Species Interactions Research Articles

Lycium barbarum arabinogalactan inhibits Escherichia coli and improves Lactobacillus plantarum growth in a defined microbial model of human gut microbiome

Arabinogalactan from Lycium barbarum (LBP-3) is the major biological activity component and contributes to maintaining intestinal immune homeostasis by targeting gut microbiota and their metabolites. However, it is still a big challenge to know the specific ecological microbe-metabolite interaction regulated by LBP-3 due to the host complex diet webs and lots of unidentified intestinal bacteria. Therefore, the present study constructed a defined microbial consortium, composing of symbiotic Bacteroides caccae, Phocaeicola vulgatus, B. thetaiotaomicron, B. uniformis, B. ovatus, Lactobacillus plantarum and Escherichia coli in vitro, to investigate the impact of LBP-3 on the interactions of species and their metabolites. The utilization of LBP-3 by the defined microbial consortium of 7 intestinal species was 22.5%. Full-length 16S rRNA analysis demonstrated that LBP-3 significantly modulated the composition of synthetic bacterial community, especially inhibiting the level of opportunistic pathogenic E. coli and improving the relative abundance of probiotic L. plantarum and all Bacteroidetes species except B. ovatus. LBP-3 remarkedly influenced 27 differential metabolites, mainly belong to amino acids, peptides and analogues (37.04%) and carboxylic acid and derivatives (7.41%). Moreover, targeted metabolomics revealed that the levels of acetate, propionate, tryptophan and 5-methylthioadenosine were improved by LBP-3. In addition, LBP-3 modulated the pathways of tryptophan metabolism, cysteine and methionine metabolism, and biosynthesis of siderophore group nonribosomal peptides. Notably, some amino acids (e.g., proline and glutamate) and their related metabolic pathways (e.g., arginine and proline metabolism) altered by LBP-3 were significantly correlated with the levels of species, such as B. uniformis, P. vulgatus, and B. thetaiotaomicron.

Navigating neighborhoods: Density, size, and species diversity influences on tree survival in subtropical secondary forests

Neighborhood effects significantly impact individual tree survival through mechanisms of resource competition and species interactions within forest ecosystems. However, the specific impacts of these effects, particularly how biodiversity feedback affects survival across diverse life stages and spatial scales in subtropical secondary forests, remains insufficiently documented. To bridge this gap, we conducted an extensive analysis of neighborhood effects at different life stages and spatial scales in the subtropical secondary forests of the Wuyi Mountains in Southeast China, uncovering key insights into the community structure and dynamics. Our investigation over a five-year period encompassed all individuals within our study plot, quantifying effects of neighborhood density, individual size in resource competition, and species diversity in the neighborhood across various life stages. Employing mixed-effects models, we established quantitative relationships to assess the impact of these factors on tree survival at different spatial scales. Our findings confirmed that density dependence adversely affects sapling survival at smaller scales. As trees mature and spatial test scales expand, the detrimental effects of conspecific resource competition on individual mortality decrease, whereas heterospecific resource competition notably impairs survival probabilities in later successional stages, suggesting a shift from intraspecific to interspecific competition as the dominant influence on mortality during secondary succession. Additionally, we detected consistent patterns in conspecific and heterospecific density effects with increasing spatial scale. These effects transitioned from interspecific differences at smaller scales to more uniform positive effects at larger scales, which may be attributed to the strong spatial dependency of neighborhood density effects, potentially due to increased habitat heterogeneity. Furthermore, our study highlights the crucial role of heterospecific neighborhood effects in enhancing survival. We observed significant positive impacts of species diversity and heterospecific interactions on individual tree survival, particularly at smaller spatial scales during the sapling stage. Importantly, our research unveils significant variability in species sensitivity to different neighborhood effects, which change with life stage and spatial scale. This underscores the necessity of accounting for life stage variations among species and the influence of spatial heterogeneity on neighborhood effects such as density. Our findings delineate distinct competition mechanisms between the earlier and later phases of forest succession, emphasizing the distinctive recovery processes of subtropical secondary forests. These insights are essential for informing forest management and conservation strategies, ensuring a nuanced understanding of forest community dynamics.

Room Temperature Sensing of Volatile Organic Compounds Using Hybrid Layered SnO Mesoflowers and Laser-Induced Graphitic Carbon Devices.

In this work, we demonstrate chemiresistive volatile organic compound (VOC) sensors prepared by drop-cast assembly of layered tin monoxide mesoflowers (SnO-MFs) on additively produced laser-induced graphene-like carbon (LIG). The SnO-MFs were synthesized below 100 °C at ambient pressure and offer a low fabrication energy alternative route to typical furnace-prepared metal-oxide materials. The additive dropcast assembly of room-temperature operating metal oxide active material allows the substitution of LIG for metal current collectors and glass for alumina, reducing the environmental footprint of the sensor. The sensors can detect methanol (150-4000 ppm) at room temperature and humidity (∼18 °C, ∼55% RH), with response and recovery times (150 ppm methanol) of t 90,resp ≈ 50 ± 10 s and t 90,rec ≈ 5 ± 0.5 s, respectively. The sensors demonstrated a limit of detection (170 ± 40 ppm) below 8 h worker safety exposure levels (200 ppm) and stable DC resistance responses ΔR/R = 9 ± 2% to 710 ppm of methanol for over 21 days in ambient laboratory conditions, n = 4. First-principles density functional theory simulations were used to elucidate the interactions of VOC species on the SnO surfaces. LIG-SnO hybrid sensors thus present a resource-efficient route to develop chemiresistive sensors for low-power applications, although with cross-selectivity to other alcohol species.

Natural selection on floral volatiles and other traits can change with snowmelt timing and summer precipitation.

Climate change is disrupting floral traits that mediate mutualistic and antagonistic species interactions. Plastic responses of these traits to multiple shifting conditions may be adaptive, depending on natural selection in new environments. We manipulated snowmelt date over three seasons (3-11 d earlier) in factorial combination with growing-season precipitation (normal, halved, or doubled) to measure plastic responses of volatile emissions and other floral traits in Ipomopsis aggregata. We quantified how precipitation and early snowmelt affected selection on traits by seed predators and pollinators. Within years, floral emissions did not respond to precipitation treatments but shifted with snowmelt treatment depending on the year. Across 3 yr, emissions correlated with both precipitation and snowmelt date. These effects were driven by changes in soil moisture. Selection on several traits changed with earlier snowmelt or reduced precipitation, in some cases driven by predispersal seed predation. Floral trait plasticity was not generally adaptive. Floral volatile emissions shifted in the face of two effects of climate change, and the new environments modulated selection imposed by interacting species. The complexity of the responses underscores the need for more studies of how climate change will affect floral volatiles and other floral traits.

Multiple stressors drive multitrophic biodiversity and ecological network dynamics in a shrinking sandy lake

Stressors endanger lake ecosystem biodiversity and networks, especially in sandy lakes of arid/semiarid regions, where impacts are poorly understood. Here, we investigate the changes in multitrophic biodiversity and ecological networks under increasing stress (temperature, nutrients, lake area) by using sedimentary DNA from a shrinking sandy lake in China over nearly 100 years. With increasing stress, species richness and stability increased, whereas species turnover decreased. Species synchronism decreased at high-trophic levels but increased at low-trophic levels. Empirical dynamic modeling showed network connectance and strength of species interactions decreased–increased–decreased over time, signaling a potential adaption–resistance–degradation change in ecosystem responses with increasing stress. Models also indicated network structure primarily depended on direct effects of nutrients and temperature under low/medium stress and on a diversity-mediated pathway under high stress. Thus, maintaining ecological network structure complexity and integrity in lake ecosystems is essential to mitigate the effects of multiple stresses.

Agricultural management reshaped the diversity patterns and community assembly of rhizosphere bacterial communities in a desert farming system

Desert farming represents human intervention in previously untouched desert landscapes, transforming natural desert areas into managed agricultural farmland. Despite the growing prevalence of desert farming, the effects of agricultural management on soil and microbial dynamics in desert environments are not well understood. This study analyzed rhizosphere bacterial community from crops (corn and wheat) in desert farms and desert plants (Artemisia desertorum) in natural desert environments to explore how bacterial communities respond to agricultural management in desert farming systems. Our results showed that the composition and diversity of rhizosphere bacterial communities were influenced more by the sampling region than by plant type. Bacterial species richness, phylogenetic diversity, and functional traits were significantly affected by moisture and temperature, whereas pH and nitrogen-related factors (total nitrogen, NO3-N, and NH4-N) were stronger correlates of functional differences in the microbial community. We found that the co-occurrence network of bacterial communities exhibited tighter and more positive interactions in natural desert, but was more stable in desert farm. Deterministic processes predominantly governed bacterial community assembly in both desert farms and natural desert, but the importance of stochastic processes increased in desert farms due to reduced environmental stress from agricultural management. These findings provide valuable insights into the impacts of agricultural management on rhizosphere bacterial communities, highlighting the roles of temperature, moisture, and nutrient supply in predicting microbial diversity, function, species interactions, and community assembly. Given the crucial functional roles of rhizosphere microbes, our conclusions can inform desert farm management practices to better manipulate microbial communities for optimized crop production.

Dynamics of Interaction of One Prey and Two Competing Predators with Population Heterogeneity

A mathematical model of two predators competing for a single prey system incorporating population heterogeneity has been proposed and analyzed by extending the system of coupled logistic equations for three species system. The local stability analysis of all the equilibria has been carried out and also globally stable in Varies 2-D planes. Through the analytical results, it is observed that the interior equilibrium of one prey and two competing predators in prey dependent case is always unstable. At the end, discussed the dynamical behavior of a three species interaction with the help of some numerical examples by using MATLAB to support the analytical presentations.

Monitoring Electrochemical Dynamics through Single-Molecule Imaging of hBN Surface Emitters in Organic Solvents.

Electrochemical techniques conventionally lack spatial resolution and average local information over an entire electrode. While advancements in spatial resolution have been made through scanning probe methods, monitoring dynamics over large areas is still challenging, and it would be beneficial to be able to decouple the probe from the electrode itself. In this work, we leverage single molecule microscopy to spatiotemporally monitor analyte surface concentrations over a wide area using unmodified hexagonal boron nitride (hBN) in organic solvents. Through a sensing scheme based on redox-active species interactions with fluorescent emitters at the surface of hBN, we observe a region of a linear decrease in the number of emitters against increasingly positive potentials applied to a nearby electrode. We find consistent trends in electrode reaction kinetics vs overpotentials between potentiostat-reported currents and optically read emitter dynamics, showing Tafel slopes greater than 290 mV·decade-1. Finally, we draw on the capabilities of spectral single-molecule localization microscopy (SMLM) to monitor the fluorescent species' identity, enabling multiplexed readout. Overall, we show dynamic measurements of analyte concentration gradients on a micrometer-length scale with nanometer-scale depth and precision. Considering the many scalable options for engineering fluorescent emitters with two-dimensional (2D) materials, our method holds promise for optically detecting a range of interacting species with exceptional localization precision.

Embracing plant–plant interactions—Rethinking predictions of species range shifts

Abstract Interactions among plants are changing across the globe resulting from a multitude of changes in the environment. Obtaining accurate predictions of plant species' range dynamics requires us to account for plant–plant interactions, but this remains challenging using the existing species distribution modelling (SDM) techniques. Advanced SDM techniques facilitate the integration of plant species interactions based on species‐to‐species associations. However, for uncharted environmental conditions in which the formerly derived species' correlations potentially no longer hold, a more process‐based alternative is expected to become increasingly relevant. We first review the most common SDM techniques that integrate plant–plant interactions and then present the concept for a novel map product: a spatial plant–plant interaction index (PII) depicting the link between a focal species’ performance and the trait signature of the interacting vegetation. The latest developments in remote sensing and the increasing availability of vegetation plot data facilitate PII mapping based on vegetation trait–environment relationships. Synthesis: PII mapping holds the potential to advance next‐generation biogeographical analyses as it can serve as a pivotal missing covariate layer necessary for the integration of plant–plant interactions into SDM applications. This data product adds flexibility to the ecologists’ toolbox to analyse species range shifts and the formation of novel communities as a response to multiple environmental changes.

Systematic Review of Multi-Species Models in Fisheries: Key Features and Current Trends

In the context of ecosystem-based fisheries management (EBFM), multi-species models offer a potential alternative to traditional single-species models for managing key species, particularly in mixed-fishery settings. These models account for interactions between different species, providing a more holistic approach to fisheries compared to traditional single-species management. There is currently no comprehensive list or recent analysis of the diverse methods used to account for species interactions in fisheries worldwide. We conducted a systematic review to objectively present the current multi-species models used in fisheries. The systematic search identified 86 multi-species models, which were then evaluated to assess their similarities. Employing a clustering analysis, three distinct groups were identified: extensions of single-species/dynamic multi-species models, aggregated ecosystem models, and end-to-end/coupled and hybrid models. The first group was among the most diverse, owing to their ability to integrate biological components, while maintaining an intermediate level of complexity. The second group, primarily defined by the EwE method, features an aggregated biomass pool structure incorporating biological components and environmental effects. The third cluster featured the most complex models, which included a comprehensive representation of size and age structure, the ability to incorporate biological components and environmental effects, as well as spatial representation. The application of these methods is primarily concentrated on small pelagic and demersal species from North America and Europe. This analysis provides a comprehensive guide for stakeholders on the development and use of multi-species models, considering data constraints and regional contexts.

A systematic review of Streptococcus Mutans and Veillonellae species interactions in dental caries progression: Positive or Negative impact?

Background The interaction between Streptococcus mutans (S. mutans) and Veillonella species (Veillonella spp.) is unclear. This study aims to investigate the interaction between S. mutans and Veillonella spp. on caries development using systematic review. Methods This systematic review was accorded to the guideline of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Three electronic databases, namely PubMed, Embase, and the Cochrane library, were used to conduct a systematic search for eligible studies from their inception until July 18, 2023. PROSPERO registration number was No. CRD42023445968. Results We initially identified 4,774 articles. After eliminating duplicates and irrelevant articles, 11 studies met the inclusion criteria. The studies revealed important aspects of the relationship between S. mutans and Veillonellae spp. in dental caries. One significant finding is that Veillonellae spp. can affect the acid production capacity of S. mutans. Some studies indicate that Veillonellae spp. can inhibit the acid production by S. mutans, potentially reducing the cariogenic process. Another aspect is the competition for substrates. Veillonellae spp. utilize lactic acid, which is a by product of S. mutans metabolism, as a source of carbon. This metabolic interaction may decrease the availability of lactic acid for S. mutans, potentially influencing its cariogenic potential. Conclusions This systematic review highlights the emerging evidence on the interaction between S. mutans and Veillonellae spp. in dental caries. The findings suggest that Veillonellae spp. can modulate the acid production, and substrate competition of S. mutans, potentially influencing the cariogenic process.

Transfer learning of species co-occurrence patterns between plant communities

AimThe use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. LocationSemiarid plant communities in Spain and México. Time period2016–2022. Major taxa studiedAngiosperms. MethodsBased on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). ResultsWhen we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusionsThis AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates.

Positive species interactions structure rhodolith bed communities at a global scale

Positive species interactions structure rhodolith bed communities at a global scale

Characterizing a stable five-species microbial community for use in experimental evolution and ecology.

Model microbial communities are regularly used to test ecological and evolutionary theory as they are easy to manipulate and have fast generation times, allowing for large-scale, high-throughput experiments. A key assumption for most model microbial communities is that they stably coexist, but this is rarely tested experimentally. Here we report the (dis)assembly of a five-species microbial community from a metacommunity of soil microbes that can be used for future experiments. Using reciprocal invasion-from-rare experiments we show that all species can coexist and we demonstrate that the community is stable for a long time (~600 generations). Crucially for future work, we show that each species can be identified by their plate morphologies, even after >1 year in co-culture. We characterise pairwise species interactions and produce high-quality reference genomes for each species. This stable five-species community can be used to test key questions in microbial ecology and evolution.

A portable multi-taxa phenotyping device to retrieve physiological performance traits

Organismal phenotyping to identify fitness traits is transforming our understanding of adaptive responses and ecological interactions of species within changing environments. Here we present a portable Multi-Taxa Phenotyping (MTP) system that can retrieve a suite of metabolic and photophysiological parameter across light, temperature, and/or chemical gradients, using real time bio-optical (oxygen and chlorophyll a fluorescence) measurements. The MTP system integrates three well-established technologies for the first time: an imaging Pulse Amplitude Modulated (PAM) chlorophyll a fluorometer, custom-designed well plates equipped with optical oxygen sensors, and a thermocycler. We demonstrate the ability of the MTP system to distinguish phenotypic performance characteristics of diverse aquatic taxa spanning corals, mangroves and algae based on metabolic parameters and Photosystem II dynamics, in a high-throughput capacity and accounting for interactions of different environmental gradients on performance. Extracted metrics from the MTP system can not only provide information on the performance of aquatic taxa exposed to differing environmental gradients, but also provide predicted phenotypic responses of key aquatic organisms to environmental change. Further work validating how rapid phenotyping tools such as the MTP system predict phenotypic responses to long term environmental changes in situ are urgently required to best inform how these tools can support management efforts.

Species interactions drive continuous assembly of freshwater communities in stochastic environments

Understanding the factors driving the maintenance of long-term biodiversity in changing environments is essential for improving restoration and sustainability strategies in the face of global environmental change. Biodiversity is shaped by both niche and stochastic processes, however the strength of deterministic processes in unpredictable environmental regimes is highly debated. Since communities continuously change over time and space—species persist, disappear or (re)appear—understanding the drivers of species gains and losses from communities should inform us about whether niche or stochastic processes dominate community dynamics. Applying a nonparametric causal discovery approach to a 30-year time series containing annual abundances of benthic invertebrates across 66 locations in New Zealand rivers, we found a strong negative causal relationship between species gains and losses directly driven by predation indicating that niche processes dominate community dynamics. Despite the unpredictable nature of these system, environmental noise was only indirectly related to species gains and losses through altering life history trait distribution. Using a stochastic birth-death framework, we demonstrate that the negative relationship between species gains and losses can not emerge without strong niche processes. Our results showed that even in systems that are dominated by unpredictable environmental variability, species interactions drive continuous community assembly.

A call to integrate non‐visual functions of pigments and their interactions with visual functions to understand global change impacts on visual systems

Abstract Animal coloration serves a variety of visually related functions in nature (e.g. mate choice, aposematism and camouflage) but the pigments in integumentary tissues such as skin, scales and feathers may also serve functions unrelated to the visual environment (e.g. temperature regulation, detoxification and pollutant protection). Our understanding of the significance of the non‐visual functions of animal integumentary pigments, as well as how they interact with the visually related functions to shape animal visual systems, remains limited. Furthermore, due to their important roles in shaping species interactions and mediating interactions in the environment, animal colour traits are likely to be impacted by global change (e.g. increased temperatures, altered habitat quality and quantity, increased environmental stochasticity, pollutants and novel species assemblages). Considering the effects of global change on both visual and non‐visual functions is important for understanding whether the selection is acting directly on the pigment or on coloration. Since changing the trait distributions can then lead to changes in visual systems, we advocate for studies to consider all potential functions of integumentary pigments, both visual and non‐visual functions and their interaction. Towards this goal, we first highlight common functions of pigments with a focus on non‐visual functions across animal systems. Then we synthesize our current understanding of how global change can impact pigmentation and discuss factors that can modify the interactions between climate change and pigment function. Lastly, we discuss how changes in colour traits can impact visual systems and provide an example using amphibians and their responses to climate change as a model. Read the free Plain Language Summary for this article on the Journal blog.

Total Polyphenol Content and Antioxidant Activity of Leaves and Fine Roots as Indicators of Drought Resistance in the Native Quercus robur and Alien Quercus rubra

Research Highlights: Environmental abiotic stressors generate secondary stresses in plants, such as osmotic and oxidative stresses, which negatively influence their normal growth, development, and metabolism. Research about other non-enzymatic components with antioxidant capacity has recently focused on polyphenols. However, their role as indicators of drought and shade tolerance in woody species leaves and roots has been poorly explored or was limited to leaves only. Background and Objectives: Under a scenario of increasing drought, understanding the seedling responses in terms of total polyphenols and their antioxidant activity, in particular at the fine root system level, may help to elucidate the native–alien species interaction. Materials and Methods: At the beginning of July, 5-month-old native Quercus robur and alien Quercus rubra seedlings were transferred indoors to the growth chamber and subjected to progressive soil drying for 21 days. Results: The decrease in soil water content was more pronounced for Q. robur (9%) than for Q. rubra (34% of field capacity). Leaf water potential significantly decreased over time in Q. robur but did not differ from the control in Q. rubra. The total polyphenol concentration in Q. robur was markedly lower in the leaves and significantly higher in the fine roots than in Q. rubra. For the leaves, both species showed markedly higher values if well-watered, and the values significantly decreased in response to drought only in Q. rubra. In contrast, the fine root values for both species were markedly higher if droughted and decreased significantly in time only in Q. robur. Differently from the polyphenol concentration, the antioxidant capacity of Q. rubra was always higher in both the leaves and fine roots. Conclusions: The higher antioxidant activity of the alien species Q. rubra revealed by this work, combined with its isohydric behaviour, could further shed some light on our understanding of its competitive performance at the seedling stage against the native Q. robur.

The Biodiversity and Climate Variability Experiment (BioCliVE): Quantifying the role of biodiversity in buffering ecosystems against climatic variability

Extreme climate events such as floods and droughts are becoming increasingly frequent and intense across the world. Future climate scenarios predict both an increase in individual extreme events, as well as chronic changes in climatic seasonality. Yet, the combined and relative effects of these pressures on ecosystems remain unknown. Concurrently, human-induced ecological disruption is accelerating species extinction rates, which are estimated to be 100 to 1000 times greater than pre-human levels. This is alarming as greater biological diversity is thought to buffer ecosystem functioning against extreme climate events, thereby safeguarding the provisioning of essential ecological services that contribute to human well-being. However, how and to what extent biodiversity buffers ecosystems against climate variability remains unclear. We recently constructed experimental grassland communities in a mesocosm-based field design representing a realistic gradient of plant diversity. Both extreme events (drought and flood) and a change in seasonality of precipitation are manipulated in a full factorial design to quantify the effects of future seasonal shifts and extremes in precipitation. We will: 1) determine to what extent higher biological diversity ensures that grasslands can continue to provide multiple ecosystem services even in the context of climate change and 2) unravel the fundamental mechanisms by which this is achieved including species asynchrony and positive species interactions. Results of our experimental approach will advance our understanding of the buffering potential of plant diversity and contribute to the development of strategies for sustainable service provisioning of our ecosystems in the face of climate change.

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.