Prediction of herbage yield in grassland: How well do Ellenberg N‐values perform?

Question: How useful are Ellenberg N‐values for predicting the herbage yield of Central European grasslands in comparison to approaches based on ordination scores of plant species composition or on soil parameters? Location: Central Germany (11°00′‐11°37’E, 50°21‐50°34’N, 500–840 m a.s.l.). Methods: Based on data from a field survey in 2001, the following models were constructed for predicting herbage yield in montane Central European grasslands: (1) Linear regression of mean Ellenberg N‐, R‐ and F‐values; (2) Linear regression of ordination scores derived from Non‐metric Multidimensional Scaling (NMDS) of vegetation data; and (3) Multiple linear regression (MLR) of soil variables. Models were evaluated by cross‐validation and validation with additional data collected in 2002. Results: Best predictions were obtained with models based on species composition. Ellenberg N‐values and NMDS scores performed equally well and better than models based on Ellenberg R‐ or F‐values. Predictions based on soil variables were least accurate. When tested with data from 2002, models based on Ellenberg N‐values or on NMDS scores accurately predicted productivity rank order of sites, but not the actual herbage yield of particular sites. Conclusions: Mean Ellenberg N‐values, which are easy to calculate, are as accurate as ordination scores in predicting herbage yield from plant species composition. In contrast, models based on soil variables may be useful for generating hypotheses about the factors limiting herbage yield, but not for prediction. We support the view that Ellenberg N‐values should be called productivity values rather than nitrogen values.     

Diagnostic parameters for selecting against novel spruce (Picea abies) decline: IV. Response of photosynthesis and transpiration to SO2+NO2 exposures

The dose- and time-response effects of 3 days of 6 h day-time sequential exposures to NO₂, SO₂ and SO₂+NO₂ of 0.45–1.81 μl l⁻¹ (ppm) SO₂ and 1.50–7.65 μl l⁻¹ NO₂ on photosynthesis, transpiration and dark respiration were examined for nine Carpatho-Ukrainian half-sib families and a population from the GFR ('Westerhof') of Norway spruce [ Piecea abies (L.) Karst.], all in their 5th growing season.
SO₂+NO₂ inhibited photosynthesis and transpiration and stimulated dark respiration more than SO₂ alone. SO₂ and SO₂+NO₂ at the lowest concentrations inhibited night transpiration, but increased it at the highest concentration, the strongest effects being obtained with combined exposures. Photosynthesis of the different half-sib families was affected significantly differently by SO₂+NO₂ exposures. NO₂ alone had no effects.
Sensitivity to transpiration decline correlated negatively with branch density. Height of trees correlated postitively with decline sensitivity in the seed orchard. The distribution of photosynthesis and transpiration sensitivities over all tested half-sib families correlated negatively with the distribution of decline sensitivity of their parents in a rural Danish seed orchard. The relative photosynthesis and transpiration sensitivities may thus serve as diagnostic parameters for selecting against novel spruce decline.     

Generation of formic acid and ethanolamine from serine in biosynthesis of linear gramicidin by a cell-free preparation of Bacillusbrevis (ATCC 8185)

A growing organism that produces antibiotic peptide was incubated with L-(U-¹⁴C)serine for labeling linear gramicidin. Linear gramicidin was isolated by a simple chromatographic method from tyrothricin (mixture of linear gramicidin and tyrocidine) applied to a column of basic aluminum oxide. The hydrolysate of labeled linear gramicidin on thin layer chromatography showed that L-(U-¹⁴C)serine was one of a precursor of ethanolamine moiety by autoradiography. L-(3-¹⁴C)serine generated formic acid in the presence of tetrahydrofolic acid by an enzyme fraction prepared with ammonium sulfate, and further formed ethanolamine binding to the protein. Formylvaline was biosynthesized by it with tetrahydrofolic acid and ATP, and subsequently released from the protein.     

Modelling the responses of Australian subtropical rainforest birds to changes in environmental conditions along elevational gradients

Montane birds face significant threats from a warming climate, so determining the environmental factors that most strongly influence the composition of such assemblages is of critical conservation importance. Changes in temperature and other environmental conditions along elevational gradients are known to influence the species richness and abundance of bird assemblages occupying mountains. However, the role of species‐specific traits in mediating the responses of bird species to changing conditions remains poorly understood. We aimed to determine whether different bird species responded differently to changing environmental conditions in a relatively understudied biodiversity hotspot in subtropical rainforest on the east coast of Australia. We examined patterns in avian species richness and abundance along two rainforest elevational gradients using monthly point counts between September 2015 and October 2016. Environmental data on temperature, wetness, canopy cover and canopy height were collected simultaneously, and trait information on body size and feeding guild membership for each bird species was obtained from the Handbook of Australian, New Zealand and Antarctic Birds. We used a generalized linear mixed modelling (GLMM) framework to determine the drivers of species richness and abundance and to quantify species’ trait–environment interactions. GLMMs indicated that temperature alone was significantly positively correlated with species richness and abundance. Species richness declined with increasing elevation. When modelling abundance, we found that feeding guild membership did not significantly affect species’ responses to environmental conditions. In contrast, the predicted abundance of a species was found to depend on its body size, due to significant positive interactions between this trait, temperature and canopy cover. Our findings indicate that large‐bodied birds are likely to increase in abundance more rapidly than small‐bodied birds with continued climatic warming. These results underline the importance of temperature as a driving factor of avian community assembly along environmental gradients.     

Stabilization of Helical Peptides by Mixed Spaced Salt Bridges

Abstract

Whether or not surface salt bridges have a strong stabilizing effect on the native structure in proteins remains uncertain. Previous studies of model peptides have shown that salt bridges spaced at i,i+4 along the chain are more stabilizing than those spaced at i,i+3, with a preference for the order acid-base rather than base-acid from N to C terminus. An analysis of the effect of spacing the ion pairs in short helical peptides is presented, in which acidic and basic side chains spaced two or three residues apart alternate along the chain. The mixed spacing proves to be stabilizing relative to pure spacings. A control peptide in which salt bridges were spaced uniformly three residues apart proved to form a β-sheet structure rather than a-helix. This is due to formation of a silk-like apolar face consisting of alanine side chains; the mesoscopic structure formed by these sheets can be imaged by scanning microscopy.     

Movement patterns of forest elephants (Loxodonta cyclotis Matschie, 1900) in the Odzala-Kokoua National Park,Republic of Congo

African forest elephants (Loxodonta cyclotis Matschie, 1900) are ecological engineers that play a fundamental role in vegetation dynamics. The species is of immediate conservation concern, yet it is relatively understudied. To narrow this knowledge gap, we studied the drivers of daily movement patterns (linear displacements) of forest elephants—characterised by a set of geographical, meteorological and anthropogenic variables—in the Odzala-Kokoua National Park, Republic of Congo. Explicitly, we used conditional random forest to model and disentangle the main environmental factors governing the displacements of six forest elephants, fitted with GPS collars and tracked over 16 months. Results indicated that females moved further distances than males, while the presence of roads or human settlements disrupted elephant behaviour resulting in faster displacements. Forest elephants moved faster along watercourses and through forest with understory dominated by Marantaceae forests and bais, but moved slower in savannahs. Finally, flood-prone areas—described by elevation and accumulated precipitation—and higher temperatures prevented longer displacements. We expect these results to improve the knowledge on the species movements through different habitats, which would benefit its conservation management.     

Varying coefficient model with unknown within-subject covariance for analysis of tumor growth curves

SUMMARY: In this article we develop a nonparametric estimation procedure for the varying coefficient model when the within-subject covariance is unknown. Extending the idea of iterative reweighted least squares to the functional setting, we iterate between estimating the coefficients conditional on the covariance and estimating the functional covariance conditional on the coefficients. Smoothing splines for correlated errors are used to estimate the functional coefficients with smoothing parameters selected via the generalized maximum likelihood. The covariance is nonparametrically estimated using a penalized estimator with smoothing parameters chosen via a Kullback-Leibler criterion. Empirical properties of the proposed method are demonstrated in simulations and the method is applied to the data collected from an ovarian tumor study in mice to analyze the effects of different chemotherapy treatments on the volumes of two classes of tumors.     

Maternal effects and maternal selection arising from variation in allocation of free amino acid to eggs

Maternal provisioning can have profound effects on offspring phenotypes, or maternal effects, especially early in life. One ubiquitous form of provisioning is in the makeup of egg. However, only a few studies examine the role of specific egg constituents in maternal effects, especially as they relate to maternal selection (a standardized selection gradient reflecting the covariance between maternal traits and offspring fitness). Here, we report on the evolutionary consequences of differences in maternal acquisition and allocation of amino acids to eggs. We manipulated acquisition by varying maternal diet (milkweed or sunflower) in the large milkweed bug, Oncopeltus fasciatus. Variation in allocation was detected by examining two source populations with different evolutionary histories and life‐history response to sunflower as food. We measured amino acids composition in eggs in this 2 × 2 design and found significant effects of source population and maternal diet on egg and nymph mass and of source population, maternal diet, and their interaction on amino acid composition of eggs. We measured significant linear and quadratic maternal selection on offspring mass associated with variation in amino acid allocation. Visualizing the performance surface along the major axes of nonlinear selection and plotting the mean amino acid profile of eggs from each treatment onto the surface revealed a saddle‐shaped fitness surface. While maternal selection appears to have influenced how females allocate amino acids, this maternal effect did not evolve equally in the two populations. Furthermore, none of the population means coincided with peak performance. Thus, we found that the composition of free amino acids in eggs was due to variation in both acquisition and allocation, which had significant fitness effects and created selection. However, although there can be an evolutionary response to novel food resources, females may be constrained from reaching phenotypic optima with regard to allocation of free amino acids.     

Estimating the variation,autocorrelation, and environmental sensitivity of phenotypic selection

Despite considerable interest in temporal and spatial variation of phenotypic selection, very few methods allow quantifying this variation while correctly accounting for the error variance of each individual estimate. Furthermore, the available methods do not estimate the autocorrelation of phenotypic selection, which is a major determinant of eco‐evolutionary dynamics in changing environments. We introduce a new method for measuring variable phenotypic selection using random regression. We rely on model selection to assess the support for stabilizing selection, and for a moving optimum that may include a trend plus (possibly autocorrelated) fluctuations. The environmental sensitivity of selection also can be estimated by including an environmental covariate. After testing our method on extensive simulations, we apply it to breeding time in a great tit population in the Netherlands. Our analysis finds support for an optimum that is well predicted by spring temperature, and occurs about 33 days before a peak in food biomass, consistent with what is known from the biology of this species. We also detect autocorrelated fluctuations in the optimum, beyond those caused by temperature and the food peak. Because our approach directly estimates parameters that appear in theoretical models, it should be particularly useful for predicting eco‐evolutionary responses to environmental change.