Influence of Metal, Ligand and Solvent on Supramolecular Polymerizations with Transition-Metal Compounds: A Theoretical Study

The nature of intermolecular interactions governing supramolecular polymerizations is very important to control their cooperativity. In order to address this problem, supramolecular columns made of Pt(II) and Pd(II) complexes of oligo(phenyleneethynylene)-based pyridine (OPE) and tetrazolyl-pyridine ligands (TEP) were investigated through the dispersion-corrected PM6 method. Aromatic, CH-π, M-Cl and metallophilic interactions helped stabilize the supramolecules studied, and their geometries and associated cooperativities were in excellent agreement with experimental data. The OPE ligand and/or the presence of Pt(II) have led to stronger metallophilic interactions and also to cooperative supramolecular polymerizations, which clearly suggests that metallophilic interactions are a key factor to control cooperativity. The results indicate that sequential monomer addition is in general less spontaneous than the combination of two larger pre-formed stacks. The present theoretical investigations contribute to the further understanding of the relation between the thermodynamics of supramolecular polymerizations and the nature of different synthons

Which Factors Determine Spatial Segregation in the South American Opossums (Didelphis aurita and D. albiventris)? An Ecological Niche Modelling and Geometric Morphometrics Approach

Didelphis albiventris and D. aurita are Neotropical marsupials that share a unique evolutionary history and both are largely distributed throughout South America, being primarily allopatric throughout their ranges. In the Araucaria moist forest of Southern Brazil these species are sympatric and they might potentially compete having similar ecology. For this reason, they are ideal biological models to address questions about ecological character displacement and how closely related species might share their geographic space. Little is known about how two morphologically similar species of marsupials may affect each other through competition, if by competitive exclusion and competitive release. We combined ecological niche modeling and geometric morphometrics to explore the possible effects of competition on their distributional ranges and skull morphology. Ecological niche modeling was used to predict their potential distribution and this method enabled us to identify a case of biotic exclusion where the habit generalist D. albiventris is excluded by the presence of the specialist D. aurita. The morphometric analyses show that a degree of shape discrimination occurs between the species, strengthened by allometric differences, which possibly allowed them to occupy marginally different feeding niches supplemented by behavioral shift in contact areas. Overlap in skull morphology is shown between sympatric and allopatric specimens and a significant, but weak, shift in shape occurs only in D. aurita in sympatric areas. This could be a residual evidence of a higher past competition between both species, when contact zones were possibly larger than today. Therefore, the specialist D. aurita acts a biotic barrier to D. albiventris when niche diversity is not available for coexistence. On the other hand, when there is niche diversification (e.g. habitat mosaic), both species are capable to coexist with a minimal competitive effect on the morphology of D. aurita

Effects of the Training Dataset Characteristics on the Performance of Nine Species Distribution Models: Application to Diabrotica virgifera virgifera

Many distribution models developed to predict the presence/absence of invasive alien species need to be fitted to a training dataset before practical use. The training dataset is characterized by the number of recorded presences/absences and by their geographical locations. The aim of this paper is to study the effect of the training dataset characteristics on model performance and to compare the relative importance of three factors influencing model predictive capability; size of training dataset, stage of the biological invasion, and choice of input variables. Nine models were assessed for their ability to predict the distribution of the western corn rootworm, Diabrotica virgifera virgifera, a major pest of corn in North America that has recently invaded Europe. Twenty-six training datasets of various sizes (from 10 to 428 presence records) corresponding to two different stages of invasion (1955 and 1980) and three sets of input bioclimatic variables (19 variables, six variables selected using information on insect biology, and three linear combinations of 19 variables derived from Principal Component Analysis) were considered. The models were fitted to each training dataset in turn and their performance was assessed using independent data from North America and Europe. The models were ranked according to the area under the Receiver Operating Characteristic curve and the likelihood ratio. Model performance was highly sensitive to the geographical area used for calibration; most of the models performed poorly when fitted to a restricted area corresponding to an early stage of the invasion. Our results also showed that Principal Component Analysis was useful in reducing the number of model input variables for the models that performed poorly with 19 input variables. DOMAIN, Environmental Distance, MAXENT, and Envelope Score were the most accurate models but all the models tested in this study led to a substantial rate of mis-classification