Regime shift dynamics, tipping points and the success of fisheries management.

Recovery of depleted fish stocks is an important goal for fisheries management and crucial to sustain important ecosystem functions as well as global food security. Successful recovery requires adjusting fishing mortality to stock productivity but can be prevented or inhibited by additional anthropogenic impacts such as climate change. Despite management measures to recover fish stocks being in place in legislations such as the European Union´s Common Fisheries Policy (CFP), recovery can be hindered by the occurrence of regime shift dynamics. Such non-linear discontinuous dynamics imply tipping points and bear the characteristics of abrupt change, hysteresis and non-stationary functional relationships. We here used the recent reform of the CFP as a natural experiment to investigate the existence of regime shift dynamics and its potential effects on the recovery potential on six strongly fished or even depleted commercial fish stocks in the North Sea. Using a set of statistical approaches we show that regime shift dynamics exist in all six fish stocks as a response to changes in fishing pressure and temperature. Our results furthermore demonstrate the context-dependence of such dynamics and hence the ability of management measures to rebuild depleted fish stocks, leading to either failed recovery or positive tipping.

Innate immune responses at the asymptomatic stage of influenza A viral infections of Streptococcus pneumoniae colonized and non-colonized mice.

Seasonal Influenza A virus (IAV) infections can promote dissemination of upper respiratory tract commensals such as Streptococcus pneumoniae to the lower respiratory tract resulting in severe life-threatening pneumonia. Here, we aimed to compare innate immune responses in the lungs of healthy colonized and non-colonized mice after IAV challenge at the initial asymptomatic stage of infection. Responses during a severe bacterial pneumonia were profiled for comparison. Cytokine and innate immune cell imprints of the lungs were analyzed. Irrespective of the colonization status, mild H1N1 IAV infection was characterized by a bi-phasic disease progression resulting in full recovery of the animals. Already at the asymptomatic stage of viral infection, the pro-inflammatory cytokine response was as high as in pneumococcal pneumonia. Flow cytometry analyses revealed an early influx of inflammatory monocytes into the lungs. Neutrophil influx was mostly limited to bacterial infections. The majority of cells, except monocytes, displayed an activated phenotype characterized by elevated CCR2 and MHCII expression. In conclusion, we show that IAV challenge of colonized healthy mice does not automatically result in severe co-infection. However, a general local inflammatory response was noted at the asymptomatic stage of infection irrespective of the infection type.

Tipping point realized in cod fishery.

Understanding tipping point dynamics in harvested ecosystems is of crucial importance for sustainable resource management because ignoring their existence imperils social-ecological systems that depend on them. Fisheries collapses provide the best known examples for realizing tipping points with catastrophic ecological, economic and social consequences. However, present-day fisheries management systems still largely ignore the potential of their resources to exhibit such abrupt changes towards irreversible low productive states. Using a combination of statistical changepoint analysis and stochastic cusp modelling, here we show that Western Baltic cod is beyond such a tipping point caused by unsustainable exploitation levels that failed to account for changing environmental conditions. Furthermore, climate change stabilizes a novel and likely irreversible low productivity state of this fish stock that is not adapted to a fast warming environment. We hence argue that ignorance of non-linear resource dynamics has caused the demise of an economically and culturally important social-ecological system which calls for better adaptation of fisheries systems to climate change.

The rise of novelty in marine ecosystems: The Baltic Sea case.

Global environmental changes have accelerated at an unprecedented rate in recent decades due to human activities. As a consequence, the incidence of novel abiotic conditions and biotic communities, which have been continuously emerging in the Earth system, has rapidly risen. Despite growing attention to the incidence and challenges posed by novelty in terrestrial ecosystems, novelty has not yet been quantified in marine ecosystems. Here, we measured for the rate of novelty (RoN) in abiotic conditions and community structure for three trophic levels, i.e., phytoplankton, zooplankton, and fish, in a large marine system - the Baltic Sea. We measured RoN as the degree of dissimilarity relative to a specific spatial and temporal baseline, and contrasted this with the rate of change as a measure of within-basin change over time. We found that over the past 35 years abiotic and biotic RoN showed complex dynamics varying in time and space, depending on the baseline conditions. RoN in abiotic conditions was smaller in the open Central Baltic Sea than in the Kattegat and the more enclosed Gulf of Bothnia, Gulf of Riga, and Gulf of Finland in the north. We found a similar spatial pattern for biotic assemblages, which resulted from changes in composition and stock size. We identified sea-surface temperature and salinity as key drivers of RoN in biotic communities. Hence, future environmental changes that are expected to affect the biogeochemistry of the Baltic Sea, may favor the rise of biotic novelty. Our results highlighted the need for a deeper understanding of novelty development in marine ecosystems, including interactions between species and trophic levels, ecosystem functioning under novel abiotic conditions, and considering novelty in future management interventions.

Catastrophic dynamics limit Atlantic cod recovery.

Collapses and regime changes are pervasive in complex systems (such as marine ecosystems) governed by multiple stressors. The demise of Atlantic cod ( Gadus morhua) stocks constitutes a text book example of the consequences of overexploiting marine living resources, yet the drivers of these nearly synchronous collapses are still debated. Moreover, it is still unclear why rebuilding of collapsed fish stocks such as cod is often slow or absent. Here, we apply the stochastic cusp model, based on catastrophe theory, and show that collapse and recovery of cod stocks are potentially driven by the specific interaction between exploitation pressure and environmental drivers. Our statistical modelling study demonstrates that for most of the cod stocks, ocean warming could induce a nonlinear discontinuous relationship between fishing pressure and stock size, which would explain hysteresis in their response to reduced exploitation pressure. Our study suggests further that a continuing increase in ocean temperatures will probably limit productivity and hence future fishing opportunities for most cod stocks of the Atlantic Ocean. Moreover, our study contributes to the ongoing discussion on the importance of climate and fishing effects on commercially exploited fish stocks, highlighting the importance of considering discontinuous dynamics in holistic ecosystem-based management approaches, particularly under climate change.

Correction: Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities.

[This corrects the article DOI: 10.1371/journal.pone.0188205.].

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities.

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.

Habitat heterogeneity determines climate impact on zooplankton community structure and dynamics.

Understanding and predicting species distribution in space and time and consequently community structure and dynamics is an important issue in ecology, and particularly in climate change research. A crucial factor determining the composition and dynamics of animal populations is habitat heterogeneity, i.e., the number of structural elements in a given locality. In the marine pelagic environment habitat heterogeneity is represented by the distribution of physical oceanographic parameters such as temperature, salinity and oxygen that are closely linked to atmospheric conditions. Little attention has been given, however, to the role of habitat heterogeneity in modulating the response of animal communities to external climate forcing. Here we investigate the long-term dynamics of Acartia spp., Temora longicornis, and Pseudocalanus acuspes, three dominant zooplankton species inhabiting different pelagic habitats in the Central Baltic Sea (CBS). We use the three copepods as indicator species for changes in the CBS zooplankton community and apply non-linear statistical modeling techniques to compare spatial population trends and to identify their drivers. We demonstrate that effects of climate variability and change depend strongly on species-specific habitat utilization, being more direct and pronounced at the upper water layer. We propose that the differential functional response to climate-related drivers in relation to strong habitat segregation is due to alterations of the species' environmental niches. We stress the importance of understanding how anticipated climate change will affect ecological niches and habitats in order to project spatio-temporal changes in species abundance and distribution.

Ecological network indicators of ecosystem status and change in the Baltic Sea.

Several marine ecosystems under anthropogenic pressure have experienced shifts from one ecological state to another. In the central Baltic Sea, the regime shift of the 1980s has been associated with food-web reorganization and redirection of energy flow pathways. These long-term dynamics from 1974 to 2006 have been simulated here using a food-web model forced by climate and fishing. Ecological network analysis was performed to calculate indices of ecosystem change. The model replicated the regime shift. The analyses of indicators suggested that the system's resilience was higher prior to 1988 and lower thereafter. The ecosystem topology also changed from a web-like structure to a linearized food-web.

Early detection of ecosystem regime shifts: a multiple method evaluation for management application.

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change.