Synthetic model ecosystem of 12 cryopreservable microbial species allowing for a noninvasive approach.

Simultaneous understanding of both population and ecosystem dynamics is crucial in an era marked by the degradation of ecosystem services. Experimental ecosystems are a powerful tool for understanding these dynamics; however, they often face technical challenges, typically falling into two categories: "complex but with limited replicability microcosms" and "highly replicable but overly simplistic microcosms." Herein, we present a high-throughput synthetic microcosm system comprising 12 functionally and phylogenetically diverse microbial species. These species are axenically culturable, cryopreservable, and can be measured noninvasively via microscopy, aided by machine learning. This system includes prokaryotic and eukaryotic producers and decomposers, and eukaryotic consumers to ensure functional redundancy. Our model system exhibited key features of a complex ecosystem: (i) various positive and negative interspecific interactions, (ii) higher-order interactions beyond two-species dynamics, (iii) probabilistic dynamics leading to divergent outcomes, and (iv) stable nonlinear transitions. We identified several conditions under which at least one species from each of the three functional groups-producers, consumers, and decomposers-and one functionally redundant species, persisted for over six months. These conditions set the stage for detailed investigations in the future. Given its designability and experimental replicability, our model ecosystem offers a promising platform for deeper insights integrating both population and ecosystem dynamics.

Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems.

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, it was suggested that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories-fluctuations, rapid environmental responses, and long-term changes-converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems.

Dynamics-based characterization and classification of biodiversity indicators.

Various biodiversity indicators, such as species richness, total abundance, and species diversity indices, have been developed to capture the state of ecological communities over space and time. As biodiversity is a multifaceted concept, it is important to understand the dimension of biodiversity reflected by each indicator for successful conservation and management. Here we utilized the responsiveness of biodiversity indicators' dynamics to environmental changes (i.e., environmental responsiveness) as a signature of the dimension of biodiversity. We present a method for characterizing and classifying biodiversity indicators according to environmental responsiveness and apply the methodology to monitoring data for a marine fish community under intermittent anthropogenic warm water discharge. Our analysis showed that 10 biodiversity indicators can be classified into three super-groups based on the dimension of biodiversity that is reflected. Group I (species richness and community mean of latitudinal center of distribution (cCOD)) showed the greatest robustness to temperature changes; Group II (species diversity and total abundance) showed an abrupt change in the middle of the monitoring period, presumably due to a change in temperature; Group III (species evenness) exhibited the highest sensitivity to environmental changes, including temperature. These results had several ecological implications. First, the responsiveness of species diversity and species evenness to temperature changes might be related to changes in the species abundance distribution. Second, the similar environmental responsiveness of species richness and cCOD implies that fish migration from lower latitudes is a major driver of species compositional changes. The study methodology may be useful in selecting appropriate indicators for efficient biodiversity monitoring.

Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity.

Microbial life in low-energy ecosystems relies on individual energy conservation, optimizing energy use in response to interspecific competition and mutualistic interspecific syntrophy. Our study proposes a novel community-level strategy for increasing energy use efficiency. By utilizing an oxidation-reduction (redox) reaction network model that represents microbial redox metabolic interactions, we investigated multiple species-level competition and cooperation within the network. Our results suggest that microbial functional diversity allows for metabolic handoffs, which in turn leads to increased energy use efficiency. Furthermore, the mutualistic division of labour and the resulting complexity of redox pathways actively drive material cycling, further promoting energy exploitation. Our findings reveal the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with important implications for microbial ecosystem functioning and the co-evolution of life and Earth.

Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations.

As interest in natural capital grows and society increasingly recognizes the value of biodiversity, we must discuss how ecosystem observations to detect changes in biodiversity can be sustained through collaboration across regions and sectors. However, there are many barriers to establishing and sustaining large-scale, fine-resolution ecosystem observations. First, comprehensive monitoring data on both biodiversity and possible anthropogenic factors are lacking. Second, some in situ ecosystem observations cannot be systematically established and maintained across locations. Third, equitable solutions across sectors and countries are needed to build a global network. Here, by examining individual cases and emerging frameworks, mainly from (but not limited to) Japan, we illustrate how ecological science relies on long-term data and how neglecting basic monitoring of our home planet further reduces our chances of overcoming the environmental crisis. We also discuss emerging techniques and opportunities, such as environmental DNA and citizen science as well as using the existing and forgotten sites of monitoring, that can help overcome some of the difficulties in establishing and sustaining ecosystem observations at a large scale with fine resolution. Overall, this paper presents a call to action for joint monitoring of biodiversity and anthropogenic factors, the systematic establishment and maintenance of in situ observations, and equitable solutions across sectors and countries to build a global network, beyond cultures, languages, and economic status. We hope that our proposed framework and the examples from Japan can serve as a starting point for further discussions and collaborations among stakeholders across multiple sectors of society. It is time to take the next step in detecting changes in socio-ecological systems, and if monitoring and observation can be made more equitable and feasible, they will play an even more important role in ensuring global sustainability for future generations. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Predator discrimination of prey promotes the predator-mediated coexistence of prey species.

The predator discrimination of prey can affect predation intensity and the prey density dependence of predators, which has the potential to alter the coexistence of prey species. We used a predator-prey population dynamics model accounting for the predator's adaptive diet choice and predator discrimination of prey to investigate how the latter influences prey coexistence. The model revealed that (i) prey species that are perceived as belonging to the same species by a predator are attacked in the same manner, and it is more difficult for them to coexist than those that are recognized as different prey species, and (ii) prey species that are not discriminated by a predator-and therefore cannot coexist-may coexist in the presence of an alternative predator that does discriminate between them. These results suggest that prey diversity, which favours the predator discrimination of prey, and the different capabilities of predators to identify prey species both enhance prey coexistence.

Reconstruction of a Soil Microbial Network Induced by Stress Temperature.

The microbial community is viewed as a network of diverse microorganisms connected by various interspecific interactions. While the stress gradient hypothesis (SGH) predicts that positive interactions are favored in more stressful environments, the prediction has been less explored in complex microbial communities due to the challenges of identifying interactions. Here, by applying a nonlinear time series analysis to the amplicon-based diversity time series data of the soil microbiota cultured under less stressful (30°C) or more stressful (37°C) temperature conditions, we show how the microbial network responds to temperature stress. While the genera that persisted only under the less stressful condition showed fewer positive effects, the genera that appeared only under the more stressful condition received more positive effects, in agreement with SGH. However, temperature difference also induced reconstruction of the community network, leading to an increased proportion of negative interactions at the whole-community level. The anti-SGH pattern can be explained by the stronger competition caused by increased metabolic rate and population densities. IMPORTANCE By combining amplicon-based diversity survey with recently developed nonlinear analytical tools, we successfully determined the interaction networks of more than 150 natural soil microbial genera under less or more temperature stress and explored the applicability of the stress gradient hypothesis to soil microbiota, shedding new light on the well-known hypothesis.

Are networks of trophic interactions sufficient for understanding the dynamics of multi-trophic communities? Analysis of a tri-trophic insect food-web time-series.

Resource-consumer interactions are considered a major driving force of population and community dynamics. However, species also interact in many non-trophic and indirect ways and it is currently not known to what extent the dynamic coupling of species corresponds to the distribution of trophic links. Here, using a 10-year data set of monthly observations of a 40-species tri-trophic insect community and nonlinear time series analysis, we compare the occurrence and strengths of both the trophic and dynamic interactions in the insect community. The matching between observed trophic and dynamic interactions provides evidence that population dynamic interactions reflect resource-consumer interactions in the many-species community. However, the presence of a trophic interaction does not always correspond to a detectable dynamic interaction especially for top-down effects. Moreover a considerable proportion of dynamic interactions are not attributable to direct trophic interactions, suggesting the unignorable role of non-trophic and indirect interactions as co-drivers of community dynamics.

Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modelling.

Molecular analysis of DNA left in the environment, known as environmental DNA (eDNA), has proven to be a powerful and cost-effective approach to infer occurrence of species. Nonetheless, relating measurements of eDNA concentration to population abundance remains difficult because detailed knowledge on the processes that govern spatial and temporal distribution of eDNA should be integrated to reconstruct the underlying distribution and abundance of a target species. In this study, we propose a general framework of abundance estimation for aquatic systems on the basis of spatially replicated measurements of eDNA. The proposed method explicitly accounts for production, transport and degradation of eDNA by utilizing numerical hydrodynamic models that can simulate the distribution of eDNA concentrations within an aquatic area. It turns out that, under certain assumptions, population abundance can be estimated via a Bayesian inference of a generalized linear model. Application to a Japanese jack mackerel (Trachurus japonicus) population in Maizuru Bay revealed that the proposed method gives an estimate of population abundance comparable to that of a quantitative echo sounder method. Furthermore, the method successfully identified a source of exogenous input of eDNA (a fish market), which may render a quantitative application of eDNA difficult to interpret unless its effect is taken into account. These findings indicate the ability of eDNA to reliably reflect population abundance of aquatic macroorganisms; when the "ecology of eDNA" is adequately accounted for, population abundance can be quantified on the basis of measurements of eDNA concentration.

Evolutionary stability of plant-pollinator networks: efficient communities and a pollination dilemma.

Mutualistic interactions between species are ubiquitous in nature and essential for ecosystem functioning. Often dozens or even hundreds of species with different degrees of specialisation form complex networks. How this complexity evolves is a fundamental question in ecology. Here, we present a new game theoretical approach to model complex coevolutionary processes and apply it to pollination networks. A theoretical analysis reveals multiple evolutionary stable network structures that depend on the availability of pollination service. In particular, we find efficient communities, in which a high percentage of pollen are transported conspecifically, to evolve only when plant and pollinator abundances are well balanced. Both pollinator shortage and oversupply select for more inefficient network structures. The results suggest that availability of pollination services is a key factor structuring pollination networks and may offer a new explanation for geographical differences in pollination communities that have long been recognised by ecologists.

Intraspecific Adaptation Load: A Mechanism for Species Coexistence.

Evolutionary ecological theory suggests that selection arising from interactions with conspecifics, such as sexual and kin selection, may result in evolution of intraspecific conflicts and evolutionary 'tragedy of the commons'. Here, we propose that such an evolution of conspecific conflicts may affect population dynamics in a way that enhances species coexistence. Empirical evidence and theoretical models suggest that more abundant species is more susceptible to invasion of 'selfish' individuals that increase their own reproductive success at the expense of population growth (intraspecific adaptation load). The density-dependent intraspecific adaptation load gives rise to a self-regulation mechanism at the population level, and stabilizes species coexistence at the community level by negative frequency-dependence.

Does past evolutionary history under different mating regimes influence the demographic dynamics of interspecific competition?

Interspecific interactions are contingent upon organism phenotypes, and thus phenotypic evolution can modify interspecific interactions and affect ecological dynamics. Recent studies have suggested that male-male competition within a species selects for capability to reproductively interfere with a closely related species. Here, we examine the effect of past evolutionary history under different mating regimes on the demographic dynamics of interspecific competition in Callosobruchus seed beetles. We used previously established experimental evolution lines of Callosobruchus chinensis that evolved under either forced lifelong monogamy or polygamy for 17 generations, and examined the demographic dynamics of competition between these C. chinensis lines and a congener, Callosobruchus maculatus. Callosobruchus chinensis was competitively excluded by C. maculatus in all trials. Time series data analyses suggested that reproductive interference from C. chinensis was relatively more important in the trials involving polygamous C. chinensis than those involving monogamous C. chinensis, in accordance with the potentially higher reproductive interference capability of polygamous C. chinensis. However, the estimated signs and magnitudes of interspecific interactions were not fully consistent with this explanation, implying the evolution of not only reproductive interference but also other interaction mechanisms. Our study thus suggests multifaceted effects of sexually selected traits on interspecific competitive dynamics.

Evaluation of detection probabilities at the water-filtering and initial PCR steps in environmental DNA metabarcoding using a multispecies site occupancy model.

Environmental DNA (eDNA) metabarcoding is a recently developed method to assess biodiversity based on a high-throughput parallel DNA sequencing applied to DNA present in the ecosystem. Although eDNA metabarcoding enables a rapid assessment of biodiversity, it is prone to species detection errors that may occur at sequential steps in field sampling, laboratory experiments, and bioinformatics. In this study, we illustrate how the error rates in the eDNA metabarcoding-based species detection can be accounted for by applying the multispecies occupancy modelling framework. We report a case study with the eDNA sample from an aquarium tank in which the detection probabilities of species in the two major steps of eDNA metabarcoding, filtration and PCR, across a range of PCR annealing temperatures, were examined. We also show that the results can be used to examine the efficiency of species detection under a given experimental design and setting, in terms of the efficiency of species detection, highlighting the usefulness of the multispecies site occupancy modelling framework to study the optimum conditions for molecular experiments.

Correction: The Release Rate of Environmental DNA from Juvenile and Adult Fish.

[This corrects the article DOI: 10.1371/journal.pone.0114639.].

Using food network unfolding to evaluate food-web complexity in terms of biodiversity: theory and applications.

Food-web complexity often hinders disentangling functionally relevant aspects of food-web structure and its relationships to biodiversity. Here, we present a theoretical framework to evaluate food-web complexity in terms of biodiversity. Food network unfolding is a theoretical method to transform a complex food web into a linear food chain based on ecosystem processes. Based on this method, we can define three biodiversity indices, horizontal diversity (DH ), vertical diversity (DV ) and range diversity (DR ), which are associated with the species diversity within each trophic level, diversity of trophic levels, and diversity in resource use, respectively. These indices are related to Shannon's diversity index (H'), where H' = DH  + DV  - DR . Application of the framework to three riverine macroinvertebrate communities revealed that D indices, calculated from biomass and stable isotope features, captured well the anthropogenic, seasonal, or other within-site changes in food-web structures that could not be captured with H' alone.

Density-dependent interspecific interactions and the complexity-stability relationship.

Ever since May theorized that communities with larger numbers of species or interspecific interactions are inherently unstable, the mechanism allowing for the stable existence of complex communities in nature has been a central question in ecology. The main efforts to answer this question have sought to identify non-random features of ecological systems that can reverse a negative complexity-stability relationship into a positive one, but are far from successful, especially in their generality. Here, using the traditional community matrix analysis, we show that variation in the density dependence of interspecific interactions, which should be ubiquitous in nature, can dramatically affect the complexity-stability relationship. More specifically, we reveal that a positive complexity-stability relationship arises when harmful interspecific effects have larger density dependence than beneficial ones, regardless of the signs (i.e. positive or negative) of their dependence. Furthermore, numerical simulations demonstrated the synergistic stabilizing effect of interaction type diversity and density-dependence variation. Thus, this concept of density-dependence variation advances our understanding of the complexity-stability relationship in the real world.

Fluctuating interaction network and time-varying stability of a natural fish community.

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

Environmental DNA enables detection of terrestrial mammals from forest pond water.

Terrestrial animals must have frequent contact with water to survive, implying that environmental DNA (eDNA) originating from those animals should be detectable from places containing water in terrestrial ecosystems. Aiming to detect the presence of terrestrial mammals using forest water samples, we applied a set of universal PCR primers (MiMammal, a modified version of fish universal primers) for metabarcoding mammalian eDNA. The versatility of MiMammal primers was tested in silico and by amplifying DNAs extracted from tissues. The results suggested that MiMammal primers are capable of amplifying and distinguishing a diverse group of mammalian species. In addition, analyses of water samples from zoo cages of mammals with known species composition suggested that MiMammal primers could successfully detect mammalian species from water samples in the field. Then, we performed an experiment to detect mammals from natural ecosystems by collecting five 500-ml water samples from ponds in two cool-temperate forests in Hokkaido, northern Japan. MiMammal amplicon libraries were constructed using eDNA extracted from water samples, and sequences generated by Illumina MiSeq were subjected to data processing and taxonomic assignment. We thereby detected multiple species of mammals common to the sampling areas, including deer (Cervus nippon), mouse (Mus musculus), vole (Myodes rufocanus), raccoon (Procyon lotor), rat (Rattus norvegicus) and shrew (Sorex unguiculatus). Many previous applications of the eDNA metabarcoding approach have been limited to aquatic/semiaquatic systems, but the results presented here show that the approach is also promising even for forest mammal biodiversity surveys.

Integrated trophic position decreases in more diverse communities of stream food webs.

The relationship between biodiversity and ecosystem functioning is an important theme in environmental sciences. We propose a new index for configuration of the biomass pyramid in an ecosystem, named integrated trophic position (iTP). The iTP is defined as a sum of trophic positions (i.e. the average number of steps involved in biomass transfer) of all the animals in a food web integrated by their individual biomass. The observed iTP for stream macroinvertebrates ranged from 2.39 to 2.79 and was negatively correlated with the species density and the Shannon-Wiener diversity index of the local community. The results indicate a lower efficiency of biomass transfer in more diverse communities, which may be explained by the variance in edibility hypothesis and/or the trophic omnivory hypothesis. We found a negative effect of biodiversity on ecosystem functioning.