Transformation starts at the periphery of networks where pushback is less.

Complex systems ranging from societies to ecological communities and power grids may be viewed as networks of connected elements. Such systems can go through critical transitions driven by an avalanche of contagious change. Here we ask, where in a complex network such a systemic shift is most likely to start. Intuitively, a central node seems the most likely source of such change. Indeed, topological studies suggest that central nodes can be the Achilles heel for attacks. We argue that the opposite is true for the class of networks in which all nodes tend to follow the state of their neighbors, a category we call two-way pull networks. In this case, a well-connected central node is an unlikely starting point of a systemic shift due to the buffering effect of connected neighbors. As a result, change is most likely to cascade through the network if it spreads first among relatively poorly connected nodes in the periphery. The probability of such initial spread is highest when the perturbation starts from intermediately connected nodes at the periphery, or more specifically, nodes with intermediate degree and relatively low closeness centrality. Our finding is consistent with empirical observations on social innovation, and may be relevant to topics as different as the sources of originality of art, collapse of financial and ecological networks and the onset of psychiatric disorders.

Unveiling functional linkages between habitats and organisms: Macroalgal habitats as influential factors of fish functional traits.

Understanding the relationship between the characteristics of habitats and their associated community is essential to comprehend the functioning of ecological systems and prevent their degradation. This is particularly relevant for in decline, habitat-forming species, such as macroalgae, which support diverse communities of fish in temperate rocky reefs. To understand the link between the functional habitats of macroalgae and the functional dimension of their associated fish communities, we used a standardized underwater visual census to quantify the macroalgal functional diversity, as well as the functional diversity, redundancy, and richness of fish communities in 400 sites scattered in three southern temperate marine realms. Our findings reveal that functional macroalgal habitats can be classified into three groups that shape the functional diversity, redundancy, and richness of fish when considering trait commonness. These results enhance our comprehension of the functional connections between the habitat and coexisting fish within marine ecosystems, providing valuable insights for the preservation of these habitats.

Farmland practices are driving bird population decline across Europe.

Declines in European bird populations are reported for decades but the direct effect of major anthropogenic pressures on such declines remains unquantified. Causal relationships between pressures and bird population responses are difficult to identify as pressures interact at different spatial scales and responses vary among species. Here, we uncover direct relationships between population time-series of 170 common bird species, monitored at more than 20,000 sites in 28 European countries, over 37 y, and four widespread anthropogenic pressures: agricultural intensification, change in forest cover, urbanisation and temperature change over the last decades. We quantify the influence of each pressure on population time-series and its importance relative to other pressures, and we identify traits of most affected species. We find that agricultural intensification, in particular pesticides and fertiliser use, is the main pressure for most bird population declines, especially for invertebrate feeders. Responses to changes in forest cover, urbanisation and temperature are more species-specific. Specifically, forest cover is associated with a positive effect and growing urbanisation with a negative effect on population dynamics, while temperature change has an effect on the dynamics of a large number of bird populations, the magnitude and direction of which depend on species' thermal preferences. Our results not only confirm the pervasive and strong effects of anthropogenic pressures on common breeding birds, but quantify the relative strength of these effects stressing the urgent need for transformative changes in the way of inhabiting the world in European countries, if bird populations shall have a chance of recovering.

Early-warning signals of impending speciation.

Species formation is a central topic in biology, and a large body of theoretical work has explored the conditions under which speciation occurs, including whether speciation dynamics are gradual or abrupt. In some cases of abrupt speciation, differentiation slowly builds up until it reaches a threshold, at which point linkage disequilibrium (LD) and divergent selection enter a positive feedback loop that triggers accelerated change. Notably, such abrupt transitions powered by a positive feedback have also been observed in a range of other systems. Efforts to anticipate abrupt transitions have led to the development of "early warning signals" (EWS), that is, specific statistical patterns preceding abrupt transitions. Examples of EWS are rising autocorrelation and variance in time-series data due to the reduction of the ability of the system to recover from disturbances. Here, we investigate whether speciation dynamics in theoretical models also exhibit EWS. Using a model of genetic divergence between two populations, we search for EWS before gradual and abrupt speciation events. We do so using six different metrics of differentiation: the effective migration rate, the number of selected loci, the mean fitness of our studied population, LD, FST, and Dabs, a metric analogous to DXY. We find evidence for EWS, with a heterogeneity in their strength among differentiation metrics. We specifically identify FST and the effective migration rate as the most reliable EWS of upcoming abrupt speciation events. Our results provide initial insights into potential EWS of impending speciation and contribute to efforts to generalize the mechanisms underlying EWS.

Evolutionary emergence of alternative stable states in shallow lakes.

Ecosystems under stress may respond abruptly and irreversibly through tipping points. Although mechanisms leading to alternative stable states are much studied, little is known about how such ecosystems could have emerged in the first place. We investigate whether evolution by natural selection along resource gradients leads to bistability, using shallow lakes as an example. There, tipping points occur between two alternative states dominated by either submersed or floating macrophytes depending on nutrient loading. We model the evolution of macrophyte depth in the lake, identify the conditions under which the ancestor population diversifies and investigate whether alternative stable states dominated by different macrophyte phenotypes occur. We find that eco-evolutionary dynamics may lead to alternative stable states, but under restrictive conditions. Such dynamics require sufficient asymmetries in the acquisition of both light and nutrient. Our analysis suggests that competitive asymmetries along opposing resource gradients may allow bistability to emerge by natural selection.

Atmospheric CO2 forcing on Mediterranean biomes during the past 500 kyrs.

There is growing concern on the survival of Mediterranean forests under the projected near-future droughts as a result of anthropogenic climate change. Here we determine the resilience of Mediterranean forests across the entire range of climatic boundary conditions realized during the past 500 kyrs based on continuous pollen and geochemical records of (sub)centennial-scale resolution from drillcores from Tenaghi Philippon, Greece. Using convergent cross-mapping we provide empirical confirmation that global atmospheric carbon dioxide (CO2) may affect Mediterranean vegetation through forcing on moisture availability. Our analysis documents two stable vegetation regimes across the wide range of CO2 and moisture levels realized during the past four glacial-interglacial cycles, with abrupt shifts from forest to steppe biomes occurring when a threshold in precipitation is crossed. Our approach highlights that a CO2-driven moisture decrease in the near future may bear an impending risk for abrupt vegetation regime shifts prompting forest loss in the Mediterranean region.

Anticipating the occurrence and type of critical transitions.

Critical transition can occur in many real-world systems. The ability to forecast the occurrence of transition is of major interest in a range of contexts. Various early warning signals (EWSs) have been developed to anticipate the coming critical transition or distinguish types of transition. However, no effective method allows to establish practical threshold indicating the condition when the critical transition is most likely to occur. Here, we introduce a powerful EWS, named dynamical eigenvalue (DEV), that is rooted in bifurcation theory of dynamical systems to estimate the dominant eigenvalue of the system. Theoretically, the absolute value of DEV approaches 1 when the system approaches bifurcation, while its position in the complex plane indicates the type of transition. We demonstrate the efficacy of the DEV approach in model systems with known bifurcation types and also test the DEV approach on various critical transitions in real-world systems.

Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics.

Managing ecological communities requires fast detection of species that are sensitive to perturbations. Yet, the focus on recovery to equilibrium has prevented us from assessing species responses to perturbations when abundances fluctuate over time. Here, we introduce two data-driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian matrix to rank species over time according to their sensitivity to perturbations on abundances. Using several population dynamics models, we demonstrate that we can infer these rankings from time-series data to predict the order of species sensitivities. We find that the most sensitive species are not always the ones with the most rapidly changing or lowest abundance, which are typical criteria used to monitor populations. Finally, using two empirical time series, we show that sensitive species tend to be harder to forecast. Our results suggest that incorporating information on species interactions can improve how we manage communities out of equilibrium.

Predicting effects of multiple interacting global change drivers across trophic levels.

Global change encompasses many co-occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction-norm perspective can improve our ability to make predictions of interactions at higher levels of organization-that is, community and food web. Building on the framework of consumer-resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof-of-concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.

Prevalence and drivers of abrupt vegetation shifts in global drylands.

The constant provision of plant productivity is integral to supporting the liability of ecosystems and human wellbeing in global drylands. Drylands are paradigmatic examples of systems prone to experiencing abrupt changes in their functioning. Indeed, space-for-time substitution approaches suggest that abrupt changes in plant productivity are widespread, but this evidence is less clear using observational time series or experimental data at a large scale. Studying the prevalence and, most importantly, the unknown drivers of abrupt (rather than gradual) dynamical patterns in drylands may help to unveil hotspots of current and future dynamical instabilities in drylands. Using a 20-y global satellite-derived temporal assessment of dryland Normalized Difference Vegetation Index (NDVI), we show that 50% of all dryland ecosystems exhibiting gains or losses of NDVI are characterized by abrupt positive/negative temporal dynamics. We further show that abrupt changes are more common among negative than positive NDVI trends and can be found in global regions suffering recent droughts, particularly around critical aridity thresholds. Positive abrupt dynamics are found most in ecosystems with low seasonal variability or high aridity. Our work unveils the high importance of climate variability on triggering abrupt shifts in vegetation and it provides missing evidence of increasing abruptness in systems intensively managed by humans, with low soil organic carbon contents, or around specific aridity thresholds. These results highlight that abrupt changes in dryland dynamics are very common, especially for productivity losses, pinpoint global hotspots of dryland vulnerability, and identify drivers that could be targeted for effective dryland management.

Using functional indicators to detect state changes in terrestrial ecosystems.

Indicators to predict ecosystem state change are urgently needed to cope with the degradation of ecosystem services caused by global change. With the development of new technologies for measuring ecosystem function with fine spatiotemporal resolution over broad areas, we are in the era of 'big data'. However, it is unclear how large, emerging datasets can be used to anticipate ecosystem state change. We propose the construction of indicators based on functional variables (flows) and state variables (pools) to predict future ecosystem state changes. The indicators identified here may be useful signals for doing so. In addition, functional indicators have explicit ecological meanings that can identify the ecological mechanism that is causing state changes, and can thus be used to improve ecosystem models.

Regime shifts, trends, and variability of lake productivity at a global scale.

Lakes are often described as sentinels of global change. Phenomena like lake eutrophication, algal blooms, or reorganization in community composition belong to the most studied ecosystem regime shifts. However, although regime shifts have been well documented in several lakes, a global assessment of the prevalence of regime shifts is still missing, and, more in general, of the factors altering stability in lake status, is missing. Here, we provide a first global assessment of regime shifts and stability in the productivity of 1,015 lakes worldwide using trophic state index (TSI) time series derived from satellite imagery. We find that 12.8% of the lakes studied show regime shifts whose signatures are compatible with tipping points, while the number of detected regime shifts from low to high TSI has increased over time. Although our results suggest an overall stable picture for global lake dynamics, the limited instability signatures do not mean that lakes are insensitive to global change. Modeling the interaction between lake climatic, geophysical, and socioeconomic features and their stability properties, we find that the probability of a lake experiencing a tipping point increases with human population density in its catchment, while it decreases as the gross domestic product of that population increases. Our results show how quantifying lake productivity dynamics at a global scale highlights socioeconomic inequalities in conserving natural environments.

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 indicators3-5, 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.

Interaction fidelity is less common than expected in plant-pollinator communities.

Pairs of plants and pollinators species sometimes consistently interact throughout time and across space. Such consistency can be interpreted as a sign of interaction fidelity, that is a consistent interaction between two species when they co-occur in the same place. But how common interaction fidelity is and what determines interaction fidelity in plant-pollinator communities remain open questions. We aim to assess how frequent is interaction fidelity between plants and their pollinators and what drives interaction fidelity across plant-pollinator communities. Using a dataset of 141 networks around the world, we quantify whether the interaction between pairs of plant and pollinator species happens more ('interaction fidelity') or less ('interaction avoidance') often than expected by chance given the structure of the networks in which they co-occur. We also explore the relationship between interaction fidelity and species' degree (i.e. number of interactions), and the taxonomy of the species involved in the interaction. Our findings reveal that most plant-pollinator interactions do not differ from random expectations, in other words show neither fidelity nor avoidance. Out of the total 44,814 co-occurring species pairs we found 7,877 unique pair interactions (18%). Only 551 (7%) of the 7,877 plant-pollinator interactions did show significant interaction fidelity, meaning that these pairs interact in a consistent and non-random way across networks. We also find that 39 (0.09%) out of 44,814 plant-pollinator pairs showed significant interaction avoidance. Our results suggest that interactions involving specialist species have a high probability to show interaction fidelity and a low probability of interaction avoidance. In addition, we find that particular associations between plant and insect orders, as for example interactions between Hymenoptera and Fabales, showed high fidelity and low avoidance. Although niche and neutral processes simultaneously influence patterns of interaction in ecological communities, our findings suggest that it is rather neutral processes that are shaping the patterns of interactions in plant-pollinator networks.

A resilience sensing system for the biosphere.

We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here, we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilizing the generic mathematical behaviour of complex systems-loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales could aid management and governance at these different scales, and identify priorities for further work. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.

Adaptive Evolution Can Both Prevent Ecosystem Collapse and Delay Ecosystem Recovery.

AbstractThere is growing concern about the dire socioecological consequences of abrupt transitions between alternative ecosystem states in response to environmental changes. At the same time, environmental change can trigger evolutionary responses that could stabilize or destabilize ecosystem dynamics. However, we know little about how coupled ecological and evolutionary processes affect the risk of transition between alternative ecosystem states. Using shallow lakes as a model ecosystem, we investigate how trait evolution of a key species affects ecosystem resilience under environmental stress. We find that adaptive evolution of macrophytes can increase ecosystem resilience by shifting the critical threshold, which marks the transition from a clear-water state to a turbid-water state to a higher level of environmental stress. However, following the transition, adaptation to the turbid-water state can delay the ecosystem recovery back to the clear-water state. This implies that restoration could be more effective when implemented early enough after a transition occurs and before organisms adapt to the alternative state. Our findings provide new insights into how to prevent and mitigate the occurrence of regime shifts in ecosystems and highlight the need to understand ecosystem responses to environmental change in the context of coupled ecological and evolutionary processes.

Probabilistic early warning signals.

Ecological communities and other complex systems can undergo abrupt and long-lasting reorganization, a regime shift, when deterministic or stochastic factors bring them to the vicinity of a tipping point between alternative states. Such changes can be large and often arise unexpectedly. However, theoretical and experimental analyses have shown that changes in correlation structure, variance, and other standard indicators of biomass, abundance, or other descriptive variables are often observed prior to a state shift, providing early warnings of an anticipated transition. Natural systems manifest unknown mixtures of ecological and environmental processes, hampered by noise and limited observations. As data quality often cannot be improved, it is important to choose the best modeling tools available for the analysis.We investigate three autoregressive models and analyze their theoretical differences and practical performance. We formulate a novel probabilistic method for early warning signal detection and demonstrate performance improvements compared to nonprobabilistic alternatives based on simulation and publicly available experimental time series.The probabilistic formulation provides a novel approach to early warning signal detection and analysis, with enhanced robustness and treatment of uncertainties. In real experimental time series, the new probabilistic method produces results that are consistent with previously reported findings.Robustness to uncertainties is instrumental in the common scenario where mechanistic understanding of the complex system dynamics is not available. The probabilistic approach provides a new family of robust methods for early warning signal detection that can be naturally extended to incorporate variable modeling assumptions and prior knowledge.

Are geochemical regime shifts identifiable in river waters? Exploring the compositional dynamics of the Tiber River (Italy).

Rivers are dynamic and sensitive systems that change their chemical composition from source to mouth. This is due to the influence of a set of variables controlled by hydro-litho-eco-atmospheric processes and anthropic pressures which are, in turn, affected by catchment attributes. This work proposes a new way of thinking about river geochemistry focused on environmental interconnections rather than single chemical variables. Abrupt changes in the system state (composition) of a certain environmental media, driven by perturbations, may trigger Geochemical Regime Shifts (GRSs). This eventuality is explored in the Tiber River (central Italy) chemistry by Compositional Data Analysis, robust Principal Component Analysis and score-distance graphs. Data variability and the interlinks between response and forcing variables are investigated for different drained areas. A potential GRS is detected for major elements in the lower reaches resulting from a threshold-like state response caused by lithological forcing. On the contrary, trace elements respond gradually to environmental drivers, showing no abrupt changes. The findings outline mechanisms and factors influencing the river's self-restoring capability at a basin-wide scale, providing a better comprehension of the circumstances controlling the equilibrium dynamics of river water systems.

Estimating the risk of species interaction loss in mutualistic communities.

Interactions between species generate the functions on which ecosystems and humans depend. However, we lack an understanding of the risk that interaction loss poses to ecological communities. Here, we quantify the risk of interaction loss for 4,330 species interactions from 41 empirical pollination and seed dispersal networks across 6 continents. We estimate risk as a function of interaction vulnerability to extinction (likelihood of loss) and contribution to network feasibility, a measure of how much an interaction helps a community tolerate environmental perturbations. Remarkably, we find that more vulnerable interactions have higher contributions to network feasibility. Furthermore, interactions tend to have more similar vulnerability and contribution to feasibility across networks than expected by chance, suggesting that vulnerability and feasibility contribution may be intrinsic properties of interactions, rather than only a function of ecological context. These results may provide a starting point for prioritising interactions for conservation in species interaction networks in the future.

Foreseeing the future of mutualistic communities beyond collapse.

Changing conditions may lead to sudden shifts in the state of ecosystems when critical thresholds are passed. Some well-studied drivers of such transitions lead to predictable outcomes such as a turbid lake or a degraded landscape. Many ecosystems are, however, complex systems of many interacting species. While detecting upcoming transitions in such systems is challenging, predicting what comes after a critical transition is terra incognita altogether. The problem is that complex ecosystems may shift to many different, alternative states. Whether an impending transition has minor, positive or catastrophic effects is thus unclear. Some systems may, however, behave more predictably than others. The dynamics of mutualistic communities can be expected to be relatively simple, because delayed negative feedbacks leading to oscillatory or other complex dynamics are weak. Here, we address the question of whether this relative simplicity allows us to foresee a community's future state. As a case study, we use a model of a bipartite mutualistic network and show that a network's post-transition state is indicated by the way in which a system recovers from minor disturbances. Similar results obtained with a unipartite model of facilitation suggest that our results are of relevance to a wide range of mutualistic systems.