Direct observation of increasing recovery length before collapse of a marine benthic ecosystem

Modularity buffers the spread of spatial perturbations in macroalgal networks

Theory predicts that spatial modular networks contain the propagation of local disturbances, but field experimental tests of this hypothesis are lacking. We combined a field experiment with a metacommunity model to assess the role of modularity in buffering the spatial spread of algal turfs in three replicated canopy-dominated macroalgal networks. Experimental networks included three modules where plots with intact canopy cover (nodes) were connected through canopy-thinned corridors. The local perturbation consisted of removal of the canopy and understory species from four nodes within a single module to enable the establishment of algal turfs, which could then spread vegetatively to other untouched nodes through the canopy-thinned links. Results show that algal turfs invade mainly untouched nodes in the perturbed module, in agreement with the hypothesis that modularity can effectively constrain the spread of a spatial perturbation. The metacommunity model supports the empirical findings, illustrating greater resistance to perturbations of modular than random macroalgal canopy networks and making alternative explanations for the observed results unlikely. Evidence that the buffering effect of modularity can operate in natural environmental conditions has important implications for designing more robust networks of protected areas and less-fragile human-dominated fragmented landscapes.

Spatial signatures of an approaching regime shift in Posidonia oceanica meadows

Determining the proximity of ecosystems to tipping points is a critical yet complex task, heightened by the growing severity of climate change and local anthropogenic stressors on ecosystem integrity. Spatial Early Warning Signals (EWS) have been recognized for their potential in preemptively signaling regime shifts to degraded states, but their performance in natural systems remains uncertain. In this study, we investigated the performance of 'recovery length' - the spatial extent of recovery from a perturbation - and spatial EWS as early warnings of regime shifts in Posidonia oceanica meadows. Our experimental approach involved progressively thinning the P. oceanica canopy, from 0 to 100%, at the edge of a dead-matte area - a structure formed by dead P. oceanica rhizomes and colonized by algal turfs - to promote the propagation of algal turfs. We calculated recovery length as the distance from the dead-matte edge to the point where algal turfs colonized the canopy-thinned region. Our results showed a linear increase in recovery length with canopy thinning, successfully anticipated the degradation of P. oceanica. While spatial skewness decline with increased canopy degradation, other spatial EWS, such as Moran correlation at lag-1, low-frequency spatial spectra, and spatial variance, were ineffective in signaling this degradation. These findings underscore the potential of recovery length as a reliable early warning indicator of regime shifts in marine coastal ecosystems.

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.

Diversity of Molluscan Assemblage in Relation to Biotic and Abiotic Variables in Brown Algal Forests

Canopy-forming macroalgae, mainly those belonging to the order Fucales, form the so-called brown algal forests, which are among the most productive assemblages in shallow coastal zones. Their vertical, branching canopies increase nearshore primary production, provide nursery areas for juvenile fish, and sustain understory assemblages of smaller algae and both sessile and vagile fauna. The majority of benthic invertebrates inhabiting these forests have larval stages that spend some time floating freely or swimming in the plankton. Therefore, canopy-forming macroalgae play an important role as species collectors related to larval supply and hydrodynamic processes. During the past several decades, brown algal forests have significantly reduced their extension and coverage in the Mediterranean basin, due to multiple interacting natural and anthropogenic pressures, with negative consequences also for the related fauna. The aim of this research was to examine how differences in macrophyte abundance and structure, as well as environmental variables, affect the associated molluscan communities in the shallow northern Adriatic Sea. Sampling sites with well-developed vegetation cover dominated by different canopy-forming species were selected in the shallow infralittoral belt of the northern Adriatic Sea in the spring–summer period of the years 2019 and 2020. Our results confirm the importance of algal forests for molluscan assemblage, with a total of 68 taxa of molluscs found associated with macrophytes. Gastropods showed the highest richness and abundance, followed by bivalves. Mollusc richness and diversity (in terms of biotic indices) were not related with the degree of development of canopy-forming species (in terms of total cover and total volume), nor with the ecological status of benthic macroalgae at different depths. On the contrary, the variability in molluscan taxa abundances was explained by some environmental variables, such as temperature, pH, light, and nitrates concentration.

Scaling up our understanding of tipping points

Anthropogenic activities are increasingly affecting ecosystems across the globe. Meanwhile, empirical and theoretical evidence suggest that natural systems can exhibit abrupt collapses in response to incremental increases in the stressors, sometimes with dramatic ecological and economic consequences. These catastrophic shifts are faster and larger than expected from the changes in the stressors and happen once a tipping point is crossed. The primary mechanisms that drive ecosystem responses to perturbations lie in their architecture of relationships, i.e. how species interact with each other and with the physical environment and the spatial structure of the environment. Nonetheless, existing theoretical work on catastrophic shifts has so far largely focused on relatively simple systems that have either few species and/or no spatial structure. This work has laid a critical foundation for understanding how abrupt responses to incremental stressors are possible, but it remains difficult to predict (let alone manage) where or when they are most likely to occur in more complex real-world settings. Here, we discuss how scaling up our investigations of catastrophic shifts from simple to more complex-species rich and spatially structured-systems could contribute to expanding our understanding of how nature works and improve our ability to anticipate the effects of global change on ecological systems. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.

Evaluating the performance of temporal and spatial early warning statistics of algal blooms

Regime shifts have large consequences for ecosystems and the services they provide. However, understanding the potential for, causes of, proximity to, and thresholds for regime shifts in nearly all settings is difficult. Generic statistical indicators of resilience have been proposed and studied in a wide range of ecosystems as a method to detect when regime shifts are becoming more likely without direct knowledge of underlying system dynamics or thresholds. These early warning statistics (EWS) have been studied separately but there have been few examples that directly compare temporal and spatial EWS in ecosystem-scale empirical data. To test these methods, we collected high-frequency time series and high-resolution spatial data during a whole-lake fertilization experiment while also monitoring an adjacent reference lake. We calculated two common EWS, standard deviation and autocorrelation, in both time series and spatial data to evaluate their performance prior to the resulting algal bloom. We also applied the quickest detection method to generate binary alarms of resilience change from temporal EWS. One temporal EWS, rolling window standard deviation, provided advanced warning in most variables prior to the bloom, showing trends and between-lake patterns consistent with theory. In contrast, temporal autocorrelation and both measures of spatial EWS (spatial SD, Moran's  I) provided little or no warning. By compiling time series data from this and past experiments with and without nutrient additions, we were able to evaluate temporal EWS performance for both constant and changing resilience conditions. True positive alarm rates were 2.5-8.3 times higher for rolling window standard deviation when a lake was being pushed towards a bloom than the rate of false positives when it was not. For rolling window autocorrelation, alarm rates were much lower and no variable had a higher true positive than false positive alarm rate. Our findings suggest temporal EWS provide advanced warning of algal blooms and that this approach could help managers prepare for and/or minimize negative bloom impacts.

Spatiotemporal variability in Swedish lake ecosystems

Studying ecosystem dynamics is critical to monitoring and managing linked systems of humans and nature. Due to the growth of tools and techniques for collecting data, information on the condition of these systems is more widely available. While there are a variety of approaches for mining and assessing data, there is a need for methods to detect latent characteristics in ecosystems linked to temporal and spatial patterns of change. Resilience-based approaches have been effective at not only identifying environmental change but also providing warning in advance of critical transitions in social-ecological systems (SES). In this study, we examine the usefulness of one such method, Fisher Information (FI) for spatiotemporal analysis. FI is used to assess patterns in data and has been established as an effective tool for capturing complex system dynamics to include regimes and regime shifts. We employed FI to assess the biophysical condition of eighty-five Swedish lakes from 1996–2018. Results showed that FI captured spatiotemporal changes in the Swedish lakes and identified distinct spatial patterns above and below the Limes Norrlandicus, a hard ecotone boundary which separates northern and southern ecoregions in Sweden. Further, it revealed that spatial variance changed approaching this boundary. Our results demonstrate the utility of this resilience-based approach for spatiotemporal and spatial regimes analyses linked to monitoring and managing critical watersheds and waterbodies impacted by accelerating environmental change.

Robots as models of evolving systems

SignificanceWe present a fully realized adaptive resource landscape with diploid three-gene robots presenting interacting roles of population dynamics, mutations, breeding, death, and birth. Although modeling and theory serves as a guide here, the inherent complexity of our robobiology world makes it an experiment in exploring rules of Darwinian natural selection at a level difficult to simulate. We find that the lower the genetic diversity, the lower the survival probability of the robot population. We propose that diploid gene robots can act as avatars of diploid mammalian cells to explore novel programs of administration of drugs.

An integrated assessment of the Good Environmental Status of Mediterranean Marine Protected Areas

Local, regional and global targets have been set to halt marine biodiversity loss. Europe has set its own policy targets to achieve Good Environmental Status (GES) of marine ecosystems by implementing the Marine Strategy Framework Directive (MSFD) across member states. We combined an extensive dataset across five Mediterranean ecoregions including 26 Marine Protected Areas (MPAs), their reference unprotected areas, and a no-trawl case study. Our aim was to assess if MPAs reach GES, if their effects are local or can be detected at ecoregion level or up to a Mediterranean scale, and which are the ecosystem components driving GES achievement. This was undertaken by using the analytical tool NEAT (Nested Environmental status Assessment Tool), which allows an integrated assessment of the status of marine systems. We adopted an ecosystem approach by integrating data from several ecosystem components: the seagrass Posidonia oceanica, macroalgae, sea urchins and fish. Thresholds to define the GES were set by dedicated workshops and literature review. In the Western Mediterranean, most MPAs are in good/high status, with P. oceanica and fish driving this result within MPAs. However, GES is achieved only at a local level, and the Mediterranean Sea, as a whole, results in a moderate environmental status. Macroalgal forests are overall in bad condition, confirming their status at risk. The results are significantly affected by the assumption that discrete observations over small spatial scales are representative of the total extension investigated. This calls for large-scale, dedicated assessments to realistically detect environmental status changes under different conditions. Understanding MPAs effectiveness in reaching GES is crucial to assess their role as sentinel observatories of marine systems. MPAs and trawling bans can locally contribute to the attainment of GES and to the fulfillment of the MSFD objectives. Building confidence in setting thresholds between GES and non-GES, investing in long-term monitoring, increasing the spatial extent of sampling areas, rethinking and broadening the scope of complementary tools of protection (e.g., Natura 2000 Sites), are indicated as solutions to ameliorate the status of the basin.

Unveiling the complexity and ecological function of aquatic macrophyte-animal networks in coastal ecosystems

Network theory offers innovative tools to explore the complex ecological mechanisms regulating species associations and interactions. Although interest in ecological networks has grown steadily during the last two decades, the application of network approaches has been unequally distributed across different study systems: while some kinds of interactions (e.g. plant-pollinator and host-parasite) have been extensively investigated, others remain relatively unexplored. Among the latter, aquatic macrophyte-animal associations in coastal environments have been largely neglected, despite their major role in littoral ecosystems. The ubiquity of macrophyte systems, their accessibility and multi-faceted ecological, economical and societal importance make macrophyte-animal systems an ideal subject for ecological network science. In fact, macrophyte-animal networks offer an aquatic counterpart to terrestrial plant-animal networks. In this review, we show how the application of network analysis to aquatic macrophyte-animal associations has the potential to broaden our understanding of how coastal ecosystems function. Network analysis can also provide a key to understanding how such ecosystems will respond to on-going and future threats from anthropogenic disturbance and environmental change. For this, we: (i) identify key issues that have limited the application of network theory and modelling to aquatic animal-macrophyte associations; (ii) illustrate through examples based on empirical data how network analysis can offer new insights on the complexity and functioning of coastal ecosystems; and (iii) provide suggestions for how to design future studies and establish this new research line into network ecology.

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

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

Can we preserve and restore overlooked macroalgal forests?

Habitat degradation and loss are severely affecting macroalgal forests worldwide, and their successful mitigation depends on the identification of the drivers of loss and the implementation of effective conservation and restoration actions. We made an extensive literature review 1- to document the historical (1789-1999) and recent (2000-2020) occurrence of the genus Cystoseira, Ericaria and Gongolaria reported in the literature along the 8000 km of the coasts of Italy, 2- to assess their decline and patterns of extinction, 3- to ascertain the drivers responsible for these changes, 4- to highlight the existence of success stories in their conservation and natural recovery. In the last twenty years, overall information on the distribution of Cystoseira s.l. exponentially increased, although research focused almost exclusively on intertidal reefs. Despite the lack of systematic monitoring programs, the local extinction of 371 populations of 19 different species of Cystoseira s.l. was documented across several regions, since 2000. Coastal engineering and poor quality of waters due to urban, agricultural or industrial activities were often documented as leading causes of habitat loss. However, the drivers of extinction were actually unknown for the majority of the populations and cause-effects relationships are scarcely documented. Although the proportion of protected populations increased to 77.8%, Marine Protected Areas are unlikely to guarantee adequate conservation efficacy, possibly also for the widespread lack of management and monitoring plans dealing specifically with Cystoseira s.l. species, and few evidences of natural recovery were observed. Our review shows the dramatic lack of baseline information for macroalgal forests, highlighting the urgent need for the monitoring of less accessible habitats, the collection of long-term data to unveil drivers of loss, and an updated reporting about the conservation status of the species of interest to plan future interventions.

Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts

Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics - underpinning persistent criticality and neutrality or independence among areas - is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.

Mediterranean rocky reefs in the Anthropocene: Present status and future concerns

Global change is striking harder and faster in the Mediterranean Sea than elsewhere, where high levels of human pressure and proneness to climate change interact in modifying the structure and disrupting regulative mechanisms of marine ecosystems. Rocky reefs are particularly exposed to such environmental changes with ongoing trends of degradation being impressive. Due to the variety of habitat types and associated marine biodiversity, rocky reefs are critical for the functioning of marine ecosystems, and their decline could profoundly affect the provision of essential goods and services which human populations in coastal areas rely upon. Here, we provide an up-to-date overview of the status of rocky reefs, trends in human-driven changes undermining their integrity, and current and upcoming management and conservation strategies, attempting a projection on what could be the future of this essential component of Mediterranean marine ecosystems.

Highly restricted dispersal in habitat-forming seaweed may impede natural recovery of disturbed populations

Cystoseira sensu lato (Class Phaeophyceae, Order Fucales, Family Sargassaceae) forests play a central role in marine Mediterranean ecosystems. Over the last decades, Cystoseira s.l. suffered from a severe loss as a result of multiple anthropogenic stressors. In particular, Gongolaria barbata has faced multiple human-induced threats, and, despite its ecological importance in structuring rocky communities and hosting a large number of species, the natural recovery of G. barbata depleted populations is uncertain. Here, we used nine microsatellite loci specifically developed for G. barbata to assess the genetic diversity of this species and its genetic connectivity among fifteen sites located in the Ionian, the Adriatic and the Black Seas. In line with strong and significant heterozygosity deficiencies across loci, likely explained by Wahlund effect, high genetic structure was observed among the three seas (ENA corrected FST = 0.355, IC = [0.283, 0.440]), with an estimated dispersal distance per generation smaller than 600 m, both in the Adriatic and Black Sea. This strong genetic structure likely results from restricted gene flow driven by geographic distances and limited dispersal abilities, along with genetic drift within isolated populations. The presence of genetically disconnected populations at small spatial scales (< 10 km) has important implications for the identification of relevant conservation and management measures for G. barbata: each population should be considered as separated evolutionary units with dedicated conservation efforts.

First Restoration Experiment for Gongolaria barbata in Slovenian Coastal Waters. What Can Go Wrong?

The global decline of brown algal forests along rocky coasts is causing an exceptional biodiversity loss. Regardless of conservation efforts, different techniques have been developed for large-scale restoration strategies in the Mediterranean Sea. In this study we tested ex situ pilot restoration of Gongolaria barbata (=Treptacantha barbata) for the first time in Slovenian coastal waters. Healthy apical fronds of the species were collected and the development of recruits on clay tiles was followed under laboratory conditions for 20 days. Despite the experimental difficulties experienced, especially due to the lack of antibiotics to prevent the growth of the biofilm, G. barbata recruits were outplanted in the sea on two concrete plates with 48 tiles each, protected by purpose-built cages to avoid grazing by herbivorous fish. The high survival rate of juveniles after four months in the field (89% of the tiles on the plate that was constantly protected) suggests that outplanting G. barbata is an operable approach for restoration efforts in the northern Adriatic Sea. Our first experiment in Slovenian coastal waters provides new information for the optimization of the best practices during the laboratory cultivation and addresses the early steps of restoration and introduction of young thalli in the natural environment.

Impairment of microbial and meiofaunal ecosystem functions linked to algal forest loss

Habitat loss is jeopardizing marine biodiversity. In the Mediterranean Sea, the algal forests of Cystoseira spp. form one of the most complex, productive and vulnerable shallow-water habitats. These forests are rapidly regressing with negative impact on the associated biodiversity, and potential consequences in terms of ecosystem functioning. Here, by comparing healthy Cystoseira forests and barren grounds (i.e., habitats where the macroalgal forests disappeared), we assessed the effects of habitat loss on meiofaunal and nematode biodiversity, and on some ecosystem functions (here measured in terms of prokaryotic and meiofaunal biomass). Overall, our results suggest that the loss of Cystoseira forests and the consequent barren formation is associated with the loss of meiofaunal higher taxa and a decrease of nematode biodiversity, leading to the collapse of the microbial and meiofaunal variables of ecosystem functions. We conclude that, given the very limited resilience of these ecosystems, active restoration of these vulnerable habitats is needed, in order to recover their biodiversity, ecosystem functions and associated services.

Community dynamics and ecological shifts on Mediterranean vermetid reefs

Mediterranean coastal ecosystems experience many local and global stressors and require long-term monitoring to detect and follow trends in community structure. Between 2009 and 2017, we seasonally and annually monitored the spatiotemporal community dynamics at 11 sites on the rocky shores of the southeastern Mediterranean, focusing on the understudied intertidal vermetid reef ecosystem. Marked seasonal trends were found in biodiversity, with the highest diversity in winter and spring. Canopy-forming brown algae, dominating the northwestern Mediterranean intertidal reefs, were generally scarce on the reef platform and almost only found in tidepools. Interannual shifts in community structure were driven mostly by sharp fluctuations in a few dominant native and alien species and the regional mass mortality of an Indo-Pacific mussel in summer 2016. Compared to an older macroalgae dataset, dating back to 1973-1995, we found that some warm-affinity (summer) taxa became more dominant and cold-affinity (winter) species less dominant, while one once conspicuous species, Halimeda tuna, completely disappeared. The observed community shifts are probably driven mostly by stressors related to climate change. We encourage forming a network of long-term, multi-site ecological monitoring programs in the Mediterranean to improve our understanding of ecosystem change and to enable making better predictions at the basin scale.

A review of social-ecological system resilience: Mechanism, assessment and management

Social-ecological system (SES) resilience involves the large information and complex relationships of nature, society and economy. To promote multi-disciplinary integration to jointly balance current well-being and long-term sustainability, it is necessary to sort resilience studies on different perspectives into a comprehensive framework to establish interdisciplinary consensus. Based on literature analysis and review, this paper presents an analytical framework for resilience in regional management, and gives a review of SES resilience studies in terms of mechanism, assessment, and management. We outline the current state of resilience research, identify the remaining challenges, and make key recommendations for future research. Our recommendations include promoting interdisciplinary consensus, emphasising dynamic adaptation processes, synthesizing multiple systems and scales, building comprehensive databases, and using mixed methods approach. The paper offers a framework for researchers, practitioners and policy makers to have a more comprehensive understanding of resilience as a whole, and thus helps navigate more fully the challenge of adapting complex resource and environmental problems.

Recovery ability of lowshore sessile assemblages in a highly contaminated post-industrial area

The inheritance of environmental contamination left by abandoned industrial plants is widespread globally. Here we compared the patterns of recovery of lowshore algal and invertebrate assemblages between the post-industrial site of Bagnoli-Coroglio and four reference sites distributed along the coast in the Gulf of Naples, southern Tyrrhenian Sea. The structure of whole assemblages, richness of taxa and abundance of individual taxa were followed during one year since an event of experimental disturbance consisting in the removal of all erect organisms from the rocky substrate. Our main findings suggest that the examined benthic assemblages recovered effectively and quickly after a pulse disturbance and, contrarily to initial expectations, that this ability was comparable between the post-industrial site and the reference sites. This result is discussed in terms of several plausible processes and mechanisms, including the general capability of intertidal organisms to recover from physical disturbance, the potential high level of environmental stress affecting the reference sites too, the chance that the most intense impacts of contamination remained restricted to the sediments of the post-industrial site without propagating to adjacent rocky habitats, and the large natural variability of reference sites that may have masked weak effects of the historical contamination. Irrespective of the actual causes, we emphasize the need for including natural variability of the examined system in any future restoration interventions, to guarantee representation of the range of variation of target organisms and of their underlying processes, and to avoid confounding the intended post-industrial impact with the effects of other natural and anthropogenic processes.

Inferring critical thresholds of ecosystem transitions from spatial data

Ecosystems can undergo abrupt transitions between alternative stable states when the driver crosses a critical threshold. Dynamical systems theory shows that when ecosystems approach the point of loss of stability associated with these transitions, they take a long time to recover from perturbations, a phenomenon known as critical slowing down. This generic feature of dynamical systems can offer early warning signals of abrupt transitions. However, these signals are qualitative and cannot quantify the thresholds of drivers at which transition may occur. Here, we propose a method to estimate critical thresholds from spatial data. We show that two spatial metrics, spatial variance and autocorrelation of ecosystem state variable, computed along driver gradients can be used to estimate critical thresholds. First, we investigate cellular-automaton models of ecosystem dynamics that show a transition from a high-density state to a bare state. Our models show that critical thresholds can be estimated as the ecosystem state and the driver values at which spatial variance and spatial autocorrelation of the ecosystem state are maximum. Next, to demonstrate the application of the method, we choose remotely sensed vegetation data (Enhanced Vegetation Index, EVI) from regions in central Africa and northeast Australia that exhibit alternative states in woody cover. We draw transects (8 × 90 km) that span alternative stable states along rainfall gradients. Our analyses of spatial variance and autocorrelation of EVI along transects yield estimates of critical thresholds. These estimates match reasonably well with those obtained by an independent method that uses large-scale (250 × 200 km) spatial data sets. Given the generality of the principles that underlie our method, our method can be applied to a variety of ecosystems that exhibit alternative stable states.

An information theory-based approach to assessing spatial patterns in complex systems

Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. Methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. Results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems.

Temporal clustering of extreme climate events drives a regime shift in rocky intertidal biofilms

Research on regime shifts has focused primarily on how changes in the intensity and duration of press disturbances precipitate natural systems into undesirable, alternative states. By contrast, the role of recurrent pulse perturbations, such as extreme climatic events, has been largely neglected, hindering our understanding of how historical processes regulate the onset of a regime shift. We performed field manipulations to evaluate whether combinations of extreme events of temperature and sediment deposition that differed in their degree of temporal clustering generated alternative states in rocky intertidal epilithic microphytobenthos (biofilms) on rocky shores. The likelihood of biofilms to shift from a vegetated to a bare state depended on the degree of temporal clustering of events, with biofilm biomass showing both states under a regime of non-clustered (60 d apart) perturbations while collapsing in the clustered (15 d apart) scenario. Our results indicate that time since the last perturbation can be an important predictor of collapse in systems exhibiting alternative states and that consideration of historical effects in studies of regime shifts may largely improve our understanding of ecosystem dynamics under climate change.

Ecological feedback mechanisms and variable disturbance regimes: the uncertain future of Mediterranean macroalgal forests

Loss of algal canopies can result in a shift towards a turf-dominated state, where variability in species life-history traits can determine new mechanisms of feedback, and influence the degraded system under variable regimes of disturbance. By focusing on rockpools dominated by Cystoseira brachycarpa, we tested the hypothesis that the alga Dictyopteris polypodioides could take advantage of extreme regimes of disturbance related to storms, and outcompete other turfs through a distinctive combination of life traits. Replacement of the canopy was initially driven by a mix of taxon-specific life-traits and resulting assemblages were susceptible to intense events of disturbance. Subsequently, D. polypodioides dominated removal quadrats, favored by density-dependent abilities to intercept more light and reach larger size than the rest of turf. These new positive feedbacks may contribute to maintain the modified state of the system and influence its ability to withstand extreme abiotic conditions.

Experimental evidence of spatial signatures of approaching regime shifts in macroalgal canopies

Developing early warning signals to predict regime shifts in ecosystems is a central issue in current ecological research. While there are many studies addressing temporal early warning indicators, research into spatial indicators is far behind, with field experiments even more rare. Here, we tested the performance of spatial early warning signals in an intertidal macroalgal system, where removal of algal canopies pushed the system toward a tipping point (corresponding to approximately 75% of canopy loss), marking the transition between a canopy- to a turf-dominated state. We performed a two-year experiment where spatial early warning indicators were assessed in transects where the canopy was differentially removed (from 0 to 100%). Unlike Moran correlation coefficient at lag-1, spatial variance, skewness, and spatial spectra at low frequency increased along the gradient of canopy degradation and dropped, or did not show any further increase beyond the transition point from a canopy- to a turf-dominated state (100% canopy removal). Our study provides direct evidence of the suitability of spatial early warning signals to anticipate regime shifts in natural ecosystems, emphasizing the importance of field experiments as a powerful tool to establish causal relationships between environmental stressors and early warning indicators.

Rate of recovery from perturbations as a means to forecast future stability of living systems

Anticipating critical transitions in complex ecological and living systems is an important need because it is often difficult to restore a system to its pre-transition state once the transition occurs. Recent studies demonstrate that several indicators based on changes in ecological time series can indicate that the system is approaching an impending transition. An exciting question is, however, whether we can predict more characteristics of the future system stability using measurements taken away from the transition. We address this question by introducing a model-less forecasting method to forecast catastrophic transition of an experimental ecological system. The experiment is based on the dynamics of a yeast population, which is known to exhibit a catastrophic transition as the environment deteriorates. By measuring the system's response to perturbations prior to transition, we forecast the distance to the upcoming transition, the type of the transition (i.e., catastrophic/non-catastrophic) and the future equilibrium points within a range near the transition. Experimental results suggest a strong potential for practical applicability of this approach for ecological systems which are at risk of catastrophic transitions, where there is a pressing need for information about upcoming thresholds.

Ocean acidification conditions increase resilience of marine diatoms

The fate of diatoms in future acidified oceans could have dramatic implications on marine ecosystems, because they account for ~40% of marine primary production. Here, we quantify resilience of Thalassiosira pseudonana in mid-20th century (300 ppm CO2) and future (1000 ppm CO2) conditions that cause ocean acidification, using a stress test that probes its ability to recover from incrementally higher amount of low-dose ultraviolet A (UVA) and B (UVB) radiation and re-initiate growth in day-night cycles, limited by nitrogen. While all cultures eventually collapse, those growing at 300 ppm CO2 succumb sooner. The underlying mechanism for collapse appears to be a system failure resulting from "loss of relational resilience," that is, inability to adopt physiological states matched to N-availability and phase of the diurnal cycle. Importantly, under elevated CO2 conditions diatoms sustain relational resilience over a longer timeframe, demonstrating increased resilience to future acidified ocean conditions. This stress test framework can be extended to evaluate and predict how various climate change associated stressors may impact microbial community resilience.

Choosing the future of Antarctica

We present two narratives on the future of Antarctica and the Southern Ocean, from the perspective of an observer looking back from 2070. In the first scenario, greenhouse gas emissions remained unchecked, the climate continued to warm, and the policy response was ineffective; this had large ramifications in Antarctica and the Southern Ocean, with worldwide impacts. In the second scenario, ambitious action was taken to limit greenhouse gas emissions and to establish policies that reduced anthropogenic pressure on the environment, slowing the rate of change in Antarctica. Choices made in the next decade will determine what trajectory is realized.

Essential ocean variables for global sustained observations of biodiversity and ecosystem changes

Sustained observations of marine biodiversity and ecosystems focused on specific conservation and management problems are needed around the world to effectively mitigate or manage changes resulting from anthropogenic pressures. These observations, while complex and expensive, are required by the international scientific, governance and policy communities to provide baselines against which the effects of human pressures and climate change may be measured and reported, and resources allocated to implement solutions. To identify biological and ecological essential ocean variables (EOVs) for implementation within a global ocean observing system that is relevant for science, informs society, and technologically feasible, we used a driver-pressure-state-impact-response (DPSIR) model. We (1) examined relevant international agreements to identify societal drivers and pressures on marine resources and ecosystems, (2) evaluated the temporal and spatial scales of variables measured by 100+ observing programs, and (3) analysed the impact and scalability of these variables and how they contribute to address societal and scientific issues. EOVs were related to the status of ecosystem components (phytoplankton and zooplankton biomass and diversity, and abundance and distribution of fish, marine turtles, birds and mammals), and to the extent and health of ecosystems (cover and composition of hard coral, seagrass, mangrove and macroalgal canopy). Benthic invertebrate abundance and distribution and microbe diversity and biomass were identified as emerging EOVs to be developed based on emerging requirements and new technologies. The temporal scale at which any shifts in biological systems will be detected will vary across the EOVs, the properties being monitored and the length of the existing time-series. Global implementation to deliver useful products will require collaboration of the scientific and policy sectors and a significant commitment to improve human and infrastructure capacity across the globe, including the development of new, more automated observing technologies, and encouraging the application of international standards and best practices.

How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs

There is growing interest in the use of critical slowing down and critical fluctuations as early warning signals for critical transitions in different complex systems. However, while some studies found them effective, others found the opposite. In this paper, we investigated why this might be so, by testing three commonly used indicators: lag-1 autocorrelation, variance, and low-frequency power spectrum at anticipating critical transitions in the very-high-frequency time series data of the Australian Dollar-Japanese Yen and Swiss Franc-Japanese Yen exchange rates. Besides testing rising trends in these indicators at a strict level of confidence using the Kendall-tau test, we also required statistically significant early warning signals to be concurrent in the three indicators, which must rise to appreciable values. We then found for our data set the optimum parameters for discovering critical transitions, and showed that the set of critical transitions found is generally insensitive to variations in the parameters. Suspecting that negative results in the literature are the results of low data frequencies, we created time series with time intervals over three orders of magnitude from the raw data, and tested them for early warning signals. Early warning signals can be reliably found only if the time interval of the data is shorter than the time scale of critical transitions in our complex system of interest. Finally, we compared the set of time windows with statistically significant early warning signals with the set of time windows followed by large movements, to conclude that the early warning signals indeed provide reliable information on impending critical transitions. This reliability becomes more compelling statistically the more events we test.

Early-warning signals of critical transition: Effect of extrinsic noise

Complex dynamical systems often have tipping points and exhibit catastrophic regime shift. Despite the notorious difficulty of predicting such transitions, accumulating studies have suggested the existence of generic early-warning signals (EWSs) preceding upcoming transitions. However, previous theories and models were based on the effect of the intrinsic noise (IN) when a system is approaching a critical point, and did not consider the pervasive environmental fluctuations or the extrinsic noise (EN). Here, we extend previous theory to investigate how the interplay of EN and IN affects EWSs. Stochastic simulations of model systems subject to both IN and EN have verified our theory and demonstrated that EN can dramatically alter and diminish the EWS. This effect is stronger with increasing amplitude and correlation time scale of the EN. In the presence of EN, the EWS can fail to predict or even give a false alarm of critical transitions.