Visual Marking of Ground Nests Might Attract Corvids

For ground-nesting birds such as waterfowl, estimating nest survival is a crucial step in assessing population dynamics, and marking nests facilitates continuous monitoring. A conventional method for marking ground nests is to use an inconspicuous rod at the nest bowl and a wooden lathe 10 m away. Nests are visually marked to allow for greater efficiency when revisiting nests and to facilitate subsequent nest searching sessions. Anecdotal evidence suggests that common ravens (Corvus corax) and American crows (C. brachyrhynchos) might learn to recognize these nest markers, resulting in artificially inflated rates of nest predation. In 2017 in central Alberta, Canada, we compared fates of nests marked with the conventional lathe-rod combination versus only a rod. We also tested the prevalence of corvid predation of marked nests in areas with and without high observations of corvid activity, using data from a study of dabbling duck (Anas spp.) nest survival. Our results suggest that marking nests with a lathe can increase predation by corvids and that nests marked with a rod only were more likely to hatch. Evaluation and use of alternate nest-marking methods would be beneficial for future studies of ground-nesting birds in areas where corvids are common. Our work highlights the importance of re-evaluating the efficacy of well-established field methods

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent