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While rock temperature is a key factor for many geomorphic phenomena in high mountains, assessing the small-scale temperature distribution remains challenging. We adapted the program package WUFI®, originally designed to calculate energy and moisture fluxes in building walls, for modeling natural rock faces. We determined physical rock properties and local meteorological parameters as input data for the simulations at rock walls in the Dachstein Mountains, Austria, and calculated annual temperature fluctuations at the surface and at 3, 10, 30, 50 and 100 cm depth, including different elevations, aspects and slope angles. Thirty-seven temperature sensors inserted into drillholes of different depths at ten locations (altitudes from 2300 to 2700 m) were used to validate the simulation. Additionally, surface temperatures were determined from infrared images; these were calibrated for surface emissivity using further temperature sensors. Simulated temperatures and sensor data show very good agreement, with a mean correlation coefficient of 0.94, and the validation of surface temperatures by thermal imaging also delivered good results (r > 0.9). This means that the WUFI software can be used to model complex temperature distributions in natural rock wall settings, thus making it a valuable tool for geomorphological investigations. [ABSTRACT FROM AUTHOR]

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Multi-temporal digital terrain models (DTMs) derived from airborne or uncrewed aerial vehicle (UAV)-borne light detection and ranging (LiDAR) platforms are frequently used tools in geomorphic impact studies. Accurate estimation of mobilized sediments from multi-temporal DTMs is indispensable for hazard assessment. To study volumetric changes in alpine environments it is crucial to identify and discuss different kind of error sources in multi-temporal data. We subdivided errors into those caused by data acquisition, data processing, and spatial properties of the terrain. In terms of the quantification of surface changes, the propagation of errors can lead to high uncertainties. Three alpine catchments with different LiDAR point clouds of different origins (airborne laser scanning [ALS], UAV-borne laser scanning [ULS]), varying point densities, accuracies and qualities were analysed, and used as basis for interpolating DTMs. The workflow was developed in the Schöttlbach area in Styria and later applied to further catchments in Austria. The main aim of the presented work is a comprehensive DTM uncertainty analysis specially designed for geomorphic impact studies, with a resulting uncertainty analysis serving as input for a change detection tool. Our findings reveal that geomorphic impact studies need the careful distinction between actual surface changes and different data uncertainties. ULS combines the benefits of terrestrial laser scanning with all the benefits of ALS. However, the use of ULS data does not necessarily improve the results of the analysis since the high level of detail is not always helpful in geomorphic impact studies. In order to make the different point clouds and DTMs comparable the quality of the ULS point cloud had to be reduced to fit the accuracy of the reference data (older ALS point clouds). Using a point cloud with a high point density with a regular planimetric point spacing and less data gaps, in the best case collected during leaf-off conditions (e.g., cross-flight strategy) turned out to be sufficient for our geomorphic research purposes., Competing Interests: The authors declare that there are no conflicts of interest that could be perceived as prejudicing the impartiality of the research reported., (© 2022 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.)

Accuracy of predicted breeding values (PBV) for low heritability traits may be increased in early generations by exploiting the information available in correlated traits. We compared the accuracy of PBV for 10 correlated traits with low to medium narrow-sense heritability (h2) in a genetically diverse field pea ( Pisum sativum L.) population after univariate or multivariate linear mixed model (MLMM) analysis with pedigree information. In the contra-season, we crossed and selfed S 1 parent plants, and in the main season we evaluated spaced plants of S 0 cross progeny and S 2+ (S 2 or higher) self progeny of parent plants for the 10 traits. Stem strength traits included stem buckling (SB) (h2 = 0.05), compressed stem thickness (CST) (h2 = 0.12), internode length (IL) (h2 = 0.61) and angle of the main stem above horizontal at first flower (EAngle) (h2 = 0.46). Significant genetic correlations of the additive effects occurred between SB and CST (0.61), IL and EAngle (-0.90) and IL and CST (-0.36). The average accuracy of PBVs in S 0 progeny increased from 0.799 to 0.841 and in S 2+ progeny increased from 0.835 to 0.875 in univariate vs MLMM, respectively. An optimized mating design was constructed with optimal contribution selection based on an index of PBV for the 10 traits, and predicted genetic gain in the next cycle ranged from 1.4% (SB), 5.0% (CST), 10.5% (EAngle) and -10.5% (IL), with low achieved parental coancestry of 0.12. MLMM improved the potential genetic gain in annual cycles of early generation selection in field pea by increasing the accuracy of PBV.

Crop breeding must achieve higher rates of genetic gain in grain yield (GY) and yield stability to meet future food demands in a changing climate. Optimal contributions selection (OCS) based on an index of key economic traits should increase the rate of genetic gain while minimising population inbreeding. Here we apply OCS in a global spring oilseed rape (canola) breeding program during three cycles of S 0,1 family selection in 2016, 2018, and 2020, with several field trials per cycle in Australia and Canada. Economic weights in the index promoted high GY, seed oil, protein in meal, and Phoma stem canker (blackleg) disease resistance while maintaining plant height, flowering time, oleic acid, and seed size and decreasing glucosinolate content. After factor analytic modelling of the genotype-by-environment interaction for the additive effects, the linear rate of genetic gain in GY across cycles was 0.059 or 0.087 t ha -1 y -1 (2.9% or 4.3% y -1 ) based on genotype scores for the first factor (f 1 ) expressed in trait units or average predicted breeding values across environments, respectively. Both GY and yield stability, defined as the root-mean-square deviation from the regression line associated with f 1 , were predicted to improve in the next cycle with a low achieved mean parental coancestry (0.087). These methods achieved rapid genetic gain in GY and other traits and are predicted to improve yield stability across global spring canola environments.

Sexual desire tends to subside gradually over time, with many couples failing to maintain desire in their long-term relationships. Three studies employed complementary methodologies to examine whether partner responsiveness, an intimacy-building behavior, could instill desire for one's partner. In Study 1, participants were led to believe that they would interact online with their partner. In reality, they interacted with either a responsive or an unresponsive confederate. In Study 2, participants interacted face-to-face with their partner, and judges coded their displays of responsiveness and sexual desire. Study 3 used a daily experiences methodology to examine the mechanisms underlying the responsiveness-desire linkage. Overall, responsiveness was associated with increased desire, but more strongly in women. Feeling special and perceived partner mate value explained the responsiveness-desire link, suggesting that responsive partners were seen as making one feel valued as well as better potential mates for anyone and thus as more sexually desirable. (PsycINFO Database Record, ((c) 2016 APA, all rights reserved).)

We used the animal model in S0 (F1) recurrent selection in a self-pollinating crop including, for the first time, phenotypic and relationship records from self progeny, in addition to cross progeny, in the pedigree. We tested the model in Pisum sativum, the autogamous annual species used by Mendel to demonstrate the particulate nature of inheritance. Resistance to ascochyta blight (Didymella pinodes complex) in segregating S0 cross progeny was assessed by best linear unbiased prediction over two cycles of selection. Genotypic concurrence across cycles was provided by pure-line ancestors. From cycle 1, 102/959 S0 plants were selected, and their S1 self progeny were intercrossed and selfed to produce 430 S0 and 575 S2 individuals that were evaluated in cycle 2. The analysis was improved by including all genetic relationships (with crossing and selfing in the pedigree), additive and nonadditive genetic covariances between cycles, fixed effects (cycles and spatial linear trends), and other random effects. Narrow-sense heritability for ascochyta blight resistance was 0.305 and 0.352 in cycles 1 and 2, respectively, calculated from variance components in the full model. The fitted correlation of predicted breeding values across cycles was 0.82. Average accuracy of predicted breeding values was 0.851 for S2 progeny of S1 parent plants and 0.805 for S0 progeny tested in cycle 2, and 0.878 for S1 parent plants for which no records were available. The forecasted response to selection was 11.2% in the next cycle with 20% S0 selection proportion. This is the first application of the animal model to cyclic selection in heterozygous populations of selfing plants. The method can be used in genomic selection, and for traits measured on S0-derived bulks such as grain yield., (Copyright © 2015 Cowling et al.)