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Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for […]

LongSoldier-poster for web

Ecoss hosts Layli Long Soldier reading

The Ecoss and McAllister collaboration on Community, Culture and the Environment is honored to welcome poet Layli Long Soldier to NAU February 21, 2019 for a public reading. Long Soldier is the author of Whereas, a finalist for the National Book Award in 2017. She was awarded a National Artist Fellowship […]

Clarifying the interpretation of carbon use efficiency in soil through methods comparison

Accurate estimates of microbial carbon use efficiency (CUE) are required to predict how global change will impact microbially-mediated ecosystem functions such as organic matter decomposition. Multiple approaches are currently used to quantify CUE but the extent to which estimates reflect methodological variability is unknown. This limits our ability to apply […]

Differential responses of carbon-degrading enzyme activities to warming: Implications for soil respiration

Extracellular enzymes catalyze rate-limiting steps in soil organic matter decomposition, and their activities (EEAs) play a key role in determining soil respiration (SR). Both EEAs and SR are highly sensitive to temperature, but their responses to climate warming remain poorly understood. Here, we present a meta-analysis on the response of […]

Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery

Global change is shifting the seasonality of vegetation in ecosystems around the globe. High-frequency digital camera imagery, and vegetation indices derived from that imagery, is facilitating better tracking of phenological responses to environmental variation. This method, commonly referred to as the “phenocam” approach, is well-suited to several specific applications, including: […]