Ecological opportunity and predator-prey interactions: linking eco-evolutionary processes and diversification in adaptive radiations

Abstract

Much of life's diversity has arisen through ecological opportunity and adaptive radiations, but the mechanistic underpinning of such diversification is not fully understood. Competition and predation can affect adaptive radiations, but contrasting theoretical and empirical results show that they can both promote and interrupt diversification. A mechanistic understanding of the link between microevolutionary processes and macroevolutionary patterns is thus needed, especially in trophic communities. Here, we use a trait-based eco-evolutionary model to investigate the mechanisms linking competition, predation and adaptive radiations. By combining available micro-evolutionary theory and simulations of adaptive radiations we show that intraspecific competition is crucial for diversification as it induces disruptive selection, in particular in early phases of radiation. The diversification rate is however decreased in later phases owing to interspecific competition as niche availability, and population sizes are decreased. We provide new insight into how predation tends to have a negative effect on prey diversification through decreased population sizes, decreased disruptive selection and through the exclusion of prey from parts of niche space. The seemingly disparate effects of competition and predation on adaptive radiations, listed in the literature, may thus be acting and interacting in the same adaptive radiation at different relative strength as the radiation progresses.

Keywords: adaptive radiation; community patterns; competition; ecological speciation; macroevolution; predation.

Figure 1.

Model illustration (a–c) and example of model output (d–f). A species pool of top consumers (a) with some trait z (e.g. birds of prey with body size z) and a pool of competitive consumers (b) with trait u (e.g. granivorous birds with beak size u) that interact on an island (c) defined by some implicit resource distribution with peak abundance as u_opt and width σ_K. The three trophic levels are distributed on the same trait dimension (e.g. size) here illustrated by colour. Competition between species is dictated by their niche width (black and grey Gaussian kernels), and we assume that populations with similar traits interact more than less similar ones. The invasion fitness of a mutant is thus a function of its trait-matching to its resources, the traits of its competitors on the same trophic level and their niche widths. We simulate adaptive radiations (e) and community data (f) with the assumption of ecological opportunity by seeding the system with monomorphic populations with trait value equal to u_opt. From this starting point at each evolutionary time step we computed community equilibrium, we allowed for mutations, computed mutant invasion fitness (d), and we either added the mutant population to the community or replaced the mutating population with the mutant population. Grey and red colour in (d–f) denote data associated with prey and predators, respectively.

Figure 2.

Prey fitness landscape at ecological equilibrium after simulation initiation and before first branching. (a) Prey fitness landscape for a prey system with prey niche width ranging from 0.1 to 0.7. (b) Prey fitness landscape for a predator–prey system with prey niche width equal to 0.3 and predator niche width ranging from 0.1 to 0.7. (c) Prey landscape for a predator–prey system with predator efficiency ranging from 0.0001 to 0.0005. Dashed lines in (b) and (c) illustrate predator landscape without a predator. If nothing else is stated parameters and traits were set to: u = 0; z = 0; u_opt = 0; K₀ = 10 000; σ_K = 1; r = 1; σ_α = 0.3; d = 0.2; c = 0.3; σ_a = 0.5; b_max = 0.0001.

Figure 3.

Prey community adaptive radiation data. (a) Prey diversity (mean over 10 replicated simulations) increase with evolutionary time and prey diversification rate, as well as prey diversity, is negatively related to prey niche width. (b) Niche availability, measured as the sum of invasion fitness for prey mutants evenly distributed between −3 and 3 in trait space instead decrease with evolutionary time and increased niche width. (c,d) Mean competition in the whole community (c, solid lines) and mean carrying capacity (d, solid lines) decrease as diversification progress and species evolve into the peripheral parts of trait space but the mean realized competition for a given species (c, dashed lines) increase owing to niche packing. Realized competition and low carrying capacity in peripheral parts of trait space combine to decrease population size (d, dashed lines) which in turn can decrease the evolutionary rate. If nothing else is stated parameters and traits were set to: u_opt = 0; K₀ = 10 000; σ_K = 1; r = 1; σ_α = 0.1; μ_prey = 0.01.

Figure 4.

Predator–prey community adaptive radiation data. Prey diversity (mean over 5 replicated simulations) and diversification rate are in general lower in predator–prey radiations (solid lines) compared to prey radiations (dashed line) (a–c). Prey diversity and diversification rate are also negatively related to predator niche width and efficiency (a–c). Note the increase in predator efficiency with panel columns. Predators also radiate, and predator diversity is positively related to prey diversity (d–f). Mean prey abundance decreases with evolutionary time, and prey abundance tends to be higher in prey radiations (dashed line) than in predator–prey radiations (solid lines) (g–i). If nothing else is stated parameters and traits were set to: u_opt = 0; K₀ = 10 000; σ_K = 1; r = 1; σ_α = 0.1; d = 0.2; c = 0.3; μ_pred = 0.01; μ_prey = 0.01.

Figure 5.

Predator–prey adaptive radiations across parameter space. Co-radiation at intermediate predator niche width (σ_a = 0.3), low predator mutation probability (μ_pred = 0.005) and low (a) and high (b) predator efficiency (b_max = 0.0001 (a) and 0.0005 (b)). (c) Predators excluding the prey from parts of trait space when the predator's efficiency is large (b_max = 0.0007), niche width is low (e.g. σ_a < 0.1) and mutation probability is low (μ_pred = 0.005). (d) High values of predator mutation probability (μ_pred = 0.1), in combination with high predator efficiency (b_max = 0.0007) interrupts the branching all together, only one predator and one prey population co-evolve in trait space with the predator trait (red) completely overlapping the prey (grey, barely seen). Insert in (d) illustrates the reference prey adaptive radiation showing that the width of the resource distribution tends to be less wide compared to predator–prey radiations. All the results presented are based on radiations with low prey niche width (σ_α = 0.1) and other model parameters were set to: u_opt = 0; K₀ = 10 000; σ_K = 1; r = 1; d = 0.2; c = 0.3; μ_prey = 0.01.