Modeled hydraulic redistribution in tree-grass, CAM-grass, and tree-CAM associations: the implications of crassulacean acid metabolism (CAM).

Past studies have largely focused on hydraulic redistribution (HR) in trees, shrubs, and grasses, and recognized its role in interspecies interactions. HR in plants that conduct crassulacean acid metabolism (CAM), however, remains poorly investigated, as does the effect of HR on transpiration in different vegetation associations (i.e., tree-grass, CAM-grass, and tree-CAM associations). We have developed a mechanistic model to investigate the net direction and magnitude of HR at the patch scale for tree-grass, CAM-grass, and tree-CAM associations at the growing season to yearly timescale. The modeling results show that deep-rooted CAM plants in CAM-grass associations could perform hydraulic lift at a higher rate than trees in tree-grass associations in a relatively wet environment, as explained by a significant increase in grass transpiration rate in the shallow soil layer, balancing a lower transpiration rate by CAM plants. By comparison, trees in tree-CAM associations may perform hydraulic descent at a higher rate than those in tree-grass associations in a dry environment. Model simulations also show that hydraulic lift increases the transpiration of shallow-rooted plants, while hydraulic descent increases that of deep-rooted plants. CAM plants transpire during the night and thus perform HR during the day. Based on these model simulations, we suggest that the ability of CAM plants to perform HR at a higher rate may have different effects on the surrounding plant community than those of plants with C3 or C4 photosynthetic pathways (i.e., diurnal transpiration).

Bridging implementation gaps to connect large ecological datasets and complex models.

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree-ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced-UVAFME. Through this process, we provide preliminary ecological inference about the long-term competitive dynamics between slow-growing P. mariana and relatively faster-growing P. glauca. Incorporating tree-ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster-growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska.

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990's beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR.