Integrated, efficient, and global prioritization approaches are necessary to manage the ongoing loss of species and their associated function. “Evolutionary distinctness” measures a species’ ...contribution to the total evolutionary history of its clade and is expected to capture uniquely divergent genomes and functions. Here we demonstrate how such a metric identifies species and regions of particular value for safeguarding evolutionary diversity.
Among the world’s 9,993 recognized bird species, evolutionary distinctness is very heterogeneously distributed on the phylogenetic tree and varies little with range size or threat level. Species representing the most evolutionary history over the smallest area (those with greatest “evolutionary distinctness rarity”) as well as some of the most imperiled distinct species are often concentrated outside the species-rich regions and countries, suggesting they may not be well captured by current conservation planning. We perform global cross-species and spatial analyses and generate minimum conservation sets to assess the benefits of the presented species-level metrics. We find that prioritizing imperiled species by their evolutionary distinctness and geographic rarity is a surprisingly effective and spatially economical way to maintain the total evolutionary information encompassing the world’s birds. We identify potential conservation gaps in relation to the existing reserve network that in particular highlight islands as effective priority areas.
The presented distinctness metrics are effective yet easily communicable and versatile tools to assist objective global conservation decision making. Given that most species will remain ecologically understudied, combining growing phylogenetic and spatial data may be an efficient way to retain vital aspects of biodiversity.
•Evolutionary distinctness (ED) is very heterogeneously distributed among birds•Species with spatially most concentrated ED occur away from high-richness regions•Prioritizing imperiled species by ED efficiently preserves evolutionary information•Combining phylogenetic and spatial data supports efficient biodiversity conservation
Jetz et al. use an updated global phylogeny of birds to measure the evolutionary distinctness of all species, identify regions and countries where this distinctness is most concentrated, and show how one can conserve near-maximum amounts total avian evolutionary history by concentrating on the most distinct bird species at risk of extinction.
The sialic acids are a family of 9-carbon sugar acids found predominantly on the cell-surface glycans of humans and other animals within the Deuterostomes and are also used in the biology of a wide ...range of bacteria that often live in association with these animals. For many bacteria sialic acids are simply a convenient source of food, whereas for some pathogens they are also used in immune evasion strategies. Many bacteria that use sialic acids derive them from the environment and so are dependent on sialic acid uptake. In this mini-review I will describe the discovery and characterization of bacterial sialic acids transporters, revealing that they have evolved multiple times across multiple diverse families of transporters, including the ATP-binding cassette (ABC), tripartite ATP-independent periplasmic (TRAP), major facilitator superfamily (MFS) and sodium solute symporter (SSS) transporter families. In addition there is evidence for protein-mediated transport of sialic acids across the outer membrane of Gram negative bacteria, which can be coupled to periplasmic processing of different sialic acids to the most common form, β-D-N-acetylneuraminic acid (Neu5Ac) that is most frequently taken up into the cell.
A key challenge in mobilising growing numbers of digitised biological specimens for scientific research is finding high-throughput methods to extract phenotypic measurements on these datasets. In ...this paper, we test a pose estimation approach based on Deep Learning capable of accurately placing point labels to identify key locations on specimen images. We then apply the approach to two distinct challenges that each requires identification of key features in a 2D image: (i) identifying body region-specific plumage colouration on avian specimens and (ii) measuring morphometric shape variation in Littorina snail shells. For the avian dataset, 95% of images are correctly labelled and colour measurements derived from these predicted points are highly correlated with human-based measurements. For the Littorina dataset, more than 95% of landmarks were accurately placed relative to expert-labelled landmarks and predicted landmarks reliably captured shape variation between two distinct shell ecotypes ('crab' vs 'wave'). Overall, our study shows that pose estimation based on Deep Learning can generate high-quality and high-throughput point-based measurements for digitised image-based biodiversity datasets and could mark a step change in the mobilisation of such data. We also provide general guidelines for using pose estimation methods on large-scale biological datasets.
Phylogenetic comparative methods are increasingly used to give new insights into the dynamics of trait evolution in deep time. For continuous traits the core of these methods is a suite of models ...that attempt to capture evolutionary patterns by extending the Brownian constant variance model. However, the properties of these models are often poorly understood, which can lead to the misinterpretation of results. Here we focus on one of these models – the Ornstein Uhlenbeck (OU) model. We show that the OU model is frequently incorrectly favoured over simpler models when using Likelihood ratio tests, and that many studies fitting this model use datasets that are small and prone to this problem. We also show that very small amounts of error in datasets can have profound effects on the inferences derived from OU models. Our results suggest that simulating fitted models and comparing with empirical results is critical when fitting OU and other extensions of the Brownian model. We conclude by making recommendations for best practice in fitting OU models in phylogenetic comparative analyses, and for interpreting the parameters of the OU model.
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