Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests.

Objective: Unplanned readmission to the pediatric cardiac intensive care unit (CICU) is associated with significant morbidity and mortality. The Pediatric Early Warning Score (PEWS) predicts ward patients at risk for decompensation but has not been previously reported to identify at-risk patients with cardiac disease prior to ward transfer. This study aimed to determine whether PEWS prior to transfer may serve as a predictor of unplanned readmission to the CICU.Design: All patients discharged from a tertiary children's hospital CICU from September 2012 through August 2015 were included for analysis. PEWS assessment was performed following transfer to the cardiac ward, and starting in January 2014, PEWS scores were also assigned by bedside CICU nurse prior to transfer from the CICU. Scores exceeding a predetermined threshold prompted further stability assessment by provider team prior to transfer.Results: Among 1320 discharges of 1082 patients during the study period, there were 130 unplanned readmissions during their hospitalization. Following implementation of pretransfer PEWS scoring, there was no significant reduction in unplanned readmission frequency (10.2% vs 9.2%, P 5 .39). A secondary analysis of PEWS scores revealed cardiac scoring as a strong discriminator of those likely to experience an unplanned readmission, independent of other significant clinical predictors of readmission (OR 1.78, 95% CI 1.17-2.71, P 5 .007). The resultant multivariate model was a good predictor of unplanned readmission (AUC 0.77, 95% CI 0.71-0.83, P < .001).Conclusion: While implementation of a pretransfer PEWS assessment did not reduce the frequency of unplanned readmissions in this small single-center cohort, a multivariate model including pretransfer elements of an early warning scoring system, along with other patient characteristics serves as a good discriminator of patients likely to experience an unplanned readmission following CICU discharge. Further prospective investigation is needed to define objective measures of pretransfer discharge readiness to potentially reduce the likelihood of unplanned readmissions.

With extinction rates far exceeding the natural background rate, reliable monitoring of wildlife populations has become crucial for adaptive management and conservation. Robust monitoring is often labor intensive with high economic costs, particularly in the case of those species that are subject to illegal poaching, such as elephants, which require frequent and accurate population estimates over large spatial scales. Dung counting methods are commonly employed to estimate the density of elephants; however, in the absence of a full survey calibration, these can be unreliable in heterogeneous habitats where dung decay rates may be highly variable. We explored whether motion‐sensitive cameras offer a simple, lower cost, and reliable alternative for monitoring in challenging forest environments. We estimated the density of African savanna elephants (Loxodanta africana) in a montane forest using the random encounter model and assessed the importance of surveying parameters for future survey design. We deployed motion‐sensitive cameras in 65 locations in the Aberdare Conservation Area in Kenya during June to August in 2015 to 2017, for a survey effort of 967 days, and a mean encounter rate of 0.09 ± 0.29 (SD) images/day. Elephants were captured in 16 locations. Density estimates varied between vegetation types, with estimates ranging from 6.27/km2 in shrub, 1.1/km2 in forest, 0.53/km2 in bamboo (Yushania alpine), and 0.44/km2 in the moorlands. The average speed of animal movement and the camera detection zone had the strongest linear associations with density estimates (R = −0.97). The random encounter model has the potential to offer an alternative, or complementary method within the active management framework for monitoring elephant populations in forests at a relatively low cost.