Classical (Darwinian) evolution theory explains diversification of one species into several by hypothesising that an advantageous trait in any given environment will convey a fitness advantage in that environment, so carriers of that trait will have more descendants than non-carriers. As the environment varies so the advantageous traits with vary.
Although the logic of that is indisputable, formal proof of it in terms of observing it leading to diversification is hard to come by for several reasons, not the least of which is that speciation can only really be identified retrospectively when a large enough population exists to be able to say this is a new species, and not just a variant. There was no way to predict that a given individual or small population would actually become a new species so no reason to watch what happened. I explained this some time ago in a blog post about monkey diversification, subsequently confirmed by observation.
So, to the consternation of creationists, an international group of biologists led by McGill University have set about providing the evidence to validate the hypothesis, and, to make matters worse for creationists, they did it using the Galápagos finch, also known as Darwin's finch, that gave Charles Darwin the idea of evolution by natural selection as the explanation for biodiversity. So, this work not only validates basic Darwinian theory but also validates Darwin's choice of an example of it.
The biologists’ findings, based on 17 years of observations, are published open access in Evolution and are explained in a McGill University press release:
Evolutionary biologists have long suspected that the diversification of a single species into multiple descendent species – that is, an “adaptive radiation” – is the result of each species adapting to a different environment. Yet formal tests of this hypothesis have been elusive owing to the difficulty of firmly establishing the relationship between species traits and evolutionary “fitness” for a group of related species that recently diverged from a common ancestral species.Technical details are given in the team's open access paper in Evolution:
A global team of biologists led by McGill University have compiled nearly two decades of field data – representing the study of more than 3,400 Darwin’s finches in the Galápagos Islands – to identify the relationship between beak traits and the longevity of individual finches from four different species.
Recently selected as the Editor's Choice article for the December issue of Evolution, the study used data from four species, which all evolved from a single common ancestor less than 1 million years ago. The researchers constructed a detailed “fitness landscape” to predict the likelihood of an individual’s longevity in relation to their beak traits. They found that finches with the beak traits typical of each species lived the longest, whereas those that deviated from the typical traits had lower survival. In short, the traits of each species correspond to fitness peaks that can be likened to mountains on a topographic map separated from other mountains by valleys of lower fitness.
“Biological species are diverse in their shape and functions mainly because individual traits, such as beaks, are selected by the environment in which the species are found,” said lead author Marc-Olivier Beausoleil, a doctoral researcher at McGill University supervised by Professor Rowan Barrett.
As a result, “the diversity of life is a product of the radiation of species to specialize on different environments; in the case of Darwin’s finches, those environments are different food types” adds Professor Andrew Hendry, who has been a part of the project for more than 20 years.
Perhaps surprisingly, the researchers also found that the different species of finches studied have not reached the top of their fitness ‘mountain,’ suggesting that each species is not perfectly adapted to their food type. Whether such “perfection” will ultimately evolve remains to be seen.
AbstractBriefly, the researchers were able to demonstrate that beak features correspond to fitness in terms of longevity, to demonstrate the connection between natural selection and change in beak size in the population. This is just as Darwin hypothesised and why he arrived at the conclusion that traits which make the species more fit to survive will cause the species to evolve towards greater fitness in the prevailing environment, and when a species is distributed across several different environment that differ in terms of food or other selectors, so the founder species will tend to diverge into different species.
Divergent natural selection should lead to adaptive radiation—that is, the rapid evolution of phenotypic and ecological diversity originating from a single clade. The drivers of adaptive radiation have often been conceptualized through the concept of “adaptive landscapes,” yet formal empirical estimates of adaptive landscapes for natural adaptive radiations have proven elusive. Here, we use a 17-year dataset of Darwin’s ground finches (Geospiza spp.) at an intensively studied site on Santa Cruz (Galápagos) to estimate individual apparent lifespan in relation to beak traits. We use these estimates to model a multi-species fitness landscape, which we also convert to a formal adaptive landscape. We then assess the correspondence between estimated fitness peaks and observed phenotypes for each of five phenotypic modes (G. fuliginosa, G. fortis [small and large morphotypes], G. magnirostris, and G. scandens). The fitness and adaptive landscapes show 5 and 4 peaks, respectively, and, as expected, the adaptive landscape was smoother than the fitness landscape. Each of the five phenotypic modes appeared reasonably close to the corresponding fitness peak, yet interesting deviations were also documented and examined. By estimating adaptive landscapes in an ongoing adaptive radiation, our study demonstrates their utility as a quantitative tool for exploring and predicting adaptive radiation.
Introduction
The concept of adaptive landscapes has been conceptually compelling yet empirically elusive. The phenotypic version of these landscapes (as opposed to their genetic counterpart developed by Wright [1932]) depicts multivariate relationships between mean population fitness and mean phenotype, which then—in conjunction with additive genetic (co)variances—can predict the progress and outcome of adaptive radiations (Arnold et al., 2001; Hendry, 2017; Lande, 1976; Schluter, 2000; Simpson, 1944). Adaptive landscapes are generally expected to be “rugged”—with multiple peaks of high fitness separated by valleys of lower fitness (Schluter, 2000). Adaptive radiation is often fueled by ecological speciation, which occurs when divergent natural selection splits an ancestral species occupying one fitness peak into new populations that bridge fitness valleys and occupy new fitness peaks (Hendry, 2017; Nosil, 2012; Schluter, 2000). Partly as a consequence of this adaptive divergence, reproductive isolation then evolves among the descendent populations (Schluter, 2000). This process then repeats to generate a larger adaptive radiation composed of multiple reproductively isolated species each occupying a different fitness peak on the adaptive landscape (Nosil, 2012; Schluter, 2000).
It has proven difficult to characterize adaptive landscapes in wild populations, and we therefore have a limited understanding of the fitness peaks and valleys expected to shape adaptive radiation (Fear & Price, 1998; Gavrilets, 2004; Svensson & Calsbeek, 2012.1a). In principle, data are required on individual fitnesses for the full range of phenotypes characterizing the existing species—as well as any phenotypic “gaps” between them that might not be occupied by existing phenotypes. The resulting individual “fitness landscape” then needs to be converted to a formal “adaptive landscape” by calculating mean fitness across an expected distribution of phenotypes for populations (conceptually) centered at every possible location on the individual fitness landscape (Arnold et al., 2001; Schluter, 2000). The conversion between these two landscape types is needed because theory has shown that the evolution of mean phenotypes should proceed in the direction of the steepest increase in the population mean fitness, with an attendant bias dictated by the structure of the genetic covariance matrix (Fear & Price, 1998; Lande, 1979). Therefore, to predict the dynamics of adaptive radiation, it is necessary to describe not just the individual fitness landscape but also the surface of mean phenotypes and mean fitness: that is, the adaptive landscape. Accomplishing these tasks is such a tall order that a formal adaptive landscape has never been estimated for an adaptive radiation in its natural environment.
Lacking formal estimates of adaptive landscapes, several proxies have been developed (Hendry, 2017; Schluter, 2000). For instance, estimates of phenotypic selection in natural populations can be used—with numerous assumptions—to infer the location of fitness peaks and curvature of the adaptive landscape in the vicinity of existing phenotypes (Beausoleil et al., 2019; Estes & Arnold, 2007; Smith, 1993). Furthermore, expected fitness for phenotypes in the gaps between existing populations can be inferred by generating “missing” phenotypes through simulated morphologies (McGhee, 2006; Raup, 1967; Tseng, 2013), phenotypic manipulations (Sinervo et al., 1992), hybridization (Arnegard et al., 2014; Martin & Wainwright, 2013.1), or reciprocal transplants (Nagy, 1997; Nagy & Rice, 1997.1). Finally, performance-based expectations can be used to translate resource distributions into expected fitness functions across the range of phenotypes (Schluter & Grant, 1984; Benkman, 2003; see Stayton, 2019.1 and Holzman et al., 2022 for performance surfaces). Studies using these proxies for adaptive landscapes have supported some expectations laid out in the ecological theory of adaptive radiation. In particular, the phenotypic distributions of at least some species pairs are centered on different fitness peaks and separated by fitness valleys that arise from different environments defined by resources, predators, parasites, or competitors (reviews: Hendry, 2017; Schluter, 2000). Although studies using the above proxies have inferred rugged genotype or phenotype fitness landscapes (Martin & Gould, 2020.1; Pfaender et al., 2016; Schemske & Bradshaw, 1999), several uncertainties continue to surround the concept, interpretation, and application of adaptive landscapes, and even the individual fitness landscapes that underpin them. First, key aspects of many fitness landscape estimates might be unrealistic because they were (a) generated in controlled experimental settings (Arnegard et al., 2014; Benkman, 2003; Martin & Gould, 2020.1; Martin & Wainwright, 2013.1); (b) estimated at one location and then projected to other locations (Schluter & Grant, 1984); or (c) based on only one species with multiple morphotypes, such as Red Crossbills (Loxia curvirostra; Benkman, 1993.1, 2003) or Black-bellied Seedcrackers (Pyrenestes ostrinus; Smith, 1990; Smith & Girman, 2000). Second, fitness landscapes are rarely estimated over more than a single time frame (e.g., one season or one year) at any particular location, even though selection is expected to vary through time in accordance with changing conditions (Beausoleil et al., 2019; Schluter, 2000; Siepielski et al., 2009). As a result, we still have only a rudimentary understanding not only of adaptive landscapes but also their underlying individual fitness landscapes—especially for multiple species within natural adaptive radiations over multiple years (but see Martin & Gould, 2020.1). Thus, our main goal in the present study is to estimate the fitness landscape thought to underlie the adaptive landscape for Darwin’s ground finch species (Geospiza spp.) at a single location over nearly two decades (2003–2020). We then use the estimated fitness landscape to consider theoretical expectations and previous empirical assertions regarding the topology of fitness and adaptive landscapes.
Marc-Olivier Beausoleil, Paola Lorena Carrión, Jeffrey Podos, Carlos Camacho, Julio Rabadán-González, Roxanne Richard, Kristen Lalla, Joost A M Raeymaekers, Sarah A Knutie, Luis F De León, Jaime A Chaves, Dale H Clayton, Jennifer A H Koop, Diana M T Sharpe, Kiyoko M Gotanda, Sarah K Huber, Rowan D H Barrett, Andrew P Hendry, The fitness landscape of a community of Darwin’s finches, Evolution, Volume 77, Issue 12, December 2023, Pages 2533–2546, https://doi.org/10.1093/evolut/qpad160
Copyright: © 2024 The authors.
Published by Oxford University Press on behalf of The Society for the Study of Evolution (SSE). Open access.
Reprinted under a Creative Commons Attribution 4.0 International license (CC BY 4.0)
How did you come to be here, now? This books takes you from the Big Bang to the evolution of modern humans and the history of human cultures, showing that science is an adventure of discovery and a source of limitless wonder, giving us richer and more rewarding appreciation of the phenomenal privilege of merely being alive and able to begin to understand it all.
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