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The present work is devoted to the analysis of drop impact on a deep liquid pool, focusing on the high-energy splashing regimes caused by large raindrops at high velocities. Such cases are characterized by short time scales and complex mechanisms, thus they have received very little attention until now. The BASILISK open-source solver is used to perform three-dimensional direct numerical simulations. The capabilities of octree adaptive mesh refinement techniques enable capturing of the small-scale features of the flow, while the volume of fluid approach combined with a balanced-force surface-tension calculation is applied to advect the volume fraction of the liquids and reconstruct the interfaces. The numerical results compare well with experimental visualizations: both the evolution of crown and cavity, the emanation of ligaments, the formation of bubble canopy and the growth of a downward-moving spiral jet that pierces through the cavity bottom, are correctly reproduced. Reliable quantitative agreements are also obtained regarding the time evolution of rim positions, cavity dimensions and droplet distributions through an observation window. Furthermore, simulation gives access to various aspects of the internal flows, which allows us to better explain the observed physical phenomena. Details of the early-time dynamics of bubble ring entrapment and splashing performance, the formation/collapse of bubble canopy and the spreading of drop liquid are discussed. The statistics of droplet size show the bimodal distribution in time, corroborating distinct primary mechanisms of droplet production at different stages.
Often research in judgment and decision making requires comparison of multiple competing models. Researchers invoke global measures such as the rate of correct predictions or the sum of squared (or absolute) deviations of the various models as part of this evaluation process. Reliance on such measures hides the (often very high) level of agreement between the predictions of the various models and does not highlight properly the relative performance of the competing models in those critical cases where they make distinct predictions. To address this important problem we propose the use of pair-wise comparisons of models to produce more informative and targeted comparisons of their performance, and we illustrate this procedure with data from two recently published papers. We use Multidimensional Scaling of these comparisons to map the competing models. We also demonstrate how intransitive cycles of pair-wise model performance can signal that certain models perform better for a given subset of decision problems.
Previous studies have explored the association between parenting style and offspring’s psychological well-being, and the association between offspring’s achievement attribution pattern and psychological well-being. However, little is known about the role of offspring’s achievement attribution in the relationship between parenting and offspring’s psychological well-being. We aimed to find the role of adolescents’ achievement attribution pattern in the relationship between parent–child communication quality and adolescents’ mental health.
A cross-sectional analysis was conducted on 2,725 adolescents aged from 9 to 18 years who are participating in the China Family Panel Studies. Participants supplied demographic information and completed a series of psychological scales including the Center for Epidemiologic Studies Depression scale, an adapted version of the Parental Bonding Instrument, an achievement attribution scale, and single-item measures of subjective well-being and subjective interpersonal popularity.
Linear regression analysis revealed that after controlling for demographic factors good parent–child communication negatively correlated with depression symptoms, and positively associated with subjective well-being and subjective interpersonal popularity. Next, mediation analysis found that internal attribution of achievement partly mediated the effects of parent–child communication quality on adolescents’ depression, subjective well-being, and subjective interpersonal popularity. The result was robust after controlling demographic variables.
An internal attribution pattern of achievement partially accounted for the associations between parent–child communication quality and adolescents’ psychological outcomes including depression, subjective well-being, and subjective interpersonal popularity. Future interventions for adolescents’ mental health promotion can target parent–child communication and adolescents’ positive achievement attribution pattern.
It has been suggested that psychosocial factors are related to survival time of inpatients with cancer. However, there are not many studies examining the relationship between spiritual well-being (SWB) and survival time among countries. This study investigated the relationship between SWB and survival time among three East Asian countries.
This international multicenter cohort study is a secondary analysis involving newly admitted inpatients with advanced cancer in palliative care units in Japan, South Korea, and Taiwan. SWB was measured using the Integrated Palliative Outcome Scale (IPOS) at admission. We performed multivariate analysis using the Cox proportional hazards model to identify independent prognostic factors.
A total of 2,638 patients treated at 37 palliative care units from January 2017 to September 2018 were analyzed. The median survival time was 18.0 days (95% confidence interval [CI] 16.5–19.5) in Japan, 23.0 days (95% CI 19.9–26.1) in Korea, and 15.0 days (95% CI 13.0–17.0) in Taiwan. SWB was a significant factor correlated with survival in Taiwan (hazard ratio [HR] 1.27; 95% CI 1.01–1.59; p = 0.04), while it was insignificant in Japan (HR 1.10; 95% CI 1.00–1.22; p = 0.06), and Korea (HR 1.02; 95% CI 0.77–1.35; p = 0.89).
Significance of results
SWB on admission was associated with survival in patients with advanced cancer in Taiwan but not Japan or Korea. The findings suggest the possibility of a positive relationship between spiritual care and survival time in patients with far advanced cancer.
Maps of global biomes or ecoregions show geographical clusters – unique assemblages of plants and animals that are spatially tied with associated geomorphologic and climatic features. Biomes are typically defined on the basis of broad vegetation types and the biophysical features that impose fundamental controls on the distribution of plants (Cox and Moore 2000). The concept of biomes has a deep history in ecology and has experienced waves of knowledge synthesis, reaching a recent consensus of seven points (Mucina 2019), one of which caught our attention: ‘A biome incorporates a complex of fine-scale biotic communities; it has its characteristic flora and fauna and it is home to characteristic vegetation types and animal communities.’ Macro-scale biodiversity patterns, therefore, reflect the overarching geophysical structures of the globe such as the well-known latitudinal gradients of biodiversity (Willig et al. 2003) and associated ecosystem functioning (e.g., litter decomposition in streams via detritivores; Boyero et al. 2015). Nevertheless, within constantly changing environments, the species composition and geographical boundaries of biomes (called ecotones) are not fixed, but are fluid over evolutionary timescales (Haywood et al. 2019). This biodiversity–environment coupling has been disrupted by agriculture and urbanisation, and the appetite of humans for resources and raw materials and their carelessness in handling waste. Humans are steadily altering land cover and modifying ecological processes across the globe, creating a new ecological order of anthropogenic biomes (anthromes; sensu Ellis and Ramankutty 2008). Natural biomes are facing unprecedented pressures to change, shift, dissolve, merge and emerge, at a pace on par with the most tumultuous periods of the biosphere’s history.
At the time of writing this book, we have witnessed an extreme case of biological invasion. A virus, through an evolutionary leap, has jumped onto a new host species, Homo sapiens, and has taken advantage of the new host’s ambitions and mobility in the zealous phase of globalisation, causing a worldwide pandemic and economic meltdown. The 2019 coronavirus outbreak (COVID-19) is a showcase of the core of invasion science. A list of questions spring to mind. Why this particular virus, and not others? Why now? How fast can it spread? How is its spread mediated by climatic and other environmental factors? What are its vectors and pathways of transmission? Which regions and populations are most susceptible? How much damage can it cause to public health and economies? What factors cause substantial variation in mortality between human populations in different countries? How can we control it? Can we forecast and prevent future outbreaks of emerging infectious diseases? While the whole world scrambles to make sense of COVID-19 and to combat the biggest crisis for humanity since World War II (WWII), we embark on a journey to address these questions to cover many more taxa and situations – the invasion of any biological organism into novel environments.
To assess community assembly via natural colonisation and the potential ceiling of species richness in local communities, Wilson and Simberloff (1969) fumigated nine red mangrove (Rhizophora mangle) islands in Florida Bay, United States. This exemplifies the need in ecology to elucidate the concepts regarding community succession and assembly. New species arrive at a site predominantly via chance and dispersal, while resident species interact with each other via eco-evolutionary games (Chapter 2). Biotic interactions act as engineers to form ecological networks. Together with filters and forces from environmental and disturbance gradients, these ecological interaction networks define realised ecological niches and mediate community assembly rules and trajectories, thereby building an ecological house on the hill. With limited space and resource and the inevitable minimum sustainable size required for a viable population to survive stochasticity and disturbance, there must be an upper bound on the number and kinds of species that can be accommodated in a community, either via natural or human-mediated colonisation of both regional endemics and alien species. For this reason, questions pertaining to the ways in which an ecological community absorbs new arrivals have been on the agenda of community ecology since its inception. Despite progress on that front, making precise predictions about the trajectory of community assembly, the characteristics of the eventual resident species and the realised number of resident species in a local community remains a formidable challenge.
Before diving into a discussion of open adaptive systems, we need to revisit the definition of an ecological network. Material covered in Chapters 2 and 3 showed that ecological networks are webs of co-evolving and co-fitting interactions among species residing in an ecosystem. Such networks subjected to regular incursions of new members in the form of biological invasions are a good example of Open Adaptive Systems (OASs). OASs are different from Clements’ (1916) superorganism metaphor that was further developed and scaled up into the concept of Lovelock’s (1972) Gaia theory, which posits that organisms interact to form a synergistic and self-regulating complex system. The reason for considering an ecological network (or its embedded ecological community) a system, rather than an organism or an organisation (sensu Keller 2005), lies with the type of its boundaries. A system can have either permeable or closed boundaries, while an organism cannot survive with a closed boundary. More importantly, a system has more flexible and tenuous boundaries, the positions of which are often set by the beholder. Boundaries drawn around sampling areas based on what we call an ecological community or an ecosystem are largely subjective. In contrast, the boundary of an organism is clear-cut and plays important physiological and metabolic roles. The value of a system’s boundary, albeit usually subjectively defined, is to identify and differentiate its residents from alien visitors, thereby providing the foundation for labelling entities for management purposes. In contrast, the organic boundary is inseparable from the organism; they belong to an irreducible whole.
Astrologists have predicted the occurrence of solar eclipses with increasing precision through the ages. Predicting celestial motions invokes the dynamics of a relatively simple and rigid system; it is straightforward and akin to identifying regularities in recurrent records. Discovering regularities, however, does not necessarily impart true comprehension. While we can speculate about the mechanisms and forces at work to fill gaps as we edge towards comprehension, such conjectured theories are often misleading. In early 2020, epidemiologists were confronted with a once-in-a-lifetime challenge: forecasting the number of infections of COVID-19 both regionally and globally. With little understanding of the viral transmission at the time, most forecasts failed miserably. Failed forecasts abound, especially for systems that are complex and adaptive; the bet between ecologist Paul Ehrlich and economist Julian Simon on the swings of metal price anticipated from socioeconomic impacts of overpopulation (Sabin 2013) is a good example. The forecasting conundrum is both typical and perplexing to ecologists and invasion scientists; hindsight is an exact science, while forecasting is no easier than catching the Cheshire Cat.
Until now, biological invasions have been conceptualised and studied mainly as a linear process: from introduction to establishment to spread. This volume charts a new course for the field, drawing on key developments in network ecology and complexity science. It defines an agenda for Invasion Science 2.0 by providing new framings and classification of research topics and by offering tentative solutions to vexing problems. In particular, it conceptualises a transformative ecosystem as an open adaptive network with critical transitions and turnover, with resident species heuristically learning and fine-tuning their niches and roles in a multiplayer eco-evolutionary game. It erects signposts pertaining to network interactions, structures, stability, dynamics, scaling, and invasibility. It is not a recipe book or a road map, but an atlas of possibilities: a 'hitchhiker's guide'.
This book deals with the roles and impacts of the entangled web of biotic interactions that an alien species partakes in as it infiltrates ecological networks. We partition related issues into six topics (network interactions, structures, stability, dynamics, scaling and invasibility). We start unpacking these issues here and will dive deeper into each in subsequent chapters. To embrace the complexity of ecological networks we need to introduce a few simple mathematical models and associated concepts that are fundamental to network analyses, visualisation and the ideas we develop. We keep the mathematical details to a minimum and provide intuitive explanation of their meaning and rationale; we also discuss, using simple terminology wherever possible, key procedures that lead to deductive conclusions. Most of the models we cite have been elucidated in great detail elsewhere and can be implemented in any computational language. Although we will not provide technical details, readers will be able to design their models and conduct analyses based on what is provided here to suit their own needs. Although we have tried to determine consensus views in the literature, the transdisciplinary nature of this field makes the knowledge landscape rugged and fluid. Answers are often not definite but contextualised. Let our journey begin.
Humanity’s rise is rapidly moulding the structure and functioning of the biosphere over the surface of our planet, while human-mediated translocations of organisms – an inevitable consequence of this rise – is driving further transformation (Pyšek et al. 2020b). Drawing inspiration and concepts from population ecology, Invasion Science 1.0 (see Chapter 1) has explored the myriad ways a focal alien species can negotiate geographical, ecological and environmental barriers to establish and potentially invade in new novel environments. Coordinated efforts have been made to classify introduction pathways (Hulme et al. 2008; Wilson et al. 2009); forecast invasion risks (Kumschick and Richardson 2013) and impacts (Jeschke et al. 2014); model invasive spread (Hui and Richardson 2017); unify invasion frameworks (Wilson et al. 2020); and prescribe management strategies such as early detection and rapid response to prevent, contain and eradicate problematic species (Wilson et al. 2017). However, the phenomenon of biological invasions involves all types of organisms, ecosystems and a wide range of contexts and framings; this has given rise to a plethora of invasion hypotheses and theories that seek to explain and ultimately predict aspects of invasion dynamics and the expected outcome of specific management actions (Jeschke and Heger 2018). Most invasion hypotheses are relevant in specific contexts and often fail when faced with the reality of contextual complexity. This has led to a wave of syntheses that have attempted to classify invasion cases and hypotheses based largely on three aspects – invasive traits, site characteristics and invasion pathways (Pyšek et al. 2020a). To embrace considerations that arise when attempting to merge insights from all these perspectives, a paradigm shift began emerging at the turn of the millennium, together with the rise of network science. It embraces the complexity of biotic interaction networks (Figure 7.1; e.g., Segar et al. 2020), the trait paradigm in community ecology (Figure 7.2; e.g., McGill et al. 2006; Salguero-Gómez et al. 2018), and considers how functional traits of species dictate their ecology and roles in networks (Figure 7.3; e.g., Mello et al. 2019). This new lens for drawing together threads pertaining to all facets of biological invasions (Invasion Science 2.0) seeks to elucidate the structure and function of an ecological network facing biological invasions. This book has laid out a road map of signposts, hazard warnings and shortcuts for the journey to Invasion Science 2.0, framing and classifying research topics and offering tentative solutions and travel advisories.