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- Aquatic Ecology
- Behavioral and Evolutionary Ecology
- Conservation & Restoration Ecology
- Landscape Ecology
- Plant Community Response to Changes in Water
- Using Landsat Time Series Data to Examine Patterns in Water Surface Temperature in the Chesapeake Bay
- Extinction Risk of the Delmarva Fox Squirrel
- Potomac Initiative
- Quantifying Feedbacks in Desert Vegetation
- Remote Sensing and Forest Disturbance
- Medium-resolution Phenology and Forest Productivity
- Biologically-Optimized Environmental Classification of Maryland Streams
- Predicting Vulnerability to Sea Level Rise
- Landscape Controls on Seasonal Timing and Growing Season Length
- Watershed Hydrology and Biogeochemistry
- Acid-Base Status of Western Maryland Streams
- BMP's for Natural Gas Drilling
- Modeling Stream Distribution and Stream Burial in Large River Basins
- Improvements in Surface Water Quality Due to Declining Atmospheric N Deposition
- Land Use Changes on Stormflow Dynamics
- Piney Creek Reservoir Assessment
- Relationship Between Wetlands and Mercury in Brook Trout
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- Chesapeake Watershed CESU
- Central Appalachians Stable Isotope Facility
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Landscape Controls on Seasonal Timing and Growing Season Length
One of the largest, most easily predicted, and already documented biological impacts of climate change on temperate ecosystems is a lengthening of the growing season. In summer-active/winter-dormant systems, the timing of spring and autumn has a profound impact on the water, carbon, and energy balance of forests, grasslands, and agricultural fields. A longer growing season might be expected to increase carbon uptake by temperate forests, providing an important negative feedback to greenhouse gas concentrations and global warming. Understanding landscape and climatic controls on the trajectory of leaf area development, summer maturity, and senescence is therefore key to understanding the impact of feedback processes and ecosystem response to climate change.
Limited field-based and remote-sensing observations have suggested complex spatial patterns related to geographic features that influence climate. However, much of this variability occurs at spatial scales that inhibit a detailed understanding of even the dominant drivers.
Recognizing the limitations of existing observations, we used nonlinear inverse modeling of medium-resolution remote sensing data, organized by day of year, to explore the influence of climate-related landscape factors on the timing of spring and autumn leaf-area trajectories in forests in a highly fragmented landscape in the mid-Atlantic region of the United States, which is undergoing considerable pressure from land-use and climate changes.
We also examined the extent to which declining summer greenness (greendown) degrades the precision and accuracy of observations of autumn offset of greenness.
Of the dominant drivers of landscape phenology, elevation was the strongest, explaining up to 70% of the spatial variation in the onset of greenness. Urban land cover was second in importance, influencing spring onset and autumn offset to a distance of 32 km from large cities. Distance to tidal water also influenced phenological timing, but only within ~5 km of shorelines.
Additionally, we observed that (i) growing season length unexpectedly increased with increasing elevation at elevations below 275 m; (ii) along gradients in urban land cover, timing of autumn offset has a stronger effect on growing season length than does time of spring onset; and (iii) summer greendown introduces bias and uncertainty into observation of the autumn offset of greenness.
These results demonstrate the power of medium grain analyses of landscape-scale phenology for understanding environmental controls on growing season length, and predicting how these might be affected by climate change.
Project PI: Andrew Elmore