The likelihood of extinction of iconic and dominant herbivores and detritivores of coral reefs: the parrotfishes and surgeonfishes

Parrotfishes and surgeonfishes perform important functional roles in the dynamics of coral reef systems. This is a consequence of their varied feeding behaviors ranging from targeted consumption of living plant material (primarily surgeonfishes) to feeding on detrital aggregates that are either scraped from the reef surface or excavated from the deeper reef substratum (primarily parrotfishes). Increased fishing pressure and widespread habitat destruction have led to population declines for several species of these two groups. Species-specific data on global distribution, population status, life history characteristics, and major threats were compiled for each of the 179 known species of parrotfishes and surgeonfishes to determine the likelihood of extinction of each species under the Categories and Criteria of the IUCN Red List of Threatened Species. Due in part to the extensive distributions of most species and the life history traits exhibited in these two families, only three (1.7%) of the species are listed at an elevated risk of global extinction. The majority of the parrotfishes and surgeonfishes (86%) are listed as Least Concern, 10% are listed as Data Deficient and 1% are listed as Near Threatened. The risk of localized extinction, however, is higher in some areas, particularly in the Coral Triangle region. The relatively low proportion of species globally listed in threatened Categories is highly encouraging, and some conservation successes are attributed to concentrated conservation efforts. However, with the growing realization of man's profound impact on the planet, conservation actions such as improved marine reserve networks, more stringent fishing regulations, and continued monitoring of the population status at the species and community levels are imperative for the prevention of species loss in these groups of important and iconic coral reef fishes.     

Carbon Footprint of Telemedicine Solutions - Unexplored Opportunity for Reducing Carbon Emissions in the Health Sector

Background

The healthcare sector is a significant contributor to global carbon emissions, in part due to extensive travelling by patients and health workers.

Objectives

To evaluate the potential of telemedicine services based on videoconferencing technology to reduce travelling and thus carbon emissions in the healthcare sector.

Methods

A life cycle inventory was performed to evaluate the carbon reduction potential of telemedicine activities beyond a reduction in travel related emissions. The study included two rehabilitation units at Umeå University Hospital in Sweden. Carbon emissions generated during telemedicine appointments were compared with care-as-usual scenarios. Upper and lower bound emissions scenarios were created based on different teleconferencing solutions and thresholds for when telemedicine becomes favorable were estimated. Sensitivity analyses were performed to pinpoint the most important contributors to emissions for different set-ups and use cases.

Results

Replacing physical visits with telemedicine appointments resulted in a significant 40–70 times decrease in carbon emissions. Factors such as meeting duration, bandwidth and use rates influence emissions to various extents. According to the lower bound scenario, telemedicine becomes a greener choice at a distance of a few kilometers when the alternative is transport by car.

Conclusions

Telemedicine is a potent carbon reduction strategy in the health sector. But to contribute significantly to climate change mitigation, a paradigm shift might be required where telemedicine is regarded as an essential component of ordinary health care activities and not only considered to be a service to the few who lack access to care due to geography, isolation or other constraints.     

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.