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Impact of a severe microburst on the structure of a mixed deciduous forest on the Maryland coastal plain Geoffrey Parker Smithsonian Environmental Research Center
overview • introduce a large-scale forest study • describe the onset of a severe weather event • approaches for evaluating the damage • summarize the various effects • ecological context of the event • next steps
Big Tree stem project • 9528 Trees (DBH of 20 cm or greater) • 46.4 hectares (square) • 106.5 football fields • 2840 volleyball courts • 34 species of woody plants • other information • location • mapping stage • crown class • condition • diameter at breast height (DBH) • elevation
ecological disturbance • a disturbance is defined as a relatively discrete event that disrupts the structure of an ecosystem, community, or population, and changes resource availability or the physical environment (White and Pickett 1985). • disturbance causes, patterns, dynamics and consequences are major research topics in ecology (Romme and Knight 1982; Risser et al. 1984; Turner 1987b, 1989; Baker 1989a, 1989c; Turner and Dale 1998).
disturbances in forest ecology • disturbances play an important role in forest ecosystems and communities • trees are sessile organisms and they care about where they live and who their neighbors are • trees must adjust to the new conditions produced by the disturbance • forest community structure • forest development • biomass production
why bother with tree elevation? contour map of the Big Tree Plot • drought and flood tolerance • elevation ranges from about sea level (floodplain) to 87ft above sea level • how can one get elevation information for each tree? • county contour maps are insufficient resolution for tree-level elevations 5 ft contours
March 2002 • Leaf-off • High accuracy LIght Detecting and Ranging (LIDAR)
crown class determination D = Dominant CD = Co-dominant I = Intermediate S = Suppressed
objectives and hypotheses • objectives: • survey the damage in the Big Tree Plot • incorporate the damage data into the existing database • determine disturbance trends and potential consequences • hypotheses: • Damage distribution was not random: • species • crown class • DBH • elevation • tree fall direction was not random • forest biomass production was dramatically affected
methods • storm damage survey • a categorical classification • compass readings of tree fall direction • causal agent • storm (direct damage) • tree (indirect damage) • Geographic Information Systems (GIS) • spatial representation of the Big Tree Plot and storm damage • statistical analyses
damage classification DIRECT INDIRECT DEAD LIVE DEAD LIVE MAJOR MINOR MAJOR MINOR TOPPED SNAPPED UPROOTED CROWN DAMAGE BENT BY WIND CROWN DAMAGE BENT BY WIND BENT BY FALLING TREE BARK SCRAPED OFF TOPPED SNAPPED UPROOTED
15ft 25ft 20ft Snapped Mockernut White Oak – Minor Crown Damage BackRoad Snapped Sweetgum Snapped White Oak Topped Dead Sweetgum Snapped Sycamore example of direct and indirect damage
preliminary damage survey • 21 different species affected • 22 different categories of damage • 408/9528 trees = 4.28% • generally fell in an easterly direction (40-90°)
statistical analyses • the distribution of the damage was not random among different tree species, crown classes, DBH or elevation • contingency table comparing damaged and undamaged trees • Chi-square test to determine expected damage, which will be compared to the actual • tree fall direction was not random • circular statistics to determine angular distribution • the forest biomass production was dramatically reduced • estimations of biomass production and predicted loss, which is heavily dependant on crown class
distribution of damage by species 35% of total trees damaged 6% of total trees damaged
damage by crown class crown classes D = dominant CD = co-dominant I = intermediate S = suppressed
damaged pasture trees • all damage was significantly higher than expected • 13 of the 54 known pasture trees were damaged
distribution of damage by elevation elevation range: 0 = 0 – 5m 5 = 5 – 10m 10 = 10 – 15m 15 = 15 – 20m 20 = 20 – 25m 25 = 25 – 30m
tree fall direction • only considered in cases of major damage • mean fall direction was 65°, with a mean angular deviation of 49° • live trees had comparable fall directions to the mean • dead trees varied dramatically from the mean
tree fall direction • indicative of potential wind pattern • landscape features may have affected tree fall direction • variation in fall direction can be explained by tree condition and damage classification direct, major (live and dead) damage
tree fall direction – classification differences Direct, Major, Live Direct, Major, Dead Indirect, Major, Dead Indirect, Major, Live
Ecosystem Consequences: Biomass Production annual above-ground biomass production: annual wood production = 4.3 Mg ha-1 annual foliage production = 3.9 Mg ha-1 Total Production = +8.2 Mg ha-1 existing above-ground biomass: prior to the storm = 235.4 Mg ha-1 after the storm = 229.1 Mg h1 Biomass Change = -6.3 Mg ha-1 Almost one year’s worth of growth was destroyed.
conclusions • distribution of the damage was non-random: • tulip poplars had the most number of trees damaged, but southern red oaks had the highest percentage of damage by species • dominant and co-dominant trees experienced more damage than expected, while intermediate and suppressed trees experienced less damage than expected • In terms of DBH, even though most major damage was experienced by smaller trees the damage was less than expected, while larger trees had higher than expected damage • pasture trees damage was higher than expected • higher than expected damage in low elevation ranges for both major and minor damage categories
more conclusions • tree fall direction was not random, but mostly between 40 and 90°, which may be indicative of an easterly wind direction • biomass production of the forest was set back almost a year in this one event that only affected about 4% of the trees in the Big Tree Plot
future directions • drought effect on root stability • windward vs. leeward location effect on damage severity/occurrence • lack of objective data on the strength of the storm … need to obtain meteorological information for the event • explore the effect of the storm on the canopy structure and the production of gaps and increased understory light
many thanks • Brianna Miles • Canopy Lab • Michelle Berger • Rehanna Chaudhri • George Rasberry • Nancy Lee • UMCP – Geography Department • Steve Prince • Marcia Snyder • Volunteers: • Dawn Miller • Joyce Schick • Melissa Parker • David Miles • Karen Yee • Kate Levendosky • Naomi Hosaka • Mandy Clancy • April Chiriboga • Jackie Allen • Chaquettea Felton