The predictability of ecological stability in a noisy world

The Stability of Competitive Metacommunities Is Insensitive to Dispersal Connectivity in a Fluctuating Environment

AbstractMaintaining the stability of ecological communities is critical for conservation, yet we lack a clear understanding of what attributes of metacommunity structure control stability. Some theories suggest that greater dispersal promotes metacommunity stability by stabilizing local populations, while others suggest that dispersal synchronizes fluctuations across patches and leads to global instability. These effects of dispersal on stability may be mediated by metacommunity structure: the number of patches, the pattern of connections across patches, and levels of spatiotemporal correlation in the environment. Thus, we need theory to investigate metacommunity dynamics under different spatial structures and ecological scenarios. Here, we use simulations to investigate whether stability is primarily affected by connectivity, including dispersal rate and topology of connectivity network, or by mechanisms related to the number of patches. We find that in competitive metacommunities with environmental stochasticity, network topology has little effect on stability on the metacommunity scale even while it could change spatial diversity patterns. In contrast, the number of connected patches is the dominant factor promoting stability through averaging stochastic fluctuations across more patches, rather than due to more habitat heterogeneity per se. These results broaden our understanding of how metacommunity structure changes metacommunity stability, which is relevant for designing effective conservation strategies.

Early warning signals have limited applicability to empirical lake data

Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.

Understanding and applying biological resilience, from genes to ecosystems

The natural world is under unprecedented and accelerating pressure. Much work on understanding resilience to local and global environmental change has, so far, focussed on ecosystems. However, understanding a system's behaviour requires knowledge of its component parts and their interactions. Here we call for increased efforts to understand 'biological resilience', or the processes that enable components across biological levels, from genes to communities, to resist or recover from perturbations. Although ecologists and evolutionary biologists have the tool-boxes to examine form and function, efforts to integrate this knowledge across biological levels and take advantage of big data (e.g. ecological and genomic) are only just beginning. We argue that combining eco-evolutionary knowledge with ecosystem-level concepts of resilience will provide the mechanistic basis necessary to improve management of human, natural and agricultural ecosystems, and outline some of the challenges in achieving an understanding of biological resilience.

Toward spatially polarized human pressure? A dynamic factor analysis of ecological stability and the role of territorial gradients in Czech Republic

In light of global change, research on ecosystem dynamics and the related environmental policies are increasingly required to face with the inherent polarization in areas with low and high human pressure. Differential levels of human pressure are hypothesized to reflect development paths toward ecological stability of local systems vis à vis socioeconomic resilience. To delineate the latent nexus between socioeconomic development paths and ecological stability of local systems, we proposed a multidimensional, diachronic analysis of 28 indicators of territorial disparities, and ecological stability in 206 homogeneous administrative units of Czech Republic over almost 30 years (1990-2018). Mixing time-invariant factors with time-varying socio-environmental attributes, a dynamic factor analysis investigated the latent relationship between ecosystem functions, environmental pressures, and the background socioeconomic characteristics of the selected spatial units. We identified four geographical gradients in Czech Republic (namely elevation, economic agglomeration, demographic structure, and soil imperviousness) at the base of territorial divides associated with the increased polarization in areas with low and high human pressure. The role of urbanization, agriculture, and loss of natural habitats reflective of rising human pressure was illustrated along the selected gradients. Finally, policy implications of the (changing) geography of ecological disturbances and local development paths in Czech Republic were briefly discussed.

Rhizosphere engineering for sustainable crop production: entropy-based insights

There is a growing interest in exploring interactions at root-soil interface in natural and agricultural ecosystems, but an entropy-based understanding of these dynamic rhizosphere processes is lacking. We have developed a new conceptual model of rhizosphere regulation by localized nutrient supply using thermodynamic entropy. Increased nutrient-use efficiency is achieved by rhizosphere management based on self-organization and minimized entropy via equilibrium attractors comprising (i) optimized root strategies for nutrient acquisition and (ii) improved information exchange related to root-soil-microbe interactions. The cascading effects through different hierarchical levels amplify the underlying processes in plant-soil system. We propose a strategy for manipulating rhizosphere dynamics and improving nutrient-use efficiency by localized nutrient supply with minimization of entropy to underpin sustainable food/feed/fiber production.

The color of environmental noise in river networks

Despite its far-reaching implications for conservation and natural resource management, little is known about the color of environmental noise, or the structure of temporal autocorrelation in random environmental variation, in streams and rivers. Here, we analyze the geography, drivers, and timescale-dependence of noise color in streamflow across the U.S. hydrography, using streamflow time series from 7504 gages. We find that daily and annual flows are dominated by red and white spectra respectively, and spatial variation in noise color is explained by a combination of geographic, hydroclimatic, and anthropogenic variables. Noise color at the daily scale is influenced by stream network position, and land use and water management explain around one third of the spatial variation in noise color irrespective of the timescale considered. Our results highlight the peculiarities of environmental variation regimes in riverine systems, and reveal a strong human fingerprint on the stochastic patterns of streamflow variation in river networks.

Effect of Coal Mining on Soil Microorganisms from Stipa krylovii Rhizosphere in Typical Grassland

The environmental changes caused by coal mining activities caused disturbances to the plant, soil, and microbial health in the mining area. Arbuscular mycorrhizal fungi (AMF) play an important role in the ecological restoration of mining areas. However, it is less understood how soil fungal communities with multiple functional groups respond to coal mining, and the quantitative impact and risk of mining disturbance. Therefore, in this study, the effect of coal mining on soil microorganisms' composition and diversity were analyzed near the edge of an opencast coal-mine dump in the Shengli mining area, Xilingol League, Inner Mongolia. The response strategy of soil fungi to coal mining and the stability of arbuscular mycorrhizal fungi (AMF) in the soil fungal community were determined. Our results showed that coal mining affected AMF and soil fungi in areas within 900 m from the coal mine. The abundance of endophytes increased with the distance between sampling sites and the mine dump, whereas the abundance of saprotroph decreased with the distance between sampling sites and the mine dump. Saprotroph was the dominant functional flora near the mining area. The nodes percentage of Septoglomus and Claroideoglomus and AMF phylogenetic diversity near the mining area were highest. AMF responded to the mining disturbance via the variety and evolution strategy of flora. Furthermore, AMF and soil fungal communities were significantly correlated with edaphic properties and parameters. Soil available phosphorus (AP) was the main influencer of soil AMF and fungal communities. These findings evaluated the risk range of coal mining on AMF and soil fungal communities and elucidated the microbial response strategy to mining disturbance.

Wet and dry extremes reduce arthropod biomass independently of leaf phenology in the wet tropics

Warming temperatures are increasing rainfall extremes, yet arthropod responses to climatic fluctuations remain poorly understood. Here, we used spatiotemporal variation in tropical montane climate as a natural experiment to compare the importance of biotic versus abiotic drivers in regulating arthropod biomass. We combined intensive field data on arthropods, leaf phenology and in situ weather across a 1700-3100 m elevation and rainfall gradient, along with desiccation-resistance experiments and multi-decadal modelling. We found limited support for biotic drivers with weak increases in some herbivorous taxa on shrubs with new leaves, but no landscape-scale effects of leaf phenology, which tracked light and cloud cover. Instead, rainfall explained extensive interannual variability with maximum biomass at intermediate rainfall (130 mm month^-1 ) as both 3 months of high and low rainfall reduced arthropods by half. Based on 50 years of regional rainfall, our dynamic arthropod model predicted shifts in the timing of biomass maxima within cloud forests before plant communities transition to seasonally deciduous dry forests (mean annual rainfall 1000-2500 mm vs. <800 mm). Rainfall magnitude was the primary driver, but during high solar insolation, the 'drying power of air' (VPD_max ) reduced biomass within days contributing to drought related to the El Niño-Southern Oscillation (ENSO). Highlighting risks from drought, experiments demonstrated community-wide susceptibility to desiccation except for some caterpillars in which melanin-based coloration appeared to reduce the effects of evaporative drying. Overall, we provide multiple lines of evidence that several months of heavy rain or drought reduce arthropod biomass independently of deep-rooted plants with the potential to destabilize insectivore food webs.

Spatial and Ecological Scaling of Stability in Spatial Community Networks

There are many scales at which to quantify stability in spatial and ecological networks. Local-scale analyses focus on specific nodes of the spatial network, while regional-scale analyses consider the whole network. Similarly, species- and community-level analyses either account for single species or for the whole community. Furthermore, stability itself can be defined in multiple ways, including resistance (the inverse of the relative displacement caused by a perturbation), initial resilience (the rate of return after a perturbation), and invariability (the inverse of the relative amplitude of the population fluctuations). Here, we analyze the scale-dependence of these stability properties. More specifically, we ask how spatial scale (local vs. regional) and ecological scale (species vs. community) influence these stability properties. We find that regional initial resilience is the weighted arithmetic mean of the local initial resiliences. The regional resistance is the harmonic mean of local resistances, which makes regional resistance particularly vulnerable to nodes with low stability, unlike regional initial resilience. Analogous results hold for the relationship between community- and species-level initial resilience and resistance. Both resistance and initial resilience are “scale-free” properties: regional and community values are simply the biomass-weighted means of the local and species values, respectively. Thus, one can easily estimate both stability metrics of whole networks from partial sampling. In contrast, invariability generally is greater at the regional and community-level than at the local and species-level, respectively. Hence, estimating the invariability of spatial or ecological networks from measurements at the local or species level is more complicated, requiring an unbiased estimate of the network (i.e., region or community) size. In conclusion, we find that scaling of stability depends on the metric considered, and we present a reliable framework to estimate these metrics.

Faunal communities mediate the effects of plant richness, drought, and invasion on ecosystem multifunctional stability

Understanding the stability of ecosystem multifunctionality is imperative for maintaining ecosystem health and sustainability under augmented global change. However it remains unknown whether and how biological communities mediate multifunctional stability in response to biodiversity loss and disturbances. Here, we conducted a 3-year experiment by exposing 270 plant communities of four plant richness levels, i.e., 1, 2, 4, or 8 species, to drought and exotic plant invasion disturbances. Then, the direct effects of plant richness, drought and invasion, and their indirect effects mediated by the stability of plant, litter-faunal, and soil-faunal communities on multifunctional stability were disentangled. We found that plant richness increased, while drought and invasion decreased ecosystem multifunctional stability, which were mediated by plant or faunal community stability. By incorporating the stability of communities into the complex ecological mechanisms, the completeness and goodness of ecological models for explaining and maintaining the stability of ecosystem multifunctionality will be improved.

Machine learning methods trained on simple models can predict critical transitions in complex natural systems

Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical models to train a deep neural network to detect critical transitions-the Early Warning Signal Network (EWSNet). We then demonstrate that this network, trained on simulated data, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.

Temporal variability in population and community dynamics

Populations and communities fluctuate in their overall numbers through time, and the magnitude of fluctuations in individual species may scale to communities. However, the composite variability at the community scale is expected to be tempered by opposing fluctuations in individual populations, a phenomenon often called the portfolio effect. Understanding population variability, how it scales to community variability, and the spatial scaling in this variability are pressing needs given shifting environmental conditions and community composition. We explore evidence for portfolio effects using null community simulations and a large collection of empirical community time series from the BioTIME database. Additionally, we explore the relative roles of habitat type and geographic location on population and community temporal variability. We find strong portfolio effects in our theoretical community model, but weak effects in empirical data, suggesting a role for shared environmental responses, interspecific competition, or a litany of other factors. Furthermore, we observe a clear latitudinal signal - and differences among habitat types - in population and community variability. Together, this highlights the need to develop realistic models of community dynamics, and hints at spatial, and underlying environmental, gradients in variability in both population and community dynamics.

Extreme flood disturbance effects on multiple dimensions of river invertebrate community stability

Multidimensional analysis of community stability has recently emerged as an overarching approach to evaluating ecosystem response to disturbance. However, the approach has previously been applied only in experimental and modelling studies. We applied this concept to an 18-year time series (2000-2017) of macroinvertebrate community dynamics from a southeast Alaskan river to further develop and test the approach in relation to the effects of two extreme flood events occurring in 2005 (event 1) and 2014 (event 2). Five components of stability were calculated for pairs of pre- or post-event years. Individual components were tested for differences between pre- and post-event time periods. Stability components' pairwise correlations were assessed and ellipsoids of stability were developed for each time period and compared to a null model derived from the permuted dataset. Only one stability component demonstrated a significant difference between time periods. In contrast, 80% of moderate and significant correlations between stability components were degraded post-disturbance and significant changes to the form of stability ellipsoids were observed. Ellipsoids of stability for all periods after the initial disturbance (2005) were not different to the null model. Our results illustrate that the dimensionality of stability approach can be applied to natural ecosystem time-series data. The major increase in dimensionality of stability observed following disturbance potentially indicates significant shifts in the processes which drive stability following disturbance. This evidence improves our understanding of community response beyond what is possible through analysis of individual stability components.

Effects of multiple stressors on the dimensionality of ecological stability

Ecological stability is a multidimensional construct. Investigating multiple stability dimensions is key to understand how ecosystems respond to disturbance. Here, we evaluated the single and combined effects of common agricultural stressors (insecticide, herbicide and nutrients) on four dimensions of stability (resistance, resilience, recovery and invariability) and on the overall dimensionality of stability (DS) using the results of a freshwater mesocosm experiment. Functional recovery and resilience to pesticides were enhanced in nutrient-enriched systems, whereas compositional recovery was generally not achieved. Pesticides did not affect compositional DS, whereas functional DS was significantly increased by the insecticide only in non-enriched systems. Stressor interactions acted non-additively on single stability dimensions as well as on functional DS. Moreover we demonstrate that pesticides can modify the correlation between functional and compositional aspects of stability. Our study shows that different disturbance types, and their interactions, require specific management actions to promote ecosystem stability.

A resilience concept based on system functioning: A dynamical systems perspective

We introduce a new framework for resilience, which is traditionally understood as the ability of a system to absorb disturbances and maintain its state, by proposing a shift from a state-based to a system functioning-based approach to resilience, which takes into account that several different coexisting stable states could fulfill the same functioning. As a consequence, not every regime shift, i.e., transition from one stable state to another, is associated with a lack or loss of resilience. We emphasize the importance of flexibility-the ability of a system to shift between different stable states while still maintaining system functioning. Furthermore, we provide a classification of system responses based on the phenomenological properties of possible disturbances, including the role of their timescales. Therefore, we discern fluctuations, shocks, press disturbances, and trends as possible disturbances. We distinguish between two types of mechanisms of resilience: (i) tolerance and flexibility, which are properties of the system, and (ii) adaptation and transformation, which are processes that alter the system's tolerance and flexibility. Furthermore, we discuss quantitative methods to investigate resilience in model systems based on approaches developed in dynamical systems theory.

Toward a Universal Theoretical Framework to Understand Robustness and Resilience: From Cells to Systems

Research across a range of biological subdisciplines and scales, ranging from molecular to ecosystemic, provides ample evidence that living systems generally exhibit both a degree of resistance to disruption and an ability to recover following disturbance. Not only do mechanisms of robustness and resilience exist across and between systems, but those mechanisms exhibit ubiquitous and scalable commonalities in pattern and function. Mechanisms such as redundancy, plasticity, interconnectivity, and coordination of subunits appear to be crucial internal players in the determination of stability. Similarly, factors external to the system such as the amplitude, frequency, and predictability of disruptors, or the prevalence of key limiting resources, may constrain pathways of response. In the face of a rapidly changing environment, there is a pressing need to develop a common framework for describing, assessing, and predicting robustness and resilience within and across living systems.

Trait-based ecology at large scales: Assessing functional trait correlations, phylogenetic constraints and spatial variability using open data

The growing use of functional traits in ecological research has brought new insights into biodiversity responses to global environmental change. However, further progress depends on overcoming three major challenges involving (a) statistical correlations between traits, (b) phylogenetic constraints on the combination of traits possessed by any single species, and (c) spatial effects on trait structure and trait-environment relationships. Here, we introduce a new framework for quantifying trait correlations, phylogenetic constraints and spatial variability at large scales by combining openly available species' trait, occurrence and phylogenetic data with gridded, high-resolution environmental layers and computational modelling. Our approach is suitable for use among a wide range of taxonomic groups inhabiting terrestrial, marine and freshwater habitats. We demonstrate its application using freshwater macroinvertebrate data from 35 countries in Europe. We identified a subset of available macroinvertebrate traits, corresponding to a life-history model with axes of resistance, resilience and resource use, as relatively unaffected by correlations and phylogenetic constraints. Trait structure responded more consistently to environmental variation than taxonomic structure, regardless of location. A re-analysis of existing data on macroinvertebrate communities of European alpine streams supported this conclusion, and demonstrated that occurrence-based functional diversity indices are highly sensitive to the traits included in their calculation. Overall, our findings suggest that the search for quantitative trait-environment relationships using single traits or simple combinations of multiple traits is unlikely to be productive. Instead, there is a need to embrace the value of conceptual frameworks linking community responses to environmental change via traits which correspond to the axes of life-history models. Through a novel integration of tools and databases, our flexible framework can address this need.

Individual species provide multifaceted contributions to the stability of ecosystems

Exploration of the relationship between species diversity and ecological stability has occupied a prominent place in ecological research for decades. Yet, a key component of this puzzle-the contributions of individual species to the overall stability of ecosystems-remains largely unknown. Here, we show that individual species simultaneously stabilize and destabilize ecosystems along different dimensions of stability, and also that their contributions to functional (biomass) and compositional stability are largely independent. By simulating experimentally the extinction of three consumer species (the limpet Patella, the periwinkle Littorina and the topshell Gibbula) from a coastal rocky shore, we found that the capacity to predict the combined contribution of species to stability from the sum of their individual contributions varied among stability dimensions. This implies that the nature of the diversity-stability relationship depends upon the dimension of stability under consideration, and may be additive, synergistic or antagonistic. We conclude that, although the profoundly multifaceted and context-dependent consequences of species loss pose a significant challenge, the predictability of cumulative species contributions to some dimensions of stability provide a way forward for ecologists trying to conserve ecosystems and manage their stability under global change.

Structural forecasting of species persistence under changing environments

The persistence of a species in a given place not only depends on its intrinsic capacity to consume and transform resources into offspring, but also on how changing environmental conditions affect its growth rate. However, the complexity of factors has typically taken us to choose between understanding and predicting the persistence of species. To tackle this limitation, we propose a probabilistic approach rooted on the statistical concepts of ensemble theory applied to statistical mechanics and on the mathematical concepts of structural stability applied to population dynamics models - what we call structural forecasting. We show how this new approach allows us to estimate a probability of persistence for single species in local communities; to understand and interpret this probability conditional on the information we have concerning a system; and to provide out-of-sample predictions of species persistence as good as the best experimental approaches without the need of extensive amounts of data.

Towards a Comparative Framework of Demographic Resilience

In the current global biodiversity crisis, the development of tools to define, quantify, compare, and predict resilience is essential for understanding the responses of species to global change. However, disparate interpretations of resilience have hampered the development of a common currency to quantify and compare resilience across natural systems. Most resilience frameworks focus on upper levels of biological organization, especially ecosystems or communities, which complicates measurements of resilience using empirical data. Surprisingly, there is no quantifiable definition of resilience at the demographic level. We introduce a framework of demographic resilience that draws on existing concepts from community and population ecology, as well as an accompanying set of metrics that are comparable across species.

Integrating the underlying structure of stochasticity into community ecology

Stochasticity is a core component of ecology, as it underlies key processes that structure and create variability in nature. Despite its fundamental importance in ecological systems, the concept is often treated as synonymous with unpredictability in community ecology, and studies tend to focus on single forms of stochasticity rather than taking a more holistic view. This has led to multiple narratives for how stochasticity mediates community dynamics. Here, we present a framework that describes how different forms of stochasticity (notably demographic and environmental stochasticity) combine to provide underlying and predictable structure in diverse communities. This framework builds on the deep ecological understanding of stochastic processes acting at individual and population levels and in modules of a few interacting species. We support our framework with a mathematical model that we use to synthesize key literature, demonstrating that stochasticity is more than simple uncertainty. Rather, stochasticity has profound and predictable effects on community dynamics that are critical for understanding how diversity is maintained. We propose next steps that ecologists might use to explore the role of stochasticity for structuring communities in theoretical and empirical systems, and thereby enhance our understanding of community dynamics.

Unveiling dimensions of stability in complex ecological networks

Understanding the stability of ecological communities is a matter of increasing importance in the context of global environmental change. Yet it has proved to be a challenging task. Different metrics are used to assess the stability of ecological systems, and the choice of one metric over another may result in conflicting conclusions. Although each of the multitude of metrics is useful for answering a specific question about stability, the relationship among metrics is poorly understood. Such lack of understanding prevents scientists from developing a unified concept of stability. Instead, by investigating these relationships we can unveil how many dimensions of stability there are (i.e., in how many independent components stability metrics can be grouped), which should help build a more comprehensive concept of stability. Here we simultaneously measured 27 stability metrics frequently used in ecological studies. Our approach is based on dynamical simulations of multispecies trophic communities under different perturbation scenarios. Mapping the relationships between the metrics revealed that they can be lumped into 3 main groups of relatively independent stability components: early response to pulse, sensitivities to press, and distance to threshold. Selecting metrics from each of these groups allows a more accurate and comprehensive quantification of the overall stability of ecological communities. These results contribute to improving our understanding and assessment of stability in ecological communities.