123,730 research outputs found
On the evolutionary interplay between dispersal and local adaptation in heterogeneous environments
Journal ArticleCopyright © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.Dispersal, whether in the form of a dandelion seed drifting on the breeze, or a salmon migrating upstream to breed in a nonnatal stream, transports genes between locations. At these locations, local adaptation modifies the gene frequencies so their carriers are better suited to particular conditions, be those of newly disturbed soil or a quiet river pool. Both dispersal and local adaptation are major drivers of population structure; however, in general, their respective roles are not independent and the two may often be at odds with one another evolutionarily, each one exhibiting negative feedback on the evolution of the other. Here, we investigate their joint evolution within a simple, discrete-time, metapopulation model. Depending on environmental conditions, their evolutionary interplay leads to either a monomorphic population of highly dispersing generalists or a collection of rarely dispersing, locally adapted, polymorphic sub-populations, each adapted to a particular habitat type. A critical value of environmental heterogeneity divides these two selection regimes and the nature of the transition between them is determined by the level of kin competition. When kin competition is low, at the transition we observe discontinuities, bistability, and hysteresis in the evolved strategies; however, when high, kin competition moderates the evolutionary feedback and the transition is smooth.Natural Sciences and Engineering Research Council of CanadaYukon FoundationArmy Research Offic
Stochastic dynamics of adaptive trait and neutral marker driven by eco-evolutionary feedbacks
How the neutral diversity is affected by selection and adaptation is
investigated in an eco-evolutionary framework. In our model, we study a finite
population in continuous time, where each individual is characterized by a
trait under selection and a completely linked neutral marker. Population
dynamics are driven by births and deaths, mutations at birth, and competition
between individuals. Trait values influence ecological processes (demographic
events, competition), and competition generates selection on trait variation,
thus closing the eco-evolutionary feedback loop. The demographic effects of the
trait are also expected to influence the generation and maintenance of neutral
variation. We consider a large population limit with rare mutation, under the
assumption that the neutral marker mutates faster than the trait under
selection. We prove the convergence of the stochastic individual-based process
to a new measure-valued diffusive process with jumps that we call Substitution
Fleming-Viot Process (SFVP). When restricted to the trait space this process is
the Trait Substitution Sequence first introduced by Metz et al. (1996). During
the invasion of a favorable mutation, a genetical bottleneck occurs and the
marker associated with this favorable mutant is hitchhiked. By rigorously
analysing the hitchhiking effect and how the neutral diversity is restored
afterwards, we obtain the condition for a time-scale separation; under this
condition, we show that the marker distribution is approximated by a
Fleming-Viot distribution between two trait substitutions. We discuss the
implications of the SFVP for our understanding of the dynamics of neutral
variation under eco-evolutionary feedbacks and illustrate the main phenomena
with simulations. Our results highlight the joint importance of mutations,
ecological parameters, and trait values in the restoration of neutral diversity
after a selective sweep.Comment: 29 page
Eco-evolutionary dynamics in fragmented landscapes
Peer reviewedPostprin
Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions
The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions
Evolved embodied phase coordination enables robust quadruped robot locomotion
Overcoming robotics challenges in the real world requires resilient control
systems capable of handling a multitude of environments and unforeseen events.
Evolutionary optimization using simulations is a promising way to automatically
design such control systems, however, if the disparity between simulation and
the real world becomes too large, the optimization process may result in
dysfunctional real-world behaviors. In this paper, we address this challenge by
considering embodied phase coordination in the evolutionary optimization of a
quadruped robot controller based on central pattern generators. With this
method, leg phases, and indirectly also inter-leg coordination, are influenced
by sensor feedback.By comparing two very similar control systems we gain
insight into how the sensory feedback approach affects the evolved parameters
of the control system, and how the performances differs in simulation, in
transferal to the real world, and to different real-world environments. We show
that evolution enables the design of a control system with embodied phase
coordination which is more complex than previously seen approaches, and that
this system is capable of controlling a real-world multi-jointed quadruped
robot.The approach reduces the performance discrepancy between simulation and
the real world, and displays robustness towards new environments.Comment: 9 page
Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment
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