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Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

The number of COVID-19 confirmed cases rapidly grew since the SARS-CoV-2 virus was identified in late 2019. Due to the high transmissibility of this virus, more countries are experiencing the repeated waves of the COVID-19 pandemic. However, with limited manufacturing and distribution of vaccines, control measures might still be the most critical measures to contain outbreaks worldwide. Therefore, evaluating the effectiveness of various control measures is necessary to inform policymakers and improve future preparedness. In addition, there is an ongoing need to enhance our understanding of the epidemiological parameters and the transmission patterns for a better response to the COVID-19 pandemic. This review focuses on how various models were applied to guide the COVID-19 response by estimating key epidemiologic parameters and evaluating the effectiveness of control measures. We also discuss the insights obtained from the prediction of COVID-19 trajectories under different control measures scenarios.

Social-ecological systems dependent on fisheries must be resilient or adapt to remain viable in the face of change. Here, we identified possible interventions (termed “adaptation options”) from published literature, aimed at supporting social or ecological resilience and/or aiding adaptation to changes induced by environmental or social stressors. Our searches centered on nations/regions across North America, Europe, and the South Pacific, encompassing fisheries literature with and without a climate change focus, to compare how, when, and by whom interventions are currently or potentially implemented. We expected that adaptation options within a climate change context would have a greater focus on enhancing social resilience due to a connection with climate change adaptation assessment methodology. Instead, we found a greater focus on ecological resilience, likely indicating a focus on management adaptation. This pattern, along with the more extensive use of social adaptation options responsively and outside the context of climate change, along with an importance in bottom-up influences in implementing them, suggests a general lack of centralized planning and organization with regards to adaptation of stakeholders. Determining how adaptation options are created, chosen, and implemented is a crucial step within or external to ecosystem-based management, especially if planned stakeholder adaption is the goal.

Background: until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs across locations, which could reduce the effectiveness of pandemic control measures. Here we assess the extent to which socioeconomic factors are associated with reductions in population mobility during the COVID-19 pandemic, at both the city level in China and at the country level worldwide. Methods: in this retrospective, observational study, we obtained anonymised daily mobile phone location data for 358 Chinese cities from Baidu, and for 121 countries from Google COVID-19 Community Mobility Reports. We assessed the intra-city movement intensity, inflow intensity, and outflow intensity of each Chinese city between Jan 25 (when the national emergency response was implemented) and Feb 18, 2020 (when population mobility was lowest) and compared these data to the corresponding lunar calendar period from the previous year (Feb 5 to March 1, 2019). Chinese cities were classified into four socioeconomic index (SEI) groups (high SEI, high–middle SEI, middle SEI, and low SEI) and the association between socioeconomic factors and changes in population mobility were assessed using univariate and multivariable linear regression. At the country level, we compared six types of mobility (residential, transit stations, workplaces, retail and recreation, parks, and groceries and pharmacies) 35 days after the implementation of the national emergency response in each country and compared these to data from the same day of the week in the baseline period (Jan 3 to Feb 6, 2020). We assessed associations between changes in the six types of mobility and the country's sociodemographic index using univariate and multivariable linear regression. Findings: the reduction in intra-city movement intensity in China was stronger in cities with a higher SEI than in those with a lower SEI (r=–0·47, pInterpretation: although COVID-19 outbreaks are more frequently reported in larger cities, our analysis shows that future policies should prioritise the reduction of risks in areas with a low socioeconomic level—eg, by providing financial assistance and improving public health messaging. However, our study design only allows us to assess associations, and a long-term study is needed to decipher causality. Funding: Chinese Ministry of Science and Technology, Research Council of Norway, Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Beijing Advanced Innovation Program for Land Surface Science, National Natural Science Foundation of China, China Association for Science and Technology.

Here we present a developing probabilistic simulation model and tool to assess likely lead times from emergence to detection and arrival for new emerging infectious diseases (EIDs). Key aspects include combining real-world data available on multiple scales with a flexible underlying disease model. As demonstrated by the SARS-CoV-2 pandemic and other emerging infectious diseases, there is a need for scenario exploration for mitigation, surveillance and preparedness strategies. Existing simulation engines have been assessed but found to offer an insufficient set of features with regards to flexibility and control over processes, disease model structure and data sets incorporated for a wider enough range of diseases, circumstances, cofactors and scenarios (Heslop et al. 2017) to suit our aims. We are therefore developing the first version of a simulation model designed to be able to incorporate a diverse range of disease models and data sources including multiple transmission and infectivity stages, multiple host species, varying and evolving virulence, socioeconomic differences, climate events and public health countermeasures. It is designed to be flexible with respect to implementing both improvements in the model structure and data as they become available. It is based on a discrete-time (daily) structure where spatial movement and transition between categories and detection are stochastic rates dependent on spatial data and past states in the model, while being informed by the most suitable data available (Fig. 1). The probability of detection is in itself treated as a probabilistic process and treated as a variable dependent on socioeconomic factors and parameterized by past performance, yet open for manipulation in scenario exploration regarding surveillance and reporting effectiveness. Pathogen hotspot data are sourced from literature and included as a probabilistic assessment of emergence as well as a source of cofactor data (Allen et al. 2017), population data are adressed (Leyk et al. 2019) for utility and combined with data on local connectivity (Nelson et al. 2019) and transnational movement patterns (Recchi et al. 2019Fig. 1), as well as an increasing set of ecological and socioeconomic candidate variables. Model parameterization relies on a machine learning framework with matching to the often partial data available for known relevant disease cases as the training data, and assessing them for plausible ranges of input for new, hypothetical EIDs. As parameterizations improve, the range of scenarios to explore will incorporate effects of climate change and multiple stressors. When a suitable version becomes available it will be shared under a MIT license.

Since COVID-19 spread globally in early 2020 and was declared a pandemic by the World Health Organization (WHO) in March, many countries are managing the local epidemics effectively through intervention measures that limit transmission. The challenges of immigration of new infections into regions and asymptomatic infections remain. Standard deterministic compartmental models are inappropriate for sub- or peri-critical epidemics (reproductive number close to or less than one), so individual-based models are often used by simulating transmission from an infected person to others. However, to be realistic, these models require a large number of parameters, each with its own set of uncertainties and lack of analytic tractability. Here, we apply stochastic age-structured Leslie theory with a long history in ecological research to provide some new insights to epidemic dynamics fuelled by external imports. We model the dynamics of an epidemic when R 0 is below one, representing COVID-19 transmission following the successful application of intervention measures, and the transmission dynamics expected when infections migrate into a region. The model framework allows more rapid prediction of the shape and size of an epidemic to improve scaling of the response. During an epidemic when the numbers of infected individuals are rapidly changing, this will help clarify the situation of the pandemic and guide faster and more effective intervention.

Significance By linking seasonality of climate and changing human behavior, we demonstrate that collaboration on global efforts for prompt and intensive intervention is fundamental to coping with future pandemic waves of COVID-19. We propose that this collaboration can be started in locations with typically high population density and international travel, followed by other high-risk locations. We believe this tiered intervention strategy can greatly integrate global efforts and is effective and practical to improve the global emergency response to COVID-19 and many other infectious diseases., Emerging evidence suggests a resurgence of COVID-19 in the coming years. It is thus critical to optimize emergency response planning from a broad, integrated perspective. We developed a mathematical model incorporating climate-driven variation in community transmissions and movement-modulated spatial diffusions of COVID-19 into various intervention scenarios. We find that an intensive 8-wk intervention targeting the reduction of local transmissibility and international travel is efficient and effective. Practically, we suggest a tiered implementation of this strategy where interventions are first implemented at locations in what we call the Global Intervention Hub, followed by timely interventions in secondary high-risk locations. We argue that thinking globally, categorizing locations in a hub-and-spoke intervention network, and acting locally, applying interventions at high-risk areas, is a functional strategy to avert the tremendous burden that would otherwise be placed on public health and society.

Oceans are exposed to anthropogenic climate change shifting marine systems toward potential instabilities. The physical, biological and social implications of such shifts can be assessed within individual scientific disciplines, but can only be fully understood by combining knowledge and expertise across disciplines. For climate change related problems these research directions have been well-established since the publication of the first IPCC report in 1990, however it is not well-documented to what extent these directions are reflected in published research. Focusing on the Nordic region, we evaluated the development of climate change related marine science by quantifying trends in number of publications, disciplinarity, and scientific focus of 1362 research articles published between 1990 and 2011. Our analysis showed a faster increase in publications within climate change related marine science than in general marine science indicating a growing prioritisation of research with a climate change focus. The composition of scientific disciplines producing climate change related publications, which initially was dominated by physical sciences, shifted toward a distribution with almost even representation of physical and biological sciences with social sciences constituting a minor constant proportion. These trends suggest that the predominantly model-based directions of the IPCC have favoured the more quantitatively oriented natural sciences rather than the qualitative traditions of social sciences. In addition, despite being an often declared prerequisite to successful climate science, we found surprisingly limited progress in implementing interdisciplinary research indicating that further initiatives nurturing scientific interactions are required.

Human action is transforming the species composition, biogeochemistry and habitats of the world’s oceans at unprecedented rates. The cumulative effect of natural and anthropogenic drivers is challenging to measure, in part due to indirect effects and the complexity of marine systems. Building on the theory of complex adaptive systems, this thesis aims to increase our understanding of how complex, heterogeneous marine social-ecological systems (SES) may respond to changing conditions. This thesis integrates resilience research with network science and describes change and structural patterns at several SES scales in order to advance our knowledge on the effects of multiple drivers.Paper I proposes a new, quantitative fish stock collapse definition, that accounts for fish stock dynamics and enables standardization and thus comparability across a large number of commercial fish stocks. Recognizing that substantial ecosystem changes are part of SES dynamics, in Paper II we review marine regime shifts worldwide to specify how co-occurring bundles of drivers are related to degraded ecosystem services for management purposes. A more detailed ecological study on regime shifts was performed in Papers III and IV. Paper III describes the late-1980s central Baltic Sea regime shift based on a food-web model. Paper IV uses a novel structural network analysis approach to detect functional shifts in complex food webs. The results of Paper IV imply that the Baltic Sea regime shift may not be a systemwide shift. Paper V uses a network approach to analyze fishing strategy diversification and social-ecological connectivity among Swedish Baltic Sea fishers, indicating that natural resource management evaluations should not be limited only to ecosystem conditions but also take account of social conditions.Overall, this thesis provides empirical evidence for the emerging perspective that marine resource science and management must account for the complexity of system elements in order to ensure the provision of ecosystem services in the future. The first application of Exponential Random Graph Modeling in ecology and an improved fish stock collapse definition provide new advanced tools for studying oceans from an SES perspective in the future.

Marine scientists broadly agree on which major processes influence the sustainability of marine environments worldwide. Recent studies argue that such shared perceptions crucially shape scientific agendas and are subject to a confirmation bias. Based on these findings a more explicit engagement with scientists’ (shared) perceptions of global change in marine environments is called for. This paper takes stock of the shared understanding in marine science of the most pertinent, worldwide threats and impacts that currently affect marine environments. Using results from an email survey among leading academics in marine science this article explores if a shared research agenda in relation to global change in marine environments exists. The analysis demonstrates that marine scientists across disciplines are largely in agreement on some common features of global marine change. Nevertheless, the analysis also highlights where natural and social scientists diverge in their assessment. The article ends discussing what these findings imply for further improvement of interdisciplinary marine science.

International audience; The Strategic Plan for Biodiversity, adopted under the auspices of the Convention on Biological Diversity, provides the basis for taking effective action to curb biodiversity loss across the planet by 2020 an urgent imperative. Yet, Antarctica and the Southern Ocean, which encompass 10% of the planet's surface, are excluded from assessments of progress against the Strategic Plan. The situation is a lost opportunity for biodiversity conservation globally. We provide such an assessment. Our evidence suggests, surprisingly, that for a region so remote and apparently pristine as the Antarctic, the biodiversity outlook is similar to that for the rest of the planet. Promisingly, however, much scope for remedial action exists.

As the world’s social-environmental problems increasingly extend across boundaries, both disciplinary and political, there is a growing need for interdisciplinarity, not only in research per se, but also in doctoral education. We present the common pitfalls of interdisciplinary research in doctoral education, illustrating approaches towards solutions using the Nordic Centre for Research on Marine Ecosystems and Resources under Climate Change (NorMER) research network as a case study. We provide insights and detailed examples of how to overcome some of the challenges of conducting interdisciplinary research within doctoral studies that can be applied within any doctoral/postdoctoral education programme, and beyond. Results from a self-evaluation survey indicate that early-career workshops, annual meetings and research visits to other institutions were the most effective learning mechanisms, whereas single discipline-focused courses and coursework were among the least effective learning mechanisms. By identifying the strengths and weaknesses of components of NorMER, this case study can inform the design of future programmes to enhance interdisciplinarity in doctoral education, as well as be applied to science collaboration and academic research in general., This study is a product of the Nordic Centre for Research on Marine Ecosystems and Resources under Climate Change (NorMER, Project no. 36800), which is funded by the Norden Top-level Research Initiative sub-programme 'Effect Studies and Adaptation to Climate Change'. We thank Nils Chr. Stenseth for support during the preparation of this manuscript, as well as 3 anonymous reviewers for useful and constructive comments.

Few studies have reported flight responses of prey on landscape slopes in the wild, and little theoretical work is available. We present a formal model to predict whether prey should move up or down the hillside after detecting a perched raptor as determined by the duration of the attack once launched by the predator, perching height of the predator, and the landscape slope. We recorded escape responses of blue tits Cyanistes caeruleus and great tits Parus major when approached by a human in the field. This was done in the postfledging period, when the demand for food is high, and when the families usually move to a new site, presumably perceived to be safer, to continue foraging after being exposed to a predator. We found that great tit families, which prefer to forage close to the ground, flew uphill, matching the model’s prediction. However, blue tit families, which forage higher in the canopy, did not move uphill but moved higher up in the trees. Vertical movement upward may be the safest option if the predator is located on the ground. Modeling escape flights may encourage detailed studies of attack and escape behaviors in animals and provide insights in predator–prey interactions in general.

Defining reliable demographic models is essential to understand the threats of ongoing environmental change. Yet, in the most remote and threatened areas, models are often based on the survey of a single population, assuming stationarity and independence in population responses. This is the case for the Emperor penguin Aptenodytes forsteri, a flagship Antarctic species that may be at high risk continent-wide before 2100. Here, using genome-wide data from the whole Antarctic continent, we reveal that this top-predator is organized as one single global population with a shared demography since the late Quaternary. We refute the view of the local population as a relevant demographic unit, and highlight that (i) robust extinction risk estimations are only possible by including dispersal rates and (ii) colony-scaled population size is rather indicative of local stochastic events, whereas the species' response to global environmental change is likely to follow a shared evolutionary trajectory., Delimiting populations is crucial for conserving threatened species. Using genome-wide data from the whole of Antarctica, Cristofari et al. show that Emperor penguins are organised into a single global population that have shared demography since the late Quarternary.

International audience; How genetic diversity is maintained in philopatric colonial systems remains unclear, and understanding the dynamic balance of philopatry and dispersal at all spatial scales is essential to the study of the evolution of coloniality. In the King penguin, Aptenodytes patagonicus, return rates of post-fledging chicks to their natal sub-colony are remarkably high. Empirical studies have shown that adults return year after year to their previous breeding territories within a radius of a few meters. Yet, little reliable data are available on intra-and inter-colonial dispersal in this species. Here, we present the first fine-scale study of the genetic structure in a king penguin colony in the Crozet Archipelago. Samples were collected from individual chicks and analysed at 8 microsatellite loci. Precise geolocation data of hatching sites and selective pressures associated with habitat features were recorded for all sampling locations. We found that despite strong natal and breeding site fidelity, king penguins retain a high degree of panmixia and genetic diversity. Yet, genetic structure appears markedly heterogeneous across the colony, with higher-than-expected inbreeding levels, and local inbreeding and relat-edness hotspots that overlap predicted higher-quality nesting locations. This points towards heterogeneous population structure at the sub-colony level, in which fine-scale environmental features drive local philopatric behaviour, while lower-quality patches may act as genetic mixing mechanisms at the colony level. These findings show how a lack of global genetic structur-ing can emerge from small-scale heterogeneity in ecological parameters, as opposed to the classical model of homogeneous dispersal. Our results also emphasize the importance of sampling design for estimation of population parameters in colonial seabirds, as at high spatial resolution, basic genetic features are shown to be location-dependent. Finally, this study stresses the importance of understanding intra-colonial dispersal and genetic mixing mechanisms in order to better estimate species-wide gene flows and population dynamics.

How natural climate cycles, such as past glacial/interglacial patterns, have shaped species distributions at the high-latitude regions of the Southern Hemisphere is still largely unclear. Here, we show how the post-glacial warming following the Last Glacial Maximum ( ca 18 000 years ago), allowed the (re)colonization of the fragmented sub-Antarctic habitat by an upper-level marine predator, the king penguin Aptenodytes patagonicus . Using restriction site-associated DNA sequencing and standard mitochondrial data, we tested the behaviour of subsets of anonymous nuclear loci in inferring past demography through coalescent-based and allele frequency spectrum analyses. Our results show that the king penguin population breeding on Crozet archipelago steeply increased in size, closely following the Holocene warming recorded in the Epica Dome C ice core. The following population growth can be explained by a threshold model in which the ecological requirements of this species (year-round ice-free habitat for breeding and access to a major source of food such as the Antarctic Polar Front) were met on Crozet soon after the Pleistocene/Holocene climatic transition.

The Intergovernmental Panel on Climate Change (IPCC 2007) has highlighted an urgent need to assess how ecosystems respond to climate change. This has placed a large Earth and Life Sciences focus on polar regions, as these areas are so far experiencing the strongest and the most rapid global environmental changes.