Forest Floor Decomposition Following Hurricane Litter Inputs in Several Puerto Rican Forests - PowerPoint PPT Presentation

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Forest Floor Decomposition Following Hurricane Litter Inputs in Several Puerto Rican Forests

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Damage mostly to foliage, causes massive amounts of litter deposition ... 2 weeks following hurricane landfall using preexisting plots and equipment ... – PowerPoint PPT presentation

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Title: Forest Floor Decomposition Following Hurricane Litter Inputs in Several Puerto Rican Forests


1
Forest Floor Decomposition Following Hurricane
Litter Inputs in Several Puerto Rican Forests
  • Rebecca Ostertag
  • Frederick N. Scatena
  • Whendee L. Silver

2
Hurricanes as Disturbance Factors in Forests
  • Redistributes mass and nutrients in a forest
  • Damage mostly to foliage, causes massive amounts
    of litter deposition
  • Amount of damage and deposition depends on forest
    type

3
Purpose of Study
  • Examine litterfall production and disapearance
    from hurricanes and associated pools and dynamics
    of nutrients added through decomposition of
    litter inputs
  • Have pre-hurricane data for various ecosystem
    processes (litterfall rates, climate, etc.), so
    can compare to post-hurricane data later
    collected
  • Plots span elevation, climate, and species
    composition gradients

4
What They Expected
  • 3 main hypotheses
  • 1. Hurricane litter inputs best predicted from
    pre-hurricane litterfall rates
  • Ex. Sites with high litterfall production will
    have high litterfall inputs, probably due to
    environmental conditions
  • 2. Hurricane litter decomposes rapidly and
    effects are only felt for a few months (nutrient
    addition is brief and not sustained)
  • 3. High-elevation sites will experience less
    damage, less litterfall inputs but slower
    decomposition rates

5
Study Area
  • Luquillo Experimental Forest (LTER)
  • Range of conditions and forest types
  • Elevation range 300-1000 m a.s.l.
  • Rainfall range 2500-4735 mm/yr
  • 6 Mixed species to Palm forests sampled
  • Moist, Tabonuco, Palm, Colorado, Short Cloud, and
    Tall Cloud
  • Area hit by Hurricane Georges in 1998

6
Hurricane Georges
7
Methods
  • All sites had preexisting plots and data on
    litterfall and forest floor processes
  • Sampling was done 2-3 months before hurricane hit
  • Sampling was then done within 2 weeks following
    hurricane landfall using preexisting plots and
    equipment
  • Forest floor and litterfall then sampled at
    2,3,4,6,8,10,..52 weeks after hurricane
  • Soils sampled every 3 months
  • Stand level tree damage quantified using 10 m
    radius plots
  • Statistical analyses done on all data (ANOVA,
    decay rates, decay models, etc.)

8
On to the Results
9
Results Initial Inputs of Forest Floor Mass and
Nutrients
  • Hurricane Georges litterfall ranged from 0.55 to
    0.93 times annual litterfall, forest floor mass
    increased 1.1 to 2.5 times above perhurricane
    values
  • Moist forest had the largest storm litterfall
    inputs and the greatest increase in forest floor
    mass and also higher nutrient content due to
    large mass inputs

10
Types of Litter inputs to the forest floor
  • Out of five sites, approximately 50-60 of the
    forest floor mass consisted of leaves
  • 30-40 consisted of wood

11
Results Tree Damage
  • Defoliation was the most common form of damage,
    accounting for 77 to 86 of all tree damage
  • Uprooted trees were common in both moist (5.9)
    and palm forests (5.2)
  • Moist Forest had the most structural damage with
    both defoliation and uprooted trees

12
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13
Results Changes in Forest Floor Mass and
Nutrients over time
  • Forest floor mass returned to prehurricane values
    rapidly
  • 5-10 months for moist forest
  • 4-5 months for tabonuco forest
  • 1-2 months for palm forest
  • Nutrient Concentrations of the forest floor
    increased and decreased depending on the site and
    nutrient.
  • Nitrogen, P, Ca, and Mg concentrations increased
    above initial storm input levels in leaves of
    Moist forest
  • Potassium quickly decreased from leaves at all
    Moist, Tabonuco, and Palm forest due to leaching
    losses.

14
Results
15
Discussion
  • Moist forest had the greatest proportional
    increase of litterfall, but recovered more
    rapidly.
  • Upper-elevation palm forest had smaller increase
    in litterfall, but recovered at about the same
    rate due to a slower decomposition rate.
  • They also had higher percentage of leaves and
    fruit which decay faster than wood.

16
Long-term Impacts
  • Extent of damage depends on the severity of the
    storm and amount of time since the last storm.
  • Extent of recovery depends on the characteristics
    of the ecosystem
  • Individuals within the system
  • e.g., palms and ferns snap and uproot
    infrequently
  • Productivity of the system

17
Decomposition rates not accelerated
  • No difference in soil NO3-, NH4, or PO43
    concentrations were evident after 1 ½ 2 years
    after Hurricane Opal.
  • Only studies done within first four months of
    hurricanes have shown any increase in soil
    nutrients.
  • From this, they determined that if, in fact,
    hurricanes do affect ecosystem processes such as
    nutrient cycling and seedling regeneration, it is
    only within a brief period immediately following
    the event.
  • Not surprising because Georges only caused 5-10
    increase in total C in the top 10 cm.

18
Increased litterfall can be an important source
of nutrients over short-term time scales.
  • Most (gt 95) of the nutrients from
    hurricane-derived litter are taken up by plants.
  • Rapid refoliation (within 3-4 weeks)
  • Vigorous understory growth observed in grasses,
    seedlings, and vines.
  • desirable microsites.

19
Discussion
  • Finally, the take home message
  • The long-term impact of a disturbance on
    ecosystem structure and process results from the
    interactions of the initial damage and the
    recovery and repair mechanisms that operate
    during the subsequent phase or reorganization.
  • In other words, long-term impact equals how much
    damage is done and how well the system can
    recover from it.

20
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