Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning.

Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/ .

Higher Mortality Is Not a Universal Cost of Dispersal: A Case Study in African Wild Dogs.

AbstractMortality is considered one of the main costs of dispersal. A reliable evaluation of mortality, however, is often hindered by a lack of information about the fate of individuals that disappear under unexplained circumstances (i.e., missing individuals). Here, we addressed this uncertainty by applying a Bayesian mortality analysis that inferred the fate of missing individuals according to information from individuals with known fate. Specifically, we tested the hypothesis that mortality during dispersal is higher than mortality among nondispersers using 32 years of mark-resighting data from a free-ranging population of the endangered African wild dog (Lycaon pictus) in northern Botswana. Contrary to expectations, we found that mortality during dispersal was lower than mortality among nondispersers, indicating that higher mortality is not a universal cost of dispersal. Our findings suggest that group living can incur costs for certain age classes, such as limited access to resources as group density increases, that exceed the mortality costs associated with dispersal. By challenging the accepted expectation of higher mortality during dispersal, we urge for further investigations of this key life history trait and propose a robust statistical approach to reduce bias in mortality estimates.

Circular-linear copulae for animal movement data.

Animal movement is often modelled in discrete time, formulated in terms of steps taken between successive locations at regular time intervals. Steps are characterized by the distance between successive locations (step lengths) and changes in direction (turn angles). Animals commonly exhibit a mix of directed movements with large step lengths and turn angles near 0 when travelling between habitat patches and more wandering movements with small step lengths and uniform turn angles when foraging. Thus, step lengths and turn angles will typically be cross-correlated.Most models of animal movement assume that step lengths and turn angles are independent, likely due to a lack of available alternatives. Here, we show how the method of copulae can be used to fit multivariate distributions that allow for correlated step lengths and turn angles.We describe several newly developed copulae appropriate for modelling animal movement data and fit these distributions to data collected on fishers (Pekania pennanti). The copulae are able to capture the inherent correlation in the data and provide a better fit than a model that assumes independence. Further, we demonstrate via simulation that this correlation can impact movement patterns (e.g. rates of dispersion overtime).We see many opportunities to extend this framework (e.g. to consider autocorrelation in step attributes) and to integrate it into existing frameworks for modelling animal movement and habitat selection. For example, copulae could be used to more accurately sample available locations when conducting habitat-selection analyses.

Discovery of Open Cubane Core Structures for Biomimetic LnCo3 (OR)4 Water Oxidation Catalysts.

Bio-mimetic catalysts such as LnCo3 (OR)4 (Ln=Er, Tm; OR=alkoxide) cubanes have recently been in the focus of research for artificial water oxidation processes. Previously, the remarkable adaptability with respect to ligand shell, nuclear structure as well as protonation and oxidation states of those catalysts has been shown to be beneficial for the water oxidation process. We further explored the structural flexibility of those catalysts and present here a series of novel structures in which one metal center is pulled out of the cubane cage. This leads to an open cubane core, which is to some extent reminiscent of observed open/closed cubane-core forms of the oxygen-evolving complex in nature's photosystem II. We investigate how those open cubane core models alter the thermodynamics of the water oxidation cycle and how different solvation approaches influence their stability.

Exploring Solvation Effects in Ligand-Exchange Reactions via Static and Dynamic Methods.

We investigate ligand-exchange reactions of a biomimetic Co(II)-based heterocubane complex in aqueous solution by means of various approaches for consideration of solvent effects. Static calculations based on geometry optimizations carried out in vacuum, with solvent continuum models, or with several explicit solvent molecules have been carried out as well as density functional theory (DFT)-based molecular dynamics simulations. In addition, reaction pathways and barriers have been elucidated via nudged elastic band calculations and metadynamics. The results show that static approaches with approximate consideration of the solvent environment lead to reaction energies, which may change drastically depending on the method employed. A more sophisticated approach is DFT-molecular dynamics at ambient conditions with full solvation, i.e. enough solvent molecules to retain bulk water properties far from the solute, which, however, comes with a much higher computational cost. The investigated example of the exchange of an acetate ligand by a hydroxide demonstrates that entropic contributions can be vital and consideration of electronic energies alone may be a rather rough approximation.

Dehydrogenation Free Energy of Co2+(aq) from Density Functional Theory-Based Molecular Dynamics.

Electron and proton transfers are important steps occurring in chemical reactions. The often used approach of calculating the energy differences of those steps using methods based on geometry optimizations neglects the influence of dynamic effects. To further investigate this issue and inspired by research in water oxidation, we calculate in the present study the dehydrogenation free energy of aqueous Co2+, which is the free energy change associated with the first step of the water oxidation reaction mechanism of recently investigated model Co(II)-aqua catalysts. We employ a method based on a thermodynamic integration scheme with strong ties to Marcus theory to obtain free energy differences, solvent reorganization free energies, and dynamic structural information on the systems from density functional theory-based molecular dynamics. While this method is computationally orders of magnitude more expensive than a static approach, it potentially allows for predicting the validity of the approximation of neglecting dynamic effects.

Relative free enthalpies for point mutations in two proteins with highly similar sequences but different folds.

Enveloping distribution sampling was used to calculate free-enthalpy changes associated with single amino acid mutations for a pair of proteins, GA95 and GB95, that show 95% sequence identity yet fold into topologically different structures. Of the L → A, I → F, and L → Y mutations at positions 20, 30, and 45, respectively, of the 56-residue sequence, the first and the last contribute the most to the free-enthalpy difference between the native and non-native sequence-structure combinations, in agreement with the experimental findings for this protein pair. The individual free-enthalpy changes are almost sequence-independent in the four-strand/one-helix structure, the stable form of GB95, while in the three-helix bundle structure, the stable form of GA95, an interplay between residues 20 and 45 is observed.