Academic literature on the topic 'Adaptive fitness predictor'
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Journal articles on the topic "Adaptive fitness predictor"
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Drahosova, Michaela, Lukas Sekanina, and Michal Wiglasz. "Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming." Evolutionary Computation 27, no. 3 (September 2019): 497–523. http://dx.doi.org/10.1162/evco_a_00229.
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In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.
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Giosan, C., and K. Wyka. "FC01-03 - High-K reproductive strategy as a negative predictor of PTSD." European Psychiatry 26, S2 (March 2011): 1812. http://dx.doi.org/10.1016/s0924-9338(11)73516-7.
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This study examined the associations between a high-K fitness strategy (i.e., a Darwinian reproductive strategy where the individual invests significantly in a small number of offspring) and PTSD on a sample of 1400 disaster workers who had exposure to a singular traumatic event (the 9/11 attack on the World Trade Center in New York City). The participants underwent psychological evaluations consisting of1) Clinician-Administered PTSD Scale (CAPS) and2) PTSD Checklist (PCL). The participants were also administered the3) High-K Strategy Scale (HKSS) (Giosan, 2006).Other factors, such as demographics, prior trauma, prior psychiatric history, and the severity of exposure to 9/11 were also captured. The results showed that HKSS score was an important negative predictor of PTSD, accounting for 10% of the variance in the PCL and 5% of the variance in the CAPS, after controlling for demographics, prior trauma, prior psychiatric history and the severity of exposure to 9/11. The findings speak against the evolutionary view that PTSD symptoms are adaptive while strengthening the evolutionary view that PTSD is the expression of an overlearned survival response in vulnerable individuals (Silove, 1998), which, when activated, can have significant negative effects on fitness.
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Cheney, Dorothy L., Joan B. Silk, and Robert M. Seyfarth. "Network connections, dyadic bonds and fitness in wild female baboons." Royal Society Open Science 3, no. 7 (July 2016): 160255. http://dx.doi.org/10.1098/rsos.160255.
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In many social mammals, females who form close, differentiated bonds with others experience greater offspring survival and longevity. We still know little, however, about how females' relationships are structured within the social group, or whether connections beyond the level of the dyad have any adaptive value. Here, we apply social network analysis to wild baboons in order to evaluate the comparative benefits of dyadic bonds against several network measures. Results suggest that females with strong dyadic bonds also showed high eigenvector centrality, a measure of the extent to which an individual's partners are connected to others in the network. Eigenvector centrality was a better predictor of offspring survival than dyadic bond strength. Previous results have shown that female baboons derive significant fitness benefits from forming close, stable bonds with several other females. Results presented here suggest that these benefits may be further augmented if a female's social partners are themselves well connected to others within the group rather than being restricted to a smaller clique.
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Nigenda-Morales, Sergio F., Ryan J. Harrigan, and Robert K. Wayne. "Playing by the rules? Phenotypic adaptation to temperate environments in an American marsupial." PeerJ 6 (March 27, 2018): e4512. http://dx.doi.org/10.7717/peerj.4512.
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Phenotypic variation along environmental gradients can provide evidence suggesting local adaptation has shaped observed morphological disparities. These differences, in traits such as body and extremity size, as well as skin and coat pigmentation, may affect the overall fitness of individuals in their environments. The Virginia opossum (Didelphis virginiana) is a marsupial that shows phenotypic variation across its range, one that has recently expanded into temperate environments. It is unknown, however, whether the variation observed in the species fits adaptive ecogeographic patterns, or if phenotypic change is associated with any environmental factors. Using phenotypic measurements of over 300 museum specimens of Virginia opossum, collected throughout its distribution range, we applied regression analysis to determine if phenotypes change along a latitudinal gradient. Then, using predictors from remote-sensing databases and a random forest algorithm, we tested environmental models to find the most important variables driving the phenotypic variation. We found that despite the recent expansion into temperate environments, the phenotypic variation in the Virginia opossum follows a latitudinal gradient fitting three adaptive ecogeographic patterns codified under Bergmann’s, Allen’s and Gloger’s rules. Temperature seasonality was an important predictor of body size variation, with larger opossums occurring at high latitudes with more seasonal environments. Annual mean temperature predicted important variation in extremity size, with smaller extremities found in northern populations. Finally, we found that precipitation and temperature seasonality as well as low temperatures were strong environmental predictors of skin and coat pigmentation variation; darker opossums are distributed at low latitudes in warmer environments with higher precipitation seasonality. These results indicate that the adaptive mechanisms underlying the variation in body size, extremity size and pigmentation are related to the resource seasonality, heat conservation, and pathogen-resistance hypotheses, respectively. Our findings suggest that marsupials may be highly susceptible to environmental changes, and in the case of the Virginia opossum, the drastic phenotypic evolution in northern populations may have arisen rapidly, facilitating the colonization of seasonal and colder habitats of temperate North America.
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Ferguson, Moira M., and Peter E. Ihssen. "Distribution and Phenotypic Correlates of Variation at Enzyme Coding Loci in Rainbow Trout (Oncorhynchus mykiss) from the Lower Laurentian Great Lakes." Canadian Journal of Fisheries and Aquatic Sciences 48, no. 7 (July 1, 1991): 1308–15. http://dx.doi.org/10.1139/f91-157.
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The distribution of variation at 37 enzyme coding loci was determined in six samples of rainbow trout (Oncorhynchus mykiss) from hatchery and natural sources. Significant heterogeneity in allele frequencies was detected among samples of Lower Laurentian Great Lakes rainbow trout. Geographical proximity was a poor predictor of genetic similarity among samples. The adaptive significance of two loci was examined in 16 experimental families. Rainbow trout homozygous for an allele at a phosphoglucomutase structural gene (Pgm1-s), which results in no detectable activity of phosphoglucomutase (PGM1) in all tissues where this locus is normally expressed (e.g. muscle), were larger than those heterozygous or homozygous for the active allele. However, no significant differences in asymmetry of four bilateral meristic traits were detected between fish with different PGM1 phenotypes. Rainbow trout with PGM1 in liver because of a regulatory gene, Pgm1-t(b), were marginally larger than those without the allele. These data suggest a correlation between biochemical genetic variation and phenotypic characteristics associated with fitness.
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Major, John E., Debby C. Barsi, Alex Mosseler, Om P. Rajora, and Moira Campbell. "Predominant paternal inheritance pattern of light-energy processing adaptive traits in red and black spruce hybrids." Canadian Journal of Forest Research 37, no. 2 (February 2007): 293–305. http://dx.doi.org/10.1139/x06-228.
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Ecophysiological traits related to light-energy processing and freezing tolerance are important adaptive traits in plants. Our goal was to investigate the pattern of inheritance of these traits in hybrids using controlled intra- and inter-specific crosses of red spruce ( Picea rubens Sarg.) (RS) and black spruce ( Picea mariana (Mill.) BSP) (BS). Our initial working hypothesis was that expected hybrid index categories could be a predictor of adaptive traits. Species results of dark-adapted photochemical efficiency (Fv/Fm) and light-adapted light-energy processing traits, quantum yield (YLD), thermal dissipation efficiency (qN), and chlorophyll fluorescence (Fpc), were consistent with previously published open-pollinated, species provenance results. Initially, YLD, qN, and Fpc, showed an additive inheritance pattern, evident by average hybrid index 50 having a mid-parent value. Because of various crosstypes of the hybrid families, parental analysis, testing male, female, and interaction effects, and having three categories (pure RS, pure BS, and hybrid spruce), revealed significant male and nonsignificant female and interactive effects. Underlying the averaged additive results was a significant species-specific paternal inheritance pattern. Crosses with BS males had 13.7% higher YLD (P = 0.001), 15.4% lower qN (P = 0.008), and 43.0% higher Fpc (P = 0.096) than crosses with either RS or hybrid males. Fv/Fm showed a nonadditive or parental species pattern. Parental analysis of Fv/Fm showed significant male, female, and male × female interaction effects, and further analysis supports a largely species-specific and paternally inherited trait. Freezing tolerance revealed a mixed model of inheritance dominated by species effects. Total dry mass was positively correlated with YLD, and negatively correlated with Fv/Fm and qN, suggesting a biological tradeoff. We know of no other studies in trees demonstrating paternal inheritance of ecophysiological processes that affect adaptation and fitness.
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Nicolaus, Marion, Joost M. Tinbergen, Karen M. Bouwman, Stephanie P. M. Michler, Richard Ubels, Christiaan Both, Bart Kempenaers, and Niels J. Dingemanse. "Experimental evidence for adaptive personalities in a wild passerine bird." Proceedings of the Royal Society B: Biological Sciences 279, no. 1749 (October 24, 2012): 4885–92. http://dx.doi.org/10.1098/rspb.2012.1936.
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Individuals of the same species differ consistently in risky actions. Such ‘animal personality’ variation is intriguing because behavioural flexibility is often assumed to be the norm. Recent theory predicts that between-individual differences in propensity to take risks should evolve if individuals differ in future fitness expectations: individuals with high long-term fitness expectations (i.e. that have much to lose) should behave consistently more cautious than individuals with lower expectations. Consequently, any manipulation of future fitness expectations should result in within-individual changes in risky behaviour in the direction predicted by this adaptive theory. We tested this prediction and confirmed experimentally that individuals indeed adjust their ‘exploration behaviour’, a proxy for risk-taking behaviour, to their future fitness expectations. We show for wild great tits ( Parus major ) that individuals with experimentally decreased survival probability become faster explorers (i.e. increase risk-taking behaviour) compared to individuals with increased survival probability. We also show, using quantitative genetics approaches, that non-genetic effects (i.e. permanent environment effects) underpin adaptive personality variation in this species. This study thereby confirms a key prediction of adaptive personality theory based on life-history trade-offs, and implies that selection may indeed favour the evolution of personalities in situations where individuals differ in future fitness expectations.
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Liu, Ding. "Research and Application of Hybrid Adaptive Forecasting Method Based on SOA." E3S Web of Conferences 213 (2020): 02007. http://dx.doi.org/10.1051/e3sconf/202021302007.
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The current network security situation is becoming more and more severe. In order to improve the accuracy of network security situation prediction, a network security situation prediction method based on crowd search algorithm optimized BP neural network is proposed. This algorithm uses the four characteristics of egoism, altruism, pre-action and uncertain reasoning unique to the crowd search algorithm to determine the search strategy, finds the best fitness individual, obtains the optimal weights and thresholds, and then performs random initialization of the BP neural network The threshold and weight are assigned, and the predicted value is obtained after neural network training. Finally, it is compared with the predicted value obtained by the other two optimization algorithms. Experiments show that the algorithm used in network security situation prediction has higher accuracy, smaller errors, and better stability.
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McFarlane, S. Eryn, Jamieson C. Gorrell, David W. Coltman, Murray M. Humphries, Stan Boutin, and Andrew G. McAdam. "The nature of nurture in a wild mammal's fitness." Proceedings of the Royal Society B: Biological Sciences 282, no. 1806 (May 7, 2015): 20142422. http://dx.doi.org/10.1098/rspb.2014.2422.
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Genetic variation in fitness is required for the adaptive evolution of any trait but natural selection is thought to erode genetic variance in fitness. This paradox has motivated the search for mechanisms that might maintain a population's adaptive potential. Mothers make many contributions to the attributes of their developing offspring and these maternal effects can influence responses to natural selection if maternal effects are themselves heritable. Maternal genetic effects (MGEs) on fitness might, therefore, represent an underappreciated source of adaptive potential in wild populations. Here we used two decades of data from a pedigreed wild population of North American red squirrels to show that MGEs on offspring fitness increased the population's evolvability by over two orders of magnitude relative to expectations from direct genetic effects alone. MGEs are predicted to maintain more variation than direct genetic effects in the face of selection, but we also found evidence of maternal effect trade-offs. Mothers that raised high-fitness offspring in one environment raised low-fitness offspring in another environment. Such a fitness trade-off is expected to maintain maternal genetic variation in fitness, which provided additional capacity for adaptive evolution beyond that provided by direct genetic effects on fitness.
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Wilson, David Sloan, and Steven Jay Lynn. "Adaptive misbeliefs are pervasive, but the case for positive illusions is weak." Behavioral and Brain Sciences 32, no. 6 (December 2009): 539–40. http://dx.doi.org/10.1017/s0140525x09991543.
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AbstractIt is a foundational prediction of evolutionary theory that human beliefs accurately approximate reality only insofar as accurate beliefs enhance fitness. Otherwise, adaptive misbeliefs will prevail. Unlike McKay & Dennett (M&D), we think that adaptive belief systems rely heavily upon misbeliefs. However, the case for positive illusions as an example of adaptive misbelief is weak.
Dissertations / Theses on the topic "Adaptive fitness predictor"
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Hurta, Martin. "Koevoluční algoritmy a klasifikace." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445529.
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The aim of this work is to automatically design a program that is able to detect dyskinetic movement features in the measured patient's movement data. The program will be developed using Cartesian genetic programming equipped with coevolution of fitness predictors. This type of coevolution allows to speed up a design performed by Cartesian genetic programming by evaluating a quality of candidate solutions using only a part of training data. Evolved classifier achieves a performance (in terms of AUC) that is comparable with the existing solution while achieving threefold acceleration of the learning process compared to the variant without the fitness predictors, in average. Experiments with crossover methods for fitness predictors haven't shown a significant difference between investigated methods. However, interesting results were obtained while investigating integer data types that are more suitable for implementation in hardware. Using an unsigned eight-bit data type (uint8_t) we've achieved not only comparable classification performance (for significant dyskinesia AUC = 0.93 the same as for the existing solutions), with improved AUC for walking patient's data (AUC = 0.80, while existing solutions AUC = 0.73), but also nine times speedup of the design process compared to the approach without fitness predictors employing the float data type, in average.
Books on the topic "Adaptive fitness predictor"
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Railsback, Steven F., and Bret C. Harvey. Modeling Populations of Adaptive Individuals. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.001.0001.
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Ecologists now recognize that the dynamics of populations, communities, and ecosystems are strongly affected by adaptive individual behaviors. Yet until now, we have lacked effective and flexible methods for modeling such dynamics. Traditional ecological models become impractical with the inclusion of behavior, and the optimization approaches of behavioral ecology cannot be used when future conditions are unpredictable due to feedbacks from the behavior of other individuals. This book provides a comprehensive introduction to state- and prediction-based theory, or SPT, a powerful new approach to modeling trade-off behaviors in contexts such as individual-based population models where feedbacks and variability make optimization impossible. This book features a wealth of examples that range from highly simplified behavior models to complex population models in which individuals make adaptive trade-off decisions about habitat and activity selection in highly heterogeneous environments. The book explains how SPT builds on key concepts from the state-based dynamic modeling theory of behavioral ecology, and how it combines explicit predictions of future conditions with approximations of a fitness measure to represent how individuals make good—not optimal—decisions that they revise as conditions change. The resulting models are realistic, testable, adaptable, and invaluable for answering fundamental questions in ecology and forecasting ecological outcomes of real-world scenarios.
Book chapters on the topic "Adaptive fitness predictor"
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Honor, Richard, and Robert I. Colautti. "EICA 2.0: a general model of enemy release and defence in plant and animal invasions." In Plant invasions: the role of biotic interactions, 192–207. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789242171.0192.
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Abstract Plants and animals have evolved a variety of strategies to limit the negative fitness consequences of natural enemies (i.e. herbivores, predators, parasites and pathogens). Demographic bottlenecks occurring during the invasion process reduce the number of co-introduced natural enemies, providing opportunities to study rapid evolution in environments with different or reduced enemy loads. Enemy release theory provides a set of hypotheses and predictions about the role of natural enemies in the proliferation of invasive species. This body of theory includes the Enemy Release Hypothesis (ERH) and the related Evolution of Increased Competitive Ability Hypothesis (EICA), but there is often confusion about these hypotheses and the data needed to test them. We introduce a simple, general model of enemy release to identify and clarify some of the key assumptions and predictions implicit in enemy release theory and its impacts on invasion. Although introduced populations likely benefit from a reduction in the direct fitness impacts of natural enemies in the early stages of invasion, an evolutionary shift in resource allocation from defence to growth and reproduction is much less likely and depends on a delicate balance between the fitness costs and benefits of defence and the fitness impacts of natural enemies in both the native and introduced ranges. Even when the abundance of natural enemies is lower in the introduced range, the majority of scenarios do not favour evolution of less defended genotypes that are more competitive or more fecund, contrary to predictions of EICA. Perhaps surprisingly, we find that the level of damage by natural enemies in field surveys is not generally a good parameter for testing enemy release theory. Instead, common garden experiments characterizing fitness reaction norms of multiple genotypes from the native and introduced range are crucial to estimate the historic rate of adaptive evolution or predict it into the future. Incorporating spatial autocorrelation and methods from population genetics can further improve our understanding of the role of enemy release and evolution in the proliferation of invasive species.
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"A First Example: Forager Patch Selection." In Modeling Populations of Adaptive Individuals, edited by Steven F. Railsback and Bret C. Harvey, 35–51. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.003.0004.
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This chapter illustrates how to use state- and prediction-based theory (SPT) by applying it to an extremely simple model of how hypothetical foragers select among patches that differ in both food availability and predation risk. This example uses two common techniques of SPT. First, individuals predict conditions using the simplest approach that lets them adapt to changing conditions: by simply assuming that current conditions will persist until the time horizon. The second typical SPT technique is to simplify evaluation of the fitness measure for a particular decision alternative by assuming that the individual will use the alternative until the time horizon. The chapter then evaluates the quality of decisions made via SPT. It also shows how SPT allows one to make models less simple and more realistic and capable.
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"Introduction to State-and Prediction-Based Theory." In Modeling Populations of Adaptive Individuals, edited by Steven F. Railsback and Bret C. Harvey, 29–34. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.003.0003.
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This chapter discusses the state- and prediction-based theory (SPT) and its use in individual-based models (IBMs). The fundamental concept of modern theory in behavioral ecology is that behavior acts to maximize a specific measure of fitness at a specific future time, and that this fitness measure incorporates multiple elements, such as the need to avoid predators, the need to avoid starvation, and the benefits of energy accumulation for reproduction. This concept has been applied widely and successfully in dynamic state variable modeling (DSVM), and SPT was developed as a way of using the same principle in IBMs when feedback from the behavior of other individuals, combined with unpredictable environmental conditions, make the assumption of optimality used by DSVM impossible. The chapter then looks at the differences between SPT and DSVM. To model populations of adaptive individuals, SPT is implemented using five steps. These steps include embedding SPT in an IBM that simulates the processes that drive behavior, both internal to the individual and external.
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"Guidance for Using State-and Prediction-Based Theory." In Modeling Populations of Adaptive Individuals, edited by Steven F. Railsback and Bret C. Harvey, 93–122. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.003.0008.
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This chapter outlines the guidance on using state- and prediction-based theory (SPT) to build models of populations and communities of adaptive individuals, detailing five steps unique to SPT. The most important aspect of SPT to remember is that one is not trying to build optimal, or even necessarily accurate, models of how an organism's behavior affects its future fitness. Instead, one is trying to find simplistic models that produce realistic behavior in contexts where optimization is impossible. While SPT can be used like dynamic state variable modeling (DSVM), as a framework for thinking about and modeling how an individual makes a particular decision, its main purpose is to model adaptive trade-off decisions in individual-based population models. Thus, using SPT is part of the larger process of developing, analyzing, and applying an IBM to address population-level questions, and the five steps therefore include that process.
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"Example Four: Facultative Anadromy in Salmonid Fishes." In Modeling Populations of Adaptive Individuals, edited by Steven F. Railsback and Bret C. Harvey, 86–92. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.003.0007.
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This chapter offers a fourth example model, with the objective of (1) illustrating the application of state- and prediction-based theory (SPT) to a new kind of decision—a life history decision—in a case where dynamic state variable modeling (DSVM) has been applied successfully; and (2) describing the unique ability of models utilizing SPT to address population-level questions of particular interest to conservationists and managers. In this case, SPT produced individual-level decisions similar to those of DSVM, but including them in a population-level model led to quite different conclusions than those implied by the individual-level DSVM analysis. Salmonid fishes exhibit amazing life history diversity. One fundamental distinction among salmonid life histories is whether or not individuals migrate to the ocean. In general, facultative anadromy can be seen as an adaptive behavior that trades off the fitness benefits of going to the ocean versus those of remaining resident. The anadromy versus residency decision is important to fish conservation and resource management.
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Honor, Richard, and Robert L. Colautti. "EICA 2.0: a general model of enemy release and defence in plant and animal invasions." In Plant invasions: the role of biotic interactions, 192–207. CABI, 2020. http://dx.doi.org/10.1079/9781789242171.0010.
Full textAbstract:
Plants and animals have evolved a variety of strategies to limit the negative fitness consequences of natural enemies (i.e. herbivores, predators, parasites and pathogens). Demographic bottlenecks occurring during the invasion process reduce the number of co-introduced natural enemies, providing opportunities to study rapid evolution in environments with different or reduced enemy loads. Enemy release theory provides a set of hypotheses and predictions about the role of natural enemies in the proliferation of invasive species. This body of theory includes the Enemy Release Hypothesis (ERH) and the related Evolution of Increased Competitive Ability Hypothesis (EICA), but there is often confusion about these hypotheses and the data needed to test them. We introduce a simple, general model of enemy release to identify and clarify some of the key assumptions and predictions implicit in enemy release theory and its impacts on invasion. Although introduced populations likely benefit from a reduction in the direct fitness impacts of natural enemies in the early stages of invasion, an evolutionary shift in resource allocation from defence to growth and reproduction is much less likely and depends on a delicate balance between the fitness costs and benefits of defence and the fitness impacts of natural enemies in both the native and introduced ranges. Even when the abundance of natural enemies is lower in the introduced range, the majority of scenarios do not favour evolution of less defended genotypes that are more competitive or more fecund, contrary to predictions of EICA. Perhaps surprisingly, we find that the level of damage by natural enemies in field surveys is not generally a good parameter for testing enemy release theory. Instead, common garden experiments characterizing fitness reaction norms of multiple genotypes from the native and introduced range are crucial to estimate the historic rate of adaptive evolution or predict it into the future. Incorporating spatial autocorrelation and methods from population genetics can further improve our understanding of the role of enemy release and evolution in the proliferation of invasive species.
Conference papers on the topic "Adaptive fitness predictor"
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Tang, Ping, and Gordon K. Lee. "An Adaptive Fitness Function for Evolutionary Algorithms Using Heuristics and Prediction." In 2006 World Automation Congress. IEEE, 2006. http://dx.doi.org/10.1109/wac.2006.376012.
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Hoppe, Fred M., and Lin Fang. "Bayesian Prediction for the Gumbel Distribution Applied to Feeder Pipe Thicknesses." In 16th International Conference on Nuclear Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/icone16-48871.
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This paper develops Bayesian prediction intervals for the minimum of any specified number of future measurements from a Gumbel distribution based on previous observations. The need for such intervals arises in the analysis of data from outlet side feeder pipes at Ontario nuclear power plants. The issue is how to best use these measurements in order to arrive at a statistically sound conclusion concerning the minimum thickness of all remaining uninspected pipes, in particular with what confidence can it be asserted that the remaining wall thicknesses are above an acceptable minimum to ensure a sufficiently high thickness up to the end of the next operating interval. The result gives a probability measure of the potential benefit of performing additional inspections when considered against the additional radiation exposure and the cost of performing additional inspections. Previously, this problem was approached by adapting a classical prediction interval that was originally derived for normal data. Here we examine both a hybrid Bayesian method that combines Bayesian ideas with maximum likelihood and also a full Bayesian approach using Markov Chain Monte Carlo. We show that the latter gives larger lower prediction limits and therefore more margin to fitness for service.