off river, a short-term connected river after EWR, and long-term connected rivers. We analyzed community stability by investigating species composition, stochastic and deterministic dynamics interplay, and ecological network robustness. We found that EWR markedly reduced the diversity and complexity of microeukaryotic plankton, altered their community dynamics, and lessened the variation within the community. Moreover, EWR disrupted the deterministic patterns of community organization, favoring dispersal constraints, and aligning with trends observed in naturally connected rivers. The shift from an isolated to a temporarily connected river appeared to transition community structuring mechanisms from deterministic to stochastic dominance, whereas, in permanently connected rivers, both forces concurrently influenced community assembly. The ecological network in temporarily connected rivers post-EWR demonstrated significantly greater stability and intricacy compared to other river systems. This shift markedly bolstered the resilience of the ecological network. The eDNA metabarcoding insights offer a novel understanding of ecosystem resilience under EWR interventions, which could be critical in assessing the effects of river restoration projects throughout their life cycle.

Detecting the ecological footprint of selection

The structure of communities is influenced by many ecological and evolutionary processes, but the way these manifest in classic biodiversity patterns often remains unclear. Here we aim to distinguish the ecological footprint of selection-through competition or environmental filtering-from that of neutral processes that are invariant to species identity. We build on existing Massive Eco-evolutionary Synthesis Simulations (MESS), which uses information from three biodiversity axes-species abundances, genetic diversity, and trait variation-to distinguish between mechanistic processes. To correctly detect and characterise competition, we add a new and more realistic form of competition that explicitly compares the traits of each pair of individuals. Our results are qualitatively different to those of previous work in which competition is based on the distance of each individual's trait to the community mean. We find that our new form of competition is easier to identify in empirical data compared to the alternatives. This is especially true when trait data are available and used in the inference procedure. Our findings hint that signatures in empirical data previously attributed to neutrality may in fact be the result of pairwise-acting selective forces. We conclude that gathering more different types of data, together with more advanced mechanistic models and inference as done here, could be the key to unravelling the mechanisms of community assembly and question the relative roles of neutral and selective processes.

A Spatial Signal of Niche Differentiation in Tropical Forests

AbstractExplaining diversity in tropical forests remains a challenge in community ecology. Theory tells us that species differences can stabilize communities by reducing competition, while species similarities can promote diversity by reducing fitness differences and thus prolonging the time to competitive exclusion. Combined, these processes may lead to clustering of species such that species are niche differentiated across clusters and share a niche within each cluster. Here, we characterize this partial niche differentiation in a tropical forest in Panama by measuring spatial clustering of woody plants and relating these clusters to local soil conditions. We find that species were spatially clustered and the clusters were associated with specific concentrations of soil nutrients, reflecting the existence of nutrient niches. Species were almost twice as likely to recruit in their own nutrient niche. A decision tree algorithm showed that local soil conditions correctly predicted the niche of the trees with up to 85% accuracy. Iron, zinc, phosphorus, manganese, and soil pH were among the best predictors of species clusters.

On the limits to invasion prediction using coexistence outcomes

The dynamics of ecological communities in nature are typically characterized by probabilistic processes involving invasion dynamics. Because of technical challenges, however, the majority of theoretical and experimental studies have focused on coexistence dynamics. Therefore, it has become central to understand the extent to which coexistence outcomes can be used to predict analogous invasion outcomes relevant to systems in nature. Here, we study the limits to this predictability under a geometric and probabilistic Lotka-Volterra framework. We show that while individual survival probability in coexistence dynamics can be fairly closely translated into invader colonization probability in invasion dynamics, the translation is less precise between community persistence and community augmentation, and worse between exclusion probability and replacement probability. These results provide a guiding and testable theoretical framework regarding the translatability of outcomes between coexistence and invasion outcomes when communities are represented by Lotka-Volterra dynamics under environmental uncertainty.

On the De Novo Emergence of Ecological Interactions during Evolutionary Diversification: A Conceptual Framework and Experimental Test

AbstractEcological interactions are crucial to the structure and function of biological communities, but we lack a causal understanding of the forces shaping their emergence during evolutionary diversification. Here we provide a conceptual framework linking different modes of diversification (e.g., ecological diversification), which depend on environmental characteristics, to the evolution of different forms of ecological interactions (e.g., resource partitioning) in asexual lineages. We tested the framework by examining the net interactions in communities of Pseudomonas aeruginosa produced via experimental evolution in nutritionally simple (SIM) or complex (COM) environments by contrasting the productivity and competitive fitness of whole evolved communities relative to their component isolates. As expected, we found that nutritional complexity drove the evolution of communities with net positive interactions whereas SIM communities had similar performance as their component isolates. A follow-up experiment revealed that high fitness in two COM communities was driven by rare variants (frequency <0.1%) that antagonized PA14, the ancestral strain and common competitor used in fitness assays. Our study suggests that the evolution of de novo ecological interactions in asexual lineages is predictable at a broad scale from environmental conditions. Further, our work demonstrates that rare variants can disproportionately impact the function of relatively simple microbial communities.

Interaction between dissolved organic carbon and fungal network governs carbon mineralization in paddy soil under co-incorporation of green manure and biochar

Legume crops in rice cultivation are typically rotated and incorporated into the soil as green manure to improve soil fertility. Biochar has recently been co-incorporated with green manure to simultaneously stimulate soil organic carbon (SOC) mineralization and increase carbon (C) sequestration. However, few studies examine the effects of the co-incorporation of biochar and green manure on C cycling and the underlying microbial mechanisms in paddy fields. In this study, the effects of the co-incorporation of green manure and biochar on C mineralization, dissolved organic carbon (DOC) characteristics, and microbial community structures were investigated. A pot study was conducted with three treatments: inorganic NPK (NPK), inorganic NPK + green manure (GM), and inorganic NPK + green manure + biochar (GMC). Organic amendments significantly increased cumulative C mineralization, with amounts in the order GMC (3,434 mg·kg-1) > GM (2,934 mg·kg-1) > NPK (2,592 mg·kg-1). Fertilizer treatments had similar effects on DOC concentrations, with amounts in the order GMC (279 mg·kg-1) > GM (255 mg·kg-1) > NPK (193 mg·kg-1). According to fluorescence spectra, the highest microbial humic acid-like fraction and biological index were also in GMC. Co-incorporation of green manure and biochar shifted the composition of bacterial and fungal communities but more importantly, increased fungal network complexity and decreased bacterial network complexity. The increase in fungal network complexity with the increase in DOC concentrations and microbially derived components was the dominant factor in promoting C mineralization. Overall, this study reveals the underlying biochemical mechanism, the interaction between DOC and fungal network of C cycling in paddy soil under the co-incorporation of green manure and biochar management, and provides fundamental knowledge for exploring effective approaches to improve soil fertility and health in the future.

Community theory: Testing environmental stress models

Intensifying climate change and an increasing need for understanding its impacts on ecological communities places new emphasis on testing environmental stress models (ESMs). Using a prior literature search plus references from a more recent search, I evaluated empirical support for ESMs, focusing on whether consumer pressure on prey decreased (consumer stress model; CSM) or increased (prey stress model; PSM) with increasing environmental stress. Applying the criterion that testing ESMs requires conducting research at multiple sites along environmental stress gradients, the analysis found that CSMs were most frequent, with 'No Effect' and PSMs occurring at low but similar frequencies. This result contrasts to a prior survey in which 'No Effect' studies were most frequent, thus suggesting that consumers are generally more suppressed by stress than prey. Thus, increased climate change-induced environmental stress seems likely to reduce, not increase impacts of consumers on prey more often than the reverse.

A guide for comparing microbial co-occurrence networks

The article provides a pipeline for comparing microbial co-occurrence networks based on the R microeco package and meconetcomp package. It has high flexibility and expansibility and can help users efficiently compare networks built from different groups of samples or different construction approaches.

HiLi-chip: A high-throughput library construction chip for comprehensive profiling of environmental microbial communities

Investigating the contribution and associations of environmental microbes to ecological health and human well-being is in great demand with the goal of One Health proposed. To achieve the goal, there is an urgent need for accurate approaches to obtaining a large amount of high-resolution molecular information from various microbes. In this study, we developed a high-throughput library construction chip (HiLi-Chip) for profiling environmental microbial communities and evaluated its performance. The HiLi-Chip showed high conformity with the conventional Pacbio method in terms of α-diversity, community composition of abundant bacteria (>83%), as well as rare taxa (>84%) and human pathogens detection (>67%), indicating its advantages of accuracy, high-throughput, cost-efficiency, and broad practicability. It is suggested that the optimal strategy of the HiLi-Chip was a 2.4 μL PCR mixture per sample (∼2.4 ng DNA) with a 216-sample × 24-replicate format. We have successfully applied the HiLi-Chip to the Jiulongjiang River and identified 51 potential human bacterial pathogens with a total relative abundance of 0.22%. Additionally, under limited nutrients and similar upstream environments, bacteria tended to impose competitive pressures, resulting in a more connected network at the downstream river confluence (RC). Whereas narrow niche breadth of bacteria and upstream environmental heterogeneity probably promoted niche complementary and environment selection leading to fewer links at RC in the midsection of the river. Core bacteria might represent the entire bacterial community and enhance network stability through synergistic interactions with other core bacteria. Collectively, our results demonstrate that the HiLi-Chip is a robust tool for rapid comprehensive profiling of microbial communities in environmental samples and has significant implications for a profound understanding of environmental microbial interactions.

From diversity to complexity: Microbial networks in soils

Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach

Modelling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species' intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighbouring species from the diverse community that had non-generic interactions with our focal species. This sparse modelling approach facilitates exploration of species interactions in diverse communities while maintaining a manageable number of parameters.

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

A needle in the haystack? Applying species co-occurrence frameworks with fish assemblage data to identify species associations and sharpen ecological hypotheses

Different species can associate or interact in many ways, and methods exist for inferring associations and underlying mechanisms from incidence data (e.g., co-occurrence frameworks). These methods have received criticism despite their recent resurgence in the literature. However, co-occurrence frameworks for identifying nonrandomly associated species pairs (e.g., aggregated or segregated pairs) have value as heuristic tools for sharpening hypotheses concerning fish ecology. This paper provides a case study examining species co-occurrence across 33 stream fish assemblages in southeastern Oklahoma, USA, which were sampled twice (1974 and 2014). This study sought to determine (a) which species were nonrandomly associated, (b) what processes might have driven these associations and (c) how consistent patterns were across time. Associations among most pairs of species (24 species, 276 unique pairs) were not significantly different from random (>80%). Among all significant, nonrandomly associated species pairs (54 unique pairs), 78% (42 pairs) were aggregated and 22% (12 pairs) segregated. Most of these (28 pairs, 52%) were hypothesized to be driven by nonbiotic mechanisms: habitat filtering (20 pairs, 37%), dispersal limitation (two pairs, 0.4%) or both (six pairs, 11%). The remaining 26 nonrandomly associated pairs (48%) had no detectable signal of spatial or environmental factors involved with the association, therefore the potential for biotic interaction was not refuted. Only five species pairs were consistently associated across both sampling periods: stonerollers Campostoma spp. and orangebelly darter Etheostoma radiosum; red shiner Cyprinella lutrensis and bullhead minnow Pimephales vigilax; bluegill sunfish Lepomis macrochirus and redear sunfish Lepomis microlophus; redfin shiner Lythrurus umbratilis and bluntnose minnow Pimephales notatus; and bigeye shiner Notropis boops and golden shiner Notemigonus crysoleucas. Frameworks for identifying nonrandomly associated species pairs can provide insight into broader mechanisms of species assembly and point to potentially interesting species interactions (out of many possible pairs). However, this approach is best applied as a tool for sharpening hypotheses to be investigated further. Rather than a weakness, the heuristic nature is the strength of such methods, and can help guide biologists toward better questions by employing relatively cheap diversity survey data, which are often already in hand, to reduce complex interaction networks down to their nonstochastic parts which warrant further investigation.

Synzootics

Ecologists increasingly recognise coinfection as an important component of emergent epidemiological patterns, connecting aspects of ecoimmunology, behaviour, ecosystem function and even extinction risk. Building on syndemic theory in medical anthropology, we propose the term 'synzootics' to describe co-occurring enzootic or epizootic processes that produce worse health outcomes in wild animals. Using framing from syndemic theory, we describe how the synzootic concept offers new insights into the ecology and evolution of infectious diseases. We then recommend a set of empirical criteria and lines of evidence that can be used to identify synzootics in nature. We conclude by exploring how synzootics could indirectly drive the emergence of novel pathogens in human populations.

The microbial network property as a bio-indicator of antibiotic transmission in the environment

Interspecies interaction is an essential mechanism for bacterial communities to develop antibiotic resistance via horizontal gene transfer. Nonetheless, how bacterial interactions vary along the environmental transmission of antibiotics and the underpinnings remain unclear. To address it, we explore potential microbial associations by analyzing bacterial networks generated from 16S rRNA gene sequences and functional networks containing a large number of antibiotic-resistance genes (ARGs). Antibiotic concentration decreased by more than 4000-fold along the environmental transmission chain from manure samples of swine farms to aerobic compost, compost-amended agricultural soils, and neighboring agricultural soils. Both bacterial and functional networks became larger in nodes and links with decreasing antibiotic concentrations, likely resulting from lower antibiotics stress. Nonetheless, bacterial networks became less clustered with decreasing antibiotic concentrations, while functional networks became more clustered. Modularity, a key topological property that enhances system resilience to antibiotic stress, remained high for functional networks, but the modularity values of bacterial networks were the lowest when antibiotic concentrations were intermediate. To explain it, we identified a clear shift from deterministic processes, particularly variable selection, to stochastic processes at intermediate antibiotic concentrations as the dominant mechanism in shaping bacterial communities. Collectively, our results revealed microbial network dynamics and suggest that the modularity value of association networks could serve as an important indicator of antibiotic concentrations in the environment.

Geographical variation of multiplex ecological networks in marine intertidal communities

Understanding the drivers of geographical variation in species distributions, and the resulting community structure, constitutes one of the grandest challenges in ecology. Geographical patterns of species richness and composition have been relatively well studied. Less is known about how the entire set of trophic and non-trophic ecological interactions, and the complex networks that they create by gluing species together in complex communities, change across geographical extents. Here, we compiled data of species composition and three types of ecological interactions occurring between species in rocky intertidal communities across a large spatial extent (~970 km of shoreline) of central Chile, and analyzed the geographical variability in these multiplex networks (i.e., comprising several interaction types) of ecological interactions. We calculated nine network summary statistics common across interaction types, and additional network attributes specific to each of the different types of interactions. We then investigated potential environmental drivers of this multivariate network organization. These included variation in sea surface temperature and coastal upwelling, the main drivers of productivity in nearshore waters. Our results suggest that structural properties of multiplex ecological networks are affected by local species richness and modulated by factors influencing productivity and environmental predictability. Our results show that non-trophic negative interactions are more sensitive to spatially structured temporal environmental variation than feeding relationships, with non-trophic positive interactions being the least labile to it. We also show that environmental effects are partly mediated through changes in species richness and partly through direct influences on species interactions, probably associated to changes in environmental predictability and to bottom-up nutrient availability. Our findings highlight the need for a comprehensive picture of ecological interactions and their geographical variability if we are to predict potential effects of environmental changes on ecological communities.

Segregation structure in Odonata assemblages follows the latitudinal gradient

Latitude is known to deeply affect life with effects generalizable into ecological rules; the increasing species diversity toward tropics is the most paradigmatic. Several hypotheses tested patterns of biotic interactions' intensity along latitude. Negative interactions (i.e. competition and predation) are expected to be among the processes that produce checkerboard distribution of species. However, no relationship between checkerboardness and latitude has been uncovered. We tested Odonata assemblages worldwide for segregation patterns using a faunistic dataset (395 species arranged in 386 natural communities) spanning a wide latitudinal range (87°). We used co-occurrence analyses (C-score index and Standardized Effect Size) as an estimate of checkerboardness then correlated the occurrence of segregation to latitude. Odonata followed the Latitudinal Diversity Gradient at the regional scale (i.e. country scale) within our analyzed assemblages spanning, whereas local richness (i.e. community scale) did not follow the same pattern. Odonata assemblages structured with segregation are more common going from high to low latitudes, and local species richness have no effect on the pattern. We summarized hypotheses on how biotic interactions or ecological and historical processes can influence the spatial patterns in the checkerboards of assemblages and presented promising ways to help to gain a better mechanistic understanding of the drivers of the Latitudinal Diversity Gradient.

Reconstructing ecological networks with noisy dynamics

Ecosystems functioning is based on an intricate web of interactions among living entities. Most of these interactions are difficult to observe, especially when the diversity of interacting entities is large and they are of small size and abundance. To sidestep this limitation, it has become common to infer the network structure of ecosystems from time series of species abundance, but it is not clear how well can networks be reconstructed, especially in the presence of stochasticity that propagates through ecological networks. We evaluate the effects of intrinsic noise and network topology on the performance of different methods of inferring network structure from time-series data. Analysis of seven different four-species motifs using a stochastic model demonstrates that star-shaped motifs are differentially detected by these methods while rings are differentially constructed. The ability to reconstruct the network is unaffected by the magnitude of stochasticity in the population dynamics. Instead, interaction between the stochastic and deterministic parts of the system determines the path that the whole system takes to equilibrium and shapes the species covariance. We highlight the effects of long transients on the path to equilibrium and suggest a path forward for developing more ecologically sound statistical techniques.

Accounting for detection unveils the intricacy of wild boar and rabbit co-occurrence patterns in a Mediterranean landscape

The patterns of species co-occurrence have long served as a primary approach to explore concepts of interspecific interaction. However, the interpretation of such patterns is difficult as they can result from several complex ecological processes, in a scale-dependent manner. Here, we aim to investigate the co-occurrence pattern between European rabbit and wild boar in an estate in Central Portugal, using two-species occupancy modelling. With this framework, we tested species interaction for occupancy and detection, but also the interdependencies between both parameters. According to our results, the wild boar and European rabbit occurred independently in the study area. However, model averaging of the detection parameters revealed a potential positive effect of wild boar’s presence on rabbit’s detection probability. Upon further analysis of the parameter interdependencies, our results suggested that failing to account for a positive effect on rabbit’s detection could lead to potentially biased interpretations of the co-occurrence pattern. Our study, in spite of preliminary, highlights the need to understand these different pathways of species interaction to avoid erroneous inferences.

Refining predictions of metacommunity dynamics by modeling species non-independence

Predicting the dynamics of biotic communities is difficult because species' environmental responses are not independent, but covary due to shared or contrasting ecological strategies and the influence of species interactions. We used latent-variable joint species distribution models to analyze paired historical and contemporary inventories of 585 vascular plant species on 471 islands in the southwest Finnish archipelago. Larger, more heterogeneous islands were characterized by higher colonization rates and lower extinction rates. Ecological and taxonomical species groups explained small but detectable proportions of variance in species' environmental responses. To assess the potential influence of species interactions on community dynamics, we estimated species associations as species-to-species residual correlations for historical occurrences, for colorizations, and for extinctions. Historical species associations could to some extent predict joint colonization patterns, but the overall estimated influence of species associations on community dynamics was weak. These results illustrate the benefits of considering metacommunity dynamics within a joint framework, but also suggest that any influence of species interactions on community dynamics may be hard to detect from observational data.

Microbial associations and spatial proximity predict North American moose (Alces alces) gastrointestinal community composition

Microbial communities are increasingly recognized as crucial for animal health. However, our understanding of how microbial communities are structured across wildlife populations is poor. Mechanisms such as interspecific associations are important in structuring free-living communities, but we still lack an understanding of how important interspecific associations are in structuring gut microbial communities in comparison with other factors such as host characteristics or spatial proximity of hosts. Here, we ask how gut microbial communities are structured in a population of North American moose Alces alces. We identify key microbial interspecific associations within the moose gut and quantify how important they are relative to key host characteristics, such as body condition, for predicting microbial community composition. We sampled gut microbial communities from 55 moose in a population experiencing decline due to a myriad of factors, including pathogens and malnutrition. We examined microbial community dynamics in this population utilizing novel graphical network models that can explicitly incorporate spatial information. We found that interspecific associations were the most important mechanism structuring gut microbial communities in moose and detected both positive and negative associations. Models only accounting for associations between microbes had higher predictive value compared to models including moose sex, evidence of previous pathogen exposure or body condition. Adding spatial information on moose location further strengthened our model and allowed us to predict microbe occurrences with ~90% accuracy. Collectively, our results suggest that microbial interspecific associations coupled with host spatial proximity are vital in shaping gut microbial communities in a large herbivore. In this case, previous pathogen exposure and moose body condition were not as important in predicting gut microbial community composition. The approach applied here can be used to quantify interspecific associations and gain a more nuanced understanding of the spatial and host factors shaping microbial communities in non-model hosts.

Progressive Microbial Community Networks with Incremental Organic Loading Rates Underlie Higher Anaerobic Digestion Performance

Although biotic interactions among members of microbial communities have been conceived to be crucial for community assembly, it remains unclear how changes in environmental conditions affect microbial interaction and consequently system performance. Here, we adopted a random matrix theory-based network analysis to explore microbial interactions in triplicate anaerobic digestion (AD) systems, which is widely applied for organic pollutant treatments. The digesters were operated with incremental organic loading rates (OLRs) from 1.0 g volatile solids (VS)/liter/day to 1.3 g VS/liter/day and then to 1.5 g VS/liter/day, which increased VS removal and methane production proportionally. Higher resource availability led to networks with higher connectivity and shorter harmonic geodesic distance, suggestive of more intense microbial interactions and quicker responses to environmental changes. Strikingly, a number of topological properties of microbial network showed significant (P < 0.05) correlation with AD performance (i.e., methane production, biogas production, and VS removal). When controlling for environmental parameters (e.g., total ammonia, pH, and the VS load), node connectivity, especially that of the methanogenic archaeal network, still correlated with AD performance. Last, we identified the Methanothermus, Methanobacterium, Chlorobium, and Haloarcula taxa and an unclassified Thaumarchaeota taxon as keystone nodes of the network.IMPORTANCE AD is a biological process widely used for effective waste treatment throughout the world. Biotic interactions among microbes are critical to the assembly and functioning of the microbial community, but the response of microbial interactions to environmental changes and their influence on AD performance are still poorly understood. Using well-replicated time series data of 16S rRNA gene amplicons and functional gene arrays, we constructed random matrix theory-based association networks to characterize potential microbial interactions with incremental OLRs. We demonstrated striking linkage between network topological features of methanogenic archaea and AD functioning independent of environmental parameters. As the intricate balance of multiple microbial functional groups is responsible for methane production, our results suggest that microbial interaction may be an important, previously unrecognized mechanism in determining AD performance.

Synchronous shedding of multiple bat paramyxoviruses coincides with peak periods of Hendra virus spillover

Within host-parasite communities, viral co-circulation and co-infections of hosts are the norm, yet studies of significant emerging zoonoses tend to focus on a single parasite species within the host. Using a multiplexed paramyxovirus bead-based PCR on urine samples from Australian flying foxes, we show that multi-viral shedding from flying fox populations is common. We detected up to nine bat paramyxoviruses shed synchronously. Multi-viral shedding infrequently coalesced into an extreme, brief and spatially restricted shedding pulse, coinciding with peak spillover of Hendra virus, an emerging fatal zoonotic pathogen of high interest. Such extreme pulses of multi-viral shedding could easily be missed during routine surveillance yet have potentially serious consequences for spillover of novel pathogens to humans and domestic animal hosts. We also detected co-occurrence patterns suggestive of the presence of interactions among viruses, such as facilitation and cross-immunity. We propose that multiple viruses may be interacting, influencing the shedding and spillover of zoonotic pathogens. Understanding these interactions in the context of broader scale drivers, such as habitat loss, may help predict shedding pulses of Hendra virus and other fatal zoonoses.

Network parameters quantify loss of assemblage structure in human-impacted lake ecosystems

Lake biodiversity is an incomplete indicator of exogenous forcing insofar as it ignores underlying deformations of community structure. Here, we seek a proxy for deformation in a network of diatom assemblages comprising 452 species in 273 lakes across China. We test predictions from network theory that nodes of similar type will tend to self-organize in an unstressed system to a positively skewed frequency distribution of nodal degree. The empirical data reveal shifts in the frequency distributions of species associations across regions, from positive skew in lakes in west China with a history of low human impacts, to predominantly negative skew amongst lakes in highly disturbed regions in east China. Skew values relate strongly to nutrient loading from agricultural activity and urbanization, as measured by total phosphorus in lake water. Reconstructions through time show that positive skew reduces with temporal intensification of human impacts in the lake and surrounding catchments, and rises as lakes recover from disturbance. Our study illustrates how network parameters can track the loss of aquatic assemblage structure in lakes associated with human pressures.

How sample heterogeneity can obscure the signal of microbial interactions

Microbial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. A major assumption of these methods is that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species' (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena-Simpson's paradox, context-dependence, and nonlinear averaging-can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometers) and those of typical microbial community samples (millimeters to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.

Plant diversity alters the representation of motifs in food webs

Changes in the diversity of plant communities may undermine the economically and environmentally important consumer species they support. The structure of trophic interactions determines the sensitivity of food webs to perturbations, but rigorous assessments of plant diversity effects on network topology are lacking. Here, we use highly resolved networks from a grassland biodiversity experiment to test how plant diversity affects the prevalence of different food web motifs, the smaller recurrent sub-networks that form the building blocks of complex networks. We find that the representation of tri-trophic chain, apparent competition and exploitative competition motifs increases with plant species richness, while the representation of omnivory motifs decreases. Moreover, plant species richness is associated with altered patterns of local interactions among arthropod consumers in which plants are not directly involved. These findings reveal novel structuring forces that plant diversity exerts on food webs with potential implications for the persistence and functioning of multitrophic communities.

Nematodes in a polar desert reveal the relative role of biotic interactions in the coexistence of soil animals

Abiotic factors are major determinants of soil animal distributions and their dominant role is pronounced in extreme ecosystems, with biotic interactions seemingly playing a minor role. We modelled co-occurrence and distribution of the three nematode species that dominate the soil food web of the McMurdo Dry Valleys (Antarctica). Abiotic factors, other biotic groups, and autocorrelation all contributed to structuring nematode species distributions. However, after removing their effects, we found that the presence of the most abundant nematode species greatly, and negatively, affected the probability of detecting one of the other two species. We observed similar patterns in relative abundances for two out of three pairs of species. Harsh abiotic conditions alone are insufficient to explain contemporary nematode distributions whereas the role of negative biotic interactions has been largely underestimated in soil. The future challenge is to understand how the effects of global change on biotic interactions will alter species coexistence.

Tancredi Caruso et al. analyze biodiversity survey data from the McMurdo Dry Valleys, an extreme desert ecosystem in Antarctica in which abiotic factors are thought to determine species distributions. Focusing on three nematode species, they find that abiotic factors alone cannot explain the data and interaction between species have been historically underestimated.