Geographical gradients in the population dynamics of North American prairie ducks

1. Geographic gradients in population dynamics may occur because of spatial variation in resources that affect the deterministic components of the dynamics (i.e. carrying capacity, the specific growth rate at small densities or the strength of density regulation) or because of spatial variation in the effects of environmental stochasticity. To evaluate these, we used a hierarchical Bayesian approach to estimate parameters characterizing deterministic components and stochastic influences on population dynamics of eight species of ducks (mallard, northern pintail, blue-winged teal, gadwall, northern shoveler, American wigeon, canvasback and redhead (Anas platyrhynchos, A. acuta, A. discors, A. strepera, A. clypeata, A. americana, Aythya valisineria and Ay. americana, respectively) breeding in the North American prairies, and then tested whether these parameters varied latitudinally. 2. We also examined the influence of temporal variation in the availability of wetlands, spring temperature and winter precipitation on population dynamics to determine whether geographical gradients in population dynamics were related to large-scale variation in environmental effects. Population variability, as measured by the variance of the population fluctuations around the carrying capacity K, decreased with latitude for all species except canvasback. This decrease in population variability was caused by a combination of latitudinal gradients in the strength of density dependence, carrying capacity and process variance, for which details varied by species. 3. The effects of environmental covariates on population dynamics also varied latitudinally, particularly for mallard, northern pintail and northern shoveler. However, the proportion of the process variance explained by environmental covariates, with the exception of mallard, tended to be small. 4. Thus, geographical gradients in population dynamics of prairie ducks resulted from latitudinal gradients in both deterministic and stochastic components, and were likely influenced by spatial differences in the distribution of wetland types and shapes, agricultural practices and dispersal processes. 5. These results suggest that future management of these species could be improved by implementing harvest models that account explicitly for spatial variation in density effects and environmental stochasticity on population abundance.     

Genetic quality of individuals impacts population dynamics

Ample evidence exists that an increase in the inbreeding level of a population reduces the value of fitness components such as fecundity and survival. It does not follow, however, that these decreases in the components of fitness impact population dynamics in a way that increases extinction risk, because virtually all species produce far more offspring than can actually survive. We analyzed the effects of the genetic quality (mean fitness) of individuals on the population growth rate of seven natural populations in each of two species of wolf spider in the genus Rabidosa , statistically controlling for environmental factors. We show that populations of different sizes, and different inbreeding levels, differ in population dynamics for both species. Differences in population growth rates are especially pronounced during stressful environmental conditions (low food availability) and the stressful environment affects smaller populations (<500 individuals) disproportionately. Thus, even in an invertebrate with an extremely high potential growth rate and strong density-dependent mortality rates, genetic factors contribute directly to population dynamics and, therefore, to extinction risk. This is only the second study to demonstrate an impact of the genetic quality of individual genotypes on population dynamics in a wild population and the first to document strong inbreeding–environment interactions for fitness among populations. Endangered species typically exist at sizes of a few hundred individuals and human activities degrade habitats making them innately more stressful (e.g. global climate change). Therefore, the interaction between genetic factors and environmental stress has important implications for efforts aimed at conserving the Earth's biodiversity.     

The extended Moran effect and large-scale synchronous fluctuations in the size of great tit and blue tit populations

1. Synchronous fluctuations of geographically separated populations are in general explained by the Moran effect, i.e. a common influence on the local population dynamics of environmental variables that are correlated in space. Empirical support for such a Moran effect has been difficult to provide, mainly due to problems separating out effects of local population dynamics, demographic stochasticity and dispersal that also influence the spatial scaling of population processes. Here we generalize the Moran effect by decomposing the spatial autocorrelation function for fluctuations in the size of great tit Parus major and blue tit Cyanistes caeruleus populations into components due to spatial correlations in the environmental noise, local differences in the strength of density regulation and the effects of demographic stochasticity. 2. Differences between localities in the strength of density dependence and nonlinearity in the density regulation had a small effect on population synchrony, whereas demographic stochasticity reduced the effects of the spatial correlation in environmental noise on the spatial correlations in population size by 21.7% and 23.3% in the great tit and blue tit, respectively. 3. Different environmental variables, such as beech mast and climate, induce a common environmental forcing on the dynamics of central European great and blue tit populations. This generates synchronous fluctuations in the size of populations located several hundred kilometres apart. 4. Although these environmental variables were autocorrelated over large areas, their contribution to the spatial synchrony in the population fluctuations differed, dependent on the spatial scaling of their effects on the local population dynamics. We also demonstrate that this effect can lead to the paradoxical result that a common environmental variable can induce spatial desynchronization of the population fluctuations. 5. This demonstrates that a proper understanding of the ecological consequences of environmental changes, especially those that occur simultaneously over large areas, will require information about the spatial scaling of their effects on local population dynamics.     

Effects of climate on population fluctuations of ibex

Predicting the effects of the expected changes in climate on the dynamics of populations require that critical periods for climate‐induced changes in population size are identified. Based on time series analyses of 26 Swiss ibex (Capra ibex) populations, we show that variation in winter climate affected the annual changes in population size of most of the populations after accounting for the effects of density dependence and demographic stochasticity. In addition, precipitation during early summer also influenced the population fluctuations. This suggests that the major influences of climate on ibex population dynamics operated either through loss of individuals during winter or early summer, or through an effect on fecundity. However, spatial covariation in these climate variables was not able to synchronize the population fluctuations of ibex over larger distances, probably due to large spatial heterogeneity in the effects of single climate variables on different populations. Such spatial variation in the influence of the same climate variable on the local population dynamics suggests that predictions of influences of climate change need to account for local differences in population dynamical responses to climatic conditions.     

Maternal effects mediated by maternal age: from life histories to population dynamics

1. Maternal effects describe how mothers influence offspring life histories. In many taxa, maternal effects arise by differential resource allocation to young, often identified by variation in propagule size, and which affects individual traits and population dynamics. 2. Using a laboratory model system, the soil mite Sancassania berlesei, we show that, controlling for egg size, older mothers lay eggs that hatch later, develop more slowly, and mature at larger body sizes. 3. Such differences in life histories lead to marked population dynamical effects lasting for multiple generations, as evidenced by an experiment initiated with similarly sized eggs that came from young or old mothers. Differences in maturation from the initial cohort led to differences in population structure and life history that propagated the initial differences over time. 4. Maternal-age effects, which are not related to gross provisioning of the egg and are therefore phenotypically cryptic, can have profound implications for population dynamics, especially if environmental variation can affect the age structure of the adult population.     

Linear analysis solves two puzzles in population dynamics: the route to extinction and extinction in coloured environments

In this paper, we give simple explanations to two unsolved puzzles that have emerged in recent theoretical studies in population dynamics. First, the tendency of some model populations to go extinct from high population densities, and second, the positive effect of autocorrelated environments on extinction risks for some model populations. Both phenomena are given general explanations by simple, linear, sto-chastic models. We emphasize the predictive and explanatory power of such models.     

Modeling density dependence and climatic disturbances in caribou: a case study from the Bathurst Island complex, Canadian High Arctic

Peary caribou Rangifer tarandus pearyi is the northernmost subspecies of Rangifer in North America and endemic to the Canadian High Arctic. Because of severe population declines following years of unfavorable winter weather with ice coating on the ground or thicker snow cover, it is believed that density-independent disturbance events are the primary driver for Peary caribou population dynamics. However, it is unclear to what extent density dependence may affect population dynamics of this species. Here, we test for different levels of density dependence in a stochastic, single-stage population model, based on available empirical information for the Bathurst Island complex (BIC) population in the Canadian High Arctic. We compare predicted densities with observed densities during 1961–2001 under various assumptions of the strength of density dependence. On the basis of our model, we found that scenarios with no or very low density dependence led to population densities far above observed densities. For average observed disturbance regimes, a carrying capacity of 0.1 caribou km⁻² generated an average caribou density similar to that estimated for the BIC population over the past four decades. With our model we also tested the potential effects of climate change-related increases in the probability and severity of disturbance years, that is unusually poor winter conditions. On the basis of our simulation results, we found that, in particular, potential increases in disturbance severity (as opposed to disturbance frequency) may pose a considerable threat to the persistence of this species.     

Predicting fluctuations of reintroduced ibex populations: the importance of density dependence, environmental stochasticity and uncertain population estimates

1. Development of population projections requires estimates of observation error, parameters characterizing expected dynamics such as the specific population growth rate and the form of density regulation, the influence of stochastic factors on population dynamics, and quantification of the uncertainty in the parameter estimates. 2. Here we construct a Population Prediction Interval (PPI) based on Bayesian state space modelling of future population growth of 28 reintroduced ibex populations in Switzerland that have been censused for up to 68 years. Our aim is to examine whether the interpopulation variation in the precision of the population projections is related to differences in the parameters characterizing the expected dynamics, in the effects of environmental stochasticity, in the magnitude of uncertainty in the population parameters, or in the observation error. 3. The error in the population censuses was small. The median coefficient of variation in the estimates across populations was 5.1%. 4. Significant density regulation was present in 53.6% of the populations, but was in general weak. 5. The width of the PPI calculated for a period of 5 years showed large variation among populations, and was explained by differences in the impact of environmental stochasticity on population dynamics. 6. In spite of the high accuracy in population estimates, the uncertainty in the parameter estimates was still large. This uncertainty affected the precision in the population predictions, but it decreased with increasing length of study period, mainly due to higher precision in the estimates of the environmental variance in the longer time-series. 7. These analyses reveal that predictions of future population fluctuations of weakly density-regulated populations such as the ibex often become uncertain. Credible population predictions require that this uncertainty is properly quantified.     

越冬地东方白鹳繁殖生物学的初步研究

近年来,陆续在长江中下游越冬地发现东方白鹳(Ciconia boyciana)繁殖个体。为了了解该种在当地的繁殖对策和种群现状,2004-2006年在安庆市望江县武昌湖地区(116°51.15′-116°49.47′E,30°19.53′-30°19.79′N)对东方白鹳的繁殖生物学进行了研究。东方白鹳在当地开始营巢时间不一致,最早为2月5日,而受干扰的繁殖个体则延至5月6日。观察到的巢全在高压线塔上,巢高34.6±0.8m(n=11),巢间距908.8±1039.4m(n=6)。产卵期最早开始于2月11日,最晚6月21日,窝卵数4.2±0.4(4-5)枚(n=6)。育雏期71.0±16.1d(n=3),日育雏5.1±2.6(n=38),雏鸟离巢时间最早6月14日,最晚9月20日。2004和2005年东方白鹳在该地区共营巢8窝,产卵25枚,孵出雏鸟9只,出飞7只。繁殖失败5巢,其中,人工干扰造成4巢失败,高压电击毁1巢。繁殖不同时期,亲鸟的觅食、休息、警戒、取材、翻卵、育雏、交配、在巢、视野外行为时间分配差异显著,而飞翔、行走、理羽、击喙、整巢和其它行为差异不显著。雏鸟在发育的不同时期,觅食、飞翔、休息、整巢、在巢、行走、视野外行为时间分配差异显著,理羽、警戒、击喙和其它行为差异不显著     

Analysis of changes of insect-plant ratio-improvement of key-factor analysis for evaluating bottom-up effects in insect population dynamics

A revised key-factor analysis was presented for analyzing the temporal changes in the ratio of insect absolute number to plant resource. Ten data sets for 5 insect species were then analyzed. In this key-factor analysis, the key factor is defined as the factor contributing highly to between-year variation inR _r , the log rate of the inter-year change of the insect-plant ratio. The yearly change of plant resource was handled as a separate factor, expressed byr _pl , log ratio of plant resource in yearn to plant resource in yearn+1. The following was revealed: 1) In 7 of the 10 data sets examined,r _pl influenced variations ofR _r ; in particular in 3 casesr _pl was the main key factor. 2) Generation-to-generation fluctuations of absolute insect densities showed density dependence in 4 cases, while those of insect-plant ratios, in 8 cases. 3) The Royama model or a linear model, explained well the relationship between log insect-plant ratio (X _r ) andR _r and the relationship betweenX _r and log yearly change rate of absolute insect density (R _abs ). However, in the 7 cases in whichr _pl was a critical factor for variations ofR _r , with, increase ofX _r ,R _r showed a steeper, decrease around the equilibrium point (the point for whichR _r is 0) thanR _abs . This occurred becauser _pl tended to be negatively correlated withX _r . Consequently, in two casesX _r fluctuated cyclicly or chaotically although without the changes in plant resource, fluctuations ofX _r would be damped oscillations approaching equilibrium.