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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’.

Withdrawal StatementThe authors have withdrawn their manuscript owing to conflicts regarding the use of data. Therefore, the authors do not wish this work to be cited as reference for the project. The revised version will be updated once this issue has been resolved. If you have any questions, please contact the corresponding author.

Microbial life in an ecosystem with low energy supply has been considered to employ two energy utilization strategies. The first is energy conservation at an individual level, while the second is energy use optimization in response to the availability of energy resources. Here, using an oxidation-reduction (redox) reaction network model where microbial metabolic pathways are established through multiple species-level competition and cooperation within a redox reaction network, we hypothesize that microbial ecosystems can move forward to increase energy use efficiency, namely an energy efficiency strategy at the community level. This strategy is supported by microbial functional diversity that enables species to interact with others in various ways of metabolic handoffs. Moreover, the high energy use efficiency is attributable to the mutualistic division of labor that increases the complexity of metabolic pathways, which actively drives material cycling to exploit more energy.

Various biodiversity indicators, such as species richness, total abundance and species diversity indices, have been developed to capture the state of ecological community 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 biodiversity conservation and management. Here we present a method to characterize and classify biodiversity indicators according to their dynamical similarity and apply the methodology to the monitoring data of a marine fish community under the intermittent anthropogenic warm water discharge. Our analysis reveals that ten biodiversity indicators can be classified into three super-groups that possibly reflect the same dimension of biodiversity. Group I (species richness) with the most robust dynamics to the temperature changes; Group II (species diversity and total abundance) showing an abrupt change in the middle of the monitoring period, presumably due to a change in temperature; Group III (species evenness) with the highest sensitivity to temperature changes. The finding suggests that selecting indicators from different super-groups might help to fully assess biodiversity response to environmental change.

Species richness, evenness, and taxonomic diversity have been proposed and used as indicators of biodiversity. However, the governing equations for the dynamics of these indicators are often unknown and an understanding of their mechanism is limited. By employing a nonlinear forecasting-based method to the time series of 10 biodiversity indicators of a marine fish community, we aimed to detect changes in their dynamics and classify the indicators according to the level and timing of dynamic changes. The 10 biodiversity indicators were classified into three super-groups: Group I (species richness and community center of distribution) with the most unchanged dynamics; Group II (species diversity and total abundance) with dynamics that had an abrupt change in the middle, presumably due to an increase in local temperature; and Group III (species evenness) with highly variable dynamics. Finally, a hypothesis regarding the mechanistic basis of the dynamic similarities between the biodiversity indicators was proposed.

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.

Male–male competition for mating or fertilization opportunities may in theory select for male manipulative adaptation that can harm males’ mates, leading to sexual conflict. Evolutionary theory predicts that selection will favor the manipulation by pollen of the duration of its recipients’ receptivity. However, there is insufficient evidence to show that pollen can exert effects on the duration of receptivity, a prerequisite for the evolution of pollen manipulative adaptation. Using a sexual diploid dandelion, Taraxacum japonicum, we conducted hand pollination experiments within and between populations to examine the effects of pollen on the timing of inflorescence (flower head) closure. We also examined the potential fitness consequences to the pollen recipients by field observation of seed production in populations with different inflorescence closure timing. Within-population experiments showed that pollination induced inflorescence closure even when some of the florets in the inflorescence were not yet pollinated with compatible pollen. Furthermore, between-population crosses revealed that pollen donors, recipients, and their interaction all influenced the timing of inflorescence closure. Different populations differed in the timing of inflorescence closure, which could affect the availability to the recipients of pollen for fertilization and thereby seed production. However, there were no significant differences in the rates of natural seed production in these populations. Our results demonstrate that pollen donors, as well as recipients, contribute to the duration of floral receptivity. We discuss the implications of our findings to the evolutionary theory of plant reproduction.

Environmental DNA (eDNA) consists of DNA fragments shed from organisms into the environment, and can be used to identify species presence and abundance. This study aimed to reveal the dispersion and degradation processes of eDNA in the sea. Caged fish were set off the end of a pier in Maizuru Bay, the Sea of Japan, and their eDNA was traced at sampling stations located at the cage and 10, 30, 100, 300, 600 and 1000 m distances from the cage along two transect lines. Sea surface water was collected at each station at 0, 2, 4, 8, 24 and 48 h after setting the cage, and again after removing the cage. Quantitative PCR analyses using a species-specific primer and probe set revealed that the target DNA was detectable while the cage was present and for up to 1 h after removing the cage, but not at 2 h or later. Among the 57 amplified samples, 45 (79%) were collected within 30 m from the cage. These results suggest that eDNA can provide a snapshot of organisms present in a coastal marine environment.

We developed a synthetic ecosystem of 12 cryopreservable microbial species with diverse interactions as an experimental “model ecosystem.” We created a machine learning model that noninvasively distinguished the 12 species on micrographs enabling high-throughput measurements. Our synthetic ecosystems maintained a certain diversity for at least six months.

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.

Interspecific interaction has been a key concept in ecology to understand the structure and dynamics of ecological communities. Important, yet often overlooked, is that an interspecific interaction is a product of multiple biological processes at various temporal and spatial scales, including changes in demographic parameters such as birth and death rates, behavioral responses such as inter-habitat movements, and hiding and evolutionary responses in a longer temporal scale. Each of those mechanisms, according to ecological theory, potentially affects population dynamics and modifies the community-level properties such as community complexity and stability in different manners. Here, a question arises: how does the net interspecific interaction, which is made up with those multiple processes, look like in the real nature? How do changes depend on the temporal or spatial scale? In this chapter we show that a data-driven approach using demographic time series is a powerful tool to answer those questions. According to nonlinear dynamics theory, a time series of a variable contains information about the dynamic system that the variable belongs to. We can use this fact to identify interspecific interactions, quantify their signs and strengths and evaluate its effect to community-level dynamic properties. Some results we got by applying the time-series analysis based on nonlinear dynamics theory (called Empirical Dynamic Modeling) to empirical demographic data, experimental or observational, will be presented, which will demonstrate how fluctuating and condition-dependent the real interactions are and reveal how those interactions give rise to the dynamic properties at higher organization levels.

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.

Our human-dominant world can be viewed as being built up in two parts, social and ecological systems, each consisting of multi-level organizations that interact in a complex manner. However, there are knowledge gaps among those interactions. In this paper, we focus on studies filling two types of gaps in the socioecological system, some of which are case studies in the East Asia region and others are discussed in a more general context. First, we address the gaps between different levels of organizations in ecological systems, namely, (1) the importance of plant trait plasticity in bridging evolution and ecology, (2) linking primary producer diversity and the dynamics of blue carbon in coastal ecosystems in the Asia–Pacific region, and (3) research direction of climate change biology to fill the gaps across evolution, community, and ecosystem. Also included is (4) the gap between ecological monitoring programs and theories, which also addresses the potential of citizen science. Second, we illustrate the gaps between ecological and social systems through ongoing development of an ecosystem management framework, i.e., ecosystem-based disaster risk reduction. Finally, we summarize the benefits of filling the gaps for ecologists and society.

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.

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.

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.

Crosses have revealed the species‐specific, positively correlated intensities of paternally expressed growth enhancer (GE) and maternally expressed growth suppressor (GS), which serve as a reproductive isolation mechanism in many plants and animals. However, how this mechanism evolved has remained unanswered. A dynamic model shows that the conflict between paternally and maternally inherited genomes may drive them to an evolutionary “arms race” of their GE and GS productions, respectively. This results in paternally expressed GE and maternally expressed GS, and their evolutionarily stable intensities are both decreasing functions of species‐specific degree of paternity and GE and GS production costs, thereby establishing a mechanism for postzygotic isolation.

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.

Mutualistic interactions between species such as pollination and plant–mycorrhiza interactions are ubiquitous in nature and essential for ecosystem functioning. Often dozens or even hundreds of species with different degree of specialization form complex networks of interdependence. How the complexity evolved and is maintained are fundamental questions in ecology. Here, we present a new game theoretical approach to model complex mutualistic interactions, which we apply to pollination networks. The theoretical analysis revealed multiple evolutionary stable network structures that form a gradient from generalism toward specialism with increasing availability of pollination service. In particular, we found that efficient communities evolve only under pollination oversupply, but that pollination shortage selects for inefficient network structures due to a pollination dilemma. These results suggest that availability of pollination services is a key factor structuring pollination networks and offer a new explanation for the geographical differences in pollination faunas that have long been recognized by ecologists. The study bridges the gap between network studies, game theory, and the natural history of pollination, which have hitherto been studied largely independently.

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.

Population density can be affected by its prey [resource] and predator [consumer] abundances through two different mechanisms: the alternation of birth [or somatic growth] or death rate and inter-habitat movement. While the food-web theory has traditionally been built on the former mechanism, the latter mechanism has formed the basis of a successful theory explaining the spatial distribution of organisms in the context of behavioral and evolutionary ecology. Yet, few studies have compared these two mechanisms, leaving the question of how similar (or different) predictions derived from birth–death-based and movement-based food-web theories unanswered. Here, theoretical models of the tri-trophic (resource–consumer-top predator) food chain were used to compare food-web patterns arising from these two mechanisms. Specifically, we evaluated the response of the food-chain structure to inter-patch differences in productivity for movement-based models and birth–death-based models. Model analysis reveals that adaptive movements give rise to positively correlated responses of all trophic levels to increased productivity; however, this pattern was not observed in the corresponding birth–death-based model. The movement-based model predicts that the food chain response to productivity is determined by the sensitivity of animal movement to the environmental conditions. More specifically, increasing sensitivity of a consumer or top predator leads to smaller inter-patch variance of the resource or consumer density, while increasing inter-patch variance in the consumer or resource density. In conclusion, adaptive movement provides an alternative mechanism correlating the food-web structure to environmental conditions.

Recently, a theoretical hypothesis was proposed that the coexistence of antagonism and mutualism may stabilize ecological community and even give rise to a positive complexity-stability relationship (interaction-type diversity hypothesis). This hypothesis was derived from an analysis of community model, which was developed based on two specific assumptions about the interaction strengths: those are, (i) different interaction types, antagonism and mutualism, have quantitatively comparable magnitude of effects to population growth; and (ii) interaction strength decreases with increasing interaction links of the same interaction type. However, those assumptions do not necessarily hold in real ecosystems, leaving unclear how robust this hypothesis is. Here, using a model with those two assumptions relaxed, we show (i) that the balance of interaction strength is necessary for the positive complexity effect to arise and (ii) that interaction-type diversity hypothesis may still hold when interaction strength decreases with increasing links of all interaction type for some species.

Theory predicts that ecological communities of many interacting species are unstable, despite the fact that complex ecosystems persist in nature. A recent theoretical study hypothesised that coexistence of antagonism and mutualism can stabilise a community and even give rise to a positive complexity–stability relationship. Here, using a theoretical model, we extended the earlier hypothesis to include competition as a third major interaction type, and showed that interaction-type diversity generally enhances stability of complex communities. Furthermore, we report a new finding that the hierarchically structured antagonistic interaction network is important for the stabilizing effect of interaction type diversity to emerge in complex communities. The present study indicated that the complexities characterised by species number, connectance, species variation, and interaction type diversity synergistically contributed to maintaining communities, and posed an interesting question of how present complex communities emerged, and developed from simpler ecosystems.

Effective ecosystem conservation and resource management require quantitative monitoring of biodiversity, including accurate descriptions of species composition and temporal variations of species abundance. Therefore, quantitative monitoring of biodiversity has been performed for many ecosystems, but it is often time- and effort-consuming and costly. Recent studies have shown that environmental DNA (eDNA), which is released to the environment from macro-organisms living in a habitat, contains information about species identity and abundance. Thus, analyzing eDNA would be a promising approach for more efficient biodiversity monitoring. In the present study, we added internal standard DNAs (i.e., known amounts of short DNA fragments from fish species that have never been observed in a sampling area) to eDNA samples, which were collected weekly from a coastal marine ecosystem in Maizuru-Bay, Kyoto, Japan (from April 2015 to March 2016), and performed metabarcoding analysis using Illumina MiSeq to simultaneously identify fish species and quantify fish eDNA copy numbers. A correction equation was obtained for each sample using the relationship between the number of sequence reads and the added amount of the standard DNAs, and this equation was used to estimate the copy numbers from the sequence reads of non-standard fish eDNA. The calculated copy numbers showed significant positive correlation with those determined by quantitative PCR, suggesting that eDNA metabarcoding with standard DNA enabled useful quantification of eDNA. Furthermore, for samples that show a high level of PCR inhibition, our method might allow more accurate quantification than qPCR because the correction equations generated using internal standard DNAs would include the effect of PCR inhibition. A single run of Illumina MiSeq produced > 70 quantitative fish eDNA time series in our study, showing that our method could contribute to more efficient and quantitative monitoring of biodiversity.

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.