Diversification in evolutionary arenas-Assessment and synthesis

Abstract

Understanding how and why rates of evolutionary diversification vary is a key issue in evolutionary biology, ecology, and biogeography. Evolutionary rates are the net result of interacting processes summarized under concepts such as adaptive radiation and evolutionary stasis. Here, we review the central concepts in the evolutionary diversification literature and synthesize these into a simple, general framework for studying rates of diversification and quantifying their underlying dynamics, which can be applied across clades and regions, and across spatial and temporal scales. Our framework describes the diversification rate (d) as a function of the abiotic environment (a), the biotic environment (b), and clade-specific phenotypes or traits (c); thus, d ~ a,b,c. We refer to the four components (a-d) and their interactions collectively as the "Evolutionary Arena." We outline analytical approaches to this framework and present a case study on conifers, for which we parameterize the general model. We also discuss three conceptual examples: the Lupinus radiation in the Andes in the context of emerging ecological opportunity and fluctuating connectivity due to climatic oscillations; oceanic island radiations in the context of island formation and erosion; and biotically driven radiations of the Mediterranean orchid genus Ophrys. The results of the conifer case study are consistent with the long-standing scenario that low competition and high rates of niche evolution promote diversification. The conceptual examples illustrate how using the synthetic Evolutionary Arena framework helps to identify and structure future directions for research on evolutionary radiations. In this way, the Evolutionary Arena framework promotes a more general understanding of variation in evolutionary rates by making quantitative results comparable between case studies, thereby allowing new syntheses of evolutionary and ecological processes to emerge.

Keywords: adaptive radiation; conifer phylogeny; macroevolutionary theory; phylogenetic comparative methods; species diversification; trait disparification.

Figure 1

The Evolutionary Arena framework. The four components of the Evolutionary Arena (boxes) are illustrated with interactions (arrows) among the components, with those influencing diversification rates in larger, black arrows. We refer to the environment (abiotic [a] and the biotic [b] components; blue boxes) in combination with clade traits (c) and diversification/ disparification rate (d) of an evolutionary lineage (green boxes) as the Evolutionary Arena. This framework is potentially dynamic, because interactions among the components allow for feedback, together shaping evolution. Note that, although all components can affect each other (positively or negatively; indicated by small and/or gray arrows), we focus here on the dependence of diversification on environmental (extrinsic biotic and abiotic) and clade‐specific (intrinsic trait) factors

Figure 2

Projected potential species richness (SR) of conifers representing their abiotic arena. The abiotic arena of conifers is defined by geographic locations (quarter degree grid cells) that can support one or more of the 455 conifer species, based on projections from the process‐based physiological niche model (approximating the fundamental conifer niche). The projected potential SR (yellow‐green shading) highlights areas that are suitable for conifer species. The abiotic arena alone does not always predict patterns of conifer diversity. For example, the eastern Congo is predicted to be climatically suitable for many species but has relatively low conifer diversity (Farjon, 2018). It is likely that clade‐specific traits (c) and biotic interactions (b) limit the diversity in certain regions (see main text)

Figure 3

Quantified EvA model for the conifers. Colored trees illustrate the distribution of abiotic environment (a), competitive interactions (b), rate of niche evolution (c), and diversification rates (d) across the conifer phylogeny with bars at tips detailing the values per clade and variable. The estimated effects on net diversification rates r_ε are indicated by arrows scaled to the standardized coefficients (slopes) also showing significant levels (ns, nonsignificant; ***, p < .001). Colors on branches are rate estimates (obtained using the fastAnc function in the R package phytools; Revell, 2012; see R scripts in Nürk, Linder, et al., 2019) and illustrate parameter distribution on the tree. When competition among species is low and the rate of niche evolution in a clade is pronounced, the diversification rate of that clade accelerates

Figure 4

The EvA model for Lupinus: (d ₁, d ₂, d ₃) ~ (a ₁, a ₂, a ₃), b ₁, c ₁. Note that rates of growth‐form disparification and accelerated gene evolution can influence diversification rates; consequently, the grouping of variables as the components of the EvA framework depends on the questions being investigated

Figure 5

The EvA model for island radiations: (d _1, d ₂) ~ (a ₁, a ₂, a ₃), b ₁, c ₁. Island ontogeny describes the typical life cycle of oceanic islands; isolation the degree of isolation of the insular system (e.g., distance from the continent); geographical fragmentation could be the number of islands (if dealing with an archipelago). Incorporating the lineage‐specific traits is complex, as several independent lineages may be involved. Trait disparification is particularly interesting in islands, for example, Hawaiian honeycreepers and silverswords

Figure 6

The EvA model for Ophrys: d ₁ ~ a ₁, b ₁, (c ₁, c ₂). Floral traits (c _1.1 geometry, c _1.2 epidermis micromorphology, and c _1.3 color and patterns), also including scent, are probably the most important regulators of the highly specific pollination system, but the role of mycorrhiza and geographical isolation on the Mediterranean islands is poorly understood