Jaclyn A. Hatala

Gross ecosystem photosynthesis causes a diurnal pattern in methane emission from rice

Author(s): Hatala, JA; Detto, M; Baldocchi, DD | Abstract: Understanding the relative contribution of environmental and substrate controls on rice paddy methanogenesis is critical for developing mechanistic models of landscape-scale methane (CH4) flux. A diurnal pattern in observed rice paddy CH4 flux has been attributed to fluctuations in soil temperature physically driving diffusive CH4 transport from the soil to atmosphere. Here we make direct landscape-scale measurements of carbon dioxide and CH4 fluxes and show that gross ecosystem photosynthesis (GEP) is the dominant cause of the diurnal pattern in CH4 flux, even after accounting for the effects of soil temperature. The time series of GEP and CH4 flux show strong spectral coherency throughout the rice growing season at the diurnal timescale, where the peak in GEP leads that of CH 4 flux by 1.3±0.08 hours. By applying the method of conditional Granger causality in the spectral domain, we demonstrated that the diurnal pattern in CH4 flux is primarily caused by GEP. Copyright 2012 by the American Geophysical Union.

An ecosystem-scale model for the spread of a host-specific forest pathogen in the Greater Yellowstone Ecosystem

The introduction of nonnative pathogens is altering the scale, magnitude, and persistence of forest disturbance regimes in the western United States. In the high-altitude whitebark pine (Pinus albicaulis) forests of the Greater Yellowstone Ecosystem (GYE), white pine blister rust (Cronartium ribicola) is an introduced fungal pathogen that is now the principal cause of tree mortality in many locations. Although blister rust eradication has failed in the past, there is nonetheless substantial interest in monitoring the disease and its rate of progression in order to predict the future impact of forest disturbances within this critical ecosystem. This study integrates data from five different field-monitoring campaigns from 1968 to 2008 to create a blister rust infection model for sites located throughout the GYE. Our model parameterizes the past rates of blister rust spread in order to project its future impact on high-altitude whitebark pine forests. Because the process of blister rust infection and mortality of individuals occurs over the time frame of many years, the model in this paper operates on a yearly time step and defines a series of whitebark pine infection classes: susceptible, slightly infected, moderately infected, and dead. In our analysis, we evaluate four different infection models that compare local vs. global density dependence on the dynamics of blister rust infection. We compare models in which blister rust infection is: (1) independent of the density of infected trees, (2) locally density-dependent, (3) locally density-dependent with a static global infection rate among all sites, and (4) both locally and globally density-dependent. Model evaluation through the predictive loss criterion for Bayesian analysis supports the model that is both locally and globally density-dependent. Using this best-fit model, we predicted the average residence times for the four stages of blister rust infection in our model, and we found that, on average, whitebark pine trees within the GYE remain susceptible for 6.7 years, take 10.9 years to transition from slightly infected to moderately infected, and take 9.4 years to transition from moderately infected to dead. Using our best-fit model, we project the future levels of blister rust infestation in the GYE at critical sites over the next 20 years.