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One of the dominant narratives about pastoral systems is that livestock populations have the potential to grow exponentially and destroy common-pool grazing resources. However, longitudinal, interdisciplinary research has shown that pastoralists are able to sustainably manage common-pool resources and that livestock populations are not growing exponentially. The common explanation for limits on livestock population growth is that reoccurring droughts, diseases, and other disasters keep populations in check. However, we hypothesize that coupled demographic processes at the level of the household also may keep livestock population growth in check. Our hypothesis is that two mechanisms at the herd-household level explain why livestock populations grow much slower in pastoral systems than predicted by conventional Malthusian models. The two mechanisms are: (1) the domestic cycle of the household, and (2) the effects of scale and stochasticity. We developed an agent-based model of a pastoral system to evaluate the hypothesis. The results from our simulations show that the couplings between herd and household do indeed constrain the growth of both human and livestock populations. In particular, the domestic cycle of the household limits herd growth and ultimately constrains the growth of livestock populations. The study shows that the misfortunes that affect individual households every day cumulatively have a major impact on the growth of human and livestock populations. 

Aim:Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905–2) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features.Methods:Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants’ Mini-Mental State Examination (MMSE) scores.Results:Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869].Conclusions:Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.