Modeling directional spatio-temporal processes in island biogeography

The Importance of Including Spatial Autocorrelation When Modelling Species Richness in Archipelagos: A Bayesian Approach

One of the aims of island biogeography theory is to explain the number of species in an archipelago. Traditionally, the variables used to explain the species richness on an island are its area and distance to the mainland. However, increasing evidence suggests that accounting for other variables is essential for better estimates. In particular, the distance between islands should play a role in determining species richness. This work uses a Bayesian framework using Gaussian processes to assess whether distance to neighbouring islands (spatial autocorrelation) can better explain arthropod species richness patterns in the Azores Archipelago and in the Canary Islands. This method is flexible and allows the inclusion of other variables, such as maximum altitude above sea level (elevation). The results show that accounting for spatial autocorrelation provides the best results for both archipelagos, but overall, spatial autocorrelation seems to be more important in the Canary archipelago. Similarly, elevation plays a more important role in determining species richness in the Canary Islands. We recommend that spatial autocorrelation should always be considered when modelling an archipelago’s species richness.

Macaronesia as a Fruitful Arena for Ecology, Evolution, and Conservation Biology

Research in Macaronesia has led to substantial advances in ecology, evolution and conservation biology. We review the scientific developments achieved in this region, and outline promising research avenues enhancing conservation. Some of these discoveries indicate that the Macaronesian flora and fauna are composed of rather young lineages, not Tertiary relicts, predominantly of European origin. Macaronesia also seems to be an important source region for back-colonisation of continental fringe regions on both sides of the Atlantic. This group of archipelagos (Azores, Madeira, Selvagens, Canary Islands, and Cabo Verde) has been crucial to learn about the particularities of macroecological patterns and interaction networks on islands, providing evidence for the development of the General Dynamic Model of oceanic island biogeography and subsequent updates. However, in addition to exceptionally high richness of endemic species, Macaronesia is also home to a growing number of threatened species, along with invasive alien plants and animals. Several innovative conservation and management actions are in place to protect its biodiversity from these and other drivers of global change. The Macaronesian Islands are a well-suited field of study for island ecology and evolution research, mostly due to its special geological layout with 40 islands grouped within five archipelagos differing in geological age, climate and isolation. A large amount of data is now available for several groups of organisms on and around many of these islands. However, continued efforts should be made toward compiling new information on their biodiversity, to pursue various fruitful research avenues and develop appropriate conservation management tools.

Temporal area approach for distributional data in biogeography

A structural approach to temporality in distributional data for use in palaeobiogeography is described herein. Pre-established areas in the distributional data matrix are split temporally, allowing a single geographical space to have multiple iterations [e.g. Area A (Lower Devonian), Area A (Middle Devonian)]. The resulting temporal matrix will allow the representation and capture of any differing relationships through time. Designed primarily for Parsimony Analysis of Endemicity (PAE) and biotic similarity analyses, this approach simply structures distributional data within a temporal partition, meaning that numerical methods can be used to assess relationships between areas to find a branching diagram. Created through the application of the temporal matrix to a given analysis, Temporal Area Approach (TAAp) is a structural approach that facilitates exploration of the data rather than being a hypothesis-driven model following analysis. Understanding the behaviour of non-phylogenetic palaeobiogeographical data and reducing the prevalence of temporal artefacts will lead to more robust area classifications.

Comparative phylogeography of oceanic archipelagos: Hotspots for inferences of evolutionary process

Remote island archipelagos offer superb opportunities to study the evolution of community assembly because of their relatively young and simple communities where speciation contributes to the origin and evolution of community structure. There is great potential for common phylogeographic patterns among remote archipelagos that originate through hotspot volcanism, particularly when the islands formed are spatially isolated and linearly arranged. The progression rule is characterized by a phylogeographic concordance between island age and lineage age in a species radiation. Progression is most likely to arise when a species radiation begins on an older island before the emergence of younger islands of a hotspot archipelago. In the simplest form of progression, colonization of younger islands as they emerge and offer appropriate habitat, is coincident with cladogenesis. In this paper, we review recent discoveries of the progression rule on seven hotspot archipelagos. We then discuss advantages that progression offers to the study of community assembly, and insights that community dynamics may offer toward understanding the evolution of progression. We describe results from two compelling cases of progression where the mosaic genome may offer insights into contrasting demographic histories that shed light on mechanisms of speciation and progression on remote archipelagos.

Oceanic island biogeography through the lens of the general dynamic model: assessment and prospect

The general dynamic model of oceanic island biogeography (GDM) has added a new dimension to theoretical island biogeography in recognizing that geological processes are key drivers of the evolutionary processes of diversification and extinction within remote islands. It provides a dynamic and essentially non-equilibrium framework generating novel predictions for emergent diversity properties of oceanic islands and archipelagos. Its publication in 2008 coincided with, and spurred on, renewed attention to the dynamics of remote islands. We review progress, both in testing the GDM's predictions and in developing and enhancing ecological-evolutionary understanding of oceanic island systems through the lens of the GDM. In particular, we focus on four main themes: (i) macroecological tests using a space-for-time rationale; (ii) extensions of theory to islands following different patterns of ontogeny; (iii) the implications of GDM dynamics for lineage diversification and trait evolution; and (iv) the potential for downscaling GDM dynamics to local-scale ecological patterns and processes within islands. We also consider the implications of the GDM for understanding patterns of non-native species diversity. We demonstrate the vitality of the field of island biogeography by identifying a range of potentially productive lines for future research.

Modeling directional spatio-temporal processes in island biogeography

A key challenge in island biogeography is to quantity the role of dispersal in shaping biodiversity patterns among the islands of a given archipelago. Here, we propose such a framework. Dispersal within oceanic archipelagos may be conceptualized as a spatio‐temporal process dependent on: (1) the spatial distribution of islands, because the probability of successful dispersal is inversely related to the spatial distance between islands and (2) the chronological sequence of island formation that determines the directional asymmetry of dispersal (hypothesized to be predominantly from older to younger islands). From these premises, directional network models may be constructed, representing putative connections among islands. These models may be translated to eigenfunctions in order to be incorporated into statistical analysis. The framework was tested with 12 datasets from the Hawaii, Azores, and Canaries. The explanatory power of directional network models for explaining species composition patterns, assessed by the Jaccard dissimilarity index, was compared with simpler time‐isolation models. The amount of variation explained by the network models ranged from 5.5% (for Coleoptera in Hawaii) to 60.2% (for Pteridophytes in Canary Islands). In relation to the four studied taxa, the variation explained by network models was higher for Pteridophytes in the three archipelagos. By the contrary, small fractions of explained variation were observed for Coleoptera (5.5%) and Araneae (8.6%) in Hawaii. Time‐isolation models were, in general, not statistical significant and explained less variation than the equivalent directional network models for all the datasets. Directional network models provide a way for evaluating the spatio‐temporal signature of species dispersal. The method allows building scenarios against which hypotheses about dispersal within archipelagos may be tested. The new framework may help to uncover the pathways via which species have colonized the islands of a given archipelago and to understand the origins of insular biodiversity.