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Accordingly a new adaptive peak is achieved and the system enters large scale, low gain.
1994; Orr 1995; Orr and Orr 1996; Gavrilets and Hastings 1996) assume that viable genotypes form "clusters" in genotype space so that the population can move from one adaptive peak to another one separated by an adaptive valley following a "ridge" of well-fit genotypes without crossing any deep adaptive valleys.
According to this idea, genetic factors are extremely interactive, such that some combinations generate high fitness and become "peaks" on the adaptive landscape, while other combinations have low fitness and represent "valleys." Wright, in his shifting balance theory (SBT) of evolution, posited that a species becomes stuck on the local equilibrium of an adaptive peak, and can only move to the domain of attraction of a higher peak by the actions of genetic drift followed by subsequent selection (Wright 1931a, 1932; Simpson 1953; Barton and Rouhani 1987, 1993).
(This requires the approximations that fitnesses are independent of genotype frequencies and that selection is weak enough relative to recombination that linkage disequilibrium is negligible.) If the landscape stays constant, a local maximum of mean fitness (an adaptive peak) will eventually be reached (at least approximately - mutation and recombination usually decrease fitness).
Second, a population in the neighborhood of a single adaptive peak will eventually climb the peak regardless of the pattern of genetic (co)variances (Lande 1979; Via and Lande 1985; Zeng 1988), in which case the role of quantitative genetics is only temporary.
This is in contrast to Barton (1992) who argued that even the less adaptive peak can easily take over.
Each of these allows a species to evolve through a valley to a new adaptive peak by a combination of stochastic and deterministic processes.
We therefore calculated the percentage of all 30 populations (i.e., trials) at each migration rate that shifted entirely to the higher adaptive peak.
Based on the principle of universal pleiotropy, Wright (1932) envisaged an adaptive landscape where each local population was defined by a coadapted combination of loci representing an adaptive peak. Such a multiplicity of peaks has been termed "multiple-peak epistasis." Universal pleiotropy and multiple-peak epistasis are two of the four premises that underlie the shifting-balance theory of evolution (Wright 1970), which also assumes the existence of polymorphisms at most loci and a subdivided population structure to allow different peaks to be reached in different populations.
Since we expect populations to be centered near an adaptive peak, the common question has been how shifts from one peak to another may occur.
We can all articulate the central error of such a perspective: since organisms help to create their own environments, adaptive peaks are built by interaction and undergo complex shifts as populations move in morphospace; organisms cannot climb stable mountains of an engineer's fancy.
It is thus unclear how commonly natural populations are characterized by multiple adaptive peaks; and hence, in general what the potential is for multiple peaks to influence evolutionary processes in natural populations.
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- adaptation energy
- adaptation model
- adaptation response
- adaptation syndrome
- adaptation, genetic
- adaptation, physiological
- adapted clothing
- adapter protein
- adaptin complex
- adaptive behavior
- adaptive behavior scale
- adaptive behavior scales
- adaptive device
- adaptive enzyme
- Adaptive Filtration
- Adaptive Gridlock
- adaptive hypertrophy
- adaptive immune system
- adaptive immunity
- Adaptive Landscape
- adaptive mutation
- adaptive peak
- adaptive radiation
- adaptive seating device
- adaptive support ventilation
- adaptive therapy
- adaptive thermogenesis
- adaptive trial
- adaptive value
- adaptor hypothesis
- Adcortyl in Orabase
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- Adaptive Noise Shaping
- Adaptive Non-linear Coherent Processor
- Adaptive Non-Linear Guidance
- Adaptive Non-Uniform Quantization Algorithm
- adaptive norm
- Adaptive Normalized Matched Filter
- Adaptive Null Steering Arrays
- Adaptive Object Based Evaluation
- Adaptive Onestep
- Adaptive Onestep Plus Adaptive Twostep
- Adaptive Optical Music Recognition
- Adaptive optics
- Adaptive optics
- Adaptive optics
- Adaptive Optics Associates
- Adaptive Optics for Extremely Large Telescopes
- Adaptive Optics Module
- Adaptive Optics Scanning Laser Ophthalmoscope
- Adaptive Order Statistic
- Adaptive Orthogonal Frequency Division Multiplexing
- Adaptive Orthogonal Modulation
- Adaptive Packet Assembly
- Adaptive Packet Marking
- Adaptive Parametric Linearization
- Adaptive Particle Swarm Optimization
- Adaptive Pattern Perceiving Electronic Computer System
- Adaptive Pattern Recognition Process
- Adaptive Pattern Recognition Processing
- Adaptive Payload Carrier
- Adaptive Pcm
- adaptive peak
- Adaptive Penalty Method
- Adaptive Periosteal Cambian
- Adaptive Pfc
- Adaptive Phase Tracking
- Adaptive Phased Array
- Adaptive Pixel Subdivision
- Adaptive Planning and Execution
- Adaptive Planning Option
- Adaptive Plot Refinement
- Adaptive Poisson-Boltzmann Solver
- Adaptive Policy Based Management
- Adaptive Polling Technique
- Adaptive Population size Genetic Algorithm
- Adaptive Power Allocation
- Adaptive Power Control
- Adaptive Power Residue
- Adaptive Prediction
- Adaptive predictive coding
- Adaptive Predictive-Ratio Closed-Loop Power Control
- Adaptive Preferential Defense
- Adaptive Prefetch Dropping
- Adaptive Principal component EXtraction
- Adaptive Private Networking
- Adaptive Probabilistic Concept Modeling
- Adaptive Probabilistic Neural Network
- Adaptive Process Guidance
- Adaptive Processing of Data Structures
- Adaptive Processor, SONAR
- Adaptive Programmable Interface Unit
- Adaptive Project Framework