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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. July 2000 DISPERSAL AND SPATIAL HETEROGENEITY 2019 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 2021 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 July 2000 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. 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