Generation time and the maximum growth rate for populations with age-specific fecundities and unknown juvenile survival

In age-classified population models where all parameters are known, the generation time and growth rate are calculated in a straightforward manner. For many populations, some parameters, such as juvenile survival, are difficult to estimate accurately. In a simplified population model where fecundity and survival are constant from the onset of breeding, it is known that generation time may be calculated given only adult survival, age at first reproduction, and the population growth rate. However, the assumption of constant fecundity from the onset of breeding does not hold for many populations. An extended population model allows calculation of generation time with the additional knowledge of the ratio of age-specific fecundities compared to a maximum fecundity rate. When these relative fecundities are unknown, an ad hoc adjustment to the simplified model performs well.When the study population is in an ideal environment, the optimal generation time and maximum growth rate are linked, and both may be approximated knowing only adult survival, age at first reproduction, and the relative fecundities. The maximum growth rate has important conservation implications, and calculating it correctly is therefore important. Improper use of the simplified population model to calculate the maximum growth rate, combined with a simple decision rule, leads to an average overharvest of 36%, and >60% for three of six bird species studied, compared to the full population model. By comparison, using the approximation from the extended or adjusted models results in average overharvests of only 8% (extended model) and 5% (adjusted model), and <50% for all six species (either model).     

Updating coarse-scale species distribution models using small fine-scale samples

Large, fine-grained samples are ideal for predictive species distribution models used for management purposes, but such datasets are not available for most species and conducting such surveys is costly. We attempted to overcome this obstacle by updating previously available coarse-grained logistic regression models with small fine-grained samples using a recalibration approach. Recalibration involves re-estimation of the intercept or slope of the linear predictor and may improve calibration (level of agreement between predicted and actual probabilities). If reliable estimates of occurrence likelihood are required (e.g., for species selection in ecological restoration) calibration should be preferred to other model performance measures. This updating approach is not expected to improve discrimination (the ability of the model to rank sites according to species suitability), because the rank order of predictions is not altered. We tested different updating methods and sample sizes with tree distribution data from Spain. Updated models were compared to models fitted using only fine-grained data (refitted models). Updated models performed reasonably well at fine scales and outperformed refitted models with small samples (10-100 occurrences). If a coarse-grained model is available (or could be easily developed) and fine-grained predictions are to be generated from a limited sample size, updating previous models may be a more accurate option than fitting a new model. Our results encourage further studies on model updating in other situations where species distribution models are used under different conditions from their training (e.g., different time periods, different regions).     

Choosing classes for size projection matrix models

Projection matrix models are intensely used in ecology to model the dynamics of structured populations. When dealing with size-structured populations, there is no satisfactory algorithm to partition size into discrete classes. We show that the Vandermeer-Moloney algorithm for choosing classes is inconsistent with the Usher model, and systematically selects the finest classes. Considering that the matrix model is a discrete approximation of a continuous model, we define an approximation error as the sum of a distribution error (the difference between the discrete distribution and its continuous counterpart), and a sample error. The optimal partition of size into classes is the one that minimizes the approximation error. This method for choosing classes also shows that the choice of the class width cannot be disconnected from the choice of the time step. When applied to 520 trees of Dicorynia guianensis in French Guiana, this algorithm identified 8 classes of 11.4 cm in width, which is in agreement with the empirical choice of foresters.     

Roads,Interrupted Dispersal,and Genetic Diversity in Timber Rattlesnakes

Abstract: Anthropogenic habitat modification often creates barriers to animal movement, transforming formerly contiguous habitat into a patchwork of habitat islands with low connectivity. Roadways are a feature of most landscapes that can act as barriers or filters to migration among local populations. Even small and recently constructed roads can have a significant impact on population genetic structure of some species, but not others. We developed a research approach that combines fine‐scale molecular genetics with behavioral and ecological data to understand the impacts of roads on population structure and connectivity. We used microsatellite markers to characterize genetic variation within and among populations of timber rattlesnakes (Crotalus horridus) occupying communal hibernacula (dens) in regions bisected by roadways. We examined the impact of roads on seasonal migration, genetic diversity, and gene flow among populations. Snakes in hibernacula isolated by roads had significantly lower genetic diversity and higher genetic differentiation than snakes in hibernacula in contiguous habitat. Genetic‐assignment analyses revealed that interruption to seasonal migration was the mechanism underlying these patterns. Our results underscore the sizeable impact of roads on this species, despite their relatively recent construction at our study sites (7 to 10 generations of rattlesnakes), the utility of population genetics for studies of road ecology, and the need for mitigating effects of roads.     

关中-天水经济区人口-经济-生态协调发展研究

在经济迅速发展的背景下,我国人口、经济和生态的矛盾日益突出。选择关中-天水经济区为研究对象,基于变异系数的协调函数模型对其人口-经济-生态协调度进行了测算。结果表明：1）1999—2008年,关中-天水经济区各地市人口-经济-生态协调度整体呈现上升的趋势,总体由失调状态转向勉强协调,其中天水市呈直线上升趋势,咸阳、宝鸡、铜川3市的协调度在波动中有所增加,西安市变化幅度不大;2）人口、经济、生态3个子系统的变化都会影响到整个系统的变化,西安市生态子系统协调度的下降,制约了整个系统的协调发展;3）协调度在空间上表现为关中-天水经济区中部地市的协调度较高,渭南和天水市的协调度始终处于该地区的较低水平。     

水安全智能识别模型的建立及应用

通过人口迁移算法优化投影寻踪模型,提出了一种新的水安全智能识别模型。与遗传算法优化的投影模型相对比,人口迁移算法的自身优势有效地避免了网络早熟现象及寻找全局最优解的困扰。从水安全的评价结果来看,用人口迁移算法优化投影寻踪是可行的,并显示出优越性。人口迁移算法为求解投影寻踪模型的非线性约束提供了新的优化方法,并为水安全评价工作提供了新的智能识别模型。     

森林资源价值及人力成本对森林火灾扑救方式的影响

介绍了我国现行的一些林火扑救的基本方式,应用经济分析的方法,根据我国未来经济政治变化趋势,分析森林资源价值及人力成本变化对我国林火扑救方式的影响,并对我国未来的森林火灾扑救方式选择进行定性的讨论和预测。     

河流水环境有机污染物的自组织预测模型及应用

自组织预测模型是前苏联学者伊万年科提出的一种非线性建模预测方法,它能有效地解决复杂非线性系统的数据处理和建模问题。该文应用它进行水环境有机污染物预测,以实际监测数据为基础,建立了一个河流有机物浓度预测的自组织模型,其检验样本的预测误差在５％以内      

Temperature niche composition change inside and outside protected areas under climate warming

Conservation of biodiversity relies heavily on protected areas but their role and effectiveness under a warming climate is still debated. We estimated the climate-driven changes in the temperature niche compositions of bird communities inside and outside protected areas in southern Canada. We hypothesized that communities inside protected areas include a higher proportion of cold-dwelling species than communities outside protected areas. We also hypothesized that communities shift to warm-dwelling species more slowly inside protected areas than outside. To study community changes, we used large-scale and long-term (1997–2019) data from the Breeding Bird Survey of Canada. To describe the temperature niche compositions of bird communities, we calculated the community temperature index (CTI) annually for each community inside and outside protected areas. Generally, warm-dwelling species dominated communities with high CTI values. We modeled temporal changes in CTI as a function of protection status with linear mixed-effect models. We also determined which species contributed most to the temporal changes in CTI with a jackknife approach. As anticipated, CTI was lower inside protected areas than outside. However, contrary to our expectation, CTI increased faster over time inside than outside protected areas and warm-dwelling species contributed most to CTI change inside protected areas. These results highlight the ubiquitous impacts of climate warming. Currently, protected areas can aid cold-dwelling species by providing habitat, but as the climate warms, the communities’ temperature compositions inside protected areas quickly begin to resemble those outside protected areas, suggesting that protected areas delay the impacts of climate warming on cold-dwelling species.