Empirical models of the conductance of leaves in apple orchards

Abstract. Seasonal data on leaf conductance (g_l) for three different apple cultivars grown in four separate orchards with different aged trees was studied between 1979 and 1985. A number of empirical models for predicting leaf conductance from environmental measurements were compared using this data and a general method for adapting such models for the prediction of different data sets is proposed. Although stepwise multiple regression identified relative humidity or vapour pressure as important variables, it frequently did not identify the optimal set of independent variables, which often did not include either of these. There was no advantage in regressing g_l against principal components of the environment, rather than against the raw environmental variables. A simple model involving air vapour pressure deficit, air temperature and a hyperbolic function of irradiance was found to explain between 32 and 62% of the variance in g_l for the different data sets. Parameters fitted for one data set led to the effective prediction of g_l in other years or plots. The model fit could generally be improved significantly by including soil moisture deficit among the independent variables.     

Ecological assessment of an intermittent Mediterranean river using community structure and function: evaluating the role of different organism groups

1. Reliable lotic ecological monitoring requires knowledge of river typology, environmental factors, the effect of stressors known here as ‘pressures’ and appropriate indicators of anthropogenically induced change. We sampled benthic macroinvertebrate, fish, bird and macrophyte communities along an intermittent Mediterranean river and analysed community structure (relative abundance) and function (metrics) relative to environmental and pressure gradients in order to identify suitable indicator group(s) for future monitoring and mitigation programmes. 2. Principal components analysis revealed that scale‐dependent longitudinal differences in valley form separated narrower higher lying sites and tributaries with good quality habitats from more open degraded sites lower down the river continuum on a small floodplain and large scale pressures describing changes in land use related to agriculture with associated physical bankside and channel impacts. 3. Forward selection of variables in redundancy analysis (RDA) showed that reach scale environmental variables were selected more frequently than pressure variables for each organism group. Altitude and pH were highly redundant within and between groups, indicating essentially longitudinal structural and functional distribution patterns. Redundancy was far lower between selected pressure variables, but single or no pressure variables were retained for some organism groups indicating poor association of functional data, in particular, with the identified pressures. All RDA results indicated a longitudinal pH gradient, highlighting the combined effect of multiple environmental and pressure based mechanisms on organism groups. 4. Large, mobile organisms such as fish and birds provided a reliable link between organism structure and function, environmental factors and physical disturbance of the channel, bankside and wider river corridor. Benthic macroinvertebrate and macrophyte structural data revealed distribution patterns in relation to water velocity, a key parameter for developing appropriate compensation measures. 5. Results clearly show the importance of assessing patterns of both functional and structural change across multiple organism groups in order to identify typologically appropriate links with complex environmental and pressure gradients and develop and implement appropriate monitoring systems.     

Path analysis of standing crop and environmental variables in the field layer of two Belgian riverine forests

Seasonal variation in standing crop, litter, pH and relative light intensity was measured in the field layer of two riverine forests near Bruges, Belgium. At each site the living fraction in 10 randomly selected 0.25 m² quadrats was separated into various components and graphed in relation to the environmental variables. The effect of the environmental variables on standing crop was assessed using path analysis, based on the standardized partial regression coefficients obtained in separate stepwise multiple regressions. In order to linearize the relation between the standing crop and the environmental variables, and to meet other statistical assumptions, a log-transformation was applied to the standing crop data. On an annual basis, 44% of the total variation in log-standing crop was explained by the variables litter, relative light intensity and pH, while seasonally the coefficient of determination ranged from 0.23 (November) to 0.72 (April). A general path diagram, based on all data, indicated predominant direct effect of litter (path coefficient: ?0.55) and pH (0.26). Relative light intensity had only an indirect effect through its impact on litter and pH. Considerable seasonal changes occurred in the importance of litter, relative light intensity and pH.     

Relative influence of temperature and electron donor and electron acceptor concentrations on bacterial sulfate reduction in saltmarsh sediment

The relative importance of three environmental variables known to influence the rate of bacterial sulfate reduction was examined using sediment from a saltmarsh pan. The variables investigated were temperature, electron donor concentration, and electron acceptor concentration. Their relative influence on the rate of bacterial sulfate reduction was examined with multiple replicate sediment samples in which the variables were experimentally adjusted. Sulfate reduction rates were measured with³⁵SO ₄ ^2– .The relative importance of each variable to sulfate reduction rate was assessed with multiple regression analysis by calculating the standardized partial regression coefficients, and the results were compared with the ranges of the three variables encountered in the natural sediment. Temperature proved to have the greatest influence, followed by electron donor and electron acceptor concentrations, in that order. The sulfate concentration was shown to have little influence on sulfate reduction rate at seawater concentrations of sulfate, but its effect increased if sulfate concentrations were diminished compared to those of seawater.     

Different roles of environmental variables and spatial factors in structuring stream benthic diatom and macroinvertebrate in Yangtze River Delta,China

Exploring the relative contribution of spatial factors and environmental variables in shaping communities is of widespread interest in biodiversity conservation and environmental management. Stream communities are hierarchically regulated by environmental variables over multiple spatial scales, and the reaction of different organisms to stressors are still equivocal. We sampled both macroinvertebrates and diatom at 80 sites and additional 10 sites for macroinvertebrates, field measured and laboratory analyzed environmental variables, from the tributaries of Qiantang River, Yangtze River Delta China in 2011. We used PCNM (principal coordinates of neighbor matrices) to generate spatial predictors. We applied redundancy analysis and variation partitioning procedures to identify key spatial and environmental factors, and to quantify their relative roles in shaping diatom and macroinvertebrate assemblages. Our results demonstrated the role of spatial and environmental variables differed in shaping benthic diatom and macroinvertebrate. Diatom assemblage variations were better explained by spatial factors, however macroinvertebrate assemblage variations were better explained by environmental variables. In terms of environmental variables, catchment scale variables (e.g., land use estimators, land use diversity) played the primary role in determining the patterns of both diatom and macroinvertebrate assemblages, whereas the influence of reach-scale variables (e.g., pH, substrates, and nutrients) appeared less. However, nutrients were the stronger factors influencing benthic diatom, whereas physical habitat (e.g., substrates) played more important role than water chemistry in structuring macroinvertebrates. Our results provided more evidence to the incorporation of spatial factors interpreting spatial patterns of stream organisms, and highlighted the useful of multiple organisms and environmental variables at different spatial scales in diagnosing mechanism of stream degradation and in building a sound stream conditions monitoring program for Yangtze River Delta.     

The relationship between species richness and ecosystem variability is shaped by the mechanism of coexistence

Theory relating species richness to ecosystem variability typically ignores the potential for environmental variability to promote species coexistence. Failure to account for fluctuation‐dependent coexistence may explain deviations from the expected negative diversity–ecosystem variability relationship, and limits our ability to predict the consequences of increases in environmental variability. We use a consumer‐resource model to explore how coexistence via the temporal storage effect and relative nonlinearity affects ecosystem variability. We show that a positive, rather than negative, diversity–ecosystem variability relationship is possible when ecosystem function is sampled across a natural gradient in environmental variability and diversity. We also show how fluctuation‐dependent coexistence can buffer ecosystem functioning against increasing environmental variability by promoting species richness and portfolio effects. Our work provides a general explanation for variation in observed diversity–ecosystem variability relationships and highlights the importance of conserving regional species pools to help buffer ecosystems against predicted increases in environmental variability.     

The relative roles of environment and history as controls of tree species composition and richness in Europe

Aim This study investigates the determinants of European‐scale patterns in tree species composition and richness, addressing the following questions: (1) What is the relative importance of environment and history? History refers to lasting effects of past large‐scale events and time‐dependent cumulative effects of ongoing processes, notably dispersal limited range dynamics. (2) Among the environmental determinants, what is the relative importance of climate, soils, and forest cover? (3) Do the answers to questions 1 and 2 differ between conifers and Fagales, the two major monophyletic groups of European trees? Location The study area comprises most of Europe (34° N–72° N and 11° W–32° E). Methods Atlas data on native distributions of 54 large tree species at 50 × 50 km resolution were linked with climatic, edaphic, and forest cover maps in a geographical information system. Unconstrained (principal components analysis using Hellinger distance transformation and detrended correspondence analysis) and constrained ordinations (redundancy analysis using Hellinger distance transformation and canonical correspondence analysis) and multiple linear regressions were used to investigate the determinants of species composition and species richness, respectively. History is expected to leave its mark as broad spatial patterns and was represented by the nine spatial terms of a cubic trend surface polynomial. Results The main floristic pattern identified by all ordinations was a latitude‐temperature gradient, while the lower axes corresponded mostly to spatial variables. Partitioning the floristic variation using constrained ordinations showed the mixed spatial‐environmental and pure spatial fractions to be much greater than the pure environmental fraction. Biplots, forward variable selection, and partial analyses all suggested climatic variables as more important floristic determinants than forest cover or soil variables. Tree species richness peaked in the mountainous regions of East‐Central and Southern Europe, except the Far West. Variation partitioning of species richness found the mixed spatial‐environmental and pure spatial fractions to be much greater than the pure environmental fraction for all species combined and Fagales, but not for conifers. The scaled regression coefficients indicated climate as a stronger determinant of richness than soils or forest cover. While the dominant patterns were similar for conifers and Fagales, conifers exhibited less predictable patterns overall, a smaller pure spatial variation fraction relative to pure environmental fraction, and a greater relative importance of climate; all differences being more pronounced for species richness than for species composition. Main conclusions The analyses suggest that history is at least as important as current environment in controlling species composition and richness of European trees, with the exception of conifer species richness. Strong support for interpreting the spatial patterns as outcomes of historical processes, notably dispersal limitation, came from the observation that many European tree species naturalize extensively outside their native ranges. Furthermore, it was confirmed that climate predominates among environmental determinants of distribution and diversity patterns at large spatial scales. Finally, the particular patterns exhibited by conifers probably reflect greater environmental specialization and greater human impact. These findings warn against expecting the European tree flora to be able track fast future climate changes on its own.     

Generating Correlation Matrices Based on the Boundaries of Their Coefficients

Correlation coefficients among multiple variables are commonly described in the form of matrices. Applications of such correlation matrices can be found in many fields, such as finance, engineering, statistics, and medicine. This article proposes an efficient way to sequentially obtain the theoretical bounds of correlation coefficients together with an algorithm to generate n n correlation matrices using any bounded random variables. Interestingly, the correlation matrices generated by this method using uniform random variables as an example produce more extreme relationships among the variables than other methods, which might be useful for modeling complex biological systems where rare cases are very important.     

Evolutionary patterns in the family Ostreidae: Larviparity vs. oviparity

A comparison was made of the evolutionary patterns among larviparous and oviparous species of the family Ostreidae. The data reveal that larviparous species have experienced a wider range of environmental variables, life history traits, and levels of genetic variation than have oviparous species. Non-parametric correlation coefficients were obtained among fifteen variables (i.e., two genetic, four environmental and nine life-history variables). Among the life-history variables, mode of larval development, fecundity, egg size, initial size of the planktonic larva and planktonic larval period were found to covary significantly with the genetic variables. In a comparison of environmental and life-history variables, the mode of larval development and habitat water depth were found to covary. The implications of these results are discussed with reference to the evolution of the family Ostreidae.     

Richness patterns, species distributions and the principle of extreme deconstruction

Aim To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle. Location Global. Methods We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world‐wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small‐bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns. Results Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from –0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM‐OLS coefficients and R² values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population‐level processes acting on species geographical ranges.     

A note on the use of multiple linear regression in molecular ecology

Multiple linear regression analyses (also often referred to as generalized linear models – GLMs, or generalized linear mixed models – GLMMs) are widely used in the analysis of data in molecular ecology, often to assess the relative effects of genetic characteristics on individual fitness or traits, or how environmental characteristics influence patterns of genetic differentiation. However, the coefficients resulting from multiple regression analyses are sometimes misinterpreted, which can lead to incorrect interpretations and conclusions within individual studies, and can propagate to wider‐spread errors in the general understanding of a topic. The primary issue revolves around the interpretation of coefficients for independent variables when interaction terms are also included in the analyses. In this scenario, the coefficients associated with each independent variable are often interpreted as the independent effect of each predictor variable on the predicted variable. However, this interpretation is incorrect. The correct interpretation is that these coefficients represent the effect of each predictor variable on the predicted variable when all other predictor variables are zero. This difference may sound subtle, but the ramifications cannot be overstated. Here, my goals are to raise awareness of this issue, to demonstrate and emphasize the problems that can result and to provide alternative approaches for obtaining the desired information.     

Species turnover in Amazonian frogs: low predictability and large differences among forests

The factors explaining species turnover at different spatial scales have been intensively studied, but most work in Amazonia has mainly focused on plants. For animals, it is not as obvious which environmental variables most affect differences in species composition among sites. We sought to identify what causes anuran turnover in Amazonian terra firme forests, and how the effectiveness of these factors varies among regions and across spatial scales. We sampled frogs in 56 plots along ponds and streams distributed over three terra firme forest areas in Eastern Amazonia. Using multiple regressions on distance matrices, we partitioned the variation in species turnover into components explained by variation in environmental and spatial distances. This was done in parallel for each area separately, and for all areas together, to assess the consistency of results between scales and across areas at the same scale. Each community seemed to respond to a set of factors specific to that area, and the identity of the variables that emerged as significant differed among areas and scales. Both geographical distances and environmental differences had larger explanatory power at the regional scale than at the local scale. The large differences among results from different areas caution against making broad generalizations about species turnover patterns from a single community, as real differences may exist among areas.     

Floristic distributional patterns in a diverse ecotonal area in South America

The Paraguayan territory and region, in the centre of South America, is a huge transition area with a succession of various vegetation types. However, this area has received little attention from researchers, with few works published on its flora and its delimitations. We aimed to identify the most important environmental driving forces and delimit floristic patterns in this region, since understanding the forces that drive floristic variations in this ecotonal region could help comprehend the distribution of vegetation not only in this region but throughout South America. We obtained 1234 tree species occurrence records, 205 geographic coordinates and 23 environmental variables and altitude from the ‘NeoTropTree’ database and verified the influence and contribution of environmental factors through variance partition. We tested the floristic consistency of the different vegetation types using dendrogram, indicator species and ordination analyses. We also constructed multiple linear models to check the correlation between species distribution and environmental variables. We found eight consistent vegetation types. The spatial variables coupled with environmental variables were more important than individual environmental or spatial variables. Among the environmental variables, the aridity index was the most important. Despite the importance of spatial factors, due to environmental heterogeneity, we found a gradient related to climate and edaphic variables related to tree flora. The results confirm that the Paraguayan territory and region can be considered to be a diversified and important ecotone area in South America with respect to tree flora.     

Evolution of environmental cues for phenotypic plasticity

Phenotypically plastic characters may respond to multiple variables in their environment, but the evolutionary consequences of this phenomenon have rarely been addressed theoretically. We model the evolution of linear reaction norms in response to several correlated environmental variables, in a population undergoing stationary environmental fluctuations. At evolutionary equilibrium, the linear combination of environmental variables that acts as a developmental cue for the plastic trait is the multivariate best linear predictor of changes in the optimum. However, the reaction norm with respect to any single environmental variable may exhibit nonintuitive patterns. Apparently maladaptive, or hyperadaptive plasticity can evolve with respect to single environmental variables, and costs of plasticity may increase, rather than reduce, plasticity in response to some variables. We also find conditions for the evolution of an indirect environmental indicator that affects expression of a plastic phenotype, despite not influencing natural selection on it.     

Tree Diversity,Environmental Heterogeneity,and Productivity in a Mexican Tropical Dry Forest1

Species diversity–environmental heterogeneity (D–EH) and species diversity–productivity (D–P) relationships have seldom been analyzed simultaneously even though such analyses could help to understand the processes underlying contrasts in species diversity among sites. Here we analyzed both relationships at a local scale for a highly diverse tropical dry forest of Mexico. We posed the following questions: (1) are environmental heterogeneity and productivity related?; (2) what are the shapes of D–EH and D–P relationships?; (3) what are individual, and interactive, contributions of these two variables to the observed variance in species diversity?; and (4) are patterns affected by sample size, or by partitioning into average local diversity and spatial species turnover? All trees (diameter at breast height ≥5 cm) within twenty‐six 0.2‐ha transects were censused; four environmental variables associated with water availability were combined into an environmental heterogeneity index; aboveground standing biomass was used as a productivity estimator. Simple and multiple linear and nonlinear regression models were run. Environmental heterogeneity and productivity were not correlated. We found consistently positive log‐linear D–EH and D–P relationships. Productivity explained a larger fraction of among‐transect variance in species diversity than did environmental heterogeneity. No effects of sample size were found. Different components of diversity varied in sensitivity to environmental heterogeneity and productivity. Our results suggest that species' differentiation along water availability gradients and species exclusion at the lowest productivity (driest) sites occur simultaneously, independently, and in a scale‐dependent fashion on the tree community of this forest.     

Bioclimatic indices as a tool in pollen forecasting

The use of bioclimatic indices could be a major step forward in the methodology of pollen forecasting. The basis for this proposal is that simple meteorological parameters do not reflect the global status of the atmosphere, but merely some static measurements. However, pollen dispersal is, above all, a dynamic phenomenon, and this fact should be reflected in the variables we used to explain it. Here, we test the two methodologies for routine pollen forecasting by comparing correlation coefficients using the same daily Poaceae airborne pollen data base from León (6 years, from 1994 to 1999) as the dependent variable and either simple daily meteorological variables or compound daily bioclimatic indices as independent variables. Both simple and compound indices reproduced the same profile of evolution of plant eco-physiological requirements, as the length of the study period during the pollen season increased. However, for time frames larger than the main pollen period, bioclimatic indices gave superior coefficients, which seems to indicate that these could be more valuable for pre-season pollen forecasting. The continentality index produced the highest mean coefficient, higher than those generated by any meteorological variable. Furthermore, at least for a Mediterranean climate, site location and evapotranspiration in relation to precipitation seem to be the most promising factors for increasing success when forecasting Poaceae airborne pollen concentration.     

Modeling of the evolution of bacterial densities in an eutrophic ecosystem (sewage lagoons)

The process of wastewater treatment was studied by modeling the relationships between physical, chemical, and biological (bacteria, phytoplankton, zooplankton) components of the sewage treatment lagoons of an urban wastewater center, based upon a two-year sampling program. The models of interactions between variables were tested by path analysis. The path coefficients were computed from the results of ridge regression, instead of linear multiple regression. The results show that fecal coliforms were effectively controlled by the environmental variables included in the model, which have a cyclic seasonal behavior. This control grew stronger with distance from the input (R ²=0.71) to the output (R ²=0.88) of the treatment plant, resulting in effective elimination of most enteric bacteria. Simultaneously, the ecosystem's community of aerobic heterotrophic bacteria became more independent from the model's predictive variables, with increased distance from the sewage input, thus demonstrating its maturation as an autonomous community in the lagoon ecosystem. Consequences of modeling are discussed, with respect to the understanding of biological wastewater treatment mechanisms and ecosystem dynamics and to plant management.     

Canonical Correspondence Analysis with variation partitioning: some comments and an application

Abstract. This study presents an alternative treatment of data from a comprehensive vegetation study in which the main gradient structure of boreal coniferous forest vegetation in southern Norway was investigated by ordination techniques. The data sets include vegetation samples of different plot sizes, supplied with measurements of 33 environmental explanatory variables (classified in four groups) and nine spatial explanatory variables derived from geographical coordinates. Partitioning the variation of the species-sample plot matrices on different sets of explanatory variables is performed by use of (partial) Canonical Correspondence Analysis. Several aspects of vegetation-environment relationships in the investigation area are discussed on the basis of results obtained by the new method. Generally, ca. 35% of the variation in species abundances are explained by environmental and spatial variables. The results indicate support for the hypothesis of macro-scale topographic control over the differentiation of the vegetation, more strongly so in pine than in spruce forest where soil nutrients play a major role. Towards finer scales, the primary topographical and topographically dependent factors lose importance, and vegetational differentiation is more strongly affected by the accumulated effects of the vegetation (including the tree stand) on soils, shading, litter fall, etc. The fraction of variation in species abundance explained by significant environmental variables was found to be ca. twice as large as the fraction explained by spatial variables. The fraction of variation explained by the supplied variables differed between data sets; it was lower for cryptogams than for vascular plants, and lower for smaller than for larger sample plots. Possible reasons for these patterns are discussed. Some methodological aspects of CCA with variation partitioning are discussed: improvements, necessary precautions, and the advantages over alternative methods.     

Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies

This paper outlines a multiple imputation method for handling missing data in designed longitudinal studies. A random coefficients model is developed to accommodate incomplete multivariate continuous longitudinal data. Multivariate repeated measures are jointly modeled; specifically, an i.i.d. normal model is assumed for time-independent variables and a hierarchical random coefficients model is assumed for time-dependent variables in a regression model conditional on the time-independent variables and time, with heterogeneous error variances across variables and time points. Gibbs sampling is used to draw model parameters and for imputations of missing observations. An application to data from a study of startle reactions illustrates the model. A simulation study compares the multiple imputation procedure to the weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106-121) that can be used to address similar data structures.