We briefly describe the species-area relationship (SAR) and summarize the different types, including the main dichotomy of island species–area relationship (ISAR) and species accumulation curves (SAC). We discuss the classification of the ISAR as a fundamental ecological law, despite its protean nature. Exploring this protean behaviour, we review the different ways in which ISAR form has been shown to vary between datasets. The final section outlines the structure of the book and provides a summary of the remaining chapters.

We build on the results of a recent paper that introduced a ‘global model of ISARs’; a structural equation model that provides a solid foundation for understanding ISAR variation across archipelagos. While revealing, the global ISAR model is incomplete, and here we pick on two issues for further scrutiny: (i) improved quantification of island isolation and configuration and (ii) addition of habitat islands. Including archipelago isolation metrics in our models, and adding in 65 habitat island datasets, we find our best models are similar to those presented in the previous study: a result that points to the robustness of the global model of ISARs. Overall, we find a negative relationship between ISAR intercept and slope as a function of archipelago species richness. Within our best models, archipelago isolation did not have an effect on ISAR model parameters. However, mean inter-island distance was found to be important in certain models. This finding suggests that intra-archipelago processes might be more important drivers of ISAR form than archipelago isolation. Unfortunately, the explanatory power of the best model based only on habitat island datasets was low, suggesting that we are some way from developing a predictive model for use in conservation applications.

By taking advantage of spatially explicit modelling and network analysis, we investigated how species–area relationships (SARs) emerge and are maintained by dispersal and how the spatial arrangement of islands affects colonization/extinction dynamics of SARs. In particular, we generated different archipelagos characterized by varying geometric properties and then we simulated inter-island dispersal/colonization patterns. As the model proceeds through time, species accumulate on different islands according to their dispersal ability and depending on island size and isolation. During each time step, the model fit a power function that thus enabled us to track the emergence of island SARs (ISARs). After equilibrium was reached, we simulated a phase of reduced dispersal. Each simulated archipelago was analysed as a network in which each island was a node connected to other nodes (islands) based on pairwise spatial distances. We found that basic properties of the underlying connectivity network were correlated with ISAR properties, although the best predictor of richness was almost always island area. In nearly all simulations, the ISAR weakened after reducing the dispersal ability of the species. Our study demonstrates that a spatially explicit dispersal simulation model and network analysis can provide meaningful insight into the evolution and robustness of ISARs.