Running title: Disturbance-driven changes in variability
DISTURBANCE-DRIVEN CHANGES IN THE VARIABILITY OF
ECOLOGICAL PATTERNS AND PROCESSES
Appendix S1. Studies included in the review of disturbance effects on variability.
Authors
Response*
T#
(disturbance
return
interval /
recovery
time)
S†
Prediction based
on S-T‡
Prediction
consistent
with
empirical
results?
Ecosystem
Disturbance(s)
Bennett 2004
Prairie
Agriculture
Soil phosphorus
concentration
(1/2)/50
1
Unstable system
Yes
Diekmann et al. 2007
Forest
Shifting
cultivation
Soil nutrient
concentrations
10/ (recovery
range:10-20)
1
Lower-higher
variability
Yes
Fuhlendorf et al. 2006
Prairie
Fire and grazing
Vegetation cover
and structure
3/1
0.3
Lower-higher
variability
Yes
Kashian et al. 2005
Forest
Fire
Stand density
200/2
1
Higher-much
higher variability
Yes
Spatial increase
Fraterrigo and Rusak
Reed et al. 2000
Marine
El Niño
Kelp forest
community
abundance
Schlesinger et al.
1996
Semi-arid
grassland
Grazing and
drought
Soil nutrient
concentrations
For grazing:
(1/10)/40
1
Unstable system
Yes
Su et al. 2006
Semi-arid
grassland
Grazing
Soil nutrient
concentrations
(1/10)/40
1
Unstable system
Yes
Terlizzi et al. 2005
Marine
Sewage
discharge
(1/10)/10
0.3
Unstable system
Yes
Fraterrigo et al. 2005
Regenerated
Forest
Agriculture,
harvesting
50/60
1
Lower-higher
variability
Yes
Guo et al. 2004
Forest
Harvest
50/50
1
Much higher
variability
Yes
Guo et al. 2004
Forest
Girdling
Forest floor litter
mass, soil nutrient
concentrations
50/20
1
Much higher
variability
Yes
Mou et al. 2005
Forest
Harvest,
girdling
Cover of species
regenerating from
seed bank
50/50
1
Higher-much
higher variability
Yes
Stark et al. 2003
Marine
Sewage
discharge
Soft-sediment
benthic
abundances
1/1
0.5
Higher-much
higher variability
Yes
Total molluscan
population
abundance
Soil phosphorus,
magnesium, and
potassium
concentrations
Forest floor litter
mass, soil nutrient
concentrations
~3/2
1
Higher-much
higher variability
Yes
2
Fraterrigo and Rusak
Marine
Industrial
effluent
Coleman et al. 2006
Marine
Release from
grazing
Piazzi et al. 2004
Marine
Sewage
discharge
Warwick & Clarke
1993
Marine
Organic inputs,
drilling, El
Niño, mining
Warwick & Clarke
1993
Marine
Organic inputs,
drilling, El
Niño, mining
Balestri et al. 2004
Sea grass growth
and morphological (1/10)/30
characteristics
1
Unstable system
No
1/3
0.5
Lower variability
No
(1/10)/30
1
Unstable system
No
Meiobenthos and
macrobenthos
1/5
0.5
Lower variability
No
Reef fish
(1/4)/10
0.5
Unstable system
No
Benthic intertidal
community
composition
1
1
Higher-much
higher variability
Yes
Benthic intertidal
community
composition
1.5
1
Higher-much
higher variability
Yes
Benthic intertidal
community
composition
3
1
Higher-much
higher variability
Yes
Rocky intertidal
algal cover and
biomass
Epiphyte
community
composition and
population
abundance
Temporal increase
Bertocci et al. 2005
Marine
Bertocci et al. 2005
Marine
Bertocci et al. 2005
Marine
Biomass
removal, low
intensity
Biomass
removal,
medium
intensity
Biomass
removal, high
intensity
3
Fraterrigo and Rusak
Bradley & Mustard
2005
Semi-arid
grassland
Invasive by an
exotic plant
Annual plant
productivity
1/~0 (no
recovery)
1
Unstable system
Yes
Collins 2000
Prairie
Fire, high
frequency
Plant community
composition
1/1
1
Higher-much
higher variability
Yes
Collins 2000
Prairie
Fire, medium
frequency
Plant community
composition
4/1
1
Higher-much
higher variability
Yes
Collins 2000
Prairie
Fire, low
frequency
Plant community
composition
1
Higher-much
higher variability
Yes
Collins 2000
Prairie
Fire, medium
frequency
Grasshopper
community
composition
4/3
1
Much higher
variability
Yes
Collins 2000
Prairie
Fire, low
frequency
Grasshopper
community
composition
20/3
1
Much higher
variability
Yes
Cottingham et al.
2000
Freshwater
Eutrophication
Algal pigment
concentrations
4/2
1
Much higher
variability
Yes
Hsieh et al. 2006
Marine
Commercial
fish harvest
Fish population
abundance
5/10
1
Unstable system
Yes
Micheli et al. 1999
Terrestrial
Predator
removal
Bird abundance
(1/10)/10
1
Unstable system
Yes
20/1
4
Fraterrigo and Rusak
Freshwater
Invasion by an
exotic
planktivore
Zooplankton
abundance
(1/10)/20
1
Unstable system
Yes
Freshwater
Predator
addition
Zooplankton
populations and
community
abundance
3/20
1
Unstable system
Yes
Prairie
Fire, high
frequency
Grasshopper
community
composition
1/3
1
Lower variabilityunstable system
No
Marine
Sewage
discharge
Molluscan
community
composition
(1/10)/365
0.3
Unstable system
Yes
Stark et al. 2003
Marine
Sewage
discharge
1/12
0.5
Lower variability
Yes
Fraterrigo et al. 2005
Regenerated
forest
Agriculture,
harvesting
50/60
1
Lower-higher
variability
Yes
Guo et al. 2004
Forest
Harvest,
girdling
Total plant cover
50/50
1
Much higher
variability
No
Lane & BassiriRad
2005
Prairie
Agriculture
Soil organic
matter content
50/50
1
Much higher
variability
No
Micheli et al. 1999
Rusak et al. 2001
Collins 2000
Spatial decrease
Terlizzi et al. 2005
Benthic
community
composition
Soil carbon,
nitrogen, and
calcium
concentrations
5
Fraterrigo and Rusak
Forest
Harvest,
girdling
Collins 2000
Prairie
Fire, high
frequency
Jones et al. in press
Freshwater
Typhoon
Collins 2000
Prairie
Fire, medium
frequency
Collins 2000
Prairie
Fire, low
frequency
Marine
Oil spill
Marine
Sewage
discharge
Mou et al. 2005
Cover of species
regenerating from
stumps or
dispersed seeds
50/50
1
Higher-much
higher variability
No
1/3
1
Lower variabilityunstable system
Yes
(1/300)/2
1
Much higher
variability
No
4/3
1
Much higher
variability
No
20/3
1
Much higher
variability
No
Intertidal
macroinvertebrate
abundance
>100
1
Unchanged-lower
variability
Yes
Encrusting
benthos
abundance
(1/10)/30
0.5
Unstable system
No
Temporal decrease
Bird and small
mammal
community
composition
Bacterioplankton
community
composition
Bird and small
mammal
community
composition
Bird and small
mammal
community
composition
No effect
Queiroz et al. 2006
Chapman et al. 1995
6
Fraterrigo and Rusak
7
Meio- and
macrobenthic
(1/2)/30
1/4
Unstable system
No
anundance
* Different metrics were used to calculate variability, but are unspecified here. Refer to the individual studies for more detail.
Lardicci et al. 1999
Marine
Thermal
pollution
# S refers to the spatial extent of disturbance, calculated as the size of disturbance relative to ecosystem size.
Appendix S1. Continued.
† T refers to the frequency and intensity of disturbance, calculated as the disturbance return interval relative to the recovery time of the
response variable.
‡ Refer to Fig. 1 to map the state space and generate predictions for given values of S and T.
Appendix S2. Parameterization and assumptions used to revise the Turner et al. (1993) model.
The landscape equilibrium model developed by Turner et al. (1993) generates predictions
about the effects of disturbance on variability using a simple simulation model of vegetation
dynamics where disturbance resets succession at one or more sites in a 100x100 landscape grid.
As they did, we used two key parameters to define the state space used to characterize the
response of landscape dynamics. A spatial parameter, S (the ratio of the size of the disturbance to
the size of the landscape / maximum = 1), characterized the x-axis, and a temporal parameter, T
(the ratio of disturbance return time [1/frequency] to recovery time), characterized the y-axis. By
conducting simulations across a variety of disturbance sizes and frequencies, a wide range of
values of T and S could be explored. Simulation output consisted of both the mean and variance
(SD) of the proportion of the landscape occupied by a particular successional stage and was
calculated over 100 time steps (Turner et al. 1993).
To facilitate comparisons among different disturbances and ecosystems, we place several
important restrictions on this successional model of landscape dynamics. First, we assume that
the most representative ecosystem state is one characterized by the mature successional stage
(stage 8), thus providing a terminal and definitive recovery target for all ecosystems. Second, we
assume that equilibrium dynamics (area of state space with low variance and a high percentage
of mature vegetation – cf. Turner et al. Fig. 5) are equivalent to unchanged conditions following
disturbance. Finally, although the area identified as "unstable" in the Turner et al. (1993) model
had a low standard deviation at the end of the simulation (100 time steps), the state space
represents large, frequent disturbance events that consistently reset recovery trajectories and
would result in an overall increase in variability compared to pre-disturbance conditions. In the
literature, systems undergoing such state changes also show an increase in variability because
Fraterrigo and Rusak
9
patterns are compared relative to the pre-disturbance state. Thus, we characterized systems as
“unstable” if response variability increased and they showed evidence of transitioning into a nonrecoverable state following disturbance. Note that plotted trajectories were extrapolated beyond
T=0.75 (Fig. 5).