Abstract. Ecosystem stability in variable environments depends on the diversity of form and function of the constituent species. Species phenotypes and ecologies are the product of evolution, and the evolutionary history represented by co-occurring species has been shown to be an important predictor of ecosystem function. If phylogenetic distance is a surrogate for ecological differences, then greater evolutionary diversity should buffer ecosystems against environmental variation and result in greater ecosystem stability. We calculated both abundance-weighted and unweighted phylogenetic measures of plant community diversity for a long-term biodiversity-ecosystem function experiment at Cedar Creek, Minnesota, USA. We calculated a detrended measure of stability in aboveground biomass production in experimental plots and showed that phylogenetic relatedness explained variation in stability. Our results indicate that communities where species are evenly and distantly related to one another are more stable compared to communities where phylogenetic relationships are more clumped. This result could be explained by a phylogenetic sampling effect, where some lineages show greater stability in productivity compared to other lineages, and greater evolutionary distances reduce the chance of sampling only unstable groups. However, we failed to find evidence for similar stabilities among closely related species. Alternatively, we found evidence that plot biomass variance declined with increasing phylogenetic distances, and greater evolutionary distances may represent species that are ecologically different (phylogenetic complementarity). Accounting for evolutionary relationships can reveal how diversity in form and function may affect stability.

Interdisciplinary research is widely considered a hothouse for innovation, and the only plausible approach to complex problems such as climate change. One barrier to interdisciplinary research is the widespread perception that interdisciplinary projects are less likely to be funded than those with a narrower focus. However, this commonly held belief has been difficult to evaluate objectively, partly because of lack of a comparable, quantitative measure of degree of interdisciplinarity that can be applied to funding application data. Here we compare the degree to which research proposals span disparate fields by using a biodiversity metric that captures the relative representation of different fields (balance) and their degree of difference (disparity). The Australian Research Council's Discovery Programme provides an ideal test case, because a single annual nationwide competitive grants scheme covers fundamental research in all disciplines, including arts, humanities and sciences. Using data on all 18,476 proposals submitted to the scheme over 5 consecutive years, including successful and unsuccessful applications, we show that the greater the degree of interdisciplinarity, the lower the probability of being funded. The negative impact of interdisciplinarity is significant even when number of collaborators, primary research field and type of institution are taken into account. This is the first broad-scale quantitative assessment of success rates of interdisciplinary research proposals. The interdisciplinary distance metric allows efficient evaluation of trends in research funding, and could be used to identify proposals that require assessment strategies appropriate to interdisciplinary research.

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages.