Our species displays remarkable linguistic diversity. Although the uneven distribution of this diversity demands explanation, the drivers of these patterns have not been conclusively determined. We address this issue in two steps: First, we review previous empirical studies whose authors have suggested environmental, geographical, and sociocultural drivers of linguistic diversification. However, contradictory results and methodological variation make it difficult to draw general conclusions. Second, we outline a program for future research. We suggest that future analyses should account for interactions among causal factors, the lack of spatial and phylogenetic independence of the data, and transitory patterns. Recent analytical advances in biogeography and evolutionary biology, such as simulation modeling of diversity patterns, hold promise for testing four key mechanisms of language diversification proposed here: neutral change, population movement, contact, and selection. Future modeling approaches should also evaluate how the outcomes of these processes are influenced by demography, environmental heterogeneity, and time.

Ecosystem services research has been focused on the ways that humans directly benefit from goods and services, and economic valuation techniques have been used to measure those benefits. We argue that, although it is appropriate in some cases, this focus on direct use and economic quantification is often limiting and can detract from environmental research and effective management, in part by crowding out other understandings of human—environment relationships. Instead, we make the case that the systematic consideration of multiple metaphors of such relationships in assessing social—ecological systems will foster better understanding of the many ways in which humans relate to, care for, and value ecosystems. Where it is possible, we encourage a deliberative approach to ecosystem management whereby ecosystem researchers actively engage conservationists and local resource users to make explicit, through open deliberation, the types of metaphors salient to their conservation problem.

International collaboration can be crucial in determining the outcomes of conservation actions. Here, we propose a framework for incorporating demographic, socioeconomic, and political data into conservation prioritization in complex regions shared by multiple countries. As a case study, we quantitatively apply this approach to one of the world's most complex and threatened biodiversity hotspots: the Mediterranean Basin. Our analysis of 22 countries surrounding the Mediterranean Sea showed that the strongest economic, trade, tourism, and political ties are clearly among the three northwestern countries of Italy, France, and Spain. Although economic activity between countries is often seen as a threat, it may also serve as an indicator of the potential of collaboration in conservation. Using data for threatened marine vertebrate species, we show how areas prioritized for conservation shift spatially when economic factors are used as a surrogate to favor areas where collaborative potential in conservation is more likely.

The relationship between inter- and transdisciplinary research and potentially transformative science is poorly understood. We use a case study of a long-term transdisciplinary research effort on hantaviruses combined with findings from studies of team science to generate a hypothesized model that links cross-disciplinary collaboration with transformative scientific outcomes. We show that potentially transformative research depends on the existence of an interesting and worthwhile problem to which participants can contribute in salient ways, human and material foundations within disciplines, collaborative mutualism across disciplines, and a transformative learning process that enables knowledge integration across diverse perspectives. Transformative learning theory suggests that new, integrated conceptual understanding is initiated by disorienting dilemmas. We argue that engagement in cross-disciplinary collaboration produces disorienting dilemmas that initiate transformative learning. Our hypothesized model provides a generalized framework for understanding how transformative learning occurs in cross-disciplinary collaboration and how that can lead to transformative science.

Sensor networks are revolutionizing environmental monitoring by producing massive quantities of data that are being made publically available in near real time. These data streams pose a challenge for ecologists because traditional approaches to quality assurance and quality control are no longer practical when confronted with the size of these data sets and the demands of real-time processing. Automated methods for rapidly identifying and (ideally) correcting problematic data are essential. However, advances in sensor hardware have outpaced those in software, creating a need for tools to implement automated quality assurance and quality control procedures, produce graphical and statistical summaries for review, and track the provenance of the data. Use of automated tools would enhance data integrity and reliability and would reduce delays in releasing data products. Development of community-wide standards for quality assurance and quality control would instill confidence in sensor data and would improve interoperability across environmental sensor networks.

Evolution is a difficult theory for students to understand. Part of the reason for this may be the tendency of instructors to teach evolution in the context of ecological systems, isolated from genetic and cellular mechanisms. To address this, we developed a set of integrative cases that consider the evolution of traits from the genetic scale to the macroecological scale. We implemented two of these cases in a biology course and tested their effectiveness using a pre-and postcourse assessment tool. Students who successfully learned evolution in a case context were better able to explain the molecular basis of mutation, to connect mutation to phenotypic change, and to make mechanistic links between genotypes and phenotypes. These gains were independent of the students' course achievement and precourse understanding of evolution. These findings support the hypothesis that students who acquire a molecular understanding of evolutionary mechanisms will have a better overall understanding of evolution.