Enhancing quantitative approaches for assessing community resilience

Scholars from many different intellectual disciplines have attempted to measure, estimate, or quantify resilience. However, there is growing concern that lack of clarity on the operationalization of the concept will limit its application. In this paper, we discuss the theory, research development and quantitative approaches in ecological and community resilience. Upon noting the lack of methods that quantify the complexities of the linked human and natural aspects of community resilience, we identify several promising approaches within the ecological resilience tradition that may be useful in filling these gaps. Further, we discuss the challenges for consolidating these approaches into a more integrated perspective for managing social-ecological systems

Listening to the silent struggles of bipolar disorder through sonification of iMoodJournal data

First published: 07 May 2022This paper reports a preliminary case study for demonstrating the potential of data sonification for telling a real-life narrative of experienced mood swings through music. We obtained iMoodJournal data (2017–2020) from a voluntarily participating male who was diagnosed with type 2 bipolar disorder in 2015. The monitored period covers prolonged stretches of severe depression, particularly during fall, winter, and spring months. These “winter depressions” were usually superseded by remission during summer. These seasonal patterns were similar and recurring in the period between 2017 and 2019. In 2020, the depressions were relatively mild due to the patient spending winter in southern latitudes. However, another severe depression episode occurred during summer 2020 instead, which likely emanated from a period of medication discontinuation. The symptomatology was overall complex and highly dynamic, manifested in the combination of mood specifying tags that the user associated with determined mood scores in the iMoodJournal. This complexity was difficult to capture in the form of the numerical scores visualized in Figures S1 and S2.David G. Angeler, Harris A. Eyre, Michael Ber

Early Warnings for State Transitions

New concepts have emerged in theoretical ecology with the intent to quantify complexities in ecological change that are unaccounted for in state-and-transition models and to provide applied ecologists with statistical early warning metrics able to predict and prevent state transitions. With its rich history of furthering ecological theory and its robust and broad-scale monitoring frameworks, the rangeland discipline is poised to empirically assess these newly proposed ideas while also serving as early adopters of novel statistical metrics that provide advanced warning of a pending shift to an alternative ecological regime. We review multivariate early warning and regime shift detection metrics, identify situations where various metrics will be most useful for rangeland science, and then highlight known shortcomings. Our review of a suite of multivariate-based regime shift/early warning indicators provides a broad range of metrics applicable to a wide variety of data types or contexts, from situations where a great deal is known about the key system drivers and a regime shift is hypothesized a priori, to situations where the key drivers and the possibility of a regime shift are both unknown. These metrics can be used to answer ecological state-and-transition questions, inform policymakers, and provide quantitative decision-making tools for managers.The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information