Addressing the Adverse Effects of Climate Change on the Human Rights of Older Persons

The common consequences of climate change events include: displacement, loss of sustainable shelter and housing, and limited access to medical care and other resources such as food, clean water, and sanitation services. These adverse effects coincide to an alarming degree with the human rights most essential to those in vulnerable or marginalized groups, including older populations. Whether through displacement or disruption of supply, the stressors of climate change events greatly exacerbate older populations’ vulnerability, especially when compounded by negative social determinants of health, such as existing social, political, and economic barriers to successful aging. Using the SDGs as a framework to develop policies around (13) climate action and the use of improved (9) industry, innovation, and infrastructure to create (11) sustainable cities and communities, it is possible to establish (10) reduced inequalities to promote overall (3) good health and well-being in our older populations.

Supervised continuous land cover change detection in a critically endangered shrubland ecosystem

Existing efforts to rapidly detect land cover change in satellite image time-series have mostly focused on forested ecosystems in the tropics and northern hemisphere. The notable difference in reflectance that occurs following deforestation allow for unsupervised methods, often with manually determined thresholds, to detect land cover change with relative accuracy. Less progress has been made in detecting change in low productivity, disturbance-prone vegetation such as grasslands and shrublands, where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5-10% of its original extent remaining in small, highly fragmented patches. I demonstrate that supervised classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence, and that trained models are suitable for operational continuous monitoring. A training dataset of precisely dated vegetation change events between 2016 and 2020 was obtained from daily, high resolution Planet labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously classify land cover change events in multivariate time-series of vegetation activity from Sentinel 2 satellites as new data becomes available. These models reached a f-score of 0.93, a 61% improvement over the f-score of 0.57 achieved using an unsupervised method designed for forested ecosystems. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for supervised approaches to continuous monitoring of habitat loss in ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated labelled datasets of land cover change events with which to train machine learning classifiers.

Early life predictors of positive change during the coronavirus disease pandemic

Background The COVID-19 pandemic is a crisis unprecedented in its size and scope. Yet studies of resilience suggest most individuals will successfully negotiate this challenge and some may even experience growth and positive change. Some evidence suggests that the capacity to enact positive change in the face of adversity may be shaped by early life experiences. Methods In a subset of 374 participants (57% female, mean age = 29 years) in the Study of Early Child Care and Youth Development (SECCYD), a longitudinal, birth cohort, prospective models were tested to determine whether early life adversities in family and neighborhood contexts predict positive change events in response to the COVID-19 pandemic. Childhood family and neighborhood contexts were assessed using a combination of self-report questionnaires and US Census data. Adulthood positive change events (e.g., becoming more appreciative of things usually taken for granted) were assessed using the Epidemic-Pandemic Impacts Inventory (EPII). Results In regression analyses, neighborhood disadvantage in childhood, measured both by objective and subjective assessments, predicted a higher number of positive change events in response to the COVID-19 pandemic (β = .18, p = .004 and β = .15, p = .006, respectively). Examination of the positive change event subscales showed neighborhood disadvantage in childhood predicted increases in events related to ‘perspective taking and charitable giving’ (β = .20, p = .022 and β = .17, p = .002, respectively) and improved ‘social relationships’ (β = .18, p = .004 and β = .13, p = .020, respectively), but not to positive ‘health behaviors’ (ps > .05). All associations were independent of sociodemographic factors and childhood family dysfunction. Conclusions Findings suggest that neighborhood disadvantage in childhood may shape prosocial responses to stress in adulthood, potentially through early life adaptions to stress that are protective when facing adversity. There are several notable implications of the study findings. Although adversity in early life has clear negative impacts, it is possible that adversity experiences may also provide opportunities to develop adaptive strategies that foster resilience and growth when facing stress. Intervention efforts should consider leveraging such stress-adapted strengths to reduce the many negative impacts of early life adversity.

A retrospective analysis of organizational change: A case study of Octaware Technologies

Purpose: The aim of this paper was to describe the change events as a case study in Octaware Technologies and analyze the change events utilizing the constructs of organizational development in order to get a better understanding of the change in an organization. Methodology: This study was conducted using a qualitative case study method to analyze the organizational change event in Octaware Technologies. Findings: The study revealed that the external environment of regulatory and economic change are the key drivers of organizational development in the company. The findings also highlighted that the organizational development of the company is demonstrated by its learning culture and outstanding management processes. Unique contribution to theory, practice, and policy: The results of the study make two contributions to the literature. First, the author sheds light on the underlying change that leads to the improvement of the specific construct of organizational development. Second, the author presented an empirically validated model conceptualized from a theoretical framework of organizational development.

Locating and Dating Land Cover Change Events in the Renosterveld, a Critically Endangered Shrubland Ecosystem

Land cover change is the leading cause of global biodiversity decline. New satellite platforms allow for monitoring of habitats in increasingly fine detail, but most applications have been limited to forested ecosystems. I demonstrate the potential for detailed mapping and accurate dating of land cover change events in a highly biodiverse, Critically Endangered, shrubland ecosystem—the Renosterveld of South Africa. Using supervised classification of Sentinel 2 data, and subsequent manual verification with very high resolution imagery, I locate all conversion of Renosterveld to non-natural land cover between 2016 and 2020. Land cover change events are further assigned dates using high temporal frequency data from Planet labs. A total area of 478.6 hectares of Renosterveld loss was observed over this period, accounting for 0.72% of the remaining natural vegetation in the region. In total, 50% of change events were dated to within two weeks of their actual occurrence, and 87% to within two months. The Renosterveld loss identified here is almost entirely attributable to conversion of natural vegetation to cropland through ploughing. Change often preceded the planting and harvesting seasons of rainfed annual grains. These results show the potential for new satellite platforms to accurately map land cover change in non-forest ecosystems, and detect change within days of its occurrence. There is potential to use this and similar datasets to automate the process of change detection and monitor change continuously.

Climate instability and emerging tick-borne disease.

This expert opinion focuses on the impact of climate change events (such as drought, extreme precipitation and wind, global warming) on the emergence and abundance of tick-borne diseases.