Biosensors, data privacy and manufacturing research highlighted at Davos
Wildfires: Fire expert urges Australian government to “prepare now for 2021”
Podcast: Drug policy, Australian megafires and London fatbergs
Please contact your Faculty Web Officer to add a Research publications feed to a page/section
The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.
The effective population size Embedded Image is a key parameter in population genetics and evolutionary biology, as it quantifies the expected distribution of changes in allele frequency due to genetic drift. Several methods of estimating Embedded Image have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator Embedded Image for contemporary effective population size using temporal data is developed in this article. The existing likelihood methods are computationally intensive and unable to handle the case when the underlying Embedded Image is large. This article tries to work around this problem by using a hidden Markov algorithm and applying continuous approximations to allele frequencies and transition probabilities. Extensive simulations are run to evaluate the performance of the proposed estimator Embedded Image, and the results show that it is more accurate and has lower variance than previous methods. The new estimator also reduces the computational time by at least 1000-fold and relaxes the upper bound of Embedded Image to several million, hence allowing the estimation of larger Embedded Image. Finally, we demonstrate how this algorithm can cope with nonconstant Embedded Image scenarios and be used as a likelihood-ratio test to test for the equality of Embedded Image throughout the sampling horizon. An R package “NB” is now available for download to implement the method described in this article.
Speciation on islands, and particularly the divergence of species in situ, has long been debated. Here, we present one of the first, complete assessments of the geographic modes of speciation for the flora of a small oceanic island. Cocos Island (Costa Rica) is pristine; it is located 550 km off the Pacific coast of Central America. It harbors 189 native plant species, 33 of which are endemic. Using phylogenetic data from insular and mainland congeneric species, we show that all of the endemic species are derived from independent colonization events rather than in situ speciation. This is in sharp contrast to the results of a study carried out in a comparable system, Lord Howe Island (Australia), where as much as 8.2% of the plant species were the product of sympatric speciation. Differences in physiography and age between the islands may be responsible for the contrasting patterns of speciation observed. Importantly, comparing phylogenetic assessments of the modes of speciation with taxonomy-based measures shows that widely used island biogeography approaches overestimate rates of in situ speciation.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.