DISTURBANCE AND STAND DYNAMICS IN PONDEROSA PINE FORESTS IN ROCKY MOUNTAIN NATIONAL PARK, USA

2003 ◽  
Vol 73 (4) ◽  
pp. 543-566 ◽  
Author(s):  
Donna S. Ehle ◽  
William L. Baker
Keyword(s):  
Ponderosa Pine ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Pine Forests ◽  
Stand Dynamics ◽  
Ponderosa Pine Forests

Historical (1860) forest structure in ponderosa pine forests of the northern Front Range, Colorado

2015 ◽  
Vol 45 (11) ◽  
pp. 1462-1473 ◽  
Author(s):  
Peter M. Brown ◽  
Michael A. Battaglia ◽  
Paula J. Fornwalt ◽  
Benjamin Gannon ◽  
Laurie S. Huckaby ◽  
...  
Keyword(s):  
Pinus Ponderosa ◽  
Forest Structure ◽  
Ponderosa Pine ◽  
Rocky Mountain ◽  
Structural Data ◽  
Pine Forests ◽  
Land Use Impacts ◽  
Quadratic Mean ◽  
Ponderosa Pine Forests ◽  
Northern Colorado

Management of many dry conifer forests in western North America is focused on promoting resilience to future wildfires, climate change, and land use impacts through restoration of historical patterns of forest structure and disturbance processes. Historical structural data provide models for past resilient conditions that inform the design of silvicultural treatments and help to assess the success of treatments at achieving desired conditions. We used dendrochronological data to reconstruct nonspatial and spatial forest structure at 1860 in fourteen 0.5 ha plots in lower elevation (∼1900–2100 m) ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) forests across two study areas in northern Colorado. Fires recorded by trees in two or more plots from 1667 to 1859 occurred, on average, every 8–15 years depending on scale of analysis. The last fire recorded in two or more plots occurred in 1859. Reconstructed 1860 stand structures were very diverse, with tree densities ranging from 0 to 320 trees·ha−1, basal areas ranging from 0.0 to 17.1 m2·ha−1, and quadratic mean diameters ranging from 0.0 to 57.5 cm. All trees in 1860 were ponderosa pine. Trees were significantly aggregated in 62% of plots in which spatial patterns could be estimated, with 10% to 90% of trees mainly occurring in groups of two to eight (maximum, 26). Current stands based on living trees with a diameter at breast height of ≥4 cm are more dense (range, 175–1010 trees·ha−1) with generally increased basal areas (4.4 to 23.1 m2·ha−1) and smaller trees (quadratic mean diameters ranging from 15.7 to 28.2 cm) and contain greater proportions of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and Rocky Mountain juniper (Juniperus scopulorum Sarg.). This is the first study to provide detailed quantitative metrics to guide restoration prescription development, implementation, and evaluation in these and similar ponderosa pine forests in northern Colorado.

Woody fuel structure and fire in subalpine fir forests, Olympic National Park, Washington

1990 ◽  
Vol 20 (2) ◽  
pp. 193-199 ◽  
Author(s):  
K. L. Taylor ◽  
R. W. Fonda
Keyword(s):  
Ponderosa Pine ◽  
National Park ◽  
Pine Forests ◽  
Fuel Moisture ◽  
Subalpine Fir ◽  
Ponderosa Pine Forests ◽  
Olympic National Park ◽  
The Many ◽  
The Relationship

The fuel structure and flammability of subalpine fir (Abieslasiocarpa (Hook.) Nutt.) stands were studied to determine the relationship between these forests and fire. It has long been known that subalpine fir forests burn catastrophically, but the contributions of fuel structure and fuel moisture to this pattern of burning have been relatively unstudied. This investigation discovered two relationships. First, over twice as much fuel in subalpine fir forests accumulated around the bases of the fir trees than in the forest as a whole, and the many dead branches on the lower trunks may allow fire to travel up into the canopy. Second, the fuels in subalpine fir forests were more flammable at the end of the summer than at the beginning, and maximum flammability was achieved in early August when the fuel moisture was between 16 and 22%. We also found that the fuel structure of subalpine fir was different from that of fire-stable ponderosa pine (Pinusponderosa Laws.) forests. The fuel around the bases of the trees in ponderosa pine forests was not significantly different from that in the entire forest, and there were few branches on the lower trunks.

scholarly journals Some Effects of Fire on Vegetation and Wildlife in Ponderosa Pine Forests of the Southern Black Hills

1981 ◽  
Vol 5 ◽  
pp. 90-95
Author(s):  
Jane Bock ◽  
Carl Bock
Keyword(s):  
South Dakota ◽  
Ponderosa Pine ◽  
National Park ◽  
Progress Report ◽  
Black Hills ◽  
Pine Forests ◽  
Ponderosa Pine Forests ◽  
Growing Seasons ◽  
Wildlife Populations ◽  
Ponderosa Pine Forest

This progress report summarized our findings in three years of research at Wind Cave National Park. Effects of prescription burns upon vegetation, birds, rodents, and bison were studied for three years in ponderosa pine forest and pine-grassland ecotone of Wind Cave National Park, South Dakota. We established study plots and analyzed vegetation in summer 1979, prior to fall 1979 and spring 1980 burning. Vegetation and wildlife populations were studied in 1980 and 1981, through two post-fire growing seasons.

Effects of Oxidant Air-pollution on Needle Health and Annual-ring Width in a Ponderosa Pine Forest

1986 ◽  
Vol 13 (3) ◽  
pp. 229-234 ◽  
Author(s):  
Wayne T. Williams ◽  
JoAnne Ackermann Williams
Keyword(s):  
Air Pollution ◽  
Sierra Nevada ◽  
Ponderosa Pine ◽  
National Park ◽  
Ring Width ◽  
Pine Forests ◽  
Annual Ring Width ◽  
Ponderosa Pine Forests ◽  
Second Year ◽  
Ponderosa Pine Forest

More than 485,000 hectares of the Ponderosa Pine forests in the southern Sierra Nevada of California are diseased as a result of air pollution, while approximately 25% of the area of Sequoia-Kings Canyon National Park receives enough oxidant air-pollution to elicit symptoms of smog injury on Ponderosa Pine, the dominant mid-elevation tree.In 1983 we re-examined four permanent study plots that had been established in 1974–75. Smog symptoms on current needles in 1983 occurred on 23.8% of the trees compared with 14.5% only in 1975. Second-year needles had 60.7% showing symptoms compared with 44.2% in 1975. Less than 0.5% of the trees retained 6-years-old needles, and 21% had only 2 years of needle retention. Mortality attributed to air pollution was observed.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

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