Group-size-mediated habitat selection and group fusion-fission dynamics of bison under predation risk

For gregarious animals the cost-benefit trade-offs that drive habitat selection may vary dynamically with group size, which plays an important role in foraging and predator avoidance strategies. We examined how habitat selection by bison (Bison bison) varied as a function of group size and interpreted these patterns by testing whether habitat selection was more strongly driven by the competing demands of forage intake vs. predator avoidance behavior. We developed an analytical framework that integrated group size into resource selection functions (RSFs). These group-size-dependent RSFs were based on a matched casecontrol design and were estimated using conditional logistic regression (mixed and populationaveraged models). Fitting RSF models to bison revealed that bison groups responded to multiple aspects of landscape heterogeneity and that selection varied seasonally and as a function of group size. For example, roads were selected in summer, but not in winter. Bison groups avoided areas of high snow water equivalent in winter. They selected areas composed of a large proportion of meadow area within a 700-m radius, and within those areas, bison selected meadows. Importantly, the strength of selection for meadows varied as a function of group size, with stronger selection being observed in larger groups. Hence the bison-habitat relationship depended in part on the dynamics of group formation and division. Group formation was most likely in meadows. In contrast, risk of group fission increased when bison moved into the forest and was higher during the time of day when movements are generally longer and more variable among individuals. We also found that stronger selection for meadows by large rather than small bison groups was caused by longer residence time in individual meadows by larger groups and that departure from meadows appears unlikely to result from a depression in food intake rate. These group-size-dependent patterns were consistent with the hypothesis that avoidance of predation risk is the strongest driver of habitat selection

Older adults with mild cognitive impairments show less driving errors after a multiple sessions simulator training program but do not exhibit long term retention

The driving performance of individuals with mild cognitive impairment (MCI) is suboptimal when compared to healthy older adults. It is expected that the driving will worsen with the progression of the cognitive decline and thus, whether or not these individuals should continue to drive is a matter of debate. The aim of the study was to provide support to the claim that individuals with MCI can benefit from a training program and improve their overall driving performance in a driving simulator. Fifteen older drivers with MCI participated in five training sessions in a simulator (over a 21-day period) and in a 6-month recall session. During training, they received automated auditory feedback on their performance when an error was noted about various maneuvers known to be suboptimal in MCI individuals (for instance, weaving, omitting to indicate a lane change, to verify a blind spot, or to engage in a visual search before crossing an intersection). The number of errors was compiled for eight different maneuvers for all sessions. For the initial five sessions, a gradual and significant decrease in the number of errors was observed, indicating learning and safer driving. The level of performance, however, was not maintained at the 6-month recall session. Nevertheless, the initial learning observed opens up possibilities to undertake more regular interventions to maintain driving skills and safe driving in MCI individuals

Experimental Method for Characterizing Electrical Steel Sheets in the Normal Direction

This paper proposes an experimental method to characterise magnetic laminations in the direction normal to the sheet plane. The principle, which is based on a static excitation to avoid planar eddy currents, is explained and specific test benches are proposed. Measurements of the flux density are made with a sensor moving in and out of an air-gap. A simple analytical model is derived in order to determine the permeability in the normal direction. The experimental results for grain oriented steel sheets are presented and a comparison is provided with values obtained from literature

Multi-mode movement decisions across widely ranging behavioral processes

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed populationlevel, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.DATA AVAILABILITY STATEMENT : The data are available at: https://osf.io/v5pnc/SUPPLEMENTARY MATERIAL : S1 Appendix. Calculation of average travelled distance using coefficient estimates associated to step length. https://doi.org/10.1371/journal.pone.0272538.s001S1 Table. Values and definition [from c] of model parameters used to simulate multi-state correlated random walks in three scenarios of landscape patchiness. https://doi.org/10.1371/journal.pone.0272538.s002S2 Table. Coefficient estimates along with their 95% confidence interval (95% CI) of the mixed-effects generalized linear model with binomial distribution (HMM-SSF + GLMM) and the multi-state correlated random walk model (HMM-CRW) to predict probability of switching from encamped to travelling mode, in 500 simulated foragers moving among resource patches and avoiding a predator. In resource patch is a dummy variable indicating whether the forager is within a resource patch (i.e., patch quality >0), equals the actual distance of the predator from the forager (dPredator) when dPredator ≤ 0.8 km and 0.8 km, otherwise. log(dPredator) is the natural logarithm of dPredator. https://doi.org/10.1371/journal.pone.0272538.s003S3 Table. Coefficient estimates along with their 95% confidence interval (95% CI) of mixed-effects generalized linear models with binomial distribution to predict probability of switching from encamped to travelling mode of movement, in plains bison during summer in Prince Albert National Park (SK, Canada). Each table represents estimates for a specific threshold probability (Pthreshold) used to categorized transition and non-transition from the conditional probabilities of being in encamped or travelling state, obtained from the fit of the HMM-SSF to plains bison trajectories. was set to the actual distance between bison and wolf (dwolf) when dwolf≤dthreshold and dthreshold, otherwise. https://doi.org/10.1371/journal.pone.0272538.s004S1 Fig. Simulated heterogeneous landscape used in the multi-state biased correlated random walk simulations, from gaussian random field with an exponential covariance function with variance = 1, nugget = 0 and a set of patch concentration (μQ) and patch size (γQ) resulting in three level of patchiness: 1) low (μQ = -1.5, γQ = 2), 2) intermediate (μQ = -0.5, γQ = 2) and 3) high (μQ = 1, γQ = 10). https://doi.org/10.1371/journal.pone.0272538.s005S2 Fig. Distribution of distance to the closest waterhole according to the mode of movement estimated from the HMM-SSF for 18 zebras in Hwange National Park during the dry hot season. The conditional probabilities of being in each state, obtained from the fit of the HMM-SFF, were dichotomized to 0–1 based on a 0.5 threshold to determine the state of the individual at each step on its trajectory. https://doi.org/10.1371/journal.pone.0272538.s006S3 Fig. Log-likelihood profile from mixed-effects generalized linear model with binomial distribution to predict probability of switching from encamped to travelling mode of movement, according to a gradient of threshold distance, dthreshold. https://doi.org/10.1371/journal.pone.0272538.s007S4 Fig. Total number of switches from encamped to travelling mode of movement according to day time, estimated using conditional probabilities of being in each state, obtained from the fit of the HMM-SFF to plains bison trajectories followed during the summers 2005–2016. We then separated the day in four periods: Night: 22:00–02:00, Dawn: 03:00–06:00, Day: 07:00–15:00 and Dusk: 16:00–21:00. https://doi.org/10.1371/journal.pone.0272538.s008http://www.plosone.orgdm2022Mammal Research InstituteZoology and Entomolog

Hypothesis tests for structured rank correlation matrices

Joint modeling of a large number of variables often requires dimension reduction strategies that lead to structural assumptions of the underlying correlation matrix, such as equal pair-wise correlations within subsets of variables. The underlying correlation matrix is thus of interest for both model specification and model validation. In this paper, we develop tests of the hypothesis that the entries of the Kendall rank correlation matrix are linear combinations of a smaller number of parameters. The asymptotic behavior of the proposed test statistics is investigated both when the dimension is fixed and when it grows with the sample size. We pay special attention to the restricted hypothesis of partial exchangeability, which contains full exchangeability as a special case. We show that under partial exchangeability, the test statistics and their large-sample distributions simplify, which leads to computational advantages and better performance of the tests. We propose various scalable numerical strategies for implementation of the proposed procedures, investigate their behavior through simulations and power calculations under local alternatives, and demonstrate their use on a real dataset of mean sea levels at various geographical locations