Nest predation in Afrotropical forest fragments shaped by inverse edge effects, timing of nest initiation and vegetation structure

High levels of nest predation influence the population dynamics of many tropical birds, especially when deforestation alters nest predator communities. The consequences of tropical forest fragmentation on nest predation, however, remain poorly understood, as natural predation patterns have only been well documented in a handful of tropical forests. Here, we show the results of an extensive study of predation on natural nests of Cabanis's Greenbul (Phyllastrephus cabanisi) during 3 years in a highly fragmented cloud forest in SE Kenya. Overall predation rates derived from 228 scrub nests averaged 69 %, matching the typical high predation level on tropical bird species. However, predation rates strongly varied in space and time, and a model that combined timing effects of fragment, edge, concealment, year and nest was best supported by our data. Nest predation rates consistently increased from forest edge to interior, opposing the classic edge effect on nest predation, and supporting the idea that classic edge effects are much rarer in Afrotropical forests than elsewhere. Nest concealment also affected predation rates, but the strength and direction of the relationship varied across breeding seasons and fragments. Apart from spatial variation, predation rates declined during the breeding season, although the strength of this pattern varied among breeding seasons. Complex and variable relationships with nest predation, such as those demonstrated here, suggest that several underlying mechanisms interact and imply that fixed nesting strategies may have variable-even opposing-fitness effects between years, sites and habitats

A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis

Background Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians. Methods We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. Results We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. Conclusion Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings