Tue, Aug 03, 2021:On Demand
Background/Question/Methods
Ecological communities are dynamic over space and time. As long-term datasets increase in scope and length, synthesis working groups can provide a powerful opportunity to understand the mechanisms and consequences of variability within ecological communities. At the same time, synthesis working groups also provide an opportunity to identify novel questions, develop and share new tools, and train a future generation of scientists. Here I trace the lineage of discoveries, methodological tools, collaborations, and lesson-learned that emerged across three successive working groups. I focus on the idea of community synchrony – the degree to which species covary or tradeoff over time – and explore the ways in which each successive working group shed new light on its causes and consequences for ecosystem stability. I quantify the number and scope of research products that can be traced back to an initial synthesis working group to highlight that the scientific impact of team-based science often extends far beyond its initial products.
Results/Conclusions A discovery made in the initial working group – that species tradeoff in variable environments and are synchronous in stable environments – formed the basis of my dissertation that experimentally identified trait tradeoffs as the mechanism for this pattern. A frustration experienced by that working group – that many measures of community structure, such as diversity indices, are “snapshots” that poorly reflect community dynamics – inspired a second working group to improve methods for ecological data synthesis. By bringing together ecologists and data scientists, this group created a statistical package to increase the accessibility of synchrony metrics while also training ecologists in open-science tools. Subsequent conversations about that work with population ecologists inspired a third working group that explored the timescale of synchrony, generating new metrics and highlighting that species tradeoff more strongly at long timescales. Key elements that contributed to the success of these groups include senior leadership that supported junior scientists, datasets identified and cleaned prior to meetings, and training in open-science tools from participants and NCEAS staff. Numerous additional questions and associated papers, grants, and working groups arose from relationships formed through these groups, reflecting the long and not always foreseeable scientific legacy of collaborative, team-based synthesis science.
Results/Conclusions A discovery made in the initial working group – that species tradeoff in variable environments and are synchronous in stable environments – formed the basis of my dissertation that experimentally identified trait tradeoffs as the mechanism for this pattern. A frustration experienced by that working group – that many measures of community structure, such as diversity indices, are “snapshots” that poorly reflect community dynamics – inspired a second working group to improve methods for ecological data synthesis. By bringing together ecologists and data scientists, this group created a statistical package to increase the accessibility of synchrony metrics while also training ecologists in open-science tools. Subsequent conversations about that work with population ecologists inspired a third working group that explored the timescale of synchrony, generating new metrics and highlighting that species tradeoff more strongly at long timescales. Key elements that contributed to the success of these groups include senior leadership that supported junior scientists, datasets identified and cleaned prior to meetings, and training in open-science tools from participants and NCEAS staff. Numerous additional questions and associated papers, grants, and working groups arose from relationships formed through these groups, reflecting the long and not always foreseeable scientific legacy of collaborative, team-based synthesis science.