Predator-driven natural selection on risk-taking behavior in anole lizards

Biologists have long debated the role of behavior in evolution, yet understanding of its role as a driver of adaptation is hampered by the scarcity of experimental studies of natural selection on behavior in nature. After showing that individual Anolis sagrei lizards vary consistently in risk-taking behaviors, we experimentally established populations on eight small islands either with or without Leiocephalus carinatus, a major ground predator. We found that selection predictably favors different risk-taking behaviors under different treatments: Exploratory behavior is favored in the absence of predators, whereas avoidance of the ground is favored in their presence. On predator islands, selection on behavior is stronger than selection on morphology, whereas the opposite holds on islands without predators. Our field experiment demonstrates that selection can shape behavioral traits, paving the way toward adaptation to varying environmental contexts

A simple model of unbounded evolutionary versatility as a largest-scale trend in organismal evolution

The idea that there are any large-scale trends in the evolution of biological organisms is highly controversial. It is commonly believed, for example, that there is a large-scale trend in evolution towards increasing complexity, but empirical and theoretical arguments undermine this belief. Natural selection results in organisms that are well adapted to their local environments, but it is not clear how local adaptation can produce a global trend. In this paper, I present a simple computational model, in which local adaptation to a randomly changing environment results in a global trend towards increasing evolutionary versatility. In this model, for evolutionary versatility to increase without bound, the environment must be highly dynamic. The model also shows that unbounded evolutionary versatility implies an accelerating evolutionary pace. I believe that unbounded increase in evolutionary versatility is a large-scale trend in evolution. I discuss some of the testable predictions about organismal evolution that are suggested by the model

Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes

The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms

Sea (in)sight: from phylogeographical insights to visual local adaptation in marine gobies = (In)zicht op zee: van fylogeografische inzichten naar visuele lokale adaptatie bij mariene grondels

Exactly 150 years ago, Charles Darwin described natural selection as the motor of the evolution of life. Nevertheless, it is not yet clear how important natural selection is for the evolution of marine organisms.The genetic adaptation to local environmental conditions as a result of natural selection, a process known as local adaptation, will be reduced by the migration of organisms due to its homogeneous character. Because of the huge potential for migration in the ‘open’ sea, for a long time biologists declared that local adaptation is rare and even absent.Nevertheless, current research shows that the sea is not as ‘open’ as it may seem. Many marine organisms are able to occupy a permanent place and hence occur in distinct populations. Since migration seems limited, the possibility of local adaptation in marine species presents an important research question. The most recent studies showed that natural selection might be an important evolutionary force in the ocean, however without any good scientific evidence.The present thesis has the ambition to prove that marine species may indeed be genetically adapted to local conditions. A promising opportunity is the possibility for local adaptation to the light regime of the sea. The light that organisms perceive varies between seas due to the differences in turbidity and the colour of the water. The importance of sight for marine animals is obvious, especially to find food and mates, and to avoid predators. Therefore, the aim of the thesis was to study local adaptation at the rhodopsin gene - the gene of the visual pigment that determines the visual capacity in dim-light - of a marine goby, the sand goby (Pomatoschistus minutus). The sand goby is a small and abundant fish species that lives along the European coasts.The results showed strong evidence that sand goby populations are genetically adapted to their specific and local light environment. They are adapted to high turbidity in the Baltic Sea and the Mediterranean lagoons, and to the more blue light of the Bay of Biscay and along the coasts of Spain and Portugal. Moreover, the sand gobies of the North Sea reveal a strategy of adaptation to the unstable local light conditions. In the current state of science, the rhodopsin gene provides one of the strongest indications that local adaptation occurs in the marine environment. Therefore, they encourage analogous studies to find further evidence for l ocal adaptation to other marine environmental conditions such as salinity tolerance and temperature. Such studies will clarify the importance of natural selection as evolutionary force for marine life.To conclude, this study reveals that the sand goby is evolutionary adapted to its light environment. There are strong indications that if the light environment changes due to either pollution or climate change, marine fishes won’t likely be able to adapt rapidly to the new circumstances. Good management of the light conditions of the marine ecosystem will be essential to support a balanced ecosystem and healthy fish stocks

Associative memory in gene regulation networks

The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organism’s gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily ‘Hebbian’ – ‘genes that fire together wire together’ – i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ‘recall’ capabilities normally reserved for cognitive systems