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In many ecological situations, resources are difficult to find but become more apparent to nearby searchers after one of their numbers discovers and begins to exploit them. If the discoverer cannot monopolize the resources, then others may benefit from joining the discoverer and sharing their discovery. Existing theories for this type of conspecific attraction have often used very simple rules for how the decision to join a discovered resource patch should be influenced by the number of individuals already exploiting that patch. We use a mechanistic, spatially explicit model to demonstrate that individuals should not necessarily simply join patches more often as the number of individuals exploiting the patch increases, because those patches are likely to be exhausted soon or joining them will intensify future local competition. Furthermore, we show that this decision should be sensitive to the nature of the resource patches, with individuals being more responsive to discoveries in general and more tolerant of larger numbers of existing exploiters on a patch when patches are resource-rich and challenging to locate alone. As such, we argue that this greater focus on underlying joining mechanisms suggests that conspecific attraction is a more sophisticated and flexible tactic than currently appreciated. 相似文献
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Estimation in linear models with censored data 总被引:1,自引:0,他引:1
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A note on optimality in lattice square designs 总被引:1,自引:0,他引:1
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Rao-Blackwellisation of sampling schemes 总被引:15,自引:0,他引:15
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Valerie Gray Hardcastle 《Biology & philosophy》1993,8(2):173-192
I examine the branch of evolutionary epistemology which tries to account for the character of cognitive mechanisms in animals and humans by extending the biological theory of evolution to the neurophysiological substrates of cognition. Like Plotkin, I construe this branch as a struggling science, and attempt to characterize the sort of theory one might expect to find this truly interdisciplinary endeavor, an endeavor which encompasses not only evolutionary biology, cognitive psychology, and developmental neuroscience, but also and especially, the computational modeling of artificial life programming; I suggest that extending Schaffner's notion of interlevel theories to include both horizontal and vertical levels of abstraction best fits the theories currently being developed in cognitive science. Finally, I support this claim with examples drawn from computational modeling data using the genetic algorithm. 相似文献
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无瓣海桑是广西从自治区外引进的外来红树林树种,采用定量化算法精确估算无瓣海桑地上生物量对红树林生态修复以及海洋蓝碳监测提供经验和方法。论文以广西茅尾海自然保护区无瓣海桑红树林为研究对象,以野外实测无瓣海桑红树林地上生物量数据和Sentinel-1/2卫星提取的后向散射数据、波段数据、植被指数数据和纹理指数数据为数据源,通过分析各遥感因子与实测红树林地上生物量之间的重要性关系,采用极端梯度提升(XGBoost)机器学习算法对比了不同的变量组合对模型精度的影响,最后基于优选的变量组合反演了无瓣海桑红树林的地上生物量。结果表明:(1)研究区无瓣海桑红树林实测树高范围为1.55—13.58m,平均值为8.37m,胸径范围为0.7—41cm,平均值为15.62cm;(2)通过XGBoost算法优选的21个特征变量组合模型拟合效果较好,其模型在测试阶段R2=0.7237,RMSE=21.70Mg/hm2。XGBoost算法反演研究区无瓣海桑地上生物量介于19.14—138.46Mg/hm2之间,平均值为51.92Mg/hm... 相似文献
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Pearlman DA 《Journal of biomolecular NMR》1996,8(1):49-66
Summary A new NMR refinement method, FINGAR (FIt NMR using a Genetic AlgoRithm), has been developed, which allows one to determine a weighted set of structures that best fits measured NMR-derived data. This method shows appreciable advantages over commonly used refinement methods. FINGAR generates an ensemble of conformations whose average reproduces the experimental NMR-derived restraints. In addition, a statistical importance weight is assigned to each of the conformations in the ensemble. As a result, one is not limited to simply presenting an envelope of sampled conformers. Instead, one can subsequently focus on a select few conformers of high weight. This is critical, because many structural analyses depend on using discrete conformations, not simply averages or ensembles. The genetic algorithm used by FINGAR allows one to simultaneously and reliably fit against many restraints, and to generate solutions which include as many conformations with non-zero weights as are necessary to generate the best fit. An added benefit of FINGAR is that because the time-consuming step in this method needs only to be performed once, in the beginning of the first run, numerous FINGAR simulations can be performed rapidly. 相似文献
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