The effect of seasonal flooding and livelihood activities on retention of nitrogen and phosphorus in Cyperus papyrus wetlands,the role of aboveground biomass

Hydrobiologia - With growing demand for food production in Africa, protecting wetlands and combining increased agricultural production with conservation of the ecological integrity of wetlands is...     

Measuring effects of linear obstacles on wildlife movements: accounting for the relationship between step length and crossing probability

Animal movements in the landscape are influenced by linear features such as rivers, roads and power lines. Prior studies have investigated how linear features, particularly roads, affect movement rates by comparing animal's movement rate measured as step lengths (i.e., the distance between consecutive observations such as GPS locations) before, during and after crossing of a linear feature. The null hypothesis has been that the length of crossing steps should not differ from other steps, and a deviation from this, mainly that steps are longer during crossing, has been taken as support for a disturbance effect of the linear feature. However, based on the simple relationship between the length of a step and its probability to cross a linear feature, we claim that this assumption is inappropriate to test for behavioural responses to linear features. The probability is related to the proportion of the total length of the trajectory (i.e., the path of movement) a step constitutes. Consequently, care should be taken when formulating hypotheses about how animal moves in relation to linear features in the landscape. Statistical tests should be set up with respect to the expected length based on the distribution of step lengths in the trajectory. We propose two methods that accounts for the bias in crossing frequency that is caused by step lengths, and illustrates their applications by using simulated animal trajectories as well as empirical data on reindeer in an area with a power line.     

Assessment of Insulin Infusion Requirements in COVID-19-Infected Patients With Diabetic Ketoacidosis

Background/ObjectiveCoronavirus disease 2019 (COVID-19) is thought to contribute to diabetic ketoacidosis (DKA) and worse outcomes in patients with diabetes. This study compared the cumulative insulin dose required to achieve DKA resolution in the intensive care unit among patients with type 2 diabetes and COVID-19 infection versus without COVID-19 infection.MethodsThis retrospective cohort study evaluated 100 patients—50 patients with COVID-19 in cohort 1 and 50 patients without COVID-19 in cohort 2—treated with insulin infusions for DKA at a tertiary care teaching hospital. The primary outcome was to compare the cumulative insulin dose required to achieve DKA resolution in each cohort. The secondary outcomes included time to DKA resolution, mean insulin infusion rate, and mean weight-based cumulative insulin infusion dose required to achieve DKA resolution. All endpoints were adjusted for confounders.ResultsThe mean cumulative insulin dose was 190.3 units in cohort 1 versus 116.4 units in cohort 2 (P = .0038). Patients receiving steroids had a mean time to DKA resolution of 35.9 hours in cohort 1 versus 15.6 hours in cohort 2 (P = .0014). In cohort 1 versus cohort 2, the mean insulin infusion rate was 7.1 units/hour versus 5.3 units/hour (P = .0025), whereas the mean weight-based cumulative insulin infusion dose was 2.1 units/kg versus 1.5 units/kg (P = .0437), respectively.ConclusionCOVID-19-infected patients required a significantly larger cumulative insulin dose, longer time to DKA resolution, higher insulin infusion rate, and higher weight-based insulin infusion dose to achieve DKA resolution versus non–COVID-19-infected patients with type 2 diabetes.