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Electronic Supplementary Material
This is the Electonic Appendix for the article
Global patterns in marine dispersal estimates: the influence of geography, taxonomic category and life history
by
Ian R. Bradbury, Benjamin Laurel, Paul V. R. Snelgrove, Paul Bentzen, and Steven E. Campana
Submitted to Proc. R. Soc. B. 2007
Electronic Appendix:
Review and Scales of Measurement of Dispersal in Marine Species:
For each estimate of dispersal, spatial and temporal scale of measurement, mean latitude, and predominant habitat of the study were also considered. If not provided (i.e. latitude, and scale of study), spatial scale was obtained using details from the text and available maps (i.e. Google Earth) and was calculated as the maximum distance between sampled locations. Temporal scale was taken as the duration of a study, and given the temporal integration of long-term genetic approaches (see below) these studies were omitted for this category.
Biogeographic Method. Biogeographic estimates of gene flow and dispersal have a long history (Darlington 1957, Nathan et al. 2003) and focuses primarily on dispersal between discrete habitats such as oceanic islands (e.g., Robertson 2001). In marine systems, this approach encompasses three types of data: information on species biogeography, occurrence of invasive species, and presence of recruits from a likely source into vacant areas (Appendix 1). Our review suggests these studies typically focus on moderate spatial scales (<100 km) over short temporal scales (< 1yr) and generate dispersal estimates that on average fall within < 40km. The main outlier in this group is an estimate by Scheltema (1971) of (teleplanic) gastropod larvae that cross ocean basin distances as much as 4500 km. Because this estimate greatly exceeded the nearest closest estimate (173 km), it was removed from further analysis.
Eulerian Methods. Eulerian approaches usually involve the fixed-location collection and counting of tagged individuals following a period of redistribution. Logistically, Eulerian studies are often less challenging because they do not require tracking individuals. However, Eulerian methods require that the number of individuals tagged be sufficient to allow recaptures despite subsequent diffusion and dilution with space and time. Studies that utilize otolith tags, either artificial or natural, have achieved some of the only estimates of limited dispersal in reef and estuarine fish (Thresher 1999; Campana and Thorrold 2001). But the challenge here is tagging and surveying enough individuals to resolve the tail of the dispersal kernel. Furthermore, tag recovery most often necessitates lethal sampling and time consuming visual and (or) expensive chemical assays, both of which significantly limit the utility of this technique with respect to large marine populations over large spatial scales. To date, Eulerian approaches have been applied to fish species with limited dispersal ability (i.e. demersal eggs, or live bearers) and have yielded results of local recruitment or homing on the order of 50-70% at an average latitude of 19 degrees N or S. However, sampling in these studies usually fail to describe the entire dispersal kernel (see Thorrold et al. 2001 for an exception.), often restricting dispersal estimates to a single point.
Lagrangian Approaches. Lagrangian approaches follow individuals in space by characterizing movements over time, either visually or through simulation studies of transport (e.g., 3-d hydrodynamic models of ocean flow). Studies that visually track individuals are limited to short distance dispersers with large larvae (e.g., ascidians) and commonly yield dispersal estimates < 10km. Modeling-based studies are more applicable to large scales, but often cannot account for behavior and mortality, thereby limiting their applicability (see Cowen et al. 2000, 2005 for exceptions). However, the utility in model-based approaches is that they may be parameterized for each species within a habitat and provide community-level resolution that is seldom attainable otherwise (Cowen et al. 2005).
Short-scale Genetic Analysis. Short-term genetic analysis refers to estimates of recent dispersal, usually within a single generation, focusing primarily on successful dispersers (i.e. effective dispersal). Short-term genetic studies utilize either parentage analysis, where all possible parents in a population are genotyped (e.g., Jones et al, 2005) or assignment tests, which assign individuals to their population of origin (e.g., Berry et al. 2004; Hauser et al. 2006). Parentage analysis is commonly used in terrestrial systems, but has recently been used to measure reef fish dispersal (Jones et al. 2005). Because a significant portion of all potential parents should be sampled in order for the approach to be effective, this technique is limited to small populations with easy access to possible parents. Assignment testing (e.g., Paetkau et al. 1999; Cornuet et al. 1999) has proven effective in measuring dispersal in situations with moderate to high genetic differentiation, and where a large number of loci have been sampled (Berry et al. 2004; Hauser et al. 2006). In these situations, assignment testing can provide estimates of dispersal on spatial scales unachievable with other methods. The limitation of this technique is that moderate differentiation is usually required for high probably assignment and at present these have rarely been applied to marine systems (but see Ruzzante et al. 2006 for a similar approach using genetic mixture analysis). Estimates of dispersal based on short-term genetic approaches suggest distances that are < 70 km or about 32% homing to a point location.
Long-term Genetic Approaches. Long-term genetic estimates of dispersal are usually achieved from spatial pattern in allelic variation and either Wright’s island model (Wright 1931) or isolation-by-distance models (IBD, Slatkin 1993). These approaches do not distinguish between historical and recent dispersal and are subject to the usual limitations of applied models (e.g., Whitlock and McCauley 1999). As such, the distinction between long-term genetic approaches and correlates discussed below is not always clear. We have chosen to restrict this cateogory to genetic studies which actually estimate dispersal distances.Although this excludes a wealth of genetic studies on marine fish, many at high latitudes (see Fig. A1), it highlights the species and habitats in which these long-term genetic approaches to estimating dispersal are being applied. In situations with high gene flow and low discrimination using assignment tests, these methods, particularly isolation-by-distance, have proven useful (Bradbury and Bentzen 2007). Kinlan and Gaines (2003) used a simulation-based approach to estimate mean dispersal in marine invertebrates and fish and found estimates were generally < 200-300 km. While encouraging, the parameterization of the stepping-stone model, the equilibrium status (Bradbury and Bentzen 2007), and the selection of species (Palumbi 2004) all contribute to uncertainty regarding interpretation of the results. IBD-based estimates of dispersal suggest distances of < 200 km for most marine species that have been examined. Several promising new approaches have been presented in the last few years. Sotka and Palumbi (2006) introduce a novel approach to dispersal estimation using clines in multi-locus genetic data, which suggests dispersal is usually <35% the clinal width in most species. In addition, coalescent-based approaches (i.e. MIGRATE, Beerli and Felsenstein 1999), which estimate dispersal and Ne simultaneously, are becoming common in terrestrial studies and allow the resolution of asymmetric gene flow. However published examples of the application of these approaches in marine species are still few.
Correlates of dispersal – genetic differentiation and PLD
Genetic differentiation. Estimates of genetic differentiation were included only from studies that had two or more subpopulations and utilized one of three types of genetic marker (i.e., mitochondrial DNA (mtDNA), microsatellites, allozymes) and any studies displaying large phylogenetic breaks associated with contemporary or historical isolation were excluded. Allozyme studies also had to survey randomly-selected loci from 15 or more individuals per subpopulation in order to be included in the database. In addition, the studies had to either provide information on FST directly (e.g., global or pairwise FST) or indirectly (e.g., allele frequency table, expected and observed heterozygosities). FST estimates based on mtDNA were corrected following Kinlan and Gaines (2003) to allow comparison with markers possessing biparental inheritance and diploid gene flow assuming Wright’s island model and using the equation FST(diploid, biparental)=[1/(4*(2Nmmt)+1)] where Nmt=0.5*[(1/FST,mt)-1].
A genetic database of 247 species was assembled using the above methods and criteria. To reduce bias associated with the overinclusion of well studied species, representative studies were chosen and attempts were made to limit multiple entries to differing marker types or when multiple values differed substaintially in which case we attempted to represent the range of values. As non-signifcant FSTvalues may be indicative of high gene flow and dispersal, these included with the exception of single locus (i.e mtDNA) estimates where low rates of mutation may reduce spatial variation and bias inferences of dispersal. The majority of included estimates were based on allozyme studies (58.1%) with 17.6% mtDNA and 24.5% microsatellite studies. Meta-analysis of genetic studies using different markers may be problematic given differencesin heterozygosity among marker types. Correction for He based on Heddrick (2005) was not possible as He estimates are rarely given for mtDNA studies, and although the correction was attempted using haplotype diversity, corrected values were not comparable and differed significantly from all other estimates. To examine the hypothesis that a bias due to different marker usage in differing regions is present we used ANOVA to test for significant effects of marker type on latitude, and explored trends within each marker type as well as overall. Furthermore, to compare FST values among multiple species, it is necessary to account for differences in the scale of geographic sampling, which was done by calculating the maximum distance (Dx) between sampled subpopulations for each study. The relationship of Dx and FST values for each species were examined statistically using linear regression after log transformation, and the residuals were used in further analysis. Spatial distances were seldom reported in most studies, but geographic information was available either from maps of sampling locations and/or a list of lat/long positions. The maximum distance separating populations was calculated using Google Earth©. If a landmass separated populations, distance measurements were made around the barrier at ca. 5 km offshore. This approach assumes that gene flow is the predominant component represented in FSTvalues. Although other factors such as phylogeography may play a role, their influence should be study / species specific and, assuming no systematic bias, they will contribute primarily to variability rather than trend. Moreover, to account for any biass due to an overrepresentation of species of a given family trends were also examined using family averages.
Planktonic larval duration. For marine invertebrates (including algae and plants) estimates of PLD encompass the period between fertilization and settlement or metamorphosis, indicated visually either through actual settlement or morphological transition. In marine fish this transition is most often delineated in otolith microstructure that characterizes the habitat transition. However, this approach neglects dispersal potential associated with the egg stage. Although species-specific egg stage duration data is largely absent from the literature, we attempt to account for differences in dispersal associated with the egg stage through the examination of egg type. The use of published data on pelagic larval duration necessitates several generalizations and assumptions. For example, the influence of factors such as temperature, salinity, and prey density on larval duration can generally not be included as available information is insufficient. Moreover, data given on larval duration often represent a single estimate of what is really a range of possible values. Whenever possible, median duration values have been estimated for a given taxa. Given that the majority of published data on dispersal potential for fish species is from studies of otolith microstructure in settled juveniles or adults, it represents the time since hatch rather than from fertilization and therefore does not account for egg stage duration. Although egg stage duration may be very short (days) in tropical areas, it may be on the order of weeks to months at higher latitudes. Currently, species-specific egg stage duration is largely absent from the literature. However, because of the potential importance of egg stage duration to dispersal potential, data was collected on egg type (i.e. pelagic or demersal) for fish species in the both the PLD and FST databases.
Because this review examines trends and patterns in published data, it is subject to the limitations of species selection made for reasons related to specific studies. We have attempted to account for selection bias in several ways. First, the overall size of each database was maximized to ensure an accurate representation of the published literature. Second, comparison among the databases of larval duration data, egg type, and genetic pattern should allow for broad-scale patterns to be identified independent of database-specific biases which may exist. Third, the regression analysis included other factors associated with species selection such as size and depth. Moreover, the persistence of observed associations within marine fish species at the family level supports the absence of a phylogenetic or selection bias in the sampled species.
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Table A1. Estimates of dispersal distance or proportion local recruitment for marine species from the published literature.
Approach / Study / Methodology / Process Measured / Taxa / Temporal Scale / Spatial Scale / Mean Latitude / Habitat / Point estimate or distance / Estimate (km)Biogeography
/ Robertson 2001 / Biogeographic review / Effective dispersal / reef fish / na / 1000 / -10 / reef / point / NaReed et al. 1988 / Presence of Zoospores / Dispersal / algae (3 sp) / 1 / 4 / 33 / coastal / distance / 4
Sammarco and Andrews 1989 / Presence / Absence / Dispersal / Corals (2sp) / 1 / 4 / 18 / reef / distance / 1
Gerrottede 1981 / Presence / Absence / Dispersal / Coral / 1 / 1 / 32 / coastal / distance / 0.0001
Keough and Chernoff 1987 / Presence / Absence / Dispersal / Bryzoan / 1 / 50 / 29 / coastal / distance / 0.1
Scheltema 1971 / Presence / Absence / Dispersal / Mollusk / 1 / 5000 / 0 / coastal / distance / 4500
Knoepffler-Peguy et al. 1985 / Spread of invasive species / Dispersal / Algae / na / na / 43 / coastal / distance / 50
Zechman and Mathieson 1985 / Presence / Absence / Dispersal / Algae / 1 / 20 / 43 / coastal / distance / 35
Meinesz et al. 1993 / Spread of invasive species / Dispersal / Algae / na / na / 43 / coastal / distance / 0.5
Davis and Butler 1989 / Direct Observations / Dispersal / Ascidian / 1 / 295 / -34 / coastal / distance / 0.0025
Grosholz and Ruiz 1995 / Spread of invasive species / Dispersal / Crustacean / na / na / 48 / coastal / distance / 173
Luczak et al. 1993 / Spread of invasive species / Dispersal / Mollusk / na / na / 51 / coastal / distance / 111
Average
/ 1 / 196.3 / 26.9 / 37.4Eulerian
/ Jones et al. 2005 / Oxytetracycline tagging / Dispersal / Reef fish / 2 / 10 / -5 / reef / point / 15.9-31.5 %Reviews: / Miller et al. 2005 / Microchemical / Dispersal / Rockfish / 1 / 500 / 44 / coastal / point / 64%
Campana and Thorrold 2001 / Thorrold et al. 2001 / Microchemical / Dispersal / Weakfish / 3 / 1500 / 35 / Estuarine / point / 60-81%
Levin 1990 / Jones et al. 1999 / Oxytetracycline tagging / Dispersal / Reef fish / 1 / 10 / -14 / reef / point / 15-60%
Orth et al. 1994 / Presence / Absence / Dispersal / Sea Grass / 3 / 5 / 38 / coastal / point / 80% or 5m
Swearer et al. 1999 / Microchemical / Dispersal / Reef fish / 1 / 50 / 17 / reef / point / 70%
Average / 1.83 / 345.83 / 19.17 / 55.0 %
Lagrangian
/ Bode and et al. (2006) / Hydrographic modelling / Effective Dispersal / Reef fish / 40 / 230 / -16 / reef / distance / 200Arnold et al. 2005 / Hydrographic modelling and Tracking / Dispersal / Bivalve / 1 / 10 / 28 / lagoon / distance / 4
Olson 1985 / Direct Observations / Dispersal / Ascidian / 1 / 5 / -14 / reef / distance / 1
Marsh et al. 2001 / Hydrographic modeling / Dispersal / Polychaete / 1 / 200 / 9 / vent / distance / 100
Stoner 1992 / Direct Observations / Dispersal / Ascidian / 1 / 10 / 19 / reef / distance / 0.02
Worcester 1994 / Direct Observations / Dispersal / Ascidian / 2 / 0.05 / 38 / coastal / Distance / 1
Average / 7.67 / 75.8 / 10.67 / 51.00
Short-term genetic analysis / Jones et al. 2005 / Paternity analysis / Effective Dispersal / Reef fish / 1 yr / 1 / -5 / reef / Point / 31.5%
Gilg and Hilbish 2003 / Hybrid zone and recruit genotyping / Effective Dispersal / Bivalve / 3 / 200 / 50 / coastal / distance / 64
Coleman and Brawely 2005 / Assignment / Effective Dispersal / Algae / 1 / 60 / 44 / coastal / distance / 0.01
Grosberg 1987 / Assignment / Effective Dispersal / Ascidian / 1 / 5 / 42 / coastal / distance / 0.01
Average
/ 1.5 / 66.5 / 32.75 / 16.08Long-term genetic analysis
Reviews:
Palumbi 2003 / Miller et al. 2005 / Microsatellite / Effective Dispersal / Rockfish / na / 500 / 44 / coastal / point / 64%
Bradbury and Bentzen / Gomez-Uchida and Banks 2005 / Microsatellite / Effective Dispersal / Rockfish / na / 1000 / 44 / coastal / distance / 111
2007 / Buonaccorsi et al. 2004 / Microsatellite / Effective Dispersal / Rockfish / na / 1400 / 38 / coastal / distance / 10
Hellberg et al. 2002
Buonaccorsi et al. 2005
Kinlan and Gaines 2003 / Microsatellite
various / Effective Dispersal
Effective Dispersal / Rockfish
various / Na
na / 2200
na / 37
na / Coastal
na / Distance
distance / 10
Various
Average / 1275 / 39.6 / 43.66 km
54 % and
Overall Average
/ 2.98 / 327.3 / 24.4 / 39.8 km1
Table A2. Database of marine invertebrate planktonic larval duration references.
Group / Species / ReferencePhaeophyta / Alaria marginata / Kusumo and Druehl 2000
Chlorophyta / Enteromorpha sp. / Jones and Babb 1968
Phaeophyceae / Laminaria hyperborea / Kain 1964
Various seaweeds / Various / Santelices (1990)
Porifera / Halichondria magniconulosa / Maldonado & Young (1996)
Porifera / Halichondria panicea / Amano(1986)
Porifera / Haliclona tubifera / Maldonado & Young (1996)
Porifera / Sigmadocia caerulea / Maldonado & Young (1996)
Porifera / Tedania ignis / Maldonado & Young (1996)
Cnidaria / Acropora hyacinthus / Harrison et al (1984)
Cnidaria / Favia fragum / Carlon & Olson (1993)
Cnidaria / Catyophyllia smithi / Tranter et al 1982
Cnidaria / Cribrinopsis fernaldi / Seibert & Spaulding( 1976)
Cnidaria / Cyphastrea serailia / Williams & Harrison (1998)
Cnidaria / Agaricia agaricites / Carlon & Olson (1993)
Cnidaria / Heteroxenia fuscenscens / Zaslow and Benayahu (1996)
Cnidaria / Metridium senile / Chia (1976); Hoffmann (1987)
Cnidaria / Urtricina crassicornis / Chia & Spaulding (1972)
Cnidaria / Stomphia didemon / Seibert (1973)
Cnidaria / Stylatula elongata / Morris et al (1980)
Polychaeta / Crucigera zygophora / Strathmann (1987)
Polychaeta / Protula pacifica / Parker & Tunnicliffe(1994)
Polychaeta / Phylloddoce maculata / Parker & Tunnicliffe(1994)
Polychaeta / Nereis procera / Strathmann (1987)
Polychaeta / Serpula vermicularis / Morris et al (1980)
Polychaeta / Lumbrineris inflata / Chia (1976); Strathmann (1987)
Polychaeta / cirratulid sp. / Morris et al (1980); Strathmann (1987)
Polychaeta / Plexauara kuna / Lasker & Kim (1996)
Crustacea / harpacticoid copepod / Hicks & Coull (1983)
Crustacea / Chorilia longipes / Hines (1986)
Crustacea / Oregonia gracilis / Hines (1986)
Crustacea / Cancridae / Hines (1986)
Crustacea / Grapsidae / Hines (1986)
Crustacea / Majidae / Hines (1986)
Crustacea / Ocypodidae / Hines (1986)
Crustacea / Pinnotheridae / Hines (1986)
Crustacea / Portunidae / Hines (1986)
Crustacea / Xanthidae / Hines (1986)
Crustacea / Chionoecetes opilio / Squires (1996)
Crustacea / Hyas araneus / Squires (1996)
Crustacea / Hyas coarctatus / Squires (1996)
Crustacea / Cancer irroratus / Squires (1996)
Crustacea / Homarus americanus / Squires (1996)
Crustacea / Pandalus borealis / Squires (1996)
Crustacea / Pandalus montagui / Squires (1996)
Crustacea / Lebbeus polaris / Squires (1996)
Crustacea / Lebbeus groenlandicus / Squires (1996)
Crustacea / Sclerocrangon boreas / Squires (1996)
Crustacea / Eualus pusiolus / Squires (1996)
Crustacea / Eualus gaimardi / Squires (1996)
Crustacea / Eualus fabricii / Squires (1996)
Mollusca / Anachis avara / Scheltema (1989)
Mollusca / Anachis translirata / Scheltema (1989)
Mollusca / Aporrhais occidentalis / Scheltema (1989)
Mollusca / Archidoris montereyensis / Morris et al (1980)
Mollusca / Architectonica nobilis / Scheltema (1989)
Mollusca / A. peracuta / Scheltema (1989)
Mollusca / Bittium varium / Scheltema (1989)
Mollusca / Bittium virginicum / Scheltema (1989)
Mollusca / Bursa thomae / Scheltema (1989)
Mollusca / Caecum cooperi / Scheltema (1989)
Mollusca / Caecum pulchellum / Scheltema (1989)
Mollusca / Calliostoma annulatum / Hadfield & Strathmann (1990); Strathmann (1987)
Mollusca / Callistoma ligatum / Holyoak (1988)
Mollusca / Cerithiopsis greeni / Scheltema (1989)
Mollusca / Cerithiopsis subulata / Scheltema (1989)
Mollusca / C. floridanum / Scheltema (1989)
Mollusca / Cheilea equestris / Scheltema (1989)
Mollusca / Cirsoterma dalli / Scheltema (1989)
Mollusca / Conus sozoni / Scheltema (1989)
Mollusca / Coralliophila deburghiae / Scheltema (1989)
Mollusca / Crassodoma gigantea / Morris et al (1980)
Mollusca / Crepidula fornicata / Scheltema (1989)
Mollusca / C. plana / Scheltema (1989)
Mollusca / Cymatium cynocephalum / Scheltema (1989)
Mollusca / C. gracile / Scheltema (1989)
Mollusca / C. labiosum / Scheltema (1989)
Mollusca / C. martinianum / Scheltema (1989)
Mollusca / C. parthenopeum / Scheltema (1989)
Mollusca / Cypraea cervus / Scheltema (1989)
Mollusca / C. spurca acicularis / Scheltema (1989)
Mollusca / C. zebra / Scheltema (1989)
Mollusca / Cypraecassis testiculus / Scheltema (1989)
Mollusca / Distorsio clathrata / Scheltema (1989)
Mollusca / D. constricta mcgintyi / Scheltema (1989)
Mollusca / Epitonium krebsii / Scheltema (1989)
Mollusca / E. rupicolumn / Scheltema (1989)
Mollusca / Erato maugeriae / Scheltema (1989)
Mollusca / Eudolium crosseanum / Scheltema (1989)
Mollusca / Kurtziella limonitella / Scheltema (1989)
Mollusca / Littorina irrorata / Scheltema (1989)
Mollusca / Lunatia heros / Scheltema (1989)
Mollusca / Macoma balthica / Gilbert (1978)
Mollusca / Mitrella lunata / Scheltema (1989)
Mollusca / Mitra nodulosa / Scheltema (1989)
Mollusca / Modulus modulus / Scheltema (1989)
Mollusca / Moliolus modiolus / deSchweinitz & Lutz (1976)
Mollusca / Nassarius ambiguus / Scheltema (1989)
Mollusca / N. obsoletus / Scheltema (1989)
Mollusca / N. trivittatus / Scheltema (1989)
Mollusca / N. vibex / Scheltema (1989)
Mollusca / Natica canrena / Scheltema (1989)
Mollusca / Neosimna acicularis / Scheltema (1989)
Mollusca / N. uniplicata / Scheltema (1989)
Mollusca / Phalium granulatum / Scheltema (1989)
Mollusca / Philippia krebsi / Scheltema (1989)
Mollusca / Polinices lacteus / Scheltema (1989)
Mollusca / Polinices duplicatus / Scheltema (1989)
Mollusca / Rissoina decussata / Scheltema (1989)
Mollusca / Seila adamsi / Scheltema (1989)
Mollusca / Strombus alatus / Scheltema (1989)
Mollusca / Thais haemastoma / Scheltema (1989)
Mollusca / Tonna galea / Scheltema (1989)
Mollusca / Torina bisculata / Scheltema (1989)
Mollusca / Triphora nigocincta / Scheltema (1989)
Mollusca / Trivia candidula / Scheltema (1989)
Mollusca / Petricola pholadiformis / Mackie (1984)
Mollusca / Sinonovacula constricta / Wang & Xu (1997)
Mollusca / solemyid sp. / Gustafson (1985)
Brachiopoda / Laqueus californianus / Valentine and Jablonski (1983)
Brachiopoda / Terebratulina sp. / Valentine and Jablonski (1983)
Brachiopoda / Platidia hornii / Rudwick (1970)
Bryozoa / Bugula stolonifera / Woollaott et al (1989)
Bryozoa / Bicrisia edwardsiana / Nielsen (1970); Woollacott & Zimmer (1978)
Bryozoa / Crisia occidentalis / Nielsen (1970); Woollacott & Zimmer (1978)
Bryozoa / Filacrisia franciscana / Nielsen (1970); Woollacott & Zimmer (1978)
Bryozoa / Rhamphostomella spinigera / Woollacott & Zimmer (1978)
Bryozoa / Borgiola pustulosa / Woollacott & Zimmer (1978)
Bryozoa / Bugula sp. / Nielsen (1970); Woollacott & Zimmer (1978); Wollacott et al (1989)
Bryozoa / Lyrula sp. / Woolacott & Zimmer (1978)
Sipuncula / Phascolosoma agassizi / Rice (1967); Strathmann (1987)
Echinodermata / Pycnopdia helianthoides / Greer (1962); Lambert(1981); Morris et al (1980); Strathmann (1978)
Echinodermata / Crossaster papposus / Lambert (1981); Strathmann (1987)
Echinodermata / Hippasterias spinosa / Lambert (1981)
Echinodermata / Florometra serratissima / Mladenov & Chia (1983); Strathmann(1978)
Echinodermata / Strongylocentrotus franciscanus / Morris et al (1980); Strathmann(1978); Strathmann (1987)
Table A3. Database of marine fish planktonic larval duration references.