Fitting Multiplet Simulations to L-Edge XAS Spectra of Transition-Metal Complexes Using an Adaptive Grid Algorithm.

A new methodology based on an adaptive grid algorithm followed by an analysis of the ground state from the fit parameters is presented to analyze and interpret experimental XAS L2,3-edge data. The fitting method is tested first in a series of multiplet calculations for d0-d7 systems and for which the solution is known. In most cases, the algorithm is able to find the solution, except for a mixed-spin Co2+ Oh complex, where it instead revealed a correlation between the crystal field and the electron repulsion parameters near spin-crossover transition points. Furthermore, the results for the fitting of previously published experimental data sets on CaO, CaF2, MnO, LiMnO2, and Mn2O3 are presented and their solution discussed. The presented methodology has allowed the evaluation of the Jahn-Teller distortion in LiMnO2, which is consistent with the observed implications in the development of batteries, which use this material. Moreover, a follow-up analysis of the ground state in Mn2O3 has demonstrated an unusual ground state for the highly distorted site which would be impossible to optimize in a perfect octahedral environment. Ultimately, the presented methodology can be used in the analysis of X-ray absorption spectroscopy data measured at the L2,3-edge for a large number of materials and molecular complexes of first-row transition metals and can be expanded to the analysis of other X-ray spectroscopic data in future studies.

A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data.

Understanding variation of traits within and among species through time and across space is central to many questions in biology. Many resources assemble species-level trait data, but the data and metadata underlying those trait measurements are often not reported. Here, we introduce FuTRES (Functional Trait Resource for Environmental Studies; pronounced few-tress), an online datastore and community resource for individual-level trait reporting that utilizes a semantic framework. FuTRES already stores millions of trait measurements for paleobiological, zooarchaeological, and modern specimens, with a current focus on mammals. We compare dynamically derived extant mammal species' body size measurements in FuTRES with summary values from other compilations, highlighting potential issues with simply reporting a single mean estimate. We then show that individual-level data improve estimates of body mass-including uncertainty-for zooarchaeological specimens. FuTRES facilitates trait data integration and discoverability, accelerating new research agendas, especially scaling from intra- to interspecific trait variability.

Predicting tropical dry forest successional attributes from space: is the key hidden in image texture?

Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R(2) = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information.