Skewed temperature dependence affects range and abundance in a warming world

Population growth metrics such as R 0 are usually asymmetric functions of temperature, with cold-skewed curves arising when the positive effects of a temperature increase outweigh the negative effects, and warm-skewed curves arising in the opposite case. Classically, cold-skewed curves are interpreted as more beneficial to a species under climate warming, because cold-skewness implies increased population growth over a larger proportion of the species's fundamental thermal niche than warm-skewness. However, inference based on the shape of the fitness curve alone, and without considering the synergistic effects of net reproduction, density and dispersal, may yield an incomplete understanding of climate change impacts. We formulate a moving-habitat integrodifference equation model to evaluate how fitness curve skewness affects species’ range size and abundance during climate warming. In contrast to classic interpretations, we find that climate warming adversely affects populations with cold-skewed fitness curves, positively affects populations with warm-skewed curves and has relatively little or mixed effects on populations with symmetric curves. Our results highlight the synergistic effects of fitness curve skewness, spatially heterogeneous densities and dispersal in climate change impact analyses, and that the common approach of mapping changes only in R 0 may be misleading.

Skewed temperature dependence affects range and abundance in a warming world

Population growth metrics such asR0are usually asymmetric functions of temperature, with cold-skewed curves arising when the positive effects of a temperature increase outweigh the negative effects, and warm-skewed curves arising in the opposite case. Classically, cold-skewed curves are interpreted as more beneficial to a species under climate warming, because cold-skewness implies increased population growth over a larger proportion of the species’ fundamental thermal niche than warm-skewness. However, inference based on the shape of the fitness curve alone, and without considering the synergistic effects of net reproduction, density, and dispersal may yield an incomplete understanding of climate change impacts. We formulate a moving-habitat integrodifference equation model to evaluate how fitness curve skewness affects species’ range size and abundance during climate warming. In contrast to classic interpretations, we find that climate warming adversely affects populations with cold-skewed fitness curves, positively affects populations with warm-skewed curves and has relatively little or mixed effects on populations with symmetric curves. Our results highlight the synergistic effects of fitness curve skewness, spatially heterogeneous densities, and dispersal in climate change impact analyses, and that the common approach of mapping changes only inR0may be misleading.

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Research on Cultural Industry Distributed Network Control Model Based on Genetic Algorithm

The network economy era brings new challenges to all aspects of society, and the construction of network culture industry also brings an urgent request. Due to the complexity of network culture industry, the construction and study of cultural network industry has certain challenging. In order to realize the massive network industrial data analysis, we introduce the genetic algorithm into the network control model, and put forward the distributed network control model based on genetic algorithm, which can realize the massive data analysis. In order to verify the effectiveness of the algorithm, this paper uses MATLAB genetic algorithms toolbox to debug the algorithm, and obtains the genetic algorithm fitness curve and the data transmission quantity statistics. It provides the theory reference for the construction of network culture industry.

Timing in a fluctuating environment: environmental variability and asymmetric fitness curves can lead to adaptively mismatched avian reproduction

Adaptation in dynamic environments depends on the grain, magnitude and predictability of ecological fluctuations experienced within and across generations. Phenotypic plasticity is a well-studied mechanism in this regard, yet the potentially complex effects of stochastic environmental variation on optimal mean trait values are often overlooked. Using an optimality model inspired by timing of reproduction in great tits, we show that temporal variation affects not only optimal reaction norm slope, but also elevation. With increased environmental variation and an asymmetric relationship between fitness and breeding date, optimal timing shifts away from the side of the fitness curve with the steepest decline. In a relatively constant environment, the timing of the birds is matched with the seasonal food peak, but they become adaptively mismatched in environments with temporal variation in temperature whenever the fitness curve is asymmetric. Various processes affecting the survival of offspring and parents influence this asymmetry, which collectively determine the ‘safest’ strategy, i.e. whether females should breed before, on, or after the food peak in a variable environment. As climate change might affect the (co)variance of environmental variables as well as their averages, risk aversion may influence how species should shift their seasonal timing in a warming world.

High genetic variability under the balance between symmetric mutation and fluctuating stabilizing selection

SummaryWe have studied variability maintained in a quantitative trait by the balance between symmetric mutation and direct stabilizing selection with a fluctuating optimum. Using a simulational computer model, we have found that wide fluctuations, such that the range of the optimum changes exceeds the width of the fitness curve, increase the trait variance, often by two or three orders of magnitude, over its value under constant selection. This happens because such fluctuations cause frequent allele substitutions at the loci that control the trait. At any particular moment the variance is increased mostly due to one or several loci where more than one allele is currently common. The data on fluctuating selection in nature are reviewed

RANK-ORDER SELECTION IS CAPABLE OF MAINTAINING ALL GENETIC POLYMORPHISMS

ABSTRACT The fitness of organisms may be due chiefly to a fitness curve imposed on their ranking in the population with respect to heterozygosity. If this is so, then the number of polymorphisms that can be retained at a particular selective equilibrium increases as the square of the population size. All of the genetic variation that we currently observe and infer to exist can probably be maintained by selection in a population of about 105 individuals. Selection acting in this way is so strong that these polymorphisms can be expected to behave very differently from neutral ones.