The genetic structure of a tribal population, the Yanomama indians. VII. Anthropometric differences among Yanomama villages

Anthropometric data on 12 variables in 19 villages of the Yanomama Indians demonstrate significant heterogeneity in physique among villages of this tribe. Mahalanobis' distances (D²) calculated from the data lead to the tentative conclusion of a general correspondence between anthropometric and geographic distances separating villages. The mean stature of the Yanomama is smaller than that of most other South American tribes which have been measured, and the Yanomama are genetically distinct from the other small Indians as shown by genetic distances based on allele frequencies for a variety of genetic markers. Since some subjects were measured more than once by the same and by different observers, it was possible to calculate approximate estimates of variance within and between observers. Univariate analysis indicates that face height and nose height are especially susceptible to systematic differences in technique between observers. The variances obtained in this field study compare favorably with those of some classical laboratory studies described in the literature. It was found that measurement error nevertheless probably makes a substantial contribution to anthropometric distance between villages. The median error variance as a fraction of that of Herskovits ('30) is 0.62 for the seven measurements in common with this study. The median value of the error variance for the 12 variables in this study is between 16% and 17% of the total variance.     

Use and misuse of the reduced major axis for line‐fitting

Many investigators use the reduced major axis (RMA) instead of ordinary least squares (OLS) to define a line of best fit for a bivariate relationship when the variable represented on the X‐axis is measured with error. OLS frequently is described as requiring the assumption that X is measured without error while RMA incorporates an assumption that there is error in X. Although an RMA fit actually involves a very specific pattern of error variance, investigators have prioritized the presence versus the absence of error rather than the pattern of error in selecting between the two methods. Another difference between RMA and OLS is that RMA is symmetric, meaning that a single line defines the bivariate relationship, regardless of which variable is X and which is Y, while OLS is asymmetric, so that the slope and resulting interpretation of the data are changed when the variables assigned to X and Y are reversed. The concept of error is reviewed and expanded from previous discussions, and it is argued that the symmetry‐asymmetry issue should be the criterion by which investigators choose between RMA and OLS. This is a biological question about the relationship between variables. It is determined by the investigator, not dictated by the pattern of error in the data. If X is measured with error but OLS should be used because the biological question is asymmetric, there are several methods available for adjusting the OLS slope to reflect the bias due to error. RMA is being used in many analyses for which OLS would be more appropriate. Am J Phys Anthropol, 2009. © 2009 Wiley‐Liss, Inc.     

Role of environmental and spatial processes in structuring anuran communities across a tropical rain forest

This study aimed to evaluate if anuran species distributions in riparian and non‐riparian areas are influenced by environmental factors (i.e. niche) and/or by spatial factors (i.e. dispersal). The environmental variables analysed were altitude, distance from the stream and leaf litter depth. Spatial factors were represented by the eigenvectors extracted from geographical coordinates by eigenfunction analysis. The study was conducted in 24 km² of terra‐firme forest in Central Amazonia, Manaus – Amazonas, Brazil. Between November 2008 and May 2009, three samples were taken from 41 plots, 21 plots being placed at non‐riparian areas and another 20 placed in riparian areas. We submitted the assemblage dataset to a partial redundancy analysis to evaluate the contributions of environmental and spatial variables (selected with a forward selection procedure). In addition, we tested if communities differ from riparian and non‐riparian areas using a db‐MANOVA. Species richness and species composition differed between riparian and non‐riparian plots. Some species were restricted to riparian areas. Altitude was the only significant variable (P = 0.005) explaining 21% of the total variance. When analysing the data from all plots using the partial redundancy analysis, 27% of the variance was explained by spatial and environmental variables. The environmental variables explained exclusively 4% of the variance in assemblage composition, and 13% was explained by environmental variables that were also structured in space (i.e. the shared fraction), while 10% was explained exclusively by spatial variables. In conclusion, our results showed differences between the assemblages of riparian and non‐riparian areas which can be explained by the distribution of anuran species along environmental gradients altitude and distance to streams, with little evidence of dispersal limitation.     

Using airborne laser scanning to model potential abundance and assemblages of forest passerines

Modelling and forecasting of the distribution and abundance of organisms using environmental variables is a major focus of applied ecological research. High-resolution airborne laser scanning is a recently developed remote-sensing method that provides data that can be used as surrogates for the vertical structure of the vegetation. These data can be used for modelling the occurrence and abundance of species or species assemblages. Until now, few studies evaluated the potential of these data for use in such models, or compared the suitability of data obtained by airborne systems with data gained by alternative methods. To fill part of this gap, we used forest passerine bird species to evaluate airborne laser scanning data for statistical modelling of potential bird abundances and composition of assemblages. Birds were counted in a mixed montane forest, on 223 1-ha plots along four transects. In the same period, these areas were scanned using Light Detection And Ranging (LiDAR) to characterise canopy structure. Additionally, we used visual interpretations of aerial photographs and field measurements on the same plots to derive habitat variables for comparison. We found clear correlations between the LiDAR variables and the other two variable sets using canonical correlation analysis. With a few exceptions, predictive power of the LiDAR data set for modelling abundances of single species, with up to 40% explained variance, was superior to that of the other two data sets. Models agreed with existing ecological knowledge for these species. For modelling of species composition with redundancy analysis, LiDAR was also superior to the other two data sets with more than 20% unique contribution to the explained variance. Our results clearly showed that LiDAR provides valuable data for describing and modelling single species as well as assemblages of forest organisms.     

The effects of lack of normality and homogeneity of variance on analysis of variance and clonal heritability in <Emphasis Type="Italic">in vitro</Emphasis> clonal propagation traits

We studied the fulfilment of assumptions of normality and homogeneity of error variance, prior to application of analysis of variance (ANOVA), for in vitro clonal propagation data. We assessed the use of data transformations and mean values for situations when the original data did not satisfy the required assumptions. The purpose of the study was to establish whether the use of original, transformed or mean values had any effect on F values, significance levels and clonal heritability values. The F values, significance levels and values of clonal heritability obtained showed analysis of variance to be reliable, despite deviations with respect to normality and homogeneity of variance and despite the fact that samples sizes were unequal. Original data may be used for ANOVA applied to measured variables such as number of shoots per explant, length of tallest shoot, number of 1-cm segments per explant and also derived variables such as the multiplication coefficient. Frequency data can be used for analysis of variance of categorical-type variables such as apical necrosis and percentage of responsive explant. For shoot colour variables, the distributions were very skewed and the variances were very different, but even though the sample sizes were not identical in all cases, lack of homogeneity of variance did not significantly affect F values, significance levels or clonal heritability values, and thus analysis of variance may be applied to the original data. The use of original and frequency data makes interpretation of the results easier than when transformed data are used and also allows us to calculate variance components more accurately than when using mean values, which do not provide as much information. Clonal heritability values from transformed data and mean values showed differences of less than one hundredth compared with those from original data. Box–Cox-transformed data showed slightly lower heritability values than those corresponding to original data, whereas clonal heritability values from both mean data and angular-transformed data were slightly higher than those obtained using original data. In clonal variability studies with single growth medium, nutritional conditions that encouraged highly unequal growth or characteristics among clones gave rise to data that were unlikely to satisfy the conditions of normality or homogeneity of variance.     

Explore the influence of soil quality on crop yield using statistically-derived pedological indicators

The aim of this paper is (1) to find and discuss the best multivariate statistical method in exploring the soil productivity function in an East-Hungarian region; (2) to evaluate and interpret the edaphic indicators and Hungarian soil quality index (HSQI); and (3) to identify the main determinant factors and indicators in this region. Soil pH, carbonate content, soluble and exchangeable Na⁺, clay, humus, available phosphorus and potassium content were analyzed. Topographical position and HSQI were evaluated as well. Yield data (maize, winter wheat, sunflower) of 10 years were standardized using calculated relative yield of each crop. Having simple indicators, stepwise linear regressions for mean relative yield were inadequate for choice uncorrelated indicators which have significant influence on yields. The variables were analyzed using principal component analysis (PCA) with Varimax rotation. According to the eigenvalues greater than 1, the PCA yielded three principal components (PCs) explaining a total of 89.471% of the variance for the entire data set. These factors could be well interpreted as derived complex indicators. Having the three PCs, a stepwise linear regression process (PCR) was conducted with dependent variables mean relative yield. The explained variance for mean relative yield was as high as adjusted R² = 0.771 (p < 0.001). The three PC factors together explained the mean relative yield better than the simple indicators and the HSQI. So, the variables can effectively explain the yield and the variability together with other variables as linear combinations. Consequently, PCR is a successful method to reveal the site specific relationship between soil properties and yields and to revision the HSQI at local level.     

Bird and mammal species composition in distinct geographic regions and their relationships with environmental factors across multiple spatial scales

Global patters of species distributions and their underlying mechanisms are a major question in ecology, and the need for multi‐scale analyses has been recognized. Previous studies recognized climate, topography, habitat heterogeneity and disturbance as important variables affecting such patterns. Here we report on analyses of species composition – environment relationships among different taxonomic groups in two continents, and the components of such relationships, in the contiguous USA and Australia. We used partial Canonical Correspondence Analysis of occurrence records of mammals and breeding birds from the Global Biodiversity Information Facility, to quantify relationships between species composition and environmental variables in remote geographic regions at multiple spatial scales, with extents ranging from 10⁵ to 10⁷ km² and sampling grids from 10 to 10,000 km². We evaluated the concept that two elements contribute to the impact of environmental variables on composition: the strength of species' affinity to an environmental variable, and the amount of variance in the variable. To disentangle these two elements, we analyzed correlations between resulting trends and the amount of variance contained in different environmental variables to isolate the mechanisms behind the observed relationships. We found that climate and land use‐land cover are responsible for most explained variance in species composition, regardless of scale, taxonomic group and geographic region. However, the amount of variance in species composition attributed to land use / land cover (LULC) was closely related to the amount of intrinsic variability in LULC in the USA, but not in Australia, while the effect of climate on species composition was negatively correlated to the variability found in the climatic variables. The low variance in climate, compared to LULC, suggests that species in both taxonomic groups have strong affinity to climate, thus it has a strong effect on species distribution and community composition, while the opposite is true for LULC.     

Improved Horvitz–Thompson Estimation of Model Parameters from Two-phase Stratified Samples: Applications in Epidemiology

The case-cohort study involves two-phase samplings: simple random sampling from an infinite superpopulation at phase one and stratified random sampling from a finite cohort at phase two. Standard analyses of case-cohort data involve solution of inverse probability weighted (IPW) estimating equations, with weights determined by the known phase two sampling fractions. The variance of parameter estimates in (semi)parametric models, including the Cox model, is the sum of two terms: (i) the model-based variance of the usual estimates that would be calculated if full data were available for the entire cohort; and (ii) the design-based variance from IPW estimation of the unknown cohort total of the efficient influence function (IF) contributions. This second variance component may be reduced by adjusting the sampling weights, either by calibration to known cohort totals of auxiliary variables correlated with the IF contributions or by their estimation using these same auxiliary variables. Both adjustment methods are implemented in the R survey package. We derive the limit laws of coefficients estimated using adjusted weights. The asymptotic results suggest practical methods for construction of auxiliary variables that are evaluated by simulation of case-cohort samples from the National Wilms Tumor Study and by log-linear modeling of case-cohort data from the Atherosclerosis Risk in Communities Study. Although not semiparametric efficient, estimators based on adjusted weights may come close to achieving full efficiency within the class of augmented IPW estimators.     

RELATIONSHIPS BETWEEN LITTORAL DIATOMS AND THEIR CHEMICAL ENVIRONMENT IN NORTHEASTERN GERMAN LAKES AND RIVERS1

We explored statistical relationships between the composition of littoral diatom assemblages and 21 chemical and physical environmental variables in 69 lakes and 15 river sites in the lowland of northeastern Germany. Canonical correspondence analysis with single treatment and with forward selection of environmental variables was used to detect 11 important ecological variables (dissolved inorganic carbon [DIC], Na ⁺ , total phosphorus [TP], dissolved organic carbon [DOC], total nitrogen [TN], pH, oxygen saturation, dissolved iron, SO₄^{2 ?} , NH₄ ⁺ , soluble reactive silicium) and maximum water depth or Ca^{2 +} or soluble reactive phosphorus that most independently explain major proportions of the total diatom variance among the habitats. Monte Carlo permutation tests showed that each contributed a significant additional proportion (P < 0.05) of the variance in species composition. Together, these 11 most important environmental variables explained 34% of the total variance in species composition among the sites and captured 73% of the explained variance from the full 21 parameters model. Weighted‐averaging regression and calibration of 304 indicator taxa with tolerance down‐weighting and classic deshrinking was used to develop transfer functions between littoral diatoms and DIC, pH, TP, TN, and Cl ^? . The DOC:TP ratio was introduced and a weighted‐averaging model was developed to infer allochthonous DOC effects in freshwater ecosystems. This diatom‐DOC/TP model was significant (P < 0.001) and explained 7.6% of the total diatom variance among the sites, surpassing the inferential power of the diatom‐TP‐transfer function (7.3% explained variance). The root‐mean‐square errors of prediction of the models were estimated by jack‐knifing and were comparable with published data sets from surface sediment diatom samples. The data set of littoral diatoms and environmental variables allows use of the diatom‐environmental transfer functions in biomonitoring and paleolimnological approaches across a broad array of natural water resources (such as floodplains, flushed lakes, estuaries, shallow lakes) in the central European lowland ecoregion.     

Shift work and sex are variants across Karolinska Sleep Questionnaire and the application of linking analysis as a step forward using Item Response Theory

The frequency of sleep disturbances is considerably higher in the night and shift workers and in females than in day workers and males, respectively. However, a subjective sleep scale must be invariant across these groups, independently of the level of their members on the scale. This study is aimed to test the invariance of the Karolinska Sleep Questionnaire’s (KSQ) items by shift work and sex. We used the data from a census that covered more than 90% (N = 1648) of the nurses from the main institute of the largest public hospital complex of Brazil. Firstly, we intend to find the KSQ’s dimensionality using factorial analysis and Item Response Theory (IRT) performed by Graded Response Model. Differential Item Functioning (DIF) was the technique used to test the invariance of each KSQ’s dimensions. In case of variance detection, we applied the linking analysis. Intending to test the KSQ’s consistency with external variables, we assessed correlations between KSQ’s dimensions with health variables, i.e., self-reported health status and musculoskeletal pain. We have found one scale and two subscales from one general and another bidimensional factor structure of the KSQ, respectively. In these dimensions, the KSQ’s items fitted well to the IRT and we have identified DIF by shift work. However, we have found DIF by sex just in one item on the general factor. Linking analysis showed as a possible step forward in the variance issue placing on to the same scale the shift work groups in the items with DIF. All correlations between KSQ’s dimensions with health variables were significant. Our findings allow us to argue that KQS’s items were variant by shift work and sex in a nursing staff census from the largest public hospital complex of Brazil, but we can go on using linking analysis. This could be used as an evidence for the construct validity should go beyond the traditional dimensionality assessment. The dimensionalities of KSQ fit well for other population but individuals living in Scandinavian countries.     

Origin and Consequences of the Relationship between Protein Mean and Variance

Cell-to-cell variance in protein levels (noise) is a ubiquitous phenomenon that can increase fitness by generating phenotypic differences within clonal populations of cells. An important challenge is to identify the specific molecular events that control noise. This task is complicated by the strong dependence of a protein''s cell-to-cell variance on its mean expression level through a power-law like relationship (σ²∝μ^1.69). Here, we dissect the nature of this relationship using a stochastic model parameterized with experimentally measured values. This framework naturally recapitulates the power-law like relationship (σ²∝μ^1.6) and accurately predicts protein variance across the yeast proteome (r² = 0.935). Using this model we identified two distinct mechanisms by which protein variance can be increased. Variables that affect promoter activation, such as nucleosome positioning, increase protein variance by changing the exponent of the power-law relationship. In contrast, variables that affect processes downstream of promoter activation, such as mRNA and protein synthesis, increase protein variance in a mean-dependent manner following the power-law. We verified our findings experimentally using an inducible gene expression system in yeast. We conclude that the power-law-like relationship between noise and protein mean is due to the kinetics of promoter activation. Our results provide a framework for understanding how molecular processes shape stochastic variation across the genome.     

Weather Variability Associated with Aedes (Stegomyia) aegypti (Dengue Vector) Oviposition Dynamics in Northwestern Argentina

This study aims to develop a forecasting model by assessing the weather variability associated with seasonal fluctuation of Aedes aegypti oviposition dynamic at a city level in Orán, in northwestern Argentina. Oviposition dynamics were assessed by weekly monitoring of 90 ovitraps in the urban area during 2005-2007. Correlations were performed between the number of eggs collected weekly and weather variables (rainfall, photoperiod, vapor pressure of water, temperature, and relative humidity) with and without time lags (1 to 6 weeks). A stepwise multiple linear regression analysis was performed with the set of meteorological variables from the first year of study with the variables in the time lags that best correlated with the oviposition. Model validation was conducted using the data from the second year of study (October 2006- 2007). Minimum temperature and rainfall were the most important variables. No eggs were found at temperatures below 10°C. The most significant time lags were 3 weeks for minimum temperature and rains, 3 weeks for water vapor pressure, and 6 weeks for maximum temperature. Aedes aegypti could be expected in Orán three weeks after rains with adequate min temperatures. The best-fit forecasting model for the combined meteorological variables explained 70 % of the variance (adj. R²). The correlation between Ae. aegypti oviposition observed and estimated by the forecasting model resulted in r_s = 0.80 (P < 0.05). The forecasting model developed would allow prediction of increases and decreases in the Ae. aegypti oviposition activity based on meteorological data for Orán city and, according to the meteorological variables, vector activity can be predicted three or four weeks in advance.     

Variance Component Estimation for Mixed Model Analysis of cDNA Microarray Data

Microarrays provide a valuable tool for the quantification of gene expression. Usually, however, there is a limited number of replicates leading to unsatisfying variance estimates in a gene‐wise mixed model analysis. As thousands of genes are available, it is desirable to combine information across genes. When more than two tissue types or treatments are to be compared it might be advisable to consider the array effect as random. Then information between arrays may be recovered, which can increase accuracy in estimation. We propose a method of variance component estimation across genes for a linear mixed model with two random effects. The method may be extended to models with more than two random effects. We assume that the variance components follow a log‐normal distribution. Assuming that the sums of squares from the gene‐wise analysis, given the true variance components, follow a scaled χ²‐distribution, we adopt an empirical Bayes approach. The variance components are estimated by the expectation of their posterior distribution. The new method is evaluated in a simulation study. Differentially expressed genes are more likely to be detected by tests based on these variance estimates than by tests based on gene‐wise variance estimates. This effect is most visible in studies with small array numbers. Analyzing a real data set on maize endosperm the method is shown to work well. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Relative influences of current and historical factors on mammal and bird diversity patterns in deglaciated North America

Aim To investigate the relative contributions of current vs. historical factors in explaining broad‐scale diversity gradients using a combination of contemporary factors and a quantitative estimate of the temporal accessibility of areas for recolonization created by glacial retreat following the most recent Ice Age. Location The part of the Nearctic region of North America that was covered by ice sheets during the glacial maximum 20 000 BP. Methods We used range maps to estimate the species richness of mammals and terrestrial birds in 48 400 km² cells. Current conditions in each cell were quantified using seven climatic and topographical variables. Historical conditions were estimated using the number of years before present when an area became exposed as the ice sheets retreated during the post‐Pleistocene climate warming. We attempted to tease apart contemporary and historical effects using multiple regression, partial regression and spatial autocorrelation analysis. Results A measure of current energy inputs, potential evapotranspiration, explained 76–82% of the variance in species richness, but time since deglaciation explained an additional 8–13% of the variance, primarily due to effects operating at large spatial scales. Because of spatial covariation between the historical climates influencing the melting of the ice sheet and current climates, it was not possible to partition their effects fully, but of the independent effects that could be identified, current climate explained two to seven times more variance in richness patterns than age. Main Conclusions Factors acting in the present appear to have the strongest influence on the diversity gradient, but an historical signal persisting at least 13 000 years is still detectable. This has implications for modelling changes in diversity patterns in response to future global warming.     

Canonical discriminant analysis applied to broiler chicken performance

The mechanisms involved in the control of growth in chickens are too complex to be explained only under univariate analysis because all related traits are biologically correlated. Therefore, we evaluated broiler chicken performance under a multivariate approach, using the canonical discriminant analysis. A total of 1920 chicks from eight treatments, defined as the combination of four broiler chicken strains (Arbor Acres, AgRoss 308, Cobb 500 and RX) from both sexes, were housed in 48 pens. Average feed intake, average live weight, feed conversion and carcass, breast and leg weights were obtained for days 1 to 42. Canonical discriminant analysis was implemented by SAS^R CANDISC procedure and differences between treatments were obtained by the F-test (P < 0.05) over the squared Mahalanobis’ distances. Multivariate performance from all treatments could be easily visualised because one graph was obtained from two first canonical variables, which explained 96.49% of total variation, using a SAS^R CONELIP macro. A clear distinction between sexes was found, where males were better than females. Also between strains, Arbor Acres, AgRoss 308 and Cobb 500 (commercial) were better than RX (experimental). Evaluation of broiler chicken performance was facilitated by the fact that the six original traits were reduced to only two canonical variables. Average live weight and carcass weight (first canonical variable) were the most important traits to discriminate treatments. The contrast between average feed intake and average live weight plus feed conversion (second canonical variable) were used to classify them. We suggest analysing performance data sets using canonical discriminant analysis.     

Community ecological modelling as an alternative to physiographic classifications for marine conservation planning

Accurate mapping of marine species and habitats is an important yet challenging component of establishing networks of representative marine protected areas. Due to limited biological data, marine classifications based on abiotic data are often used as surrogates to represent biological patterns. We tested the surrogacy of an existing physiographic marine classification using non-metric multidimensional scaling and permutational analysis of variance to determine whether species composition was significantly different among physiographic units. We also present an alternative ecological classification that incorporates biological and environmental data in a community modeling approach. We use data on 174 species of demersal fish and benthic invertebrates to identify mesoscale biological assemblages in a 100,000 km² study area in the northeast Pacific Ocean. We identified assemblages using cluster analysis then used a random forest model with 12 environmental variables to delineate mesoscale ecological units. Our community modelling approach resulted in five geographically coherent ecological units that were best explained by changes in depth, temperature and salinity. Our model showed high predictive performance (AUC = 0.93) and the resulting ecological units represent more distinct species assemblages than those delineated by physiographic variables alone. A strength of our analysis is the ability to map model uncertainty to identify transition zones at unit boundaries. The output of this study provides a biotic driven classification that can be used to better achieve representativity in the MPA planning process.     

EEG-activity patterns in visual information processing: characteristics, variance-analytical deliminiation, discriminance-analytical determination of undispensable variables and scale level

1. In order to determine the variable distributions of 5 activation dependent EEG activity patterns occurring during visual information processing, mean values and standard deviations of the percental quantities of the frequencies 4, 5, ..., 13 Hz, 14 to 20 Hz and 21 to 30 Hz, as well as the mean amplitudes in the frequency bands 3.5 ... 7.4 Hz, 7.5 ... 13.4 Hz and 13.5 to 30 Hz were determined on corresponding to 10 s samples. It could be demonstrated by regression analysis that an interval scale level can be assumed already on the basis of cethe percental quantities in the three last mentioned frequency bands. 2. On the basis of 18 relevant variables, all the adjacent activity patterns could be separated from each other by means of univariate variance analysis at pairwise mean value comparison by at least two variables. 3. After stepwise reduction of dispensable variables in the framework of a linear discriminance analysis an optimal set of variables was determined, comprising the percental quantities of the frequencies 4, 5, 6, 10, 12 Hz, and 14 to 20 Hz, as well as the mean value of the amplitudes in the frequency band 3.5 to 7.4 Hz. In 4 our of 5 elementary discriminance functions, the mean values calculated for each pattern were significantly distinguishable from each other (analysis of variance, Newman-Keuls test). 4. By linear regression analysis it could be shown that the classification system of the EEG activity patterns at visual information processing can be mapped on an interval scale after the reduction of variables, too. Finally, data about the reliability of the scoring procedure are presented.     

Interobserver error in human skin colorimetry

Twenty light-skinned adults were measured at the upper inner arm site using two commonly used reflectance spectrophotometers. Each subject was measured by each of three investigators to assess the influence of interobserver error on the reflectance readings. A repeated measures design analysis of variance showed no significant variance component due to observers.     

A comparative study of hourly and daily relationships between selected meteorological parameters and airborne fungal spore composition

Air sampling was conducted in Szczecin (Poland) throughout April–September 2013. The final data set included 177 daily and 4248 hourly samples. The total of 21 types of spores, which occurred in a number >10 in the season, were taken into account. The following meteorological parameters were analyzed: air temperature, relative humidity, precipitation and wind speed. Effects of individual weather parameters on hourly and daily concentrations of different fungal spore types were examined using Spearman’s rank association test, whereas effects of complex of meteorological factors on hourly and daily compositions of spore were assessed using detrended correspondence analysis (DCA) and redundancy analysis (RDA). Airborne fungal spore distribution patterns in relation to meteorological variables were determined by RDA, after DCA results detected a linear structure of the spore data. The RDA results obtained indicated that all the applied variables accounted for 20 and 22% of the total variance in the hourly and daily spore data, respectively. The results of stepwise forward selection of variables revealed all included hourly and daily meteorological variables were statistically significant. The largest amount of the total variance in the spore composition was explained by the air temperature in both cases (16%). Multivariate ordination did not show large differences between the hourly and daily relationships (with exception of wind speed impact), while the differences between simple hourly and daily correlations were more clear. Correlations between daily values of variables were in most cases higher than between hourly values of variables.     

QUANTITATIVE GENETICS OF BRYOZOAN PHENOTYPIC EVOLUTION. I. RATE TESTS FOR RANDOM CHANGE VERSUS SELECTION IN DIFFERENTIATION OF LIVING SPECIES

The possible roles of random genetic change and natural selection in bryozoan speciation were analyzed using quantitative genetic methods on breeding data for traits of skeletal morphology in two closely related species of the cheilostome Stylopoma. The hypothesis that morphologic differences between the species are caused entirely by mutation and genetic drift could not be rejected for reasonable rates of mutation maintained for as few as 10³ to 10⁴ generations. Divergence times this short or shorter are consistent with the abrupt appearances of many invertebrate species in the fossil record, commonly followed by millions of years of morphologic stasis. To produce these differences over 10³ generations or fewer, directional selection acting alone would require unrealistically high levels of minimum selective mortality throughout divergence. Thus, selection is unnecessary to explain the divergence of these species, except as a means of accelerating the effects of random genetic change on shorter time scales (directional selection), or decelerating them over longer ones (stabilizing selection). These results are consistent with a variety of models of phenotypic evolution involving random shifts between multiple adaptive peaks. Similar results were obtained by substituting trait heritabilities and genetic covariances reconstructed by partitioning within- and among-colony phenotypic variance in place of the values based on breeding data. Quantitative genetic analysis of speciation in fossil bryozoan lineages is thus justified.