Ecosystem tipping points in an evolving world

Hysteretic and asynchronous regime shifts of bacterial and micro-eukaryotic communities driven by nutrient loading

Nutrient pollution is pervasive in many urban rivers, while restoration measures that reduce nutrient loading but fail to improve biological communities often lack effectiveness due to the indispensable role of biota, especially multi-taxa, in enhancing ecosystem stability and function. The investigation of the response patterns of multi-taxa to the nutrient loading in urban rivers is important for the recovery of biota structure and thus ecosystem function. However, little is known about the response patterns of multi-taxa and their impact on ecosystem structure and function in urban rivers. Here, the study, from the perspective of alternative stable states theory, showed the hysteretic response of both bacterial and micro-eukaryotic communities to nutrient loading based on the field investigation and environmental DNA metabarcoding. Bistability was shown to exist in both bacterial and micro-eukaryotic communities, demonstrating that the response of microbiota to nutrient loading was a regime shifts with hysteresis. Potential analysis then indicated that the increased nutrient loading drove regime shifts in the bacterial community and the micro-eukaryotic community towards a state dominated by anaerobic bacteria and benthic Bacillariophyta, respectively. High nutrient loading was found to reduce the relative abundance of metazoan, but increase that of eukaryotic algae, which made the trophic pyramid top-lighter and bottom-heavier, probably exacerbating the degradation of ecosystem function. It should be noted that, in response to the reduced nutrient loading, the recovery threshold of micro-eukaryotic communities (nutrient loading = ∼0.5) was lower than that of bacterial communities (nutrient loading = ∼1.2), demonstrating longer hysteresis of micro-eukaryotic communities. In addition, the markedly positive correlation between the status of microbial communities and N-related enzyme activities suggested the recovery of microbial communities probably will benefit the improvement of N-cycling functionality. The obtained results provide a deep insight into the collapse and recovery trajectories of multi-trophic microbiota to the nutrient loading gradient and their impact on the N transformation potential, therefore benefiting the restoration and management of urban rivers.

Phytoplankton biomass in northern lakes reveals a complex response to global change

Global change may introduce fundamental alterations in phytoplankton biomass and community structure that can alter the productivity of northern lakes. In this study, we utilized Swedish and Finnish monitoring data from lakes that are spatially (135 lakes) and temporally (1995-2019, 110 lakes) extensive to assess how phytoplankton biomass (PB) of dominant phytoplankton groups related to changes in water temperature, pH and key nutrients [total phosphorus (TP), total nitrogen (TN), total organic carbon (TOC), iron (Fe)] along spatial (Fennoscandia) and temporal (25 years) gradients. Using a machine learning approach, we found that TP was the most important determinant of total PB and biomass of a specific species of Raphidophyceae - Gonyostomum semen - and Cyanobacteria (both typically with adverse impacts on food-webs and water quality) in spatial analyses, while Fe and pH were second in importance for G. semen and TN and pH were second and third in importance for Cyanobacteria. However, in temporal analyses, decreasing Fe and increasing pH and TOC were associated with a decrease in G. semen and an increase in Cyanobacteria. In addition, in many lakes increasing TOC seemed to have generated browning to an extent that significantly reduced PB. The identified discrepancy between the spatial and temporal results suggests that substitutions of data for space-for-time may not be adequate to characterize long-term effects of global change on phytoplankton. Further, we found that total PB exhibited contrasting temporal trends (increasing in northern- and decreasing in southern Fennoscandia), with the decline in total PB being more pronounced than the increase. Among phytoplankton, G. semen biomass showed the strongest decline, while cyanobacterial biomass showed the strongest increase over 25 years. Our findings suggest that progressing browning and changes in Fe and pH promote significant temporal changes in PB and shifts in phytoplankton community structures in northern lakes.

Stability, resilience and eco-evolutionary feedbacks of mutualistic networks to rising temperature

Ecological networks comprising of mutualistic interactions can suddenly transition to undesirable states, such as collapse, due to small changes in environmental conditions such as a rise in local environmental temperature. However, little is known about the capacity of such interaction networks to adapt to a rise in temperature and the occurrence of critical transitions. Here, combining quantitative genetics and mutualistic dynamics in an eco-evolutionary framework, we evaluated the stability and resilience of mutualistic networks to critical transitions as environmental temperature increases. Specifically, we modelled the dynamics of an optimum trait that determined the tolerance of species to local environmental temperature as well as to species interaction. We then evaluated the impact of individual trait variation and evolutionary dynamics on the stability of feasible equilibria, the occurrence of threshold temperatures at which community collapses, and the abruptness of such community collapses. We found that mutualistic network architecture, that is the size of the community and the arrangement of species interactions, interacted with evolutionary dynamics to impact the onset of network collapses. Some networks had more capacity to track the rise in temperatures than others and thereby increased the threshold temperature at which the networks collapsed. However, such a result was modulated by the amount of heritable trait variation species exhibited, with high trait variation in the mean optimum phenotypic trait increasing the environmental temperature at which networks collapsed. Furthermore, trait variation not only increased the onset of temperatures at which networks collapsed but also increased the local stability of feasible equilibria. Our study argued that mutualistic network architecture interacts with species evolutionary dynamics and increases the capacity of networks to adapt to changes in temperature and thereby delayed the occurrence of community collapses.

The interplay of facilitation and competition drives the emergence of multistability in dryland plant communities

Within communities, species are wrapped in a set of feedbacks with each other and with their environment. When such feedbacks are strong enough they can generate alternative stable states. So far, research on alternative stable states has mostly focused on systems with a small number of species and a limited diversity of interaction types. Here, we analyze a spatial model of plant community dynamics in stressed ecosystems such as drylands, where each species is characterized by a strategy, and the different species interact through facilitation and competition for space and resources, such as water. We identify three different types of multistability emerging from the interplay of competition and facilitation. Under low-stress levels, plant communities organize in small groups of coexisting species, maintained by space, competition and facilitation ("cliques"). Under higher stress levels, positive feedback from facilitation lead to the dominance of a single facilitating species ("mutual exclusion states"). At the highest stress levels, the single facilitating species left in the system coexists with the desert state. By linking community ecology and alternative stable states theory using a spatial plant community model for stressed ecosystems, our study contributes to highlight the importance of positive feedback loops for the stability of ecological communities.

Quantifying the Resilience of Coal Energy Supply in China Toward Carbon Neutrality

Facing the challenge of achieving the goal of carbon neutrality, China is decoupling the currently close dependence of its economy on coal use. The energy supply and demand decarbonization has substantial influence on the resilience of the coal supply. However, a general understanding of the precise impact of energy decarbonization on the resilience of the coal energy supply is still lacking. Here, from the perspective of network science, we propose a theoretical framework to explore the resilience of the coal market of China. We show that the processes of increasing the connectivity and the competition between the coal enterprises, which are widely believed to improve the resilience of the coal market, can undermine the sustainability of the coal supply. Moreover, our results reveal that the policy of closing small-sized coal mines may not only reduce the safety accidents in the coal production but also improve the resilience of the coal market network. Using our model, we also suggest a few practical policies for minimizing the systemic risk of the coal energy supply.

Unraveling the mesoscale organization induced by network-driven processes

Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance-which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

Harnessing ecological theory to enhance ecosystem restoration

Ecosystem restoration can increase the health and resilience of nature and humanity. As a result, the international community is championing habitat restoration as a primary solution to address the dual climate and biodiversity crises. Yet most ecosystem restoration efforts to date have underperformed, failed, or been burdened by high costs that prevent upscaling. To become a primary, scalable conservation strategy, restoration efficiency and success must increase dramatically. Here, we outline how integrating ten foundational ecological theories that have not previously received much attention - from hierarchical facilitation to macroecology - into ecosystem restoration planning and management can markedly enhance restoration success. We propose a simple, systematic approach to determining which theories best align with restoration goals and are most likely to bolster their success. Armed with a century of advances in ecological theory, restoration practitioners will be better positioned to more cost-efficiently and effectively rebuild the world's ecosystems and support the resilience of our natural resources.

Ectomycorrhizal fungi are more sensitive to high soil nitrogen levels in forests exposed to nitrogen deposition

Ectomycorrhizal fungi are essential for nitrogen (N) cycling in many temperate forests and responsive to anthropogenic N addition, which generally decreases host carbon (C) allocation to the fungi. In the boreal region, however, ectomycorrhizal fungal biomass has been found to correlate positively with soil N availability. Still, responses to anthropogenic N input, for instance through atmospheric deposition, are commonly negative. To elucidate whether variation in N supply affects ectomycorrhizal fungi differently depending on geographical context, we investigated ectomycorrhizal fungal communities along fertility gradients located in two nemo-boreal forest regions with similar ranges in soil N : C ratios and inorganic N availability but contrasting rates of N deposition. Ectomycorrhizal biomass and community composition remained relatively stable across the N gradient with low atmospheric N deposition, but biomass decreased and the community changed more drastically with increasing N availability in the gradient subjected to higher rates of N deposition. Moreover, potential activities of enzymes involved in ectomycorrhizal mobilisation of organic N decreased as N availability increased. In forests with low external input, we propose that stabilising feedbacks in tree-fungal interactions maintain ectomycorrhizal fungal biomass and communities even in N-rich soils. By contrast, anthropogenic N input seems to impair ectomycorrhizal functions.

Resilience assessment in complex natural systems

Ecological resilience is the capability of an ecosystem to maintain the same structure and function and avoid crossing catastrophic tipping points (i.e. undergoing irreversible regime shifts). While fundamental for management, concrete ways to estimate and interpret resilience in real ecosystems are still lacking. Here, we develop an empirical approach to estimate resilience based on the stochastic cusp model derived from catastrophe theory. The cusp model models tipping points derived from a cusp bifurcation. We extend cusp in order to identify the presence of stable and unstable states in complex natural systems. Our Cusp Resilience Assessment (CUSPRA) has three characteristics: (i) it provides estimates on how likely a system is to cross a tipping point (in the form of a cusp bifurcation) characterized by hysteresis, (ii) it assesses resilience in relation to multiple external drivers and (iii) it produces straightforward results for ecosystem-based management. We validate our approach using simulated data and demonstrate its application using empirical time series of an Atlantic cod population and marine ecosystems in the North Sea and the Mediterranean Sea. We show that Cusp Resilience Assessment is a powerful method to empirically estimate resilience in support of a sustainable management of our constantly adapting ecosystems under global climate change.

Foundation species stabilize an alternative eutrophic state in nutrient-disturbed ponds via selection on microbial community

Eutrophication due to nutrient addition can result in major alterations in aquatic ecosystem productivity. Foundation species, individually and interactively, whether present as invasive species or as instruments of ecosystem management and restoration, can have unwanted effects like stabilizing turbid eutrophic states. In this study, we used whole-pond experimental manipulations to investigate the impacts of disturbance by nutrient additions in the presence and absence of two foundation species: Dreissena polymorpha (a freshwater mussel) and Myriophyllum spicatum (a macrophyte). We tracked how nutrient additions to ponds changed the prokaryotic and eukaryotic communities, using 16S, 18S, and COI amplicon sequencing. The nutrient disturbance and foundation species imposed strong selection on the prokaryotic communities, but not on the microbial eukaryotic communities. The prokaryotic communities changed increasingly over time as the nutrient disturbance intensified. Post-disturbance, the foundation species stabilized the prokaryotic communities as observed by the reduced rate of change in community composition. Our analysis suggests that prokaryotic community change contributed both directly and indirectly to major changes in ecosystem properties, including pH and dissolved oxygen. Our work shows that nutrient disturbance and foundation species strongly affect the prokaryotic community composition and stability, and that the presence of foundation species can, in some cases, promote the emergence and persistence of a turbid eutrophic ecosystem state.

Spatio-temporal coupling analysis and tipping points detection of China's coastal integrated land-human activity-ocean system

The coastal zone is typically highly developed and its ocean environment is vastly exposed to the onshore activities. Land-based pollution, as the "metabolite" of terrestrial human activities, significantly impacts the ocean environment. Although numerous studies have investigated these effects, few have quantified the interactions among land-human activity-ocean across both spatial and temporal scales. In this study, we have developed a land-human activity-ocean systemic framework integrating the coupling coordination degree model and tipping point to quantify the spatiotemporal dynamic interaction mechanism among the land-based pollution, human activities, and ocean environment in China from 2001 to 2020. Our findings revealed that the overall coupling coordination degree of the China's coastal zone increased by 36.9 % over last two decades. Furthermore, the effect of human activities on China's coastal environment remained within acceptable thresholds, as no universal tipping points for coastal pollution or ocean environment has been found over the 20-year period. Notably, the lag time for algal blooms, the key indicator of ocean environment health, was found to be 0-3 years in response to the land economic development and 0-4 years in response to land-based pollution. Based on the differences in spatiotemporal interactions among land-human activity-ocean system, we employed cluster analysis to categorize China's coastal provinces into four types and to develop appropriate management measures. Quantifying the interaction mechanism within the land-human activity-ocean system could aid decision-makers in creating sustainable coastal development strategies. This enables efficient use of land and ocean resources, supports coastal conservation and restoration efforts, and fosters effective management recommendations to enhance coastal sustainability and resilience.

First Passage Times of Long Transient Dynamics in Ecology

Long transient dynamics in ecological models are characterized by extended periods in one state or regime before an eventual, and often abrupt, transition. One mechanism leading to long transient dynamics is the presence of ghost attractors, states where system dynamics slow down and the system lingers before eventually transitioning to the true attractor. This transition results solely from system dynamics rather than external factors. This paper investigates the dynamics of a classical herbivore-grazer model with the potential for ghost attractors or alternative stable states. We propose an intuitive threshold for first passage time analysis applicable to both bistable and ghost attractor regimes. By formulating the first passage time problem as a backward Kolmogorov equation, we examine how the mean first passage time changes as parameters are varied from the ghost attractor regime to the bistable one, through a saddle-node bifurcation. Our results reveal that the mean and variance of first passage times vary smoothly across the bifurcation threshold, eliminating the deterministic distinction between ghost attractors and bistable regimes. This work suggests that first passage time analysis can be an informative way to classify the length of a long transient. A better understanding of the duration of long transients may contribute to greater ecological understanding and more effective environmental management.

A tiny fraction of all species forms most of nature: Rarity as a sticky state

Using data from a wide range of natural communities including the human microbiome, plants, fish, mushrooms, rodents, beetles, and trees, we show that universally just a few percent of the species account for most of the biomass. This is in line with the classical observation that the vast bulk of biodiversity is very rare. Attempts to find traits allowing the tiny fraction of abundant species to escape rarity have remained unsuccessful. Here, we argue that this might be explained by the fact that hyper-dominance can emerge through stochastic processes. We demonstrate that in neutrally competing groups of species, rarity tends to become a trap if environmental fluctuations result in gains and losses proportional to abundances. This counter-intuitive phenomenon arises because absolute change tends to zero for very small abundances, causing rarity to become a "sticky state", a pseudoattractor that can be revealed numerically in classical ball-in-cup landscapes. As a result, the vast majority of species spend most of their time in rarity leaving space for just a few others to dominate the neutral community. However, fates remain stochastic. Provided that there is some response diversity, roles occasionally shift as stochastic events or natural enemies bring an abundant species down allowing a rare species to rise to dominance. Microbial time series spanning thousands of generations support this prediction. Our results suggest that near-neutrality within niches may allow numerous rare species to persist in the wings of the dominant ones. Stand-ins may serve as insurance when former key species collapse.

Cities of the Anthropocene: urban sustainability in an eco-evolutionary perspective

Cities across the globe are driving systemic change in social and ecological systems by accelerating the rates of interactions and intensifying the links between human activities and Earth's ecosystems, thereby expanding the scale and influence of human activities on fundamental processes that sustain life. Increasing evidence shows that cities not only alter biodiversity, they change the genetic makeup of many populations, including animals, plants, fungi and microorganisms. Urban-driven rapid evolution in species traits might have significant effects on socially relevant ecosystem functions such as nutrient cycling, pollination, water and air purification and food production. Despite increasing evidence that cities are causing rapid evolutionary change, current urban sustainability strategies often overlook these dynamics. The dominant perspectives that guide these strategies are essentially static, focusing on preserving biodiversity in its present state or restoring it to pre-urban conditions. This paper provides a systemic overview of the socio-eco-evolutionary transition associated with global urbanization. Using examples of observed changes in species traits that play a significant role in maintaining ecosystem function and resilience, I propose that these evolutionary changes significantly impact urban sustainability. Incorporating an eco-evolutionary perspective into urban sustainability science and planning is crucial for effectively reimagining the cities of the Anthropocene. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

Marine diatoms record Late Holocene regime shifts in the Pikialasorsuaq ecosystem

The Pikialasorsuaq (North Water polynya) is an area of local and global cultural and ecological significance. However, over the last decades, the region has been subject to rapid warming, and in some recent years, the seasonal ice arch that has historically defined the polynya's northern boundary has failed to form. Both factors are deemed to alter the polynya's ecosystem functioning. To understand how climate-induced changes to the Pikialasorsuaq impact the basis of the marine food web, we explored diatom community-level responses to changing conditions, from a sediment core spanning the last 3800 years. Four metrics were used: total diatom concentrations, taxonomic composition, mean size, and diversity. Generalized additive model statistics highlight significant changes at ca. 2400, 2050, 1550, 1200, and 130 cal years BP, all coeval with known transitions between colder and warmer intervals of the Late Holocene, and regime shifts in the Pikialasorsuaq. Notably, a weaker/contracted polynya during the Roman Warm Period and Medieval Climate Anomaly caused the diatom community to reorganize via shifts in species composition, with the presence of larger taxa but lower diversity, and significantly reduced export production. This study underlines the high sensitivity of primary producers to changes in the polynya dynamics and illustrates that the strong pulse of early spring cryopelagic diatoms that makes the Pikialasorsuaq exceptionally productive may be jeopardized by rapid warming and associated Nares Strait ice arch destabilization. Future alterations to the phenology of primary producers may disproportionately impact higher trophic levels and keystone species in this region, with implications for Indigenous Peoples and global diversity.

Climate change and ecosystem shifts in the southwestern United States

Climate change shifts ecosystems, altering their compositions and instigating transitions, making climate change the predominant driver of ecosystem instability. Land management agencies experience these climatic effects on ecosystems they administer yet lack applied information to inform mitigation. We address this gap, explaining ecosystem shifts by building relationships between the historical locations of 22 ecosystems (c. 2000) and abiotic data (1970-2000; bioclimate, terrain) within the southwestern United States using 'ensemble' machine learning models. These relationships identify the conditions required for establishing and maintaining southwestern ecosystems (i.e., ecosystem suitability). We projected these historical relationships to mid (2041-2060) and end-of-century (2081-2100) periods using CMIP6 generation BCC-CSM2-MR and GFDL-ESM4 climate models with SSP3-7.0 and SSP5-8.5 emission scenarios. This procedure reveals how ecosystems shift, as suitability typically increases in area (~ 50% (~ 40% SD)), elevation (12-15%) and northing (4-6%) by mid-century. We illustrate where and when ecosystems shift, by mapping suitability predictions temporally and within 52,565 properties (e.g., Federal, State, Tribal). All properties had ≥ 50% changes in suitability for ≥ 1 ecosystem within them, irrespective of size (≥ 16.7 km2). We integrated 9 climate models to quantify predictive uncertainty and exemplify its relevance. Agencies must manage ecosystem shifts transcending jurisdictions. Effective mitigation requires collective action heretofore rarely instituted. Our procedure supplies the climatic context to inform their decisions.

Shifting taxonomic and functional community composition of rivers under land use change

Land use intensification has led to conspicuous changes in plant and animal communities across the world. Shifts in trait-based functional composition have recently been hypothesized to manifest at lower levels of environmental change when compared to species-based taxonomic composition; however, little is known about the commonalities in these responses across taxonomic groups and geographic regions. We investigated this hypothesis by testing for taxonomic and geographic similarities in the composition of riverine fish and insect communities across gradients of land use in major hydrological regions of the conterminous United States. We analyzed an extensive data set representing 556 species and 33 functional trait modalities from 8023 fish communities and 1434 taxa and 50 trait modalities from 5197 aquatic insect communities. Our results demonstrate abrupt threshold changes in both taxonomic and functional community composition due to land use conversion. Functional composition consistently demonstrated lower land use threshold responses compared to taxonomic composition for both fish (urban p = 0.069; agriculture p = 0.029) and insect (urban p = 0.095; agriculture p = 0.043) communities according to gradient forest models. We found significantly lower thresholds for urban versus agricultural land use for fishes (taxonomic and functional p < 0.001) and insects (taxonomic p = 0.001; functional p = 0.033). We further revealed that threshold responses in functional composition were more geographically consistent than for taxonomic composition to both urban and agricultural land use change. Traits contributing the most to overall functional composition change differed along urban and agricultural land gradients and conformed to predicted ecological mechanisms underpinning community change. This study points to reliable early-warning thresholds that accurately forecast compositional shifts in riverine communities to land use conversion, and highlight the importance of considering trait-based indicators of community change to inform large-scale land use management strategies and policies.

What Can Evolutionary History Tell Us about the Functioning of Ecological Communities? The ASN Presidential Debate

AbstractIn January 2018, Sharon Strauss, then president of the American Society of Naturalists, organized a debate on the following topic: does evolutionary history inform the current functioning of ecological communities? The debaters-Ives, Lau, Mayfield, and Tobias-presented pro and con arguments, caricatured in standard debating format. Numerous examples show that both recent microevolutionary and longer-term macroevolutionary history are important to the ecological functioning of communities. On the other hand, many other examples illustrate that the evolutionary history of communities or community members does not influence ecological function, or at least not very much. This article aims to provide a provocative discussion of the consistent and conflicting patterns that emerge in the study of contemporary and historical evolutionary influences on community function, as well as to identify questions for further study. It is intended as a thought-provoking exercise to explore this complex field, specifically addressing (1) key assumptions and how they can lead us astray and (2) issues that need additional study. The debaters all agree that evolutionary history can inform us about at least some aspects of community function. The underlying question at the root of the debate, however, is how the fields of ecology and evolution can most profitably collaborate to provide a deeper and broader understanding of ecological communities.

Feedback loops between 3D vegetation structure and ecological functions of animals

Ecosystems function in a series of feedback loops that can change or maintain vegetation structure. Vegetation structure influences the ecological niche space available to animals, shaping many aspects of behaviour and reproduction. In turn, animals perform ecological functions that shape vegetation structure. However, most studies concerning three-dimensional vegetation structure and animal ecology consider only a single direction of this relationship. Here, we review these separate lines of research and integrate them into a unified concept that describes a feedback mechanism. We also show how remote sensing and animal tracking technologies are now available at the global scale to describe feedback loops and their consequences for ecosystem functioning. An improved understanding of how animals interact with vegetation structure in feedback loops is needed to conserve ecosystems that face major disruptions in response to climate and land-use change.

Framework for global stability analysis of dynamical systems

Dynamical systems that are used to model power grids, the brain, and other physical systems can exhibit coexisting stable states known as attractors. A powerful tool to understand such systems, as well as to better predict when they may "tip" from one stable state to the other, is global stability analysis. It involves identifying the initial conditions that converge to each attractor, known as the basins of attraction, measuring the relative volume of these basins in state space, and quantifying how these fractions change as a system parameter evolves. By improving existing approaches, we present a comprehensive framework that allows for global stability analysis of dynamical systems. Notably, our framework enables the analysis to be made efficiently and conveniently over a parameter range. As such, it becomes an essential tool for stability analysis of dynamical systems that goes beyond local stability analysis offered by alternative frameworks. We demonstrate the effectiveness of our approach on a variety of models, including climate, power grids, ecosystems, and more. Our framework is available as simple-to-use open-source code as part of the DynamicalSystems.jl library.

How nutrient loading leads to alternative stable states in microbially mediated N-cycle pathways: A new insight into bioavailable nitrogen removal in urban rivers

Excessive nutrients have disrupted pathways of microbial-mediated nitrogen (N) cycle in urban rivers and caused bioavailable N to accumulate in sediments, while remedial actions sometimes fail to recover degraded river ecosystems even when environmental quality has been improved. It is not sufficient to revert the ecosystem to its original healthy state by restoring the pre-degradation environmental conditions, as explained by alternative stable states theory. Understanding the recovery of disrupted N-cycle pathways from the perspective of alternative stable states theory can benefit effective river remediation. Previous studies have found alternative microbiota states in rivers; however, the existence and implications of alternative stable states in microbial-mediated N-cycle pathway remain unclear. Here, high-throughput sequencing and N-related enzyme activities measurement were combined in the field investigation to provide empirical evidence for the bi-stability in microbially mediated N-cycle pathways. According to the behavior of bistable ecosystems, the existence of alternative stable states in microbial-mediated N-cycle pathway have been shown, and nutrient loading, mainly total nitrogen and total phosphorus, were identified as key driver of regime shifts. In addition, potential analysis revealed that reducing nutrient loading shifted the N-cycle pathway to a desirable state characterized by high ammonification and nitrification, probably avoiding the accumulation of ammonia and organic N. It should be noted that the improvement of microbiota status can facilitate the recovery of the desirable pathway state according to the relationship between microbiota states and N-cycle pathway states. Keystone species, including Rhizobiales and Sphingomonadales, were discerned by network analysis, and the increase in their relative abundance may facilitate the improvement of microbiota status. The obtained results suggested that the nutrient reduction should be combined with microbiota management to benefit the bioavailable N removal in urban rivers, therefore providing a new insight into alleviating adverse effects of the nutrient loading on urban rivers.

Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field

Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.

Functional diversity can facilitate the collapse of an undesirable ecosystem state

Biodiversity may increase ecosystem resilience. However, we have limited understanding if this holds true for ecosystems that respond to gradual environmental change with abrupt shifts to an alternative state. We used a mathematical model of anoxic-oxic regime shifts and explored how trait diversity in three groups of bacteria influences resilience. We found that trait diversity did not always increase resilience: greater diversity in two of the groups increased but in one group decreased resilience of their preferred ecosystem state. We also found that simultaneous trait diversity in multiple groups often led to reduced or erased diversity effects. Overall, our results suggest that higher diversity can increase resilience but can also promote collapse when diversity occurs in a functional group that negatively influences the state it occurs in. We propose this mechanism as a potential management approach to facilitate the recovery of a desired ecosystem state.

Tipping points and interactive effects of chronic human disturbance and acute heat stress on coral diversity

Multiple anthropogenic stressors co-occur ubiquitously in natural ecosystems. However, multiple stressor studies often produce conflicting results, potentially because the nature and direction of stressor interactions depends upon the strength of the underlying stressors. Here, we first examine how coral α- and β-diversities vary across sites spanning a gradient of chronic local anthropogenic stress before and after a prolonged marine heatwave. Developing a multiple stressor framework that encompasses non-discrete stressors, we then examine interactions between the continuous and discrete stressors. We provide evidence of additive effects, antagonistic interactions (with heatwave-driven turnover in coral community composition diminishing as the continuous stressor increased), and tipping points (at which the response of coral Hill-richness to stressors changed from additive to near synergistic). We show that community-level responses to multiple stressors can vary, and even change qualitatively, with stressor intensity, underscoring the importance of examining complex, but realistic continuous stressors to understand stressor interactions and their ecological impacts.

A predictive timeline of wildlife population collapse

Contemporary rates of biodiversity decline emphasize the need for reliable ecological forecasting, but current methods vary in their ability to predict the declines of real-world populations. Acknowledging that stressor effects start at the individual level, and that it is the sum of these individual-level effects that drives populations to collapse, shifts the focus of predictive ecology away from using predominantly abundance data. Doing so opens new opportunities to develop predictive frameworks that utilize increasingly available multi-dimensional data, which have previously been overlooked for ecological forecasting. Here, we propose that stressed populations will exhibit a predictable sequence of observable changes through time: changes in individuals' behaviour will occur as the first sign of increasing stress, followed by changes in fitness-related morphological traits, shifts in the dynamics (for example, birth rates) of populations and finally abundance declines. We discuss how monitoring the sequential appearance of these signals may allow us to discern whether a population is increasingly at risk of collapse, or is adapting in the face of environmental change, providing a conceptual framework to develop new forecasting methods that combine multi-dimensional (for example, behaviour, morphology, life history and abundance) data.

Multiple phase transitions shape biodiversity of a migrating population

In a wide variety of natural systems, closely related microbial strains coexist stably, resulting in high levels of fine-scale biodiversity. However, the mechanisms that stabilize this coexistence are not fully understood. Spatial heterogeneity is one common stabilizing mechanism, but the rate at which organisms disperse throughout the heterogeneous environment may strongly impact the stabilizing effect that heterogeneity can provide. An intriguing example is the gut microbiome, where active mechanisms affect the movement of microbes and potentially maintain diversity. We investigate how biodiversity is affected by migration rate using a simple evolutionary model with heterogeneous selection pressure. We find that the biodiversity-migration rate relationship is shaped by multiple phase transitions, including a reentrant phase transition to coexistence. At each transition, an ecotype goes extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the statistics of fluctuations due to demographic noise-this may provide an experimental means for detecting and altering impending extinction.

Ecological Drivers of Community Cohesion

From protocellular to societal, networks of living systems are complex and multiscale. Discerning the factors that facilitate assembly of these intricate interdependencies using pairwise interactions can be nearly impossible. To facilitate a greater understanding, we developed a mathematical and computational model based on a synthetic four-strain Saccharomyces cerevisiae interdependent system. Specifically, we aimed to provide a greater understanding of how ecological factors influence community dynamics. By leveraging transiently structured ecologies, we were able to drive community cohesion. We show how ecological interventions could reverse or slow the extinction rate of a cohesive community. An interconnected system first needs to persist long enough to be a subject of natural selection. Our emulation of Darwin's "warm little ponds" with an ecology governed by transient compartmentalization provided the necessary persistence. Our results reveal utility across scales of organization, stressing the importance of cyclic processes in major evolutionary transitions, engineering of synthetic microbial consortia, and conservation biology. IMPORTANCE We are facing unprecedented disruption and collapse of ecosystems across the globe. To have any hope of mitigating this phenomenon, a much greater understanding of ecosystem dynamics is required. However, ecosystems are typically composed of highly dynamic networks of individual species. These interactions are further modulated by abiotic and biotic factors that vary temporally and spatially. Thus, ecological dynamics are obfuscated by this complexity. Here, we developed a theoretical model, informed by a synthetic experimental system, of Darwin's "warm little ponds." This cycling four-species system seeks to elucidate the ecological factors that drive or inhibit interaction. We show that these factors could provide an essential tool for avoiding the accelerating ecological collapse. Our study also provides a starting point to develop a more encompassing model to inform conservation efforts.

Rising temperature drives tipping points in mutualistic networks

The effect of climate warming on species' physiological parameters, including growth rate, mortality rate and handling time, is well established from empirical data. However, with an alarming rise in global temperature more than ever, predicting the interactive influence of these changes on mutualistic communities remains uncertain. Using 139 real plant-pollinator networks sampled across the globe and a modelling approach, we study the impact of species' individual thermal responses on mutualistic communities. We show that at low mutualistic strength plant-pollinator networks are at potential risk of rapid transitions at higher temperatures. Evidently, generalist species play a critical role in guiding tipping points in mutualistic networks. Further, we derive stability criteria for the networks in a range of temperatures using a two-dimensional reduced model. We identify network structures that can ascertain the delay of a community collapse. Until the end of this century, on account of increasing climate warming many real mutualistic networks are likely to be under the threat of sudden collapse, and we frame strategies to mitigate this. Together, our results indicate that knowing individual species' thermal responses and network structure can improve predictions for communities facing rapid transitions.

Identifying thresholds of nitrogen enrichment for substantial shifts in arbuscular mycorrhizal fungal community metrics in a temperate grassland of northern China

Nitrogen (N) enrichment poses threats to biodiversity and ecosystem stability, while arbuscular mycorrhizal (AM) fungi play important roles in ecosystem stability and functioning. However, the ecological impacts, especially thresholds of N enrichment potentially causing AM fungal community shifts have not been adequately characterized. Based on a long-term field experiment with nine N addition levels ranging from 0 to 50 g N m^-2  yr^-1 in a temperate grassland, we characterized the community response patterns of AM fungi to N enrichment. Arbuscular mycorrhizal fungal biomass continuously decreased with increasing N addition levels. However, AM fungal diversity did not significantly change below 20 g N m^-2  yr^-1 , but dramatically decreased at higher N levels, which drove the AM fungal community to a potentially unstable state. Structural equation modeling showed that the decline in AM fungal biomass could be well explained by soil acidification, whereas key driving factors for AM fungal diversity shifted from soil nitrogen : phosphorus (N : P) ratio to soil pH with increasing N levels. Different aspects of AM fungal communities (biomass, diversity and community composition) respond differently to increasing N addition levels. Thresholds for substantial community shifts in response to N enrichment in this grassland ecosystem are identified.

Mapping the planet's critical natural assets

Sustaining the organisms, ecosystems and processes that underpin human wellbeing is necessary to achieve sustainable development. Here we define critical natural assets as the natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature's contributions to people (NCP), and we map the global locations of these critical natural assets at 2 km resolution. Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP) account for 30% of total global land area and 24% of national territorial waters, while 44% of land area is required to also maintain two global-scale NCP (carbon storage and moisture recycling). These areas overlap substantially with cultural diversity (areas containing 96% of global languages) and biodiversity (covering area requirements for 73% of birds and 66% of mammals). At least 87% of the world's population live in the areas benefitting from critical natural assets for local-scale NCP, while only 16% live on the lands containing these assets. Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets and the NCP they provide could simultaneously advance development, climate and conservation goals.

Climatic threshold of crop production and climate change adaptation: A case of winter wheat production in China

Global climate change has adversely affected agricultural production. Identifying the climatic threshold is critical to judge the impact and risk of climate change and proactively adapt agriculture. However, the climatic threshold of agriculture, especially crop production, remains unclear. To bridge this gap, taking winter wheat production from 1978 to 2017 in China as an example, this study clarified the definition of the climatic threshold of crop production and calculated it based on a mechanism model considering multiple factors and their synergies. The results showed that (1) the climate presented a warmer and wetter trend from 1978 to 2017, especially after 1996. (2) Water, fertilizer, and winter wheat yields increased significantly (22.4 mm/decade, 96.4 kg/ha·decade, and 674.2 kg/ha·decade, respectively, p &lt; 0.01). (3) The average optimal temperature and water thresholds for winter wheat were 7.3°C and 569 mm, respectively. The temperature rise was unfavorable for winter wheat production, and the water supply increase was beneficial to winter wheat production. (4) Increasing irrigation and fertilization could raise the optimal temperature threshold and adapt to climate warming in most provinces, while Shandong and Shaanxi both needed to reduce fertilization. We established a generalized method for calculating the climatic threshold of agricultural production and found that multifactor synergistic effects could influence the climatic threshold. The climatic threshold of winter wheat changed with different adaptation levels. However, considering the limitations in resource availability and environmental capacity, increasing the use efficiency of water and fertilizer is more important for adapting to climate change in the future.

Estimating comparable distances to tipping points across mutualistic systems by scaled recovery rates

Mutualistic systems can experience abrupt and irreversible regime shifts caused by local or global stressors. Despite decades of efforts to understand ecosystem dynamics and determine whether a tipping point could occur, there are no current approaches to estimate distances (in state/parameter space) to tipping points and compare the distances across various mutualistic systems. Here we develop a general dimension-reduction approach that simultaneously compresses the natural control and state parameters of high-dimensional complex systems and introduces a scaling factor for recovery rates. Our theoretical framework places various systems with entirely different dynamical parameters, network structure and state perturbations on the same scale. More importantly, it compares distances to tipping points across different systems on the basis of data on abundance and topology. By applying the method to 54 real-world mutualistic networks, our analytical results unveil the network characteristics and system parameters that control a system's resilience. We contribute to the ongoing efforts in developing a general framework for mapping and predicting distance to tipping points of ecological and potentially other systems.

Bimodal distribution of seafloor microbiota diversity and function are associated with marine aquaculture

The aim of the current work was to investigate the impact of marine aquaculture on seafloor biogeochemistry and diversity from pristine environments in the northern part of Norway. Our analytical approach included analyses of 182 samples from 16 aquaculture sites using 16S and 18S rRNA, shotgun analyses, visual examination of macro-organisms, in addition to chemical measurements. We observed a clear bimodal distribution of the prokaryote composition and richness, determined by analyses of 16S rRNA gene operational taxonomic units (OTUs). The high OTU richness cluster was associated with non-perturbed environments and farness from the aquaculture sites, while the low OTU richness cluster was associated with perturbed environments and proximity to the aquaculture sites. Similar patterns were also observed for eukaryotes using 18S rRNA gene analyses and visual examination, but without a bimodal distribution of OTU richness. Shotgun sequencing showed the archaeum Nitrosopumilus as dominant for the high OTU richness cluster, and the epsilon protobacterium Sulfurovum as dominant for the low OTU richness cluster. Metabolic reconstruction of Nitrosopumilus indicates nitrification as the main metabolic pathway. Sulfurovum, on the other hand, was associated with sulfur oxidation and denitrification. Changes in nitrogen and sulfur metabolism is proposed as a potential explanation for the difference between the high and low OTU richness clusters. In conclusion, these findings suggest that pollution from elevated loads of organic waste drives the microbiota towards a complete alteration of respiratory routes and species composition, in addition to a collapse in prokaryote OTU richness.

Regime transition Shapes the Composition, Assembly Processes, and Co-occurrence Pattern of Bacterioplankton Community in a Large Eutrophic Freshwater Lake

At certain nutrient concentrations, shallow freshwater lakes are generally characterized by two contrasting ecological regimes with disparate patterns of biodiversity and biogeochemical cycles: a macrophyte-dominated regime (MDR) and a phytoplankton-dominated regime (PDR). To reveal ecological mechanisms that affect bacterioplankton along the regime shift, Illumina MiSeq sequencing of the 16S rRNA gene combined with a novel network clustering tool (Manta) were used to identify patterns of bacterioplankton community composition across the regime shift in Taihu Lake, China. Marked divergence in the composition and ecological assembly processes of bacterioplankton community was observed under the regime shift. The alpha diversity of the bacterioplankton community consistently and continuously decreased with the regime shift from MDR to PDR, while the beta diversity presents differently. Moreover, as the regime shifted from MDR to PDR, the contribution of deterministic processes (such as environmental selection) to the assembly of bacterioplankton community initially decreased and then increased again as regime shift from MDR to PDR, most likely as a consequence of differences in nutrient concentration. The topological properties, including modularity, transitivity and network diameter, of the bacterioplankton co-occurrence networks changed along the regime shift, and the co-occurrences among species changed in structure and were significantly shaped by the environmental variables along the regime transition from MDR to PDR. The divergent environmental state of the regimes with diverse nutritional status may be the most important factor that contributes to the dissimilarity of bacterioplankton community composition along the regime shift.

Emerging signals of declining forest resilience under climate change

Forest ecosystems depend on their capacity to withstand and recover from natural and anthropogenic perturbations (that is, their resilience)¹. Experimental evidence of sudden increases in tree mortality is raising concerns about variation in forest resilience², yet little is known about how it is evolving in response to climate change. Here we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators^3–5, has changed during the period 2000–2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, probably related to increased water limitations and climate variability. By contrast, boreal forests show divergent local patterns with an average increasing trend in resilience, probably benefiting from warming and CO₂ fertilization, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests, corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest primary productivity, occurring in response to slow drifting towards a critical resilience threshold. Approximately 23% of intact undisturbed forests, corresponding to 3.32 Pg C of gross primary productivity, have already reached a critical threshold and are experiencing a further degradation in resilience. Together, these signals reveal a widespread decline in the capacity of forests to withstand perturbation that should be accounted for in the design of land-based mitigation and adaptation plans.

How Do Taxonomic and Functional Diversity Metrics Change Along an Aridity Gradient in a Tropical Dry Forest?

Ecological indicators based on biodiversity metrics are valuable and cost-effective tools to quantify, track and understand the effects of climate change on ecosystems. Studying changes in these indicators along climatic gradients in space is a common approach to infer about potential impacts of climate change over time, overcoming the limitations of lack of sufficiently long time-series data. Here, we studied the response of complementary biodiversity metrics in plants: taxonomic diversity (species richness and Simpson index) and functional diversity (diversity and redundancy) in 113 sampling sites along a spatial aridity gradient (from 0.27 to 0.69 of aridity index-AI) of 700 km in a Tropical dry forest. We found different responses of taxonomic and functional diversity metrics to aridity. Species diversity showed a hump-shaped curve peaking at intermediate levels of aridity between 0.38 and 0.52 AI as an ecotone, probably because it is where most species, from both drier and more mesic environments, still find conditions to co-exist. Functional diversity showed a positive linear relation with increasing aridity, suggesting higher aridity favors drought-adapted species with diverse functional traits. In contrast, redundancy showed a negative linear relation with increasing aridity, indicating that drier sites have few species sharing the same functional traits and resource acquisition strategies. Thus, despite the increase in functional diversity toward drier sites, these communities are less resilient since they are composed of a small number of plant species with unique functions, increasing the chances that the loss of one of such "key species" could lead to the loss of key ecosystem functions. These findings show that the integration of complementary taxonomic and functional diversity metrics, beyond the individual response of each one, is essential for reliably tracking the impacts of climate change on ecosystems. This work also provides support to the use of these biodiversity metrics as ecological indicators of the potential impact of climate change on drylands over time.

Community structure determines the predictability of population collapse

Early warning signals (EWS) are phenomenological tools that have been proposed as predictors of the collapse of biological systems. Although a growing body of work has shown the utility of EWS based on either statistics derived from abundance data or shifts in phenotypic traits such as body size, so far this work has largely focused on single species populations.

However, to predict reliably the future state of ecological systems, which inherently could consist of multiple species, understanding how reliable such signals are in a community context is critical.

Here, reconciling quantitative trait evolution and Lotka–Volterra equations, which allow us to track both abundance and mean traits, we simulate the collapse of populations embedded in mutualistic and multi‐trophic predator–prey communities. Using these simulations and warning signals derived from both population‐ and community‐level data, we showed the utility of abundance‐based EWS, as well as metrics derived from stability‐landscape theory (e.g. width and depth of the basin of attraction), were fundamentally linked. Thus, the depth and width of such stability‐landscape curves could be used to identify which species should exhibit the strongest EWS of collapse.

The probability a species displays both trait and abundance‐based EWS was dependent on its position in a community, with some species able to act as indicator species. In addition, our results also demonstrated that in general trait‐based EWS were less reliable in comparison with abundance‐based EWS in forecasting species collapses in our simulated communities. Furthermore, community‐level abundance‐based EWS were fairly reliable in comparison with their species‐level counterparts in forecasting species‐level collapses.

Our study suggests a holistic framework that combines abundance‐based EWS and metrics derived from stability‐landscape theory that may help in forecasting species loss in a community context.

Avoiding critical thresholds through effective monitoring

A major challenge in sustainability science is identifying targets that maximize ecosystem benefits to humanity while minimizing the risk of crossing critical system thresholds. One critical threshold is the biomass at which populations become so depleted that their population growth rates become negative-depensation. Here, we evaluate how the value of monitoring information increases as a natural resource spends more time near the critical threshold. This benefit emerges because higher monitoring precision promotes higher yield and a greater capacity to recover from overharvest. We show that precautionary buffers that trigger increased monitoring precision as resource levels decline may offer a way to minimize monitoring costs and maximize profits. In a world of finite resources, improving our understanding of the trade-off between precision in estimates of population status and the costs of mismanagement will benefit stakeholders that shoulder the burden of these economic and social costs.

Hidden dependence of spreading vulnerability on topological complexity

Many dynamical phenomena in complex systems concern spreading that plays out on top of networks with changing architecture over time-commonly known as temporal networks. A complex system's proneness to facilitate spreading phenomena, which we abbreviate as its "spreading vulnerability," is often surmised to be related to the topology of the temporal network featured by the system. Yet, cleanly extracting spreading vulnerability of a complex system directly from the topological information of the temporal network remains a challenge. Here, using data from a diverse set of real-world complex systems, we develop the "entropy of temporal entanglement" as a quantity to measure topological complexities of temporal networks. We show that this parameter-free quantity naturally allows for topological comparisons across vastly different complex systems. Importantly, by simulating three different types of stochastic dynamical processes playing out on top of temporal networks, we demonstrate that the entropy of temporal entanglement serves as a quantitative embodiment of the systems' spreading vulnerability, irrespective of the details of the processes. In being able to do so, i.e., in being able to quantitatively extract a complex system's proneness to facilitate spreading phenomena from topology, this entropic measure opens itself for applications in a wide variety of natural, social, biological, and engineered systems.

Photo-acclimatory thresholds anticipate sudden shifts in seagrass ecosystem state under reduced light conditions

Seagrass ecosystems usually respond in a nonlinear fashion to increasing pressures and environmental changes. Feedback mechanisms operating at the ecosystem level and involving multiple interactions among the seagrass meadow, its associated community and the physical environment are known to play a major role in such nonlinear responses. Phenotypic plasticity may also be important for buffering these ecological thresholds (i.e., regime shifts) as many physiological processes show nonlinear responses to gradual environmental changes, conferring the appearance of resistance before the effects at the organism and population levels are visible. However, the potential involvement of plant plasticity in driving catastrophic shifts in seagrass ecosystems has not yet been assessed. In this study, we conducted a manipulative 6-month light-gradient experiment in the field to capture nonlinearities of the physiological and population responses of the seagrass Cymodocea nodosa to gradual light reduction. The aim was to explore if and how the photo-acclimatory responses of shaded plants are translated to the population level and, hence, to the ecosystem level. Results showed that the seagrass population was rather stable under increasing shading levels through the activation of multilevel photo-acclimative responses, which are initiated with light reduction and modulated in proportion to shading intensity. The activation of photo-physiological and metabolic compensatory responses allowed shaded plants to sustain nearly constant plant productivity (metabolic carbon balance) along a range of shading levels before losing linearity and starting to decline. The species then activated plant- and meadow-scale photo-acclimative responses and drew on its energy reserves (rhizome carbohydrates) to confer additional population resilience. However, when the integration of all these buffering mechanisms failed to counterbalance the effects of extreme light limitation, the population collapsed, giving place to a phase shift from vegetated to bare sediments with catastrophic ecosystem outcomes. Our findings evidence that ecological thresholds in seagrass ecosystems under light limitation can be explained by the role of species' compensatory responses in modulating population-level responses. The thresholds of these plastic responses anticipate the sudden loss of seagrass meadows with the potential to be used as early warning indicators signalling the imminent collapse of the ecosystem, which is of great value for the real-world management of seagrass ecosystems.

Failures to disagree are essential for environmental science to effectively influence policy development

While environmental science, and ecology in particular, is working to provide better understanding to base sustainable decisions on, the way scientific understanding is developed can at times be detrimental to this cause. Locked-in debates are often unnecessarily polarised and can compromise any common goals of the opposing camps. The present paper is inspired by a resolved debate from an unrelated field of psychology where Nobel laureate David Kahneman and Garry Klein turned what seemed to be a locked-in debate into a constructive process for their fields. The present paper is also motivated by previous discourses regarding the role of thresholds in natural systems for management and governance, but its scope of analysis targets the scientific process within complex social-ecological systems in general. We identified four features of environmental science that appear to predispose for locked-in debates: (1) The strongly context-dependent behaviour of ecological systems. (2) The dominant role of single hypothesis testing. (3) The high prominence given to theory demonstration compared investigation. (4) The effect of urgent demands to inform and steer policy. This fertile ground is further cultivated by human psychological aspects as well as the structure of funding and publication systems.

Spatiotemporal dynamics drive synergism of land use and climatic extreme events in insect meta-populations

Ecosystems are increasingly threatened by co-occurring stressors associated with anthropogenic global change. Spatial stressor patterns range from local to regional to global, and temporal stressor patterns from discrete to continuous. To date, most multiple stressor studies covered short periods and focused on local effects and interactions. However, it remains largely unknown how stressors with different spatiotemporal patterns interact in their effects over longer periods. In particular, at higher spatial scales, biotic dynamics in ecological networks complicate the understanding of stressor interactions. We used a spatially explicit meta-population model for a generic freshwater insect, parameterized based on traits of the European damselfly Coenagrion mercuriale, to simulate scenarios of discrete climatic extreme events and continuous land use-related stress. Climatic extreme events were modeled as recurring mortality in all patches, whereas land use permanently influenced meta-populations via patch qualities and network connectivity. We found that the risk of discrete climatic extreme events to meta-populations depended strongly on the proportion of land use types, with effects ranging from negligible to extinction. Land use-related stress limited recovery in meta-populations from effects of climatic extreme events, resulting in synergistic stressor interactions. Moreover, the spatial configuration of land use type influenced the combined stressor effects with clustered configurations resulting in lower effects compared to a random configuration. Finally, we found that combined stressor effects can vary with the time point at which they were determined, indicating that inconclusive results in multiple stressor research can partly be due to differences in the time of determination. We conclude that conservation should focus on regional landscape management to mitigate risks on meta-populations from future, intensified extreme climate events. Reducing land use effects, thus improving patch quality and network connectivity, can compensate for effects of additional discrete stressors and, in turn, synergistic interactions.