You-Fang Chen

As the two largest countries by population, China and India have pervasive effects on the ecosphere. Because of their human population size and long international boundary, they share biodiversity and the threats to it, as well as crops, pests and diseases. We ranked the two countries on a variety of environmental challenges and solutions, illustrating quantitatively their environmental footprint and the parallels between them regarding the threats to their human populations and biodiversity. Yet we show that China and India continue to have few co-authorships in environmental publications, even as their major funding for scientific research has expanded. An agenda for collaboration between China and India can start with the shared Himalaya, linking the countries’ scientists and institutions. A broader agenda can then be framed around environmental challenges that have regional patterns. Coordinated and collaborative research has the potential to improve the two countries’ environmental performance, with implications for global sustainability.

The species pool concept was formulated over the past several decades and has since played an important role in explaining multi-scale ecological patterns. Previous statistical methods were developed to identify species pools based on broad-scale species range maps or community similarity computed from data collected from many areas. No statistical method is available for estimating species pools for a single local community (sampling area size may be very small as ≤ 1 km2). In this study, based on limited local abundance information, we developed a simple method to estimate the area size and richness of a species pool for a local ecological community. The method involves two steps. In the first step, parameters from a truncated negative trinomial model characterizing the distributional aggregation of all species (i.e., non-random species distribution) in the local community were estimated. In the second step, we assume that the unseen species in the local community are most likely the rare species, only found in the remaining part of the species pool, and vice versa, if the remaining portion of the pool was surveyed and was contrasted with the sampled area. Therefore, we can estimate the area size of the pool, as long as an abundance threshold for defining rare species is given. Since the size of the pool is dependent on the rarity threshold, to unanimously determine the pool size, we developed an optimal method to delineate the rarity threshold based on the balance of the changing rates of species absence probabilities in the sampled and unsampled areas of the pool. For a 50 ha (0.5 km2) forest plot in the Barro Colorado Island of central Panama, our model predicted that the local, if not regional, species pool for the 0.5 km2 forest plot was nearly the entire island. Accordingly, tree species richness in this pool was estimated as around 360. When the sampling size was smaller, the upper bound of the 95% confidence interval could reach 418, which was very close to the flora record of tree richness for the island. A numerical test further demonstrated the power and reliability of the proposed method, as the true values of area size and species richness for the hypothetical species pool have been well covered by the 95% confidence intervals of the true values. Our method fills the knowledge gap on estimating species pools for a single local ecological assemblage with little information. The method is statistically robust and independent of sampling size, as proved by both empirical and numerical tests.