Temporal variation of competition and facilitation in mixed species forests in Central Europe

Facilitation, reduced competition or increased competition can arise in mixed stands and become essential to the performance of these stands when compared to pure stands. Facilitation and over‐yielding are widely held to prevail on poor sites, whereas neutral interactions or competition, leading to under‐yielding of mixed versus pure stands, can occur on fertile sites. While previous studies have focused on the spatial variation of mixing effects, we examine the temporal variation of facilitation and competition and its effect on growth. The study is based on tree ring measurement on cores from increment borings from 559 trees of Norway spruce (Picea abies [L.] Karst.), European beech (Fagus sylvatica [L.]) and sessile oak (Quercus petraea (Matt.) Liebl.) in southern Germany, half of which were in pure stands and half in adjacent mixed stands. Mean basal area growth indices were calculated from tree ring measurements for pure and mixed stands for every species and site. The temporal variation, with positive correlations between species‐specific growth indices during periods of low growth and neutral or negative correlations during periods of high growth, is more distinct in mixed than in neighbouring pure stands. We provide evidence that years with low growth trigger over‐yielding of trees in mixed as opposed to pure stands, while years with high growth lead to under‐yielding. We discuss the relevance of the results in terms of advancing our understanding and modelling of mixed stands, extension of the stress gradient hypothesis, and the performance of mixed versus pure stands in the face of climate change.     

Factors contributing to the accumulation of reproductive isolation: A mixed model approach

The analysis of large datasets describing reproductive isolation between species has been extremely influential in the study of speciation. However, the statistical methods currently used for these data limit the ability to make direct inferences about the factors predicting the evolution of reproductive isolation. As a result, our understanding of iconic patterns and rules of speciation rely on indirect analyses that have clear statistical limitations. Phylogenetic mixed models are commonly used in ecology and evolution, but have not been applied to studies of reproductive isolation. Here I describe a flexible framework using phylogenetic mixed models to analyze data collected at different evolutionary scales, to test both categorical and continuous predictor variables, and to test the effect of multiple predictors on rates and patterns of reproductive isolation simultaneously. I demonstrate the utility of this framework by re‐analyzing four classic datasets, from both animals and plants, and evaluating several hypotheses that could not be tested in the original studies: In the Drosophila and Bufonidae datasets, I found support for more rapid accumulation of reproductive isolation in sympatric species pairs compared to allopatric species pairs. Using Silene and Nolana, I found no evidence supporting the hypothesis that floral differentiation elevates postzygotic reproductive isolation. The faster accumulation of postzygotic isolation in sympatry is likely the result of species coexistence determined by the level of postzygotic isolation between species. In addition, floral trait divergence does not appear to translate into pleiotropic effects on postzygotic reproductive isolation. Overall, these methods can allow researchers to test new hypotheses using a single statistical method, while remedying the statistical limitations of several previous methods.     

Tolerance for inequity may increase with social closeness in chimpanzees

Economic decision-making depends on our social environment. Humans tend to respond differently to inequity in close relationships, yet we know little about the potential for such variation in other species. We examine responses to inequity in several groups of chimpanzees (Pan troglodytes) in a paradigm similar to that used previously in capuchin monkeys (Cebus apella). We demonstrate that, like capuchin monkeys, chimpanzees show a response to inequity of rewards that is based upon the partner receiving the reward rather than the presence of the reward alone. However, we also found a great amount of variation between groups tested, indicating that chimpanzees, like people, respond to inequity in a variable manner, which we speculate could be caused by such variables as group size, the social closeness of the group (as reflected in length of time that the group has been together) and group-specific traditions.     

Historical and recent genetic bottlenecks in European grayling, Thymallus thymallus

Sharp declines in population size, known as genetic bottlenecks, increase the level of inbreeding and reduce genetic diversity threatening population sustainability in both short- and long-term. We evaluated the presence, severity and approximate time of bottlenecks in 34 European grayling (Thymallus thymallus) populations covering the majority of the species distribution using microsatellite markers. We identified footprints of population decline in all grayling populations using the M ratio test. In contrast to earlier simulation studies assuming isolated populations, forward simulations allowing low levels of migration demonstrated that bottleneck footprints measured using the M ratio can persist within small populations much longer (up to thousands of generations) than previously anticipated. Using a coalescence approach, the beginning of population reduction was dated back to 1,000–10,000 years ago which suggests that the extremely low M ratio in European grayling is most likely caused by the last glaciation and subsequent post-glacial recolonization processes. In contrast to the M ratio, two alternative methods for bottleneck detection identified more recent bottlenecks in six populations and thus, from a conservation perspective, these populations warrant future monitoring. Based on a single time-point analysis using approximate Bayesian computation methodology, all grayling populations exhibited very small effective population sizes with the majority of N _e estimates below 50. Taken together, our results demonstrate the predominate role of genetic drift in European grayling populations in the short term but also emphasize the importance of gene flow counteracting the effects of genetic drift and loss of variation over longer evolutionary timescales.     

The Impact of Outliers on Net-Benefit Regression Model in Cost-Effectiveness Analysis

Ordinary least square (OLS) in regression has been widely used to analyze patient-level data in cost-effectiveness analysis (CEA). However, the estimates, inference and decision making in the economic evaluation based on OLS estimation may be biased by the presence of outliers. Instead, robust estimation can remain unaffected and provide result which is resistant to outliers. The objective of this study is to explore the impact of outliers on net-benefit regression (NBR) in CEA using OLS and to propose a potential solution by using robust estimations, i.e. Huber M-estimation, Hampel M-estimation, Tukey''s bisquare M-estimation, MM-estimation and least trimming square estimation. Simulations under different outlier-generating scenarios and an empirical example were used to obtain the regression estimates of NBR by OLS and five robust estimations. Empirical size and empirical power of both OLS and robust estimations were then compared in the context of hypothesis testing.Simulations showed that the five robust approaches compared with OLS estimation led to lower empirical sizes and achieved higher empirical powers in testing cost-effectiveness. Using real example of antiplatelet therapy, the estimated incremental net-benefit by OLS estimation was lower than those by robust approaches because of outliers in cost data. Robust estimations demonstrated higher probability of cost-effectiveness compared to OLS estimation. The presence of outliers can bias the results of NBR and its interpretations. It is recommended that the use of robust estimation in NBR can be an appropriate method to avoid such biased decision making.     

Difficulty in Discerning Drivers of Lake Ecosystem Metabolism with High-Frequency Data

High-frequency measurements are increasingly available and used to model ecosystem processes. This growing capability provides the opportunity to resolve key drivers of ecosystem processes at a variety of scales. We use a unique series of high-frequency measures of potential predictors to analyze daily variation in rates of gross primary production (GPP), respiration (R), and net ecosystem production (NEP = GPP − R) for two north temperate lakes. Wind speed, temperature, light, precipitation, mixed layer depth, water column stability, chlorophyll a, chromophoric dissolved organic matter (CDOM), and zooplankton biomass were measured at daily or higher-frequency intervals over two summer seasons. We hypothesized that light, chlorophyll a, and zooplankton biomass would be strongly related to variability in GPP. We also hypothesized that chlorophyll a, CDOM, and temperature would be most strongly related to variability in R, whereas NEP would be related to variation in chlorophyll a and CDOM. Consistent with our hypotheses, chlorophyll a was among the most important drivers of GPP, R, and NEP in these systems. However, multiple regression models did not necessarily include the other variables we hypothesized as most important. Despite the large number of potential predictor variables, substantial variance remained unexplained and models were inconsistent between years and between lakes. Drivers of GPP, R, and NEP were difficult to resolve at daily time scales where strong seasonal dynamics were absent. More complex models with greater integration of physical processes are needed to better identify the underlying drivers of short-term variability of ecosystem processes in lakes and other systems.     

Transformation of steroids by mixed cultures

Some results of our studios on transformation of steroids by mixed culture fermentation are presented in this paper. Arthrobacter simplex was paired in turn with each of the following: Streptomyces roseochromogenes, Curvularia lunata, Absidia coerulea, and Aspergillus ochraceus. The steroid substrates examined for multiple transformation were 16α-hydroxy-cortexolone, 16α-hydroxy-cortexolone 16,17-acetonide, 9α-fluorohydrocortisone, 9α-fluorohydrocortisone 21-acetate, and 9α-fluorohydrocortisone 21-hemisuccinate. The effects of media, steroid substrate, and microbial interaction in a mixed culture on the induction and repression of steroid transforming enzymes were unique to each case studied. The reaction mechanism of the multiple steroid transformation was also found to vary from one mixed culture system to another. Two different reaction mechanisms were observed, namely, consecutive and parallel. In the former, one of the two enzymatic reactions always preceded the other, while in the latter, two different enzyme reactions occurred simultaneously, thereby giving rise to two different intermediates. Multiple transformation of steroids by a single step mixed culture fermentation has potential economic advantages.     

The effect of social and ecological factors on the time budget of wild vervet monkeys

Animals often have a limited time to perform different fitness‐enhancing activities, such as the trade‐off between socializing versus foraging in group‐living species. Many previous studies have focused on how ecological and social factors influence activity budget at the individual or group level in various species. However, few primate studies have focused on multiple study groups living within a similar habitat. Here, we analyse group, season and sex effects on the individual activity budget of wild vervet monkeys (Chlorocebus pygerythrus) living in four groups with overlapping home ranges. Generally, our findings support previous studies on primates. Our results indicate that intragroup competition may force larger groups to spend more time feeding and less time resting. We also found that seasonal variation, and therefore food availability, has a strong influence on the monkeys’ activity budget. Females, which are the philopatric sex in vervet monkeys, spent more time socializing while, in general, males spent more time resting. However, we did not find any difference on the time spent socializing between groups. Since there is evidence that not only time constraints and habitat quality but also group size influence individual behaviours and ultimately group living, we advise that future studies should focus on multiple groups of the same species living in the same habitat in order to better understand how all these variables are interlinked.     

基于树冠重叠面积的天然混交林林木竞争指数

构建基于树冠重叠面积和林木混交关系的竞争指数,探究天然混交林林分及主要树种竞争的表达方法,并以甘肃省小陇山林区锐齿槲栎天然混交林为研究对象,采用胸径、树高、冠幅、距离等信息的2期调查数据,对新构建的基于树冠重叠面积的天然混交林林木竞争(CIM)指数进行验证,并选取Spearman系数,对CIM竞争指数和传统竞争指数进行相关性分析。结果表明,采用树冠投影重叠和考虑树高关系确定竞争木的方法能有效避免竞争木多选或漏选; CIM1、CIM2、CIM3能够表达出混交林竞争压力变化,表现出竞争压力越大林木蓄积生长量越小的趋势,将树种混交因子引入竞争指数能够提高蓄积生长量与竞争指数的相关性,更加客观的描述林分中竞争状况。竞争指数与蓄积生长量相关性由大到小排序为CIM2>CIM3>CIM1>Bella竞争指数(CIO)>简单竞争指数(CI)>CIO2>CIO1,表明竞争指数 CIM2对混交林竞争表达更具参考意义。林分中主要树种锐齿槲栎由于相邻木中同树种相邻木增加,导致竞争压力增大。天然混交林中树种组成复杂多样,林木分布不均匀,在竞争指数中引入树种混交因子能够较为真实的反映林木竞争状况。     

The effect of forest management operations on population performance of Vaccinium myrtillus on a landscape-scale

We have studied the variation in average performance of Vaccinium myrtillus L. among local populations within an intensively managed boreal forest landscape in southeast Norway. Our aim was to identify whether forest disturbance history was an important factor controlling the variation in average plant performance. We used three measures of forest maturity (maturity class, tree biomass and number of trees) as variables reflecting disturbance history. These variables were used in our analysis together with measured environmental conditions (soil moisture, pH, nitrogen, and elevation) a priori known to affect plant performance. To quantify plant performance, we used multiple measures of the local V. myrtillus populations, including percentage cover, average ramet age, height, diameter, biomass, growth, and sexual reproduction (number of fruits and number of mature seeds per fruit). Most of the explained variation in our performance variables could be related to our environmental variables, but disturbance history was also an important determinant of the performance of V. myrtillus. All performance variables were higher in younger, less mature stands, except ramet age, which was highest in mature stands. The increase in performance after clear cutting indicates that this period is of high importance for the population dynamics and persistence of V. myrtillus in intensively managed boreal forest landscapes.     

K‐Sample comparisons using propensity analysis

In this paper, we investigate K‐group comparisons on survival endpoints for observational studies. In clinical databases for observational studies, treatment for patients are chosen with probabilities varying depending on their baseline characteristics. This often results in noncomparable treatment groups because of imbalance in baseline characteristics of patients among treatment groups. In order to overcome this issue, we conduct propensity analysis and match the subjects with similar propensity scores across treatment groups or compare weighted group means (or weighted survival curves for censored outcome variables) using the inverse probability weighting (IPW). To this end, multinomial logistic regression has been a popular propensity analysis method to estimate the weights. We propose to use decision tree method as an alternative propensity analysis due to its simplicity and robustness. We also propose IPW rank statistics, called Dunnett‐type test and ANOVA‐type test, to compare 3 or more treatment groups on survival endpoints. Using simulations, we evaluate the finite sample performance of the weighted rank statistics combined with these propensity analysis methods. We demonstrate these methods with a real data example. The IPW method also allows us for unbiased estimation of population parameters of each treatment group. In this paper, we limit our discussions to survival outcomes, but all the methods can be easily modified for any type of outcomes, such as binary or continuous variables.     

A quantitative genetic analysis of leaf beetle larval performance on two natural hosts: including a mixed diet

Published quantitative genetic studies of larval performance on different host plants have always compared performance on one host species or genotype vs. performance on another species or genotype. The fact that some insects may feed on more than one plant species during their development has been neglected. We executed a quantitative genetic analysis of performance with larvae of the leaf beetle Oreinaelongata, raised on each of two sympatric host plants or on a mixture of them. Growth rate was higher for larvae feeding on Adenostylesalliariae, intermediate on the mixed diet and lowest on Cirsium spinosissimum. Development time was shortest on A. alliariae, intermediate on mixed diet and longest on C. spinosissimum. Survival was higher on the mixed diet than on both pure hosts. Genetic variation was present for all three performance traits but a genotype by host interaction was found only for growth rate. However, the reaction norms for growth rate are unlikely to evolve towards an optimal shape because of a lack of heritability of growth rate in each single environment. We found no negative genetic correlations for performance traits among hosts. Therefore, our results do not support a hypothesis predicting the existence of between‐host trade‐offs in performance when both hosts are sympatric with an insect population. We conclude that the evolution of host specialized genotypes is unlikely in the study population.     

Advantages of nonlinear mixed models for fitting avian growth curves

Our understanding of avian growth rates can benefit from the use of two statistical approaches that explicitly model the sources of intraspecific variation. First, random effects can evaluate whether there are consistent differences between individuals and groups of siblings within a population, and also account for any lack of statistical independence among data points. Second, nonlinear fixed‐effect functions can be extended to test specific biological hypotheses of interest, such as for differences between groups or populations. We illustrate the advantages of these methods by using nonlinear mixed models to study variation in the growth trajectories of nestling orange‐crowned warblers Oreothylpis celata. Specifically, we quantify the sources of variation within populations, analyze the effects of asynchronous hatching, and test for a difference in the growth rates of populations in Alaska and California, which are at the northern and southern limits of the species’ breeding distribution. We found that growth rates did not consistently vary between nests and individuals within populations and were not affected by asynchronous hatching, but were higher in Alaska than in California. Our extensions of traditional methods allowed us to accurately quantify this difference between populations, which is consistent with life history theory but has rarely been demonstrated in previous comparisons of intraspecific passerine populations. The methods we present can be applied to any taxonomic group and adjusted to fit any nonlinear function, and we provide code and implementation advice to facilitate the use of this analytical framework in future studies.     

Analysis of Group Randomized Trials with Multiple Binary Endpoints and Small Number of Groups

The group randomized trial (GRT) is a common study design to assess the effect of an intervention program aimed at health promotion or disease prevention. In GRTs, groups rather than individuals are randomized into intervention or control arms. Then, responses are measured on individuals within those groups. A number of analytical problems beset GRT designs. The major problem emerges from the likely positive intraclass correlation among observations of individuals within a group. This paper provides an overview of the analytical method for GRT data and applies this method to a randomized cancer prevention trial, where multiple binary primary endpoints were obtained. We develop an index of extra variability to investigate group-specific effects on response. The purpose of the index is to understand the influence of individual groups on evaluating the intervention effect, especially, when a GRT study involves a small number of groups. The multiple endpoints from the GRT design are analyzed using a generalized linear mixed model and the stepdown Bonferroni method of Holm.     

The blue tit's song is an inconsistent signal of male condition

Sexually selected traits are often hypothesized to signal malecondition or quality, though empirical evidence is mixed, anda number of alternative models of sexual selection do not requirecondition dependence. We examined the relationship between variousmeasures of condition and dawn songs in male blue tits (Cyanistescaeruleus). We detected 6 largely independent measures of variation(i.e., variables) in these songs. None of these variables wererelated to blue tits' ultraviolet–blue plumage, a demonstratedsexual signal, thus failing to support the redundant signalhypothesis. We found some evidence that the song variables wemeasured signaled male quality. There were correlations betweenbody size and certain song traits, though neither male age normale recapture in the subsequent breeding season (apparent localsurvival) predicted any song variation. We combined our resultswith published effect sizes comparing blue tit song with malequality variables using meta-analysis and found that a few songmeasures are correlates of male quality, though as in our fielddata, neither male age nor survival appeared related to song.Our relatively large sample sizes (>60), combined with ourmeta-analytical integration of 89 effect sizes, make the resultsregarding the signaling value of our measured components ofblue tit song robust. These results demonstrate that 1) onlycertain aspects of signal variation may be condition dependentand 2) even when components of a sexual signal appear correlatedwith condition in some studies, these signal components maybe unrelated or inconsistently related to a variety of conditionindices.     

Analysis of regional species distribution models based on radio-telemetry datasets from multiple small-scale studies

The identification of core habitat areas and resulting prediction maps are vital tools for land managers. Often, agencies have large datasets from multiple studies over time that could be combined for a more informed and complete picture of a species. Colorado Parks and Wildlife has a large database for greater sage-grouse (Centrocercus urophasianus) including 11 radio-telemetry studies completed over 12 years (1997–2008) across northwestern Colorado. We divided the 49,470-km² study area into 1-km² grids with the number of sage-grouse locations in each grid cell that contained at least 1 location counted as the response variable. We used a generalized linear mixed model (GLMM) using land cover variables as fixed effects and individual birds and populations as random effects to predict greater sage-grouse location counts during breeding, summer, and winter seasons. The mixed effects model enabled us to model correlations that may exist in grouped data (e.g., correlations among individuals and populations). We found only individual groupings accounted for variation in the summer and breeding seasons, but not the winter season. The breeding and summer seasonal models predicted sage-grouse presence in the currently delineated populations for Colorado, but we found little evidence supporting a winter season model. According to our models, about 50% of the study area in Colorado is considered highly or moderately suitable habitat in both the breeding and summer seasons. As oil and gas development and other landscape changes occur in this portion of Colorado, knowledge of where management actions can be accomplished or possible restoration can occur becomes more critical. These seasonal models provide data-driven, distribution maps that managers and biologists can use for identification and exploration when investigating greater sage-grouse issues across the Colorado range. Using historic data for future decisions on species management while accounting for issues found from combining datasets allows land managers the flexibility to use all information available. © 2013 The Wildlife Society.     

Geographical variation in morphology and its environmental correlates in a widespread North American lizard,Anolis carolinensis (Squamata: Dactyloidae)

The green anole, Anolis carolinensis, has long been an important model organism for studies of physiology and behaviour, and recently became the first reptile to have its genome sequenced. With a large and environmentally heterogeneous distribution, especially in relation to well‐studied Antillean relatives, A. carolinensis is also emerging as an important organism for novel studies of geographical differentiation and adaptation. In the present study, we quantify the degree of morphological variation in this species and test for environmental correlates of this variation. We also examine adherence to Bergmann's and Allen's rule, two eco‐geographical principles that have been well studied over large species ranges. We sampled from 14 populations across the distribution of the species in North America and measured 28 distinct morphological traits. We also collected a suite of environmental variables for each site, including those related to temperature, precipitation, and vegetation. Ultimately, we found a large degree of geographical variation in morphology, with head traits contributing the most to differences among populations. Morphological variation was correlated with variation in temperature, precipitation, and latitude across sites. We found no support for reverse Bergmann's rule typical of squamates, although we did find a trend of reverse Allen's rule. Ultimately, the present study provides a novel look at A. carolinensis and establishes it as a strong candidate for further studies of variation and adaptation over a large range.     

Generalized linear mixed models for mapping multiple quantitative trait loci

Many biological traits are discretely distributed in phenotype but continuously distributed in genetics because they are controlled by multiple genes and environmental variants. Due to the quantitative nature of the genetic background, these multiple genes are called quantitative trait loci (QTL). When the QTL effects are treated as random, they can be estimated in a single generalized linear mixed model (GLMM), even if the number of QTL may be larger than the sample size. The GLMM in its original form cannot be applied to QTL mapping for discrete traits if there are missing genotypes. We examined two alternative missing genotype-handling methods: the expectation method and the overdispersion method. Simulation studies show that the two methods are efficient for multiple QTL mapping (MQM) under the GLMM framework. The overdispersion method showed slight advantages over the expectation method in terms of smaller mean-squared errors of the estimated QTL effects. The two methods of GLMM were applied to MQM for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets.     

Sample Size Determination for Designing a Strata-Matched Case-Control Study to Detect Multiple Risk Factors

Investigations of sample size for planning case-control studies have usually been limited to detecting a single factor. In this paper, we investigate sample size for multiple risk factors in strata-matched case-control studies. We construct an omnibus statistic for testing M different risk factors based on the jointly sufficient statistics of parameters associated with the risk factors. The statistic is non-iterative, and it reduces to the Cochran statistic when M = 1. The asymptotic power function of the test is a non-central chi-square with M degrees of freedom and the sample size required for a specific power can be obtained by the inverse relationship. We find that the equal sample allocation is optimum. A Monte Carlo experiment demonstrates that an approximate formula for calculating sample size is satisfactory in typical epidemiologic studies. An approximate sample size obtained using Bonferroni's method for multiple comparisons is much larger than that obtained using the omnibus test. Approximate sample size formulas investigated in this paper using the omnibus test, as well as the individual tests, can be useful in designing case-control studies for detecting multiple risk factors.     

Cellular aggregation in Chlamydomonas (Chlorophyceae) is chimaeric and depends on traits like cell size and motility

How the variation in phenotypic traits like cell size and motility impacts predator-induced cellular aggregation is not known. Furthermore the genetic composition of cell groups in mixed populations of Chlamydomonas has not been investigated. An examination of these two questions will not only enhance our understanding of Chlamydomonas ecology, but also shed light on the primordial steps before integrated multicellular groups were established. Group living comes with viability and reproductive costs and it is not known how these are shared if groups are genetically heterogeneous. We observed that the natural predator Peranema trichophorum (Euglenoidea) induced clumping in Chlamydomonas. When co-cultured with P. trichophorum cells protected themselves by forming facultative groups (reverting back to a unicellular lifestyle once predators were removed). The dynamics of group formation in different Chlamydomonas species and strains correlated with cell size and swimming speed. Small or less motile strains aggregated more readily than large, fast-swimming ones. Interestingly, Chlamydomonas groups were both intra-species and inter-species chimaeric. This suggests that the predator-induced group formation in Chlamydomonas involved cells coming together rather than staying together and during aggregation cells showed little or no discrimination between self and non-self. These data demonstrate that the dynamics of cell aggregation, in unicellular volvocines at least, depends on phenotypic traits like cell size and motility and high genetic relatedness is not mandatory at this initial stage. These findings further our understanding of aggregation in mixed Chlamydomonas populations and have implications for understanding the very first steps on the road to simple multicellularity.