TY - JOUR
T1 - Cross-scale modeling of surface temperature and tree seedling establishment in mountain landscapes
AU - Dingman, John R.
AU - Sweet, Lynn C.
AU - McCullough, Ian
AU - Davis, Frank W.
AU - Flint, Alan
AU - Franklin, Janet
AU - Flint, Lorraine E.
N1 - Funding Information:
We gratefully acknowledge funding support from the National Science Foundation Macrosystems Biology Program, NSF #EF-1065864, and thank our collaborating investigators (A. Hall, L. Hannah, M. Moritz, M. North, K. Redmond, H. Regan, A. Syphard). The manuscript was improved by comments from H. Regan. S. McKnight and A. Shepard coordinated field site set-up, while E. Conlisk, S. Dashiell, L. di Scipio, E. Hopkins, A. MacDonald, K. Maher, J. McClure, P. Prather, E. Peck, R. Swab, and W. Wilkinson contributed to data collection and maintenance of the common gardens and field sensors. We thank the USDA Forest Service and Tejon Ranch Company for access to field sites. P. Slaughter has been instrumental with development of the field data processing system and database ingest software. Lastly, we would like to thank The Earth Research Institute staff at UC Santa Barbara for their assistance and support.
PY - 2013
Y1 - 2013
N2 - Introduction: Estimating surface temperature from above-ground field measurements is important for understanding the complex landscape patterns of plant seedling survival and establishment, processes which occur at heights of only several centimeters. Currently, future climate models predict temperature at 2 m above ground, leaving ground-surface microclimate not well characterized. Methods: Using a network of field temperature sensors and climate models, a ground-surface temperature method was used to estimate microclimate variability of minimum and maximum temperature. Temperature lapse rates were derived from field temperature sensors and distributed across the landscape capturing differences in solar radiation and cold air drainages modeled at a 30-m spatial resolution. Results: The surface temperature estimation method used for this analysis successfully estimated minimum surface temperatures on north-facing, south-facing, valley, and ridgeline topographic settings, and when compared to measured temperatures yielded an R2 of 0.88, 0.80, 0.88, and 0.80, respectively. Maximum surface temperatures generally had slightly more spatial variability than minimum surface temperatures, resulting in R2 values of 0.86, 0.77, 0.72, and 0.79 for north-facing, south-facing, valley, and ridgeline topographic settings. Quasi-Poisson regressions predicting recruitment of Quercus kelloggii (black oak) seedlings from temperature variables were significantly improved using these estimates of surface temperature compared to air temperature modeled at 2 m. Conclusion: Predicting minimum and maximum ground-surface temperatures using a downscaled climate model coupled with temperature lapse rates estimated from field measurements provides a method for modeling temperature effects on plant recruitment. Such methods could be applied to improve projections of species' range shifts under climate change. Areas of complex topography can provide intricate microclimates that may allow species to redistribute locally as climate changes.
AB - Introduction: Estimating surface temperature from above-ground field measurements is important for understanding the complex landscape patterns of plant seedling survival and establishment, processes which occur at heights of only several centimeters. Currently, future climate models predict temperature at 2 m above ground, leaving ground-surface microclimate not well characterized. Methods: Using a network of field temperature sensors and climate models, a ground-surface temperature method was used to estimate microclimate variability of minimum and maximum temperature. Temperature lapse rates were derived from field temperature sensors and distributed across the landscape capturing differences in solar radiation and cold air drainages modeled at a 30-m spatial resolution. Results: The surface temperature estimation method used for this analysis successfully estimated minimum surface temperatures on north-facing, south-facing, valley, and ridgeline topographic settings, and when compared to measured temperatures yielded an R2 of 0.88, 0.80, 0.88, and 0.80, respectively. Maximum surface temperatures generally had slightly more spatial variability than minimum surface temperatures, resulting in R2 values of 0.86, 0.77, 0.72, and 0.79 for north-facing, south-facing, valley, and ridgeline topographic settings. Quasi-Poisson regressions predicting recruitment of Quercus kelloggii (black oak) seedlings from temperature variables were significantly improved using these estimates of surface temperature compared to air temperature modeled at 2 m. Conclusion: Predicting minimum and maximum ground-surface temperatures using a downscaled climate model coupled with temperature lapse rates estimated from field measurements provides a method for modeling temperature effects on plant recruitment. Such methods could be applied to improve projections of species' range shifts under climate change. Areas of complex topography can provide intricate microclimates that may allow species to redistribute locally as climate changes.
KW - Black oak
KW - Climate change
KW - Generalized linear models
KW - Microclimate
KW - Spatial scale
KW - Surface temperature
KW - Topography
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U2 - 10.1186/2192-1709-2-30
DO - 10.1186/2192-1709-2-30
M3 - Article
AN - SCOPUS:84903610330
VL - 2
JO - Ecological Processes
JF - Ecological Processes
SN - 2192-1709
IS - 1
M1 - 30
ER -
