Download the role of dispersal and disturbance in determining spatial

Ecology, 81(7), 2000, pp. 2011–2026
q 2000 by the Ecological Society of America
THE ROLE OF DISPERSAL AND DISTURBANCE IN DETERMINING
SPATIAL HETEROGENEITY IN SEDENTARY ORGANISMS
DANIEL C. REED,1,4 PETER T. RAIMONDI,2 MARK H. CARR,2
AND
LLOYD GOLDWASSER3
1
Marine Science Institute, University of California, Santa Barbara, California 93106 USA
2Department of Biology, University of California, Santa Cruz, California 95064 USA
Southwest Fisheries Science Center, National Marine Fisheries Service/National Oceanic and
Atmospheric Administration (NMFS/NOAA), 3150 Paradise Drive, Tiburon, California 94920 USA
3
Abstract. The dispersal ability of seeds, spores, and larvae can greatly influence spatial
heterogeneity in the abundance of plants and sedentary animals, especially after a severe
and widespread disturbance has caused extensive population declines and opened large
areas for colonization by propagules. However, little work has been done on the relationship
between propagule dispersal and spatial heterogeneity in species abundance. We hypothesize
that, shortly after a disturbance, spatial variability in abundance of sedentary organisms is
negatively related to the dispersal potential of a species’ propagules, and that this negative
relationship diminishes over time as populations recover from the disturbance. To evaluate
this hypothesis, we examined temporal changes in spatial patterns of abundance in a diverse
group of sedentary marine organisms, whose propagules differ greatly in dispersal potential.
The data set was derived from long-term monitoring of the kelp forest communities of
Channel Islands National Park and contained annual estimates of abundance of 27 species
from nine phyla collected at 16 sites on five different islands during 1986–1994. Amongsite variability in the abundance of plants and animals was negatively related to the planktonic duration of a species’ propagules. The negative relationship disappeared over time
in some regions, but not in others. This disappearance resulted primarily from species with
relatively limited dispersal becoming more uniformly distributed over time; among-site
variability of species with more widespread dispersal changed little. Differences among
regions in the rate at which the negative relationship diminished over time suggested
regional differences in rates of recovery from the 1982–1984 El Niño disturbance. These
differences among regions may have resulted in part from differences in the frequency and
intensity of sea urchin grazing, and in part from regional differences in the distances
separating sites where abundances were measured. Our findings support the hypothesis that
differential dispersal after large disturbances can significantly influence spatial heterogeneity in assemblages of sedentary organisms.
Key words: Channel Islands National Park; colonization; dispersal; disturbance; El Niño; kelp
forest; larvae; propagule; sedentary organisms; spatial heterogeneity; spore; variability.
INTRODUCTION
Spatial heterogeneity in the distribution and abundance of organisms profoundly affects local and regional ecological processes (see reviews in Wiens
[1976], Pickett and White [1985], Kareiva [1994], Tilman and Kareiva [1997]). Spatial patterns of abundance
may be determined by biological characteristics of a
species and physical features of its environment. One
of the most important biological attributes contributing
to spatial patterns of species abundance is dispersal,
which, for sedentary organisms, is restricted to the dissemination of propagules.
The dispersal potential of seeds, spores, and larvae
has long been recognized for its importance in influencing large-scale patterns of distribution and geographic ranges of both terrestrial plants (Cwynar and
Manuscript received 29 October 1998; revised 19 May 1999;
accepted 28 May 1999.
4 E-mail: [email protected]
Macdonald 1987, Ashton and Mitchell 1989, Johnson
and Webb 1989) and sedentary marine organisms (Perron and Kohn 1985, Richmond 1987, Roughgarden et
al. 1988, Scheltema 1989, Gaines and Bertness 1992,
Emlet 1995). Much attention has focused on the consequences of dispersal to individual fitness (Strathmann
1974, 1982, Vance 1980, Strathmann et al. 1981, Levin
et al. 1984), genetic structure (Hamrick and Loveless
1986, Slatkin 1987, Doherty et al. 1995, Palumbi et al.
1997), population dynamics (Thorson 1950, Levin et
al. 1987, Levin and Huggett 1990), and community
composition (Shmida and Elner 1984, Gaines and
Roughgarden 1985, Schupp et al. 1989, Keough and
Raimondi 1995, 1996, Palmer et al. 1996). In contrast,
there has been little attempt to determine the degree to
which spatial heterogeneity in the abundance of plants
and sedentary animals is determined by the dispersal
potential of propagules. In the only empirical study on
this topic known to us (Carlon and Olson 1993), mean
dispersal distance of larvae and spatial variability in
2011
DANIEL C. REED ET AL.
2012
the abundance of adults of two species of coral were
negatively correlated.
Disturbance is a major factor affecting spatial heterogeneity of abundance in natural communities, and
in the absence of resistant dormant stages, recolonization by sedentary organisms after large disturbances
is dependent on propagule dispersal (Sousa 1984, Pickett and White 1985). We hypothesize that, in systems
dominated by sedentary organisms lacking resistant
dormant stages (e.g., seed banks), there is a negative
relationship between dispersal potential and spatial heterogeneity after a severe widespread disturbance. Regional recovery following such large disturbances
should occur more rapidly in species with widespread
dispersal due to their ability to disseminate propagules
over relatively large areas. In contrast, more geographically restricted colonization originating from patchily
distributed remnant populations that survived the disturbance might be expected of species with more limited dispersal. In time, as adjacent populations of species with limited dispersal recover, spatial variability
in their abundance within a region should gradually
decline, eventually causing the negative relationship
between dispersal potential and spatial variability in
abundance to disappear. The existence of such a negative relationship in communities of diverse assemblages of sessile organisms has never been documented, and the conditions that influence how long it persists after a large disturbance have yet to be explored.
The role of dispersal in determining spatial variability in the abundance of sedentary organisms may
be greatest in marine systems where, unlike most terrestrial and aquatic habitats, extreme among-species
variability in propagule dispersal is common (Strathmann 1990), and extended propagule dormancy (e.g.,
seed banks) is uncommon (Hoffmann and Santelices
1991, Reed et al. 1997). In this paper, we examine the
hypothesis that variability in abundance among sites
separated by kilometers to tens of kilometers is negatively correlated with the dispersal potential of a species’ propagules. We used data from a diverse assemblage of sedentary marine organisms collected in
southern California, USA, during a nine-year period
after one of the largest disturbances on record (the
1982–1984 El Niño–Southern Oscillation [ENSO]
event). We compare changes in this relationship over
time in three regions exposed to different oceanographic conditions.
METHODS
Analyses were done on two different data sets, both
of which were derived from the Channel Islands National Park’s kelp forest monitoring program. Data
from the period 1986–1994 were used to explore the
extent to which spatial heterogeneity in the distribution
of sedentary reef organisms is explained by dispersal
potential of propagules. Here, our approach relied
largely on simple linear regression analyses that ex-
Ecology, Vol. 81, No. 7
amined the relationship between among-site variability
in abundance and propagule dispersal potential for a
diverse array of species whose propagules varied widely in dispersal potential. A second, more limited data
set was used to evaluate the effect of, and initial recovery from, the 1982–1984 El Niño–Southern Oscillation [ENSO] event.
Data set and study area
Since 1982, the National Park Service has collected
data annually on the abundance of a wide variety of
species that inhabit kelp forests at 13 sites on five islands (three additional sites were established in subsequent years). Species abundances at each site were
estimated from nondestructive sampling of postmetamorphic individuals of a readily detectable size (hereafter referred to as benthic life stages). Each of the
Channel Islands National Park (CINP) monitoring sites
was defined by a permanent 100 3 20 m sampling area.
Divers collected data on counts or percent cover for
individual species in quadrats that were randomly
placed within the sampling area. Three sizes of quadrats were used depending on the density and distribution of the species sampled: 1.5 m2 (for species sampled as percent cover; n 5 25 quadrats/site), 2 m2 (for
common species sampled by counts; n 5 20 quadrats/
site), and 60 m2 (for less common species sampled by
counts; n 5 12 quadrats/site). The total area sampled
per technique was ;40 m2 for the two smaller quadrat
sizes and 720 m2 for the larger quadrats. Each site was
sampled once each year in the summer (June–August).
The mean abundance of a species, averaged over all
quadrats at a site in a given year, was used to derive
the dependent variable in all analyses.
Channel Islands National Park lies in the northern
portion of the Southern California Bight, where currents, prevailing winds, sea temperatures, nutrient concentrations, and bathymetry of adjacent basins vary
greatly (Fig. 1; Hickey 1992, Hendershott and Winant
1996, Harms and Winant 1998). The diverse oceanographic conditions that occur in the park give rise to
a similarly diverse array of species assemblages (Hewatt 1946, Neushul et al. 1967, Seapy and Littler 1980,
Engle 1994). Sites for long-term population monitoring
were chosen by the National Park Service to represent
the broad range of environmental conditions and biological assemblages in the park (Davis 1988). Monitoring sites were generally located in areas of continuous reef with relatively low coverage of sand and
cobble. Due to sampling logistics, sites were not located in places exposed to severe wave stress, although
such places occur in CINP. Rather, all sites were located
in areas of moderate wave exposure; however, because
sites were located on all sides of the islands, the degree
to which they were influenced by waves varied with
swell direction. The location and general characteristics of each monitoring site are given in Table 1.
The CINP kelp forest monitoring data are well suited
July 2000
DISPERSAL AND SPATIAL HETEROGENEITY
2013
FIG. 1. Channel Islands National Park (CINP). (a) Satellite-derived sea-surface temperature map for 11 October 1985
(from Hickey 1992), showing three general regions (blue, green/yellow, and orange/red) that differ with respect to ocean
circulation and temperature. The temperature scale is in degrees Celsius. (b) Location of CINP kelp forest monitoring sites:
blue circles are monitoring sites in the cold region; yellow circles are monitoring sites in the warm region; red circles are
monitoring sites on Santa Barbara Island; numbers refer to site locations and characteristics in Table 1.
DANIEL C. REED ET AL.
2014
Ecology, Vol. 81, No. 7
TABLE 1. Location and general characteristics of the 16 sites used by the National Park Service to monitor kelp forest
communities in Channel Islands National Park (CINP).
Site
Location
1
Wyckoff Ledge,
San Miguel Island
Hare Rock,
San Miguel Island
Johnson’s Lee North,
Santa Rosa Island
Johnson’s Lee South,
Santa Rosa Island
Rode’s Reef,
Santa Rosa Island
Gull Island,
Santa Cruz Island
Fry’s Harbor,
Santa Cruz Island
Pelican Bay,
Santa Cruz Island
Scorpion Anchorage,
Santa Cruz Island
Yellow Banks,
Santa Cruz Island
Admiral’s Reef,
Anacapa Island
Cathedral Cove,
Anacapa Island
Landing Cove,
Anacapa Island
SE Sea Lion Rookery,
Santa Barbara Island
Arch Point,
Santa Barbara Island
Cat Canyon,
Santa Barbara Island
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Latitude,
longitude
34800.99
120823.319
34802.849
120821.459
33853.619
120806.369
33853.359
120806.229
34801.389
120806.669
33856.669
119849.579
34803.099
119845.149
34801.679
119842.249
34802.779
119832.819
33859.009
119833.819
34800.339
119825.869
34800.719
119822.299
34801.709
119821.719
33827.459
119801.529
33828.659
119801.739
33827.249
119802.479
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
N
W
Depth
(m)
Relief
Percent
sand
Percent
cobble
Percent
rock
14–16
medium
21.1
3.2
75.7
7–10
high
6.3
15.9
77.8
8–12
medium
4.5
2.4
93.1
15–18
medium
14.8
2.3
82.9
15–18
low
9.9
9.5
80.6
15–18
high
4.3
2.1
93.6
8–20
medium
4.9
13.4
81.7
5–11
low
24.2
15.4
60.4
4–8
medium
11.1
5.3
83.6
15–16
low
11.3
16.1
72.6
11–18
high
7.5
10.4
82.1
6–11
low
19.1
19.2
61.7
5–15
high
10.6
18.6
70.8
13–15
low
12.2
6.4
81.4
6–11
medium
5.7
12.7
81.6
6–11
medium
13.6
2.7
83.7
Notes: Depth is the range spanned by the 100 3 20 m sampling area at mean lower low water (MLLW). Topographic relief
is categorized as low (, 1 m high), medium (within 1–2 m), and high (.2 m).
for evaluating the relationship between propagule dispersal potential and among-site variability in species
abundance because of the following factors: (1) they
contain information on many species whose propagules
vary widely in dispersal potential; (2) they are collected
at a relatively large number of sites, which should increase the accuracy of estimates of spatial variability
among sites; and (3) they span multiple years, which
allows changes in the relationship between dispersal
potential and spatial variability in abundance to be
evaluated over time.
Only data that met the following criteria were included in our analyses: (1) the species had to be sedentary or weakly mobile in their postmetamorphic stage
to ensure that inter-annual differences in among-site
variability in abundance could be attributed primarily
to recruitment and mortality, rather than to emigration
and immigration of benthic life stages; (2) a species
was included only if there was reliable information on
the planktonic duration of its propagules; (3) the species had to be present at most sites to ensure that
among-site variability in abundance was estimated using roughly the same sample size and over approximately the same geographic range for all species; and
(4) all sites had to be sampled in a given year for that
year to be included in the analyses, in order to ensure
that data from all years were collected over the same
geographic range. The subset of data that met these
criteria included annual estimates of abundance of 27
species from nine phyla collected at 16 sites on five
different islands during the nine year period 1986–1994
(Table 2).
Some limitations to using CINP data exist, because
the kelp forest monitoring program was not designed
specifically to evaluate the relationship between dispersal potential of a species’ planktonic propagules and
spatial variability in the abundance of its benthic life
stages. Of particular concern is that study sites around
the islands were chosen to reflect the broad range of
environmental conditions and biological assemblages
in the park. Consequently, the quality of habitat for a
species may have varied among sites, and this variation
in itself can be expected to cause a substantial amount
of spatial variability in species abundance. Because we
were not interested in investigating the consequences
of this source of variability on patterns of species abundance, we removed it from our analyses. This was accomplished for each species by dividing its abundance
at a given site during a given year by the species’ mean
abundance at that site calculated over the nine-year
DISPERSAL AND SPATIAL HETEROGENEITY
July 2000
2015
TABLE 2. Estimated planktonic periods and maximum age of the species analyzed.
Phylum
Genus species
Planktonic
period (d)
Bryozoa
Urochordata
Phaeophyta
Phaeophyta
Phaeophyta
Phaeophyta
Phaeophyta
Porifera
Annelida
Mollusca
Mollusca
Mollusca
Cnidaria
Echinodermata
Annelida
Echinodermata
Cnidaria
Mollusca
Echinodermata
Echinodermata
Echinodermata
Mollusca
Chordata
Chordata
Chordata
Echinodermata
Echinodermata
Diaperoecia californica
Styela montereyensis
Eisenia arborea
Laminaria farlowii
Pterygophora californica
Macrocystis pyrifera
Desmarestia ligulata
Tethya aurantia
Diopatra ornata
Haliotis corrugata
Haliotis rufescens
Lithopoma undosum
Urticina lofentensis
Lytechinus anemesus
Phragmatopoma californica
Strongylocentrotus purpuratus
Muricea fruticosa
Aplysia californica
Strongylocentrotus franciscanus
Asterina miniata
Parastichopus parvimensis
Crassadoma giganteus
Alloclinus holderi
Coryphopterus nicholsii
Lythrypnus dalli
Pisaster giganteus
Pycnopodia helianthoides
0.5 (1)†
0.5 (1)
1 (2)‡
1 (2)‡
1 (2)
1 (2)
1 (2)‡
2 (2)
3 (1)§
7 (13)\
7 (13)
7 (1)\
10 (3)¶
21 (4)
24 (15)
29 (1)
30 (3)#
34 (3)
37 (1)
45 (1)
60 (1)††
60 (1)
60 (6)‡‡
70 (7)
70 (7)
76 (1)§§
90 (1)
Longevity
(yr)
20
3
11
6
15
5
1
(14)
(3)
(15)
(15)
(15)
(15)
(16)
···
5 (14)
20 (3)
20 (3)
20 (14)
20 (11)
3 (11)
7 (13)
20 (3)
10 (3)
1 (3)
20 (3)
20 (11)
10 (11)
20 (3)
4 (12)
5 (10)
1 (9)
20 (11)
8 (11)
Notes: For some species, estimates of maximum age were reported as ‘‘$20 yr’’ in the original
citation. Maximum age for these species and for species with a maximum age reported as being
.20 yr are categorized as 20 yr to avoid bias when examining the relationship between planktonic duration and maximum age. Numbers in parentheses indicate sources of estimates: (1)
Strathmann (1987); (2) Reed et al. (1992); (3) Morris et al. (1980); (4) Hinegardner (1975);
(5) Eckelbarger (1975); (6) Stepien (1986); (7) M. Steele, unpublished data; (8) Foster and
Schiel (1985); (9) Behrents (1983); (10) Wiley (1973); (11) J. Pearse, unpublished data; (12)
M. Love, unpublished data; (13) Barry (1989); (14) J. Engle, personal communication; (15)
Dayton et al. (1984); (16) Abbott and Hollenberg (1976).
† Based on estimates for other bryozoa with lecitotrophic larvae.
‡ Based on estimates for Macrocystis pyrifera and Pterygophora californica.
§ Based on estimate for Diopatra cuprea.
\ Based on estimate for Haliotis rufescens.
¶ Based on estimate for Urticina crassicornis.
# Based on estimate for Muricea californica.
†† Based on estimate for Parastichopus californicus.
‡‡ Based on estimate for Heterosticus rostratus.
§§ Based on estimate for Pisaster ochraceus.
study period. This procedure resulted in an annual standardized abundance for each species–site combination,
each of which had an overall mean (averaged over nine
years) equal to one. Standardized abundances of species were used in all analyses to remove site-specific
differences in absolute abundances.
Measures of spatial variability
We used the coefficient of variation (CV 5 standard
deviation/mean abundance) as our measure of amongsite variability in the abundance of benthic life stages.
Coefficients of variation are the only common measure
of variability that are not dependent on the magnitude
of the values (Zar 1974). However, CVs vary with
means when the relationship between increasing means
and associated standard deviations is nonlinear. To test
for nonlinearly, we compared the fit of common non-
linear functions to the relationship between the standard deviation and the mean with that of a linear function. The linear function was always the better fit for
the data sets analyzed. In this study, differences in the
magnitude of abundance between species were not extreme, because we used standardized abundances. Coefficients of variation for each species were calculated
for each year from standardized abundances of the 16
sites, which spanned a linear distance of ;140 km
(each site contributed a single value for the calculation
of a species’ CV in any given year). Distributions of
CV s were tested for normality and homoscedasticity (in
cases where multiple distributions were considered),
for each of the 46 analyses involving CVs. In no case
was there a violation of these assumptions. In some
analyses, there may have been low power to detect
significant deviations from normality due to low rep-
2016
DANIEL C. REED ET AL.
lication. However, if there was significant departure
from normality, it is likely that at least one of the 46
individual tests would have been significant.
As noted, two general sizes of quadrats were used
in sampling (1.5–2 m2 and 60 m2). As a result, the total
area sampled at a site differed among species depending on the size of the quadrat used to sample them
(total area sampled was ;40 and 720 m2 for species
sampled in small and large quadrats, respectively).
Such large differences in quadrat size and total area
sampled among species could potentially affect the accuracy and precision of our estimates of among-site
variability in species abundance (Andrews and Mapstone 1987). To address this issue, we compared the
mean among-site variability for the two sizes of quadrats with a t test. Mean among-site variability in abundance was calculated as the mean of the nine yearly
CV s (1986–1994) for each of the 27 species. Because
precision depends in part on the size of the sample, it
is more appropriately measured as the ratio of the standard error (SE) to the sample mean, rather than the CV
(Andrews and Mapstone 1987). However, in our analyses of among-site variability, replication came from
sites (which was the same for all species [n 5 16 sites]),
irrespective of whether they were sampled in large or
small quadrats. Thus, our use of CVs as a measure of
precision is appropriate in this case, because the CV
and the SE/sample mean ratio provide the same relative
measure of precision (CVs can be converted to SE/sample mean by dividing them by square root of 16 sites).
To explore whether patterns of variability were related to oceanographic conditions (e.g., currents, temperature, and nutrients), we also investigated variation
among sites within different regions of CINP. Ocean
circulation in the waters surrounding CINP is very
complex and can be categorized into at least six flow
regimes with transitions among them that vary in space
and time (Harms and Winant 1998). However, the general circulation in the Santa Barbara Channel tends to
be cyclonic, which often advects cool surface waters
eastward along the northern coasts of the Channel Islands. Surface waters around the Channel Islands are
primarily mixtures of warm saline Southern California
Bight waters of subtropical origin and colder, lower
salinity coastal waters upwelled in the vicinity of Point
Conception to the north (Chelton 1984, Lynn and Simpson 1987). This mixture results in a gradient of increasing ocean temperatures from west to east in most
flow regimes, with waters in the central portion of the
park typically being colder (and more nutrient rich) on
the north sides of islands than on the south sides (Hendershott and Winant 1996). These gradients in ocean
temperature are largest in summer and fall and smallest
in winter (Harms and Winant 1998). Satellite-derived
sea surface temperature maps have been used in previous studies to examine circulation patterns in the
Southern California Bight (Cowen 1985, Hickey 1992,
Hendershott and Winant 1996, Harms and Winant
Ecology, Vol. 81, No. 7
1998). We used the maps of sea surface temperature
published in these earlier studies to categorize the 16
monitoring sites into three different oceanographic regions. The maps were derived from satellite images
taken in five different years (1981, 1982, 1985, 1993,
1994) and included data collected during all four seasons. The three oceanographic regions that we identified from these images were as follows: (1) the cold
region, which included sites around San Miguel Island,
and off the north sides of Santa Rosa and Santa Cruz
Islands; (2) the warm region, which included sites at
Anacapa Island, the eastern end of Santa Cruz Island,
and the south sides of Santa Rosa and Santa Cruz Island; and (3) the Santa Barbara Island Region, which
included the three sites at Santa Barbara Island (Fig.
1). The maximum distance between sites within regions
was ;65 km for the warm and cold regions and 7 km
for Santa Barbara Island. Our oceanographic groupings
were largely consistent with previous island groupings
based on nearshore species assemblages (Engle 1994).
Estimates of dispersal potential
Dispersal potential is closely related to the duration
of the dispersal phase for species with propagules that
disperse passively before settling (Keough 1988).
Therefore, we used the mean length in days of the
planktonic dispersal phase as our measure of the dispersal potential for species used in our analyses (length
of planktonic duration was transformed to its natural
log to linearize slopes for regressions). Estimates of
the planktonic duration of larvae and spores were obtained from the literature or by personal communications with experts (Table 2). Planktonic durations of
fish larvae were based on the mean number of daily
rings in otoliths of recently settled juveniles. This
method can accurately estimate the time an individual
larva spent in the plankton (Campana and Nielson
1985, Victor 1991). It is more difficult to determine
the length of time that an invertebrate or alga spent in
the plankton, because structures analogous to otoliths
are not found in most invertebrate larvae and algal
spores. Consequently, estimates of the planktonic duration of invertebrate larvae and algal spores were
based on laboratory studies that reported the length of
time needed for a larva or spore to reach competency.
Because laboratory studies of larval development were
frequently done at different temperatures, and temperature influences the rate of larval development (Strathmann 1987), different studies often reported different
planktonic periods for the same species. When multiple
estimates of planktonic period were available for a species, we used the estimate obtained at the temperature
that most closely resembled that of the Southern California Bight. Estimates of planktonic duration often
were based on a related species that had a similar mode
of spore/larval development.
It is important to note that the duration of a planktonic period can vary greatly between closely related
July 2000
DISPERSAL AND SPATIAL HETEROGENEITY
species, as well as within the same species (Strathmann
1987). Inaccurate estimates of planktonic duration that
arise from such variability could potentially constitute
a significant source of bias in our regression analyses
examining the relationship between spatial variability
in abundance and planktonic duration. To check for
such bias, we compared the results of a regression analysis that used estimates of planktonic duration to those
of a one-way ANOVA that grouped the species into
one of three broad categories of planktonic duration,
which corresponded to distinct types of development.
In both analyses, the mean of the nine yearly CVs
(1986–1994) for each of the 27 species was used as
the estimate of among-site variability in abundance. If
the categorization of species is accurate, and results of
the more conservative ANOVA are consistent with that
of the regression analysis, then potential errors in estimates of planktonic duration should not constitute a
significant source of bias in the regression analyses.
Available information on reproduction and development allows all 27 species to be assigned to one of
the three categories of development (R. Emlet and J.
Pearse, personal communication). The categories of
planktonic duration used in the ANOVA were as follows: (1) species that produce propagules, which are
competent to settle within one day of release (hereafter
referred to as short-range dispersers); (2) species that
release nonfeeding lecithotrophic larvae, which spend
greater than one but ,15 d in the plankton (hereafter
referred to as intermediate-range dispersers); and (3)
species that release planktotrophic larvae, which feed
for $15 d in the plankton before they become competent to settle on the bottom (hereafter referred to as
long-range dispersers). We chose 15 d for the break
between intermediate- and long-range dispersers, because it was midway between specific estimates of dispersal ability for the lecitotrophic species with the longest estimate of planktonic duration (Urticina, 10 d)
and the planktotrophic species with the shortest planktonic duration (Lytechinus, 21 d). In this way, we were
able to insure that each species was assigned to the
correct developmental category based on estimates of
its planktonic duration.
Any conclusions regarding a negative relationship
between dispersal potential and spatial heterogeneity
could be confounded, if the duration of the planktonic
phase covaried with other life history characteristics
affecting species abundance. One of the characteristics
most likely to confound the relationship is life span.
We used linear regression to test whether differences
in maximum age varied with planktonic duration. Data
on the maximum age of 26 of the 27 species were
obtained from the literature or from personal communications with knowledgeable sources (Table 2).
Generally, estimates of maximum age for fish were
based on otolith studies; estimates for algae were based
on demographic studies that followed tagged individuals over time; estimates for invertebrates were ob-
2017
tained from a variety of methods that included in situ
growth studies of marked individuals, analysis of shells
and calcareous skeletons, and long-term laboratory
studies of captive individuals. The maximum age for
several species was reported as ‘‘$20 yr.’’ Maximum
age for these species and for species with a maximum
age .20 yr was categorized as 20 yr to avoid bias when
examining the relationship between planktonic duration and maximum age.
Only 13 of the 27 species included in our analyses
had completely sessile benthic life stages. Thus, another potential factor that could confound our analyses
is the extent to which movement of benthic life stages
covaried with planktonic duration. Of the 14 species
having benthic life stages capable of movement, the
majority either were territorial and had very small (,2m2) home ranges (e.g., the fishes, as reported in Cole
[1984], Steele [1997]; M. Carr, unpublished data), or
were nonterritorial with relatively limited foraging
ranges (sea urchins, as reported in Dean et al. [1984],
Harrold and Reed [1985], Laur et al. [1986]; abalones,
as reported in Tutschulte [1976], Hines and Pearse
[1982]; sea cucumbers, as reported in Da Silva et al.
[1986]; sea stars as reported in Leonard [1994]). Because movement in these species appears limited relative to the plot size of a site (20 3 100 m) and the
distances separating sites (kilometers to tens of kilometers), we assumed that the effects of movement by
benthic life stages on our estimates of among-site variability in species abundance was minimal.
Evidence for widespread disturbance
There is ample evidence that a severe disturbance
occurred throughout CINP shortly before the start of
our time series. The El Niño of 1982–84 was one of
the largest ENSO events ever recorded, and it had profound widespread effects on reef organisms throughout
CINP (Davis 1986) and beyond (Wooster and Fluharty
1985, Paine 1986, Tegner and Dayton 1987, Glynn
1988, Dayton and Tegner 1989, Castilla and Camus
1992). Winter of 1982–83 was one of the most severe
storm seasons of the century, along the west coast of
North America (Namias and Cayan 1984, Seymour et
al. 1984). Large swells associated with these storms
broke up large portions of reefs and caused catastrophic
losses of many kelp forest species (Dayton and Tegner
1984, Harris et al. 1984, Ebeling et al. 1985, Botkin
et al. 1987, Dayton and Tegner 1989). After the storms
came a prolonged period with unusually warm, nutrient-poor water, which further reduced population levels
of many plants and animals (McGowan 1985, Zimmerman and Robertson 1985, Dean and Jacobsen 1986,
Tegner and Dayton 1987). In addition, many kelp forest
echinoderms were killed by fatal epidemic diseases,
which were believed to be caused by water-borne pathogens that flourish in warm water (Davis 1986, Jangoux
1987, Tegner and Dayton 1987).
Our ability to evaluate the effects of the 1982–1984
2018
DANIEL C. REED ET AL.
Ecology, Vol. 81, No. 7
multifactorial hierarchical ANOVA, where year, region, and dispersal potential were fixed factors. Species
abundances in this analysis were standardized using
the method described above (see Methods: Data set
and study area), with the exception that abundances
were standardized to data collected during 1982–1994,
which better represents the long-term mean abundance
of these species. Standardizing species abundances during the years 1982–1986 to that averaged over 1982–
1994 allowed us to evaluate changes in abundance associated with El Niño relative to the best available
estimate of the long-term mean abundance of a species.
Standardized abundances were transformed to ln (x 1
1) to meet the assumption of normality.
RESULTS
Validity of assumptions
FIG. 2. Mean standardized abundance (6 1 SE) of short-,
intermediate-, and long-range dispersers vs. (a) year and (b)
oceanographic region (SBI 5 Santa Barbara Island region).
Data are from 14 species at 16 sites that were collected before
and shortly after the 1982–1984 El Niño. See text for a list
of the species analyzed. Data were transformed to ln(x 1 1)
to meet the assumption of normality for ANOVA.
El Niño on spatial patterns of abundance in CINP is
somewhat limited, because the National Park Service
has no data prior to 1982, and only data collected after
the ENSO event met the criteria for our analyses. However, the entire CINP database contains information on
the abundance of 14 of the 27 species used in our
analyses dating back to 1982 (prior to 1986 data were
collected at only 9–14 of the 16 sites). Six of the 14
species were short-range dispersers (Eisenia arborea,
Diaperoecia californica, Laminaria farlowii, Macrocystis pyrifera, Pterygophora californica, and Styela
montereyensis), two were intermediate-range dispersers (Diopatra ornata and Lithopoma undosum), and six
were long-range dispersers (Asterina miniata, Parastichopus parvimensis, Phragmatopoma californica, Pisaster giganteus, Strongylocentrotus franciscanus, and
S. purpuratus). We tested for disturbance and recovery
from El Niño in these species by examining changes
in their abundance during the period 1982–1986. Because we were also interested in whether El Niño effects varied among different regions of CINP, and
among species with different planktonic durations, we
examined the main and interactive effects of year, dispersal potential (i.e., short-, intermediate-, and longrange), and oceanographic region on abundance in a
Our analyses of the 14 species for which data exist
since 1982 support the contention that the 1982–1984
El Niño had a severe and widespread impact on the kelp
forest communities in Channel Islands National Park
(CINP). Mean standardized abundances of short-, intermediate-, and long-range dispersers declined by 60%,
32%, and 47%, respectively (percentages based on untransformed data), from summer 1982 to summer 1983
following the severe El Niño storms that occurred during the intervening winter (Fig. 2a). Temporal changes
in abundances during 1982–1986 differed among the
three dispersal groups (see significant year 3 dispersal
potential interaction in Table 3). Mean standardized
abundance of short-range dispersers declined continuously to a low in 1986, which was 21% of that measured in 1982, before the onset of El Niño (Fig. 2a).
In contrast, populations of intermediate- and longrange dispersers appeared to recover rapidly from El
Niño. Mean standardized abundances of intermediateand long-range dispersers increased continuously during 1983–1986 to values that were slightly more than
twice those measured in 1982 (Fig. 2a). Although the
TABLE 3. Results of ANOVA examining the effect of year
(1982–1986), oceanographic region (cold, warm, Santa
Barbara Island), and dispersal potential (short-, intermediate-, and long-range) on standardized abundance, using
the 14 species for which there are data since 1982.
Source
Year (Y)
Region (R)
Dispersal potential (D)
Y3R
Y3D
R3D
Y3R3D
Error
Mean
square
F ratio
4
2
0.8983
0.0431
3.126
0.150
0.0164
0.8609
2
8
8
4
16
165
1.1864
0.2058
1.9070
1.1884
0.2530
0.2874
4.129
0.716
6.636
4.135
0.880
0.287
0.0178
0.6770
,0.0001
0.0032
0.5926
···
df
P
Notes: All three independent factors were considered fixed.
Standardized abundances were transformed to ln(x 1 1) to
meet the assumption of normality for ANOVA.
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DISPERSAL AND SPATIAL HETEROGENEITY
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vs. 1.20 6 0.11 for means generated from small quadrats; t1, 25 5 21.21, P 5 0.238), suggesting that differences in CVs among species did not result from differences in total area sampled. Similarly, we found no
relationship between maximum life span and planktonic duration (F1, 24 5 0.13, P 5 0.73), indicating that
differences in life span among species probably did not
confound our analyses of the relationship between dispersal potential and among-site variability in species
abundance.
Temporal changes in spatial heterogeneity
FIG. 3. Relationship between spatial variability in species
abundance and propagule planktonic duration. Standardized
abundances during the period 1986–1994 were used to calculate the coefficient of variation (CV) in abundance among
the 16 sites for each of the 27 species. Each point represents
the mean of the nine yearly CV values for one of the 27
species.
relative abundance of the three dispersal groups differed among regions (Fig. 2b; see significant region 3
dispersal potential interaction in Table 3), their patterns
of temporal change did not (see nonsignificant year 3
region and year 3 region 3 dispersal potential interactions in Table 3). This similarity in temporal changes
of abundance among regions indicated that the effects
of El Niño in CINP were widespread.
The analysis using specific estimates of planktonic
duration (i.e., regression) gave results similar to those
that grouped species into broad categories of planktonic
duration (i.e., ANOVA). In the regression, among-site
variability in species abundance was inversely correlated with the planktonic duration of a species’ propagules for the period 1986–1994 (Fig. 3; F1, 25 5 6.43,
P 5 0.018). In the ANOVA, among-site variability over
the same nine-year period was smaller for long-range
dispersers than for short-range dispersers (Fig. 4; F2,24
5 3.479, P 5 0.047). Intermediate-range dispersers
were not significantly different from either short- or
long-range dispersers; consequently, we present results
of only short- and long-range dispersers in all subsequent analyses that categorized dispersal ability. The
amount of variability in the CVs explained by the two
analyses was also very similar (R2 5 0.21 for the regression, and R2 5 0.23 for the ANOVA). The similarity in results obtained by regression and ANOVA
indicates that it is unlikely that erroneous estimates of
planktonic duration altered the results of regression
analyses investigating temporal changes in the relationship between dispersal potential and spatial heterogeneity of abundance.
We found no evidence that quadrat size affected the
accuracy or precision of our estimates of among-site
variability in species abundance (CVs were 1.42 6 0.12
[mean 6 1 SE] for means generated from large quadrats
The relationship between spatial variability in species abundance and propagule dispersal potential became progressively less negative over time (Fig. 5).
The reason for the temporal change in this relationship
was that organisms with restricted dispersal generally
became more uniformly distributed over time; the spatial variability in the abundance of long-range dispersers changed little over time (Fig. 6, Table 4).
Temporal changes in spatial variability coincided
with temporal changes in species abundance (Fig. 7).
Short-range dispersers (which as a group became more
evenly distributed over time) increased in abundance
during 1986–1994 (F1, 61 5 10.65, P 5 0.002, slope 5
0.083) , while abundances of long-range dispersers declined (F1,124 5 7.63, P 5 0.007, slope 520.054).
Differences among oceanographic regions
These analyses (Table 3, Figs. 5 and 6) do not distinguish between differences in the relationship between spatial variability and dispersal potential at different locations within CINP. To examine spatial differences in this relationship, we analyzed the three
oceanographic regions in the park separately and found
FIG. 4. Mean coefficient of variation (CV) in species abundance for short-, intermediate-, and long-range dispersers
(planktonic durations 5 0–1 d, 2–10 d, and 21–90 d, respectively). Data are from the period 1986–1994 and represent means 6 1 SE calculated from standardized abundances
of the species in each planktonic-duration group. Means that
share a letter are not significantly different from one another
at P 5 0.05 using Tukey’s studentized range test.
2020
DANIEL C. REED ET AL.
Ecology, Vol. 81, No. 7
FIG. 5. Slopes of the regressions between
the coefficient of variation (CV) of a species’
standardized abundance and propagule planktonic duration, by year. The number within or
below each bar is the P value of the regression
for that year.
that the relationship did vary spatially (Fig. 8). In the
cold region, the negative relationship between the two
variables generally persisted over time; negative slopes
were significant (P , 0.05) for six of the nine years,
and significant at P , 0.1 for all nine years (Fig. 8a).
In contrast, in the warm region, a negative relationship
was observed during only the first two years of the
study; the slopes were not different from zero in the
seven subsequent years (P . 0.19 for 1988–1994; Fig.
8b). No relationship between spatial variability in species abundance and propagule planktonic duration was
observed for any year in the Santa Barbara Island region (Fig. 8c).
In the cold region, the consistency of the relationship
FIG. 6. Temporal changes in the coefficient of variation
(CV) of a species’ standardized abundance for short- and longrange dispersers. Regression analyses were done using data
for individual species of short- and long-range dispersers.
Means (calculated across all species in each dispersal group)
are presented here for clarity. For short-range dispersers, n
5 7 species; for long-range dispersers, n 5 14 species. The
regression coefficient for long-range dispersers was not significantly different from zero (P . 0.05; see Table 4).
between spatial variability in species abundance and
propagule planktonic duration reflected the high spatial
variability in abundance of short-range dispersers, as
a group, relative to that of long-range dispersers (Table
5, Fig. 9a). This consistency contrasted sharply with
the changes observed in the warm region, where
among-site variability in the abundance of short-range
dispersers began high and then declined, while that of
long-range dispersers began and remained low (Table
5, Fig. 9b). In the Santa Barbara Island region, amongsite variability in abundance remained relatively low
for both short- and long-range dispersers (Table 5, Fig.
9c).
DISCUSSION
In the Introduction, we hypothesized that regional
recovery from a large disturbance should occur more
rapidly in species with widespread dispersal than in
species with more limited dispersal, causing the relationship between spatial heterogeneity and dispersal
potential to be strongly negative shortly after the disturbance. We further hypothesized that this negative
relationship should disappear over time, as populations
of short-range dispersers recovered from the disturbance. Our analyses of data collected from Channel
Islands National Park (CINP) following the 1982–1984
El Niño support these hypotheses. The negative relationship between spatial variability in abundance and
propagule planktonic duration was strongly significant
in 1986 and 1987, less so in 1988 and 1989, and nonsignificant from 1990 through the rest of the study (Fig.
5).
Differences in temporal trends among oceanographic
regions, however, suggest that rates of recovery following El Niño may have varied in different areas of
CINP. Greater among-site variability in the abundance
of short-range dispersers was observed in the cold region in every year of the study period (Fig. 9a). Ac-
July 2000
DISPERSAL AND SPATIAL HETEROGENEITY
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TABLE 4. Results of analyses of the relationship between spatial variability (CV), planktonic
duration, and year, using ANCOVA and linear regression (see Fig. 6).
df
F
Homogeneity of slopes
Year
Planktonic duration
1, 185
···
···
10.93
NA
NA
NA
NA
Regression line
Long-range dispersers
Short-range dispersers
1, 124
1, 61
0.51
14.13
0.476
,0.001
ANCOVA source
P
Slope of
regression line
0.001
0.013
20.090
Notes: ANCOVAs were done to test homogeneity of slopes; planktonic duration was considered a categorical variable (short- vs. long-range disperser), and year the covariate. Because
the interaction term testing homogeneity of slopes was significant (indicating that the temporal
change in spatial variability differed for long- and short-range dispersers), no tests of year or
planktonic duration were done (indicated by NA; see Neter et al. [1996]). Regression analyses
were done to determine the significance and direction of temporal change in CV for short- and
long-range dispersers.
cording to our hypothesis, the persistence of a negative
relationship between dispersal potential and spatial variability indicates slow to negligible recovery by shortrange dispersers in the cold region. In contrast, the
greater among-site variability of short-range dispersers
in the warm region disappeared by 1988 and did not
return (Figs. 8b and 9b), indicating a relatively rapid
recovery following the 1982–1984 El Niño. Recovery
seems to have been even faster in the Santa Barbara
Island region, where we did not find differences in
among-site variability between short- and long-range
dispersers, not even in 1986, which was only two years
after El Niño conditions subsided. This consistent similarity in among-site variability between short- and
long-range dispersers in the Santa Barbara Island region did not result from low disturbance there. The
results of the multifactorial ANOVA showed no effect
of oceanographic region on changes in species abundance, indicating that disturbance from El Niño on Santa Barbara Island was similar to that of other regions
in the park. The apparently faster recovery at Santa
Barbara Island may instead reflect the shorter distances
FIG. 7. Changes in the standardized abundance of short- and long-range dispersers during
1986–1994. Regression analyses were done using data for individual species of short- and
long-range dispersers. Means (calculated across
all species in each dispersal group) are presented here for clarity. For short-range dispersers, n 5 7 species; for long-range dispersers, n
5 14 species.
between sites in the Santa Barbara Island region (maximum distance, 7 km vs. ;65 km for the cold and warm
regions), which may make it easier for short-range dispersers to recolonize other sites within this region.
Although we do not know the causes of the different
patterns of recovery observed in the cold and warm
regions, observations by the National Park Service and
local knowledge of these areas provide for some interesting speculation. The rate of recovery after a disturbance depends on both the supply of propagules to
the disturbed site and their ability to colonize upon
arrival. Regional differences in both of these factors
may have contributed to the slow rate of recovery in
the cold region relative to that of the warm region. The
steep bottom contour on the north side of Santa Cruz
Island and the northeast sides of Santa Rosa Island and
San Miguel Island cause the kelp beds in this area of
the cold region to be relatively narrow and small in
area (Hodder and Mel 1978; J. Engle, personal communication). In contrast, the south sides of Santa Cruz
and Santa Rosa Islands (which are in the warm region)
are characterized by a broad, gently sloping shelf that
2022
DANIEL C. REED ET AL.
FIG. 8. Slopes of the regressions between the coefficient
of variation (CV) of a species’ standardized abundance and
propagule planktonic duration by year for (a) cold region, (b)
warm region, and (c) Santa Barbara Island region. The number within or below each histogram is the P value of the
regression for that year.
supports relatively large kelp forest communities. Production of propagules is frequently related to population size, and smaller populations in the cold region
may have caused the supply of propagules to be less
there than in the warm region.
Slow recovery in the cold region may also reflect an
inhibition of short-range dispersers by long-range dispersers that arrived and established themselves first.
For example, shortly after El Niño, sea urchins (which
are long-range dispersers) recruited at high densities
throughout CINP. Their densities remained high during
most of the study period at three of the five sites in the
cold region, while the density and percent cover of
macroalgae (which constituted five of the seven species
of short-range dispersers) at these sites remained consistently low (Kushner et al. 1993, Richards et al.
1997). In contrast, at only one of the eight sites in the
warm region were densities of sea urchins high and
those of macroalgae low. The exclusion of macroalgae
by intensive sea urchin grazing has been well documented in kelp forest communities worldwide (see
Lawrence [1975], Harrold and Pearse [1987] for reviews), and it is believed that sea urchin grazing prevented large brown algae from becoming established
Ecology, Vol. 81, No. 7
at three of the five sites in the cold region (Kushner et
al. 1993, Richards et al. 1997).
Abundances of short-range dispersers were inversely
related to abundances of long-range dispersers (Fig. 7).
Such patterns are often interpreted to mean the two
groups compete. However, drawing conclusions about
interspecific interactions based on observations of
changes in population sizes can be misleading. In this
study, the species analyzed were very diverse with respect to trophic level, resource requirements, and habits, casting doubt on the conclusion that changes in the
abundance of short- and long-range dispersers resulted
from strong competition between them. Additional information from either experiments or modeling would
be useful in examining the role of competition in generating these temporal changes in abundance.
Evidence for our hypothesis of dispersal-mediated
spatial heterogeneity is based on data from a finite set
of species collected after a single large disturbance,
which places limits on the generality of our findings.
Nonetheless, the vast majority of species that we analyzed are common inhabitants of kelp forests in the
northeast Pacific and have geographic distributions that
span several thousand kilometers. Moreover, the wide
range of organisms in the different dispersal groups
suggests our conclusions are not specific to certain taxa.
Although algae composed five of the seven species of
short-range dispersers and none of the long-range dispersers, the differences between these two dispersal
groups seem not to stem from the algae–animal dichotomy. Spatial variability of short- and intermediaterange dispersers did not differ, although intermediaterange dispersers included six species from four different animal phyla (Fig. 4).
The comparative approach taken here relied on certain assumptions to test the hypothesis of dispersalmediated spatial heterogeneity. Our evaluation of potential confounding factors that could produce spurious
correlations suggested that such sources of error were
minimal. Specifically, species-specific differences in
among-site variability seemed unaffected by speciesspecific differences in (1) maximum life span, or (2)
the size of the area in which species were sampled.
Similarly, errors in our estimates of planktonic duration
appeared unlikely to have influenced our results (regression in Fig. 3 vs. ANOVA in Fig. 4). Finally, our
assumption regarding minimal net movement of benthic life stages seemed reasonable, given the patterns
of temporal change observed in among-site variability
in species abundance. If movement of benthic stages
significantly affected temporal change in among-site
variability, then CVs of species capable of movement
would be expected to change over time, while those of
sessile species would be expected to remain constant.
However, we found just the opposite pattern; CVs of
long-range dispersers (which included mostly mobile
species) did not change over time, whereas CVs of
short-range dispersers (which included only sessile
DISPERSAL AND SPATIAL HETEROGENEITY
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2023
TABLE 5. Results of analyses of the relationship between spatial variability (CV), planktonic duration, and year for three
different oceanographic regions using ANCOVA and linear regression (see Fig. 9).
Cold region
ANCOVA source
Slope†
Homogeneity of
slopes
Year
Planktonic duration
Regression line
Long-range
dispersers
Short-range
dispersers
0.01
Warm region
df
F
1, 182
1, 183
1, 183
0.72
0.000
26.94
0.397
0.995
0.001
1, 119
0.23
0.636
0.61
0.437
20.019 1, 63
P
Slope†
df
F
Santa Barbara Island region
P
1, 190 4.77 0.03
NA
NA
0
20.061
NA
NA
1, 121 0.00 0.992
1, 69
7.90 0.006
Slope†
df
F
P
1, 147 3.76 0.06
1, 148 0.34 0.85
1, 148 1.41 0.24
0.025 1, 104 1.67 0.199
20.046 1, 43
1.86 0.18
Notes: ANCOVAs were done to test homogeneity of slopes; planktonic duration was considered a categorical variable
(short- vs. long-range dispersers), and year the covariate. If the interaction term testing homogeneity of slopes was not
significant (indicating that the temporal change in spatial variability was the same for short- and long-range dispersers), then
that term was dropped, and a reduced model was run (Neter et al. 1996). If the interaction term testing homogeneity of slopes
was significant (indicating that the temporal change in spatial variability differed for short- and long-range dispersers), then
no tests of year or planktonic duration were done (indicated by NA; see Neter et al. [1996]). In all cases, regression analyses
were done to determine the significance and direction of temporal change in CV for short- and long-range dispersers.
† Slopes reported for regression lines.
species) did. Thus, the evidence is consistent with our
hypothesis of dispersal-mediated spatial heterogeneity.
Nonetheless, undetected covariance between these or
other factors (e.g., mean life span, susceptibility to episodic mortality), and planktonic duration or amongsite variability in species abundance, may still have
influenced our results. We think this is unlikely, and
furthermore that such potentially confounding factors
would be as likely to obscure relationships as they
would be to produce spurious ones.
In conclusion, the patterns observed at CINP support
our hypothesis that differential dispersal after large disturbances can play a major role in producing spatial
heterogeneity in the abundance of sedentary and weakly mobile organisms over spatial scales of kilometers
to tens of kilometers. The dynamic nature of these patterns of spatial variability means that single estimates
of abundance are insufficient for understanding the relationship between dispersal potential and spatial heterogeneity. Such an understanding requires long-term
data collected at multiple locations. Such data are uncommon in the academic community, but are frequently
collected elsewhere as part of routine monitoring. This
study demonstrates the usefulness of such monitoring
data for exploring general ecological phenomena that
influence spatial and temporal patterns of species abundance.
FIG. 9. Temporal changes in the coefficient of variation
(CV) of a species’ standardized abundance for short- and longrange dispersers for (a) cold region, (b) warm region, and (c)
Santa Barbara Island region. Coefficients of variation were
calculated across sites within each oceanographic region. Regression analyses were done using data for individual species
of short- and long-range dispersers. Means (calculated across
all species in each dispersal group) are presented here for
clarity. For short-range dispersers, n 5 7 species; for longrange dispersers, n 5 14 species. Regressions without lines
are not significantly different from zero (P . 0.05; see Table
5).
ACKNOWLEDGMENTS
We are grateful to the National Park Service for making
their monitoring data available for analyses. In particular, we
thank the many people who spent many hours underwater
collecting these data, and G. Davis, D. Kushner, and M. Shaver for their help in facilitating our acquisition of them. J.
Estes, S. Gaines, S. Holbrook, K. Lafferty, M. Mangel, B.
Menge, S. Morgan, S. Schroeter, S. Stevens, A. Stuart-Oaten,
W. Wilson, and several anonymous reviewers provided insightful comments on earlier drafts of the manuscript. We
thank R. Emlet, J. Pearse, and M. Strathmann for reviewing
DANIEL C. REED ET AL.
2024
our estimates of larval planktonic period and J. Engle and D.
Kushner for sharing their knowledge of the natural history
of Channel Islands National Park (CINP). Support for the
preparation of this work was provided by the National Science
Foundation under grant numbers OCE-9633329 (D. C. Reed
and P. T. Raimondi), OCE-9618012 (M. H. Carr), and OCE9321892 (L. Goldwasser).
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