Culturally-transmitted feeding behaviour in primates: Evidence for accelerating learning rates

Cultural transmission implies the rapid spread of behavioural innovations when initially naïve individuals copy more informed ones. Mathematical models of transmission feature accelerating (and in most cases, logistic) rates of learning as animals that acquire an innovation provide ever increasing numbers of informers for potential learners. Conversely, non-accelerating rates have been proposed as a null hypothesis for apparent cases of cultural transmission that can best be explained by simpler mechanisms such as trial-and-error learning. Using the AIC technique for comparing models with different numbers of parameters, this paper examines the 21 cases in the primate literature where quantifiable data are available on learning rates for presumed culturally-transmitted feeding innovations. In each case, cumulative distributions over time of the frequency or proportion of individuals that acquire an innovation are compared with three accelerating functions (logistic, positive exponential, and hyperbolic sine) and two non-accelerating ones (linear and logarithmic). In 16 cases, the best fit is given by an accelerating function: nine of these support the logistic, four support the positive exponential and three, the reverse S-shaped hyperbolic sine. Individual cases often show small differences between alternative functions, but overall trends support the cultural assumption of accelerating learning rates.     

Statistical analysis of algal and water quality data

Data on cyanobacteria (blue-green algae) are generallycollected on a reactive basis, frequently in responseto bloom events. Such data presents a biased andincomplete snapshot of water quality. This paper looksat two typical data sets for UK waters showing thatwhile statistics may be used to describe the data theyare of limited use in forecasting. Suggestions ofappropriate tests for small and sparse data sets aremade.     

单纯形加速法拟合生态学中的非线性模型

本文以Logistic模型,Taylor幂法则模型,Holling功能反应模型,以及种群内禀增长力Rm等模型的拟合和参数估计为例,探讨单纯形加速法在生态模型优化拟合和参数估计中的应用.结果表明,单纯形加速法拟合生态学中的非线性模型不仅适用广泛,而且拟合过程是直接求原来非线性模型的最优拟合,因而优于生态学中通常使用的将原模型“线性化后再拟合”的方法,而与其它一些最优化方法,如:麦夸方法、枚举选优法等比较,由于单纯形法不需计算目标函数的偏导数,因而计算不受目标函数及其偏导函数复杂程度的限制,而且对于各种模型其求优计算过程十分相似,可以编制统一的计算程序.本研究所编制的计算机程序对于本文未提到的其它一些模型也是完全适用的,在应用时仅需修改定义目标函数的自定义函数语句即可.研究也发现,在求优过程中,只要搜索系数选择适当和实际数据合理,是可以保证寻优成功的.     

A regression analysis of the summer population dynamics of Polyarthra vulgaris in a northern Michigan bog lake

The population dynamics of a planktonic rotifer (Polyarthra vulgaris) were examined in a brown water, acid lake in northern Michigan, U.S.A. Predation by Chaoborus punctipennis and low food (Navicula spp. and Cyclotella spp.) concentrations were the main factors limiting P. vulgaris populations of all factors examined. The data presented here support a hypothesis for zooplankton limitation by an invertebrate predator.