Integrating team science into interdisciplinary graduate education: an exploration of the SESYNC Graduate Pursuit

Complex socio-environmental challenges require interdisciplinary, team-based research capacity. Graduate students are fundamental to building such capacity, yet formal opportunities for graduate students to develop these capacities and skills are uncommon. This paper presents an assessment of the Graduate Pursuit (GP) program, a formal interdisciplinary team science graduate research and training program administered by the National Socio-Environmental Synthesis Center (SESYNC). Quantitative and qualitative assessment of the program’s first cohort revealed that participants became significantly more comfortable with interdisciplinary research and team science approaches, increased their capacity to work across disciplines, and were enabled to produce tangible research outcomes. Qualitative analysis of four themes—(1) discipline, specialization, and shared purpose, (2) interpersonal skills and personality, (3) communication and teamwork, and (4) perceived costs and benefits—encompass participants’ positive and negative experiences and support findings from past assessments. The findings also identify challenges and benefits related to individual personality traits and team personality orientation, the importance of perceiving a sense of autonomy and independence, and the benefit of graduate training programs independent of the university and graduate program environment

Reimagining the potential of Earth observations for ecosystem service assessments

The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. This will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide

Scaling up biodiversity–ecosystem function relationships across space and over time

Understanding how to scale up effects of biological diversity on ecosystem functioning and services remains challenging. There is a general consensus that biodiversity loss alters ecosystem processes underpinning the goods and services upon which humanity depends. Yet most of that consensus stems from experiments performed at small spatial scales for short time frames, which limits transferability of conclusions to longer‐term, landscape‐scale conservation policies and management. Here we develop quantitative scaling relationships linking 374 experiments that tested plant diversity effects on biomass production across a range of scales. We show that biodiversity effects increase by factors of 1.68 and 1.10 for each 10‐fold increase in experiment temporal and spatial scales, respectively. Contrary to prior studies, our analyses suggest that the time scale of experiments, rather than their spatial scale, is the primary source of variation in biodiversity effects. But consistent with earlier research, our analyses reveal that complementarity effects, rather than selection effects, drive the positive space–time interactions for plant diversity effects. Importantly, we also demonstrate complex space–time interactions and nonlinear responses that emphasize how simple extrapolations from small‐scale experiments are likely to underestimate biodiversity effects in real‐world ecosystems. Quantitative scaling relationships from this research are a crucial step towards bridging controlled experiments that identify biological mechanisms across a range of scales. Predictions from scaling relationships like these could then be compared with observations for fine‐tuning the relationships and ultimately improving their capacities to predict consequences of biodiversity loss for ecosystem functioning and services over longer time frames across real‐world landscapes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163563/5/ecy3166-sup-0001-AppendixS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163563/4/ecy3166_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163563/3/ecy3166-sup-0003-AppendixS3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163563/2/ecy3166.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163563/1/ecy3166-sup-0002-AppendixS2.pd

Appendix A. Model selection statistics for landscape pattern effects on three hydrologic services at the subwatershed scale.

Model selection statistics for landscape pattern effects on three hydrologic services at the subwatershed scale

Appendix C. Spatial distribution of three hydrologic services at 30-m resolution.

Spatial distribution of three hydrologic services at 30-m resolution

Groundwater effects on net primary productivity and soil organic carbon: a global analysis

Groundwater affects ecosystem services (ES) by altering critical zone ecohydrological and biogeochemical processes. Previous research has demonstrated significant and nonlinear impacts of shallow groundwater on ES regionally, but it remains unclear how groundwater affects ES at the global scale and how such effects respond to environmental factors. Here, we investigated global patterns of groundwater relationships with two ES indicators—net primary productivity (NPP) and soil organic carbon (SOC)—and analyzed underlying factors that mediated groundwater influences. We quantitatively compared multiple high-resolution (∼1 km) global datasets to characterize water table depth (WTD), NPP and SOC, and performed spatial simultaneous autoregressive modeling to test how selected predictors altered WTD-NPP and WTD-SOC relationships. Our results show widespread significant WTD-NPP correlations (61.5% of all basins globally) and WTD-SOC correlations (64.7% of basins globally). Negative WTD-NPP correlations, in which NPP decreased with rising groundwater, were more common than positive correlations (62.4% vs. 37.6%). However, positive WTD-SOC relationships, in which SOC increased with rising groundwater, were slightly more common (53.1%) than negative relationships (46.9%). Climate and land use (e.g., vegetation extent) were dominant factors mediating WTD-NPP and WTD-SOC relationships, whereas topography, soil type and irrigation were also significant factors yet with lesser effects. Climate also significantly constrained WTD-NPP and WTD-SOC relationships, suggesting stronger WTD-NPP and WTD-SOC relationships with increasing temperature. Our results highlight that the relationship of groundwater with ES such as NPP and SOC are spatially extensive at the global scale and are likely to be susceptible to ongoing and future climate and land-use changes

Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes

Maximizing agricultural production on existing cropland is one pillar of meeting future global food security needs. To close crop yield gaps, it is critical to understand how climate extremes such as drought impact yield. Here, we use gridded, daily meteorological data and county-level annual yield data to quantify meteorological drought sensitivity of US maize and soybean production from 1958 to 2007. Meteorological drought negatively affects crop yield over most US crop-producing areas, and yield is most sensitive to short-term (1–3 month) droughts during critical development periods from July to August. While meteorological drought is associated with 13% of overall yield variability, substantial spatial variability in drought effects and sensitivity exists, with central and southeastern US becoming increasingly sensitive to drought over time. Our study illustrates fine-scale spatiotemporal patterns of drought effects, highlighting where variability in crop production is most strongly associated with drought, and suggests that management strategies that buffer against short-term water stress may be most effective at sustaining long-term crop productivity

Appendix B. Histogram of subwatershed size.

Histogram of subwatershed size