Rangeland Degradation in Mongolia – Using State and Transition Models to Help Understand Rangeland Dynamics

Rangeland degradation and soil erosion pose constant challenges to the management of natural resources in Mongolia. Large increases in livestock numbers since the early 1990s, together with increasing temperatures and higher frequency of extreme weather events have led to widespread degradation of rangeland resources, to the extent that today, nearly 57% of rangelands in Mongolia are considered degraded to some degree. New ways of understanding the dynamics of rangeland ecosystems and guidelines to conserve healthy and productive rangelands are urgently needed. The application of State and Transition Models (STMs) in ecosystem management has shown promise to understand the mechanistic processes behind rangeland degradation and to suggest appropriate interventions for maintaining the health or restoring degraded rangelands. The Green Gold-Animal Health project funded by the Swiss Development Agency in Mongolia was the first initiative aimed at developing and applying STMs to Mongolian rangeland management. Here we describe the development of STMs for the most common rangeland types in Mongolia, including the definition of reference and alternative rangeland states and “recovery classes”, based on the timelines and management actions needed to recover a reference state. Our results show that STMs are effective tools for analysing and interpreting rangeland health monitoring data and provide a scientific basis for planning and implementing resilience-based rangeland management. Furthermore, STMs facilitate synthesis of available knowledge and help identify areas where more information is needed. In summary, STMs have the potential to serve as a valuable tool for better communication of rangeland health assessments and decision making to facilitate appropriate management

Behavioral Adaptations of Nursing Brangus Cows to Virtual Fencing: Insights from a Training Deployment Phase

Virtual fencing systems have emerged as a promising technology for managing the distribution of livestock in extensive grazing environments. This study provides comprehensive documentation of the learning process involving two conditional behavioral mechanisms and the documentation of efficient, effective, and safe animal training for virtual fence applications on nursing Brangus cows. Two hypotheses were examined: (1) animals would learn to avoid restricted zones by increasing their use of containment zones within a virtual fence polygon, and (2) animals would progressively receive fewer audio-electric cues over time and increasingly rely on auditory cues for behavioral modification. Data from GPS coordinates, behavioral metrics derived from the collar data, and cueing events were analyzed to evaluate these hypotheses. The results supported hypothesis 1, revealing that virtual fence activation significantly increased the time spent in containment zones and reduced time in restricted zones compared to when the virtual fence was deactivated. Concurrently, behavioral metrics mirrored these findings, with cows adjusting their daily travel distances, exploration area, and cumulative activity counts in response to the allocation of areas with different virtual fence configurations. Hypothesis 2 was also supported by the results, with a decrease in cueing events over time and increased reliance with animals on audio cueing to avert receiving the mild electric pulse. These outcomes underscore the rapid learning capabilities of groups of nursing cows in responding to virtual fence boundaries

Isolation predicts compositional change after discrete disturbances in a global meta-study

Globally, anthropogenic disturbances are occurring at unprecedented rates and over extensive spatial and temporal scales. Human activities also affect natural disturbances, prompting shifts in their timing and intensities. Thus, there is an urgent need to understand and predict the response of ecosystems to disturbance. In this study, we investigated whether there are general determinants of community response to disturbance across different community types, locations, and disturbance events. We compiled 14 case studies of community response to disturbance from four continents, twelve aquatic and terrestrial ecosystem types, and eight different types of disturbance. We used community compositional differences and species richness to indicate community response. We used mixed-effects modeling to test the relationship between each of these response metrics and four potential explanatory factors: regional species pool size, isolation, number of generations passed, and relative disturbance intensity. We found that compositional similarity was higher between pre- and post-disturbance communities when the disturbed community was connected to adjacent undisturbed habitat. The number of generations that had passed since the disturbance event was a significant, but weak, predictor of community compositional change; two communities were responsible for the observed relationship. We found no significant relationships between the factors we tested and changes in species richness. To our knowledge, this is the first attempt to search for general drivers of community resilience from a diverse set of case studies. The strength of the relationship between compositional change and isolation suggests that it may be informative in resilience research and biodiversity management

Single-subject analyses of magnetoencephalographic evoked responses to the acoustic properties of affective non-verbal vocalizations

Magneto-encephalography (MEG) was used to examine the cerebral response to affective non-verbal vocalizations (ANVs) at the single-subject level. Stimuli consisted of nonverbal affect bursts from the Montreal Affective Voices morphed to parametrically vary acoustical structure and perceived emotional properties. Scalp magnetic fields were recorded in three participants while they performed a 3-alternative forced choice emotion categorization task (Anger, Fear, Pleasure). Each participant performed more than 6000 trials to allow single-subject level statistical analyses using a new toolbox which implements the general linear model (GLM) on stimulus-specific responses (LIMO-EEG). For each participant we estimated ‘simple’ models (including just one affective regressor (Arousal or Valence)) as well as ‘combined’ models (including acoustical regressors). Results from the ‘simple’ models revealed in every participant the significant early effects (as early as ~100 ms after onset) of Valence and Arousal already reported at the group-level in previous work. However, the ‘combined’ models showed that few effects of Arousal remained after removing the acoustically-explained variance, whereas significant effects of Valence remained especially at late stages. This study demonstrates (i) that single-subject analyses replicate the results observed at early stages by group-level studies and (ii) the feasibility of GLM-based analysis of MEG data. It also suggests that early modulation of MEG amplitude by affective stimuli partly reflects their acoustical properties

How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices