3 resultados para combinatorial protocol in multiple linear regressions
em Publishing Network for Geoscientific
Resumo:
Aim: To investigate shell size variation among gastropod faunas of fossil and recent long-lived European lakes and discuss potential underlying processes. Location: 23 long-lived lakes of the Miocene to Recent of Europe. Methods: Based on a dataset of 1412 species of both fossil and extant lacustrine gastropods, we assessed differences in shell size in terms of characteristics of the faunas (species richness, degree of endemism, differences in family composition) and the lakes (surface area, latitude and longitude of lake centroid, distance to closest neighbouring lake) using multiple and linear regression models. Because of a strong species-area relationship, we used resampling to determine whether any observed correlation is driven by that relationship. Results: The regression models indicated size range expansion rather than unidirectional increase or decrease as the dominant pattern of size evolution. The multiple regression models for size range and maximum and minimum size were statistically significant, while the model with mean size was not. Individual contributions and linear regressions indicated species richness and lake surface area as best predictors for size changes. Resampling analysis revealed no significant effects of species richness on the observed patterns. The correlations are comparable across families of different size classes, suggesting a general pattern. Main conclusions: Among the chosen variables, species richness and lake surface area are the most robust predictors of shell size in long-lived lake gastropods. Although the most outstanding and attractive examples for size evolution in lacustrine gastropods derive from lakes with extensive durations, shell size appears to be independent of the duration of the lake as well as longevity of a species. The analogue of long-lived lakes as 'evolutionary islands' does not hold for developments of shell size because different sets of parameters predict size changes.
Resumo:
Long chain 1,13- and 1,15-alkyl diols form the base of a number of recently proposed proxies used for climate reconstruction. However, the sources of these lipids and environmental controls on their distribution are still poorly constrained. We have analyzed the long chain alkyl diol (LCD) composition of cultures of ten eustigmatophyte species, with three species from different families grown at various temperatures, to identify the effect of species composition and growth temperature on the LCD distribution. The results were compared with the LCD distribution of sixty-two lake surface sediments, and with previously reported LCD distributions from marine environments. The different families within the Eustigmatophyceae show distinct LCD patterns, with the freshwater family Eustigmataceae most closely resembling LCD distributions in both marine and lake environments. Unlike the other two eustigmatophyte families analyzed (Monodopsidaceae and Goniochloridaceae), C28 and C30 1,13-alkyl diols and C30 and C32 1,15-alkyl diols are all relatively abundant in the family Eustigmataceae, while the mono-unsaturated C32 1,15-alkyl diol was below detection limit. In contrast to the marine environment, LCD distributions in lakes did not show a clear relationship with temperature. The Long chain Diol Index (LDI), a proxy previously proposed for sea surface temperature reconstruction, showed a relatively weak correlation (R2 = 0.33) with mean annual air temperature used as an approximation for annual mean surface temperature of the lakes. A much-improved correlation (R2 = 0.74, p-value<0.001) was observed applying a multiple linear regression analysis between LCD distributions and lake temperatures reconstructed using branched tetraether lipid distributions. The obtained regression model provides good estimates of temperatures for cultures of the family Eustigmataceae, suggesting that algae belonging to this family have an important role as a source for LCDs in lacustrine environments, or, alternatively, that the main sources of LCDs are similarly affected by temperature as the Eustigmataceae. The results suggest that LCDs may have the potential to be applicable as a palaeotemperature proxy for lacustrine environments, although further calibration work is still required.
Resumo:
The composition and abundance of algal pigments provide information on phytoplankton community characteristics such as photoacclimation, overall biomass and taxonomic composition. In particular, pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by high-performance liquid chromatography (HPLC) techniques applied to filtered water samples. This method, as well as other laboratory analyses, is time consuming and therefore limits the number of samples that can be processed in a given time. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an empirical orthogonal function (EOF) analysis to remote-sensing reflectance data derived from ship-based hyperspectral underwater radiometry and from multispectral satellite data (using the Medium Resolution Imaging Spectrometer - MERIS - Polymer product developed by Steinmetz et al., 2011, doi:10.1364/OE.19.009783) measured in the Atlantic Ocean. Subsequently we developed multiple linear regression models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multispectral resolution is chosen (i.e., eight bands, similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. As a demonstration of the utility of the approach, the fitted model based on satellite reflectance data as input was applied to 1 month of MERIS Polymer data to predict the concentration of those pigment groups for the whole eastern tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photophysiology.