Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Can Asset Pricing Models Price Idiosyncratic Risk in U.K. Stock Returns?

I examine how well different linear factor models and consumption-based asset pricing models price idiosyncratic risk in U.K. stock returns. Correctly pricing idiosyncratic risk is a significant challenge for many of the models I consider. For some consumption-based models, there is a clear tradeoff in the performance of the models between correctly pricing systematic risk and idiosyncratic risk. Linear factor models do a better job in most cases in pricing systematic risk than consumption-based models but the reverse is true for idiosyncratic risk. Copyright 2007, The Eastern Finance Association.