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.

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.