Quantifying the "bio-" components in biophysical models of larval transport in marine benthic invertebrates: advances and pitfalls.
ABSTRACT Biophysical models are being used increasingly, both as predictive tools of larval dispersal for a particular system and for general evaluation of the role of different factors in larval transport. In the results of such models, larval duration, mortality, and behavior in the water column have exhibited pronounced effects on larval dispersal of marine benthic invertebrates. The parameterization of these processes has broadly reflected values from laboratory experiments, but the accuracy of these values is unknown. The pelagic larval duration used in models should be determined by laboratory, or preferably field, studies and should incorporate environmentally dependent variability. For mortality, in situ estimates are now feasible and, likely, more accurate than the currently used values. Larval behavior can be measured in the field, by high-frequency sampling of distributional changes relative to features in the water column or by controlled larval releases in tractable systems. To successfully validate the outcomes of these models, we must either improve our techniques for measuring larval abundance at the end of larval transport immediately before settlement, or incorporate components for settlement into the models. We must also address the mismatch in sampling resolution between biological and physical processes. If used with caution, this powerful approach can significantly advance our understanding of larval transport.
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ABSTRACT: The megalopal larval stage of many estuarine brachyuran crabs appears to return to adult habitats by undergoing rhythmic vertical migrations which result in saltatory up-estuary transport on flood tides. Larval ascent into the water column during rising tides may be cued by changing hydrologic variables. To test this hypothesis, we investigated the responses of field-caught megalopae of the blue crab Callinectes sapidus and the fiddler crab Uca spp. to constant rates of pressure and salinity change under laboratory conditions. For both genera, pressure changes resulted in increased movement (barokinesis) and upward migration in the test chamber, with C. sapidus megalopae having a lower response threshold (2.810-2 mbar s-1) than Uca spp. larvae (510-2 mbar s-1). Similarly, larvae ascended in response to increasing salinity, with C. sapidus larvae being more sensitive. Larvae were negatively phototactic and failed to respond to pressure increases at light levels above 1.01015 and 1.01013 photons m-2 s-1 for C. sapidus and Uca spp. megalopae, respectively. Such responses are thought to explain the low abundances of larvae in the water column during daytime flood tides. Nevertheless, threshold sensitivities to increasing pressure for both genera were above levels experienced during floodtide conditions in the field. Similarly, it is unlikely that increasing salinity is sufficient to induce ascent in Uca spp. postlarvae. However, rates of salinity increase during midflood tide typically reach levels necessary to induce an ascent in C. sapidus megalopae. These results are consistent with the hypothesis that fiddler crab megalopae utilize an endogenous activity rhythm for flood-tide transport, while blue crab megalopae rely upon external cues, especially salinity changes, to time their sojourns in the water column.Marine Biology 04/1995; 122(3):391-400. · 2.47 Impact Factor
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ABSTRACT: Many marine dispersive propagules select specific settlement sites based on a range of environmental cues. However, the link between larval choice and post-settlement growth and survival is still poorly understood. Here we show that cypris larvae of the barnacle Balanus improvisus actively reject surfaces exposed to local flow speeds exceeding 5-10 cm/s. Field experiments show that post-settlement growth and survival decline in freestream flows above 15 cm/s. Moreover, studies in flume flow at local speeds exceeding 10 cm/s reveal that early juveniles show reduced feeding rates caused by deformation of the cirral fan, reduced retention efficiency, and a decrease in time spent feeding. We conclude that cypris larvae actively reject flow environments that will be suboptimal for suspension feeding in the early post-settlement phase. Our study suggests that larval choice can be adaptively connected to a specific part of the life cycle, in this case the very sensitive time after metamorphosis.Ecology 09/2006; 87(8):1960-6. · 5.18 Impact Factor
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ABSTRACT: The early life history of many marine benthic invertebrate and fish species involves a planktonic larval stage that allows exchange of individuals among separated adult populations. Here, we demonstrate how natural and anthropogenic trace elements can be used to determine larval origins and assess bay-ocean exchange of invertebrate larvae. Trace elements can be effective site markers for estuaries because run-off and pollutant loading often impart distinct elemental signatures to bay habitats relative to nearshore coastal environments. Crab larvae originating from San Diego Bay (SDB) were distinguished from those originating in neighboring embayments and exposed coastal habitats by comparing multiple trace-element concentrations (''fingerprints'') in individuals. Discriminant function analysis (DFA) was used to characterize stage I zoeae of the striped shore crab, Pachygrapsus crassipes,of known origin (reference larvae) via trace-elemental composition (i.e., Cu, Zn, Mn, Sr, Ca). Linear discriminant functions were used to identify the origin and characterize the exchange of stage I P. crassipes zoeae between SDB and the nearshore coastal environment during one spring tidal cycle. Elemental fingerprinting revealed that most (87%) of the stage I larvae collected at the bay entrance during the flood tide were larvae of SDB origin that were reentering the bay. Nearly one third of zoeae sampled (32%) at the entrance during ebb tide were coastal larvae leaving the bay and returning to open water. The observed bidirectional exchange contrasts with the unidirectional transport of zoeae out of the bay predicted from stage I vertical migratory behavior. Because P. crassipes zoeal survivorship is lower in SDB than in coastal waters, bay-ocean exchange has significant implications for the dynamics of P. crassipes populations. Trace-elemental fingerprinting of invertebrate larvae promises to facilitate investigations of many previously intractable questions about larval transport and dynamics.Limnology and Oceanography - LIMNOL OCEANOGR. 01/2000; 45(4):871-880.
Quantifying the “Bio-” Components in Biophysical
Models of Larval Transport in Marine Benthic
Invertebrates: Advances and Pitfalls
ANNA METAXAS* AND MEGAN SAUNDERS
Department of Oceanography, Dalhousie University, Halifax, Nova Scotia B3H 4J1, Canada
ingly, both as predictive tools of larval dispersal for a
particular system and for general evaluation of the role of
different factors in larval transport. In the results of such
models, larval duration, mortality, and behavior in the water
column have exhibited pronounced effects on larval dis-
persal of marine benthic invertebrates. The parameterization
of these processes has broadly reflected values from labo-
ratory experiments, but the accuracy of these values is
unknown. The pelagic larval duration used in models should
be determined by laboratory, or preferably field, studies and
should incorporate environmentally dependent variability.
For mortality, in situ estimates are now feasible and, likely,
more accurate than the currently used values. Larval behav-
ior can be measured in the field, by high-frequency sam-
pling of distributional changes relative to features in the
water column or by controlled larval releases in tractable
systems. To successfully validate the outcomes of these
models, we must either improve our techniques for measur-
ing larval abundance at the end of larval transport immedi-
ately before settlement, or incorporate components for set-
tlement into the models. We must also address the mismatch
in sampling resolution between biological and physical pro-
cesses. If used with caution, this powerful approach can
significantly advance our understanding of larval transport.
Biophysical models are being used increas-
Spatially fragmented populations, or metapopulations,
can be classified as “sources” or “sinks” of a particular
species depending on the balance of inputs (birth and im-
migration) and outputs (death and emigration) of individu-
als. The degree of exchange of individuals, or “connectiv-
ity,” among populations is critical for the stability of these
populations and their capacity to exploit new areas (e.g.,
invasive species) and re-colonize areas after local extirpa-
tion. The assessment of connectivity among populations of
marine invertebrates requires the quantification of the mech-
anisms that drive larval transport, retention, and supply.
Larval horizontal transport has been conventionally at-
tributed to advection along the dominant direction of flow;
recently, however, larval behavior (i.e., directional swim-
ming in response to a cue) has emerged as a potentially
significant factor influencing horizontal dispersal (Metaxas,
2001; Kingsford et al., 2002; Levin, 2006). For benthic
invertebrates, adjustments in larval vertical position are
possible because larval vertical swimming speeds are on the
same order of magnitude as vertical flow speeds. In turn,
these vertical adjustments can move larvae across water
layers with different flow velocities, modifying larval direc-
tion and speed of transport. Measurements of the effect
(and, consequently, relative importance) of larval behavior
on larval transport are in their infancy.
Because of inherent difficulties in measuring larval dis-
persal in the field, biophysical models are increasingly be-
ing used to both quantify larval transport and assess its role
in regulating population connectivity (e.g., Siegel et al.,
2003; Aiken et al., 2007). These models can be either
general circulation models (e.g., the Regional Ocean Mod-
eling System) with particle-tracking subroutines (Baums et
al., 2006; Edwards et al., 2007; Paris et al., 2007; Pfeiffer-
Herbert et al., 2007; North et al., 2008) or simpler advec-
tion-diffusion models (Hill, 1990; Dekshenieks et al., 1996;
Cowen et al., 2000; Gaylord and Gaines, 2000), and typi-
cally incorporate both physical and biological parameters.
The physical parameters most frequently include mean ve-
Received 4 November 2008; accepted 30 March 2009.
* Towhom correspondence
Abbreviations: PLD, planktonic larval duration.
Reference: Biol. Bull. 216: 257–272. (June 2009)
© 2009 Marine Biological Laboratory
locity components and their variance, the circulation being
driven by winds, tides, or density structure of the water
column (Hare et al., 1999; Siegel et al., 2003; Aiken et al.,
2007; Edwards et al., 2007). To date, the biological param-
eters have included planktonic larval duration (PLD; Siegel
et al., 2003; Aiken et al., 2007; Edwards et al., 2007); diel
and ontogenetic vertical migration—as differences in larval
vertical position in response to light or related to larval age,
respectively (Paris and Cowen, 2004; Pfeiffer-Herbert et al.,
2007; Edwards et al., 2007); and timing of larval release
(Baums et al., 2006; Edwards et al., 2007; Mitarai et al.,
2008). In most instances, larvae are modeled as passive
(non-swimming) particles (Siegel et al., 2003; Aiken et al.,
2007). However, a few modeling studies have incorporated
slightly more complex biological parameters, such as larval
developmental rate given temperature and chorophyll re-
gimes (Dekshenieks et al., 1996; Pfeiffer-Herbert et al.,
2007), and larval swimming velocity (Dekshenieks et al.,
1996) and depth distribution (North et al., 2008) in response
to a salinity gradient.
The response variable of biophysical models is a dis-
persal kernel after a predetermined period of larval devel-
opment (James et al., 2002; Baums et al., 2006; Aiken et al.,
2007). It is estimated as the frequency distribution of set-
tling larvae (number of propagules per unit area) at a series
of settlement sites (or distances from the larval release site)
normalized to the number of larvae produced at a release
site (Siegel et al., 2003; Edwards et al., 2007). Other re-
sponse variables include larval horizontal (Paris et al., 2005;
Edwards et al., 2007; North et al., 2008) and vertical (Dek-
shenieks et al., 1996) distributions; larval trajectories, or
“dispersal paths,” (Baums et al., 2006; Pfeiffer-Herbert et
al., 2007); mean dispersal distance (Edwards et al., 2007;
North et al., 2008); and abundance of adults (Gaylord and
The extent of validation of model predictions varies
among studies. In most instances, models are used only to
provide theoretical outcomes that quantify the effect of
different factors (e.g., length of PLD, different flow scenar-
ios, timing and location of larval release, larval behavior) on
dispersal trajectories or dispersal kernels (e.g., Hill, 1990;
James et al., 2002; Siegel et al., 2003; Aiken et al., 2007;
Fiksen et al., 2007; North et al., 2008), and, consequently,
are not validated. Alternatively, models have been validated
by comparing predicted spatial patterns in dispersal desti-
nations with spatial patterns in recruitment (e.g., Incze and
Naimie, 2000; Pfeiffer-Herbert et al., 2007).
The predictive performance of biophysical models is
constrained by two factors. First, the parameterization, and
resolution, of the different components of the models can
only be as accurate as our estimates of the parameters in the
field. Second, validation of model outcomes must be done
for the appropriate life-history stages. In this review, we
discuss these two constraints. With respect to model param-
eterization, we address three biological parameters: larval
growth rate, and by extension PLD; larval mortality rate;
and larval behavior in response to cues in the water column
during dispersal and on the benthos during settlement. For
each parameter, we provide an overview of current knowl-
edge, discuss its potential effect on the prediction of larval
transport, and make suggestions on approaches for its pa-
rameterization. We also provide a perspective on suggested
approaches to model validation.
Although biophysical models have been used to quantify
larval transport in both benthic invertebrates and fish, we
have focused our review on the former, and provide only a
few key examples for the latter. A number of recent reviews
have evaluated the utility and applicability of these models
either mainly or exclusively for larval fish (e.g., Werner et
al., 2001, 2007; Fiksen et al., 2007; Leis, 2007), but ours is
the first comprehensive treatment for larval benthic inver-
tebrates. In many instances, models have represented “lar-
vae” as passive particles that could fall under any taxon;
however, it is the taxon-specific behaviors that may generate
differences in model performance between taxa. By limiting
this review to benthic invertebrates, we attempt to describe
these behaviors in detail for this group. Although similar
processes operate on the two taxa, the scales over which a
particular process becomes relevant may differ between
larval fish and benthic invertebrates (Bradbury and Snel-
grove, 2001). In particular, the stronger swimming ability of
larval fish allows for greater potential dispersal distances
(Gaines et al., 2007), but also a higher probability of reten-
tion or of a successful response to a cue, than for benthic
invertebrates (Cowen and Sponaugle, 2009). A relatively
small number of studies (tens) have used biophysical mod-
els to quantify larval transport in marine benthic inverte-
brates; however, the majority has been published in the last
5 years, suggesting that the use of the approach is acceler-
ating and making our review very timely.
The planktonic larval duration (PLD), used in biophysical
transport models, is defined as the period between embryo
release in the water column and larval arrival at the settle-
ment location. PLD frequently includes both a precompe-
tent period, which is dependent upon growth rate, and a
postcompetent period, which is both species- and environ-
mentally dependent (see below). PLD can be measured in
situ for only a few species with particularly large larvae and
short development periods (hours to days), which can be
tracked visually, such as tunicates (Olson, 1985; Davis and
Butler, 1989). However, larvae of most species are too small
and spend too long in the water column (weeks to months)
to allow in situ measurements of PLD. Consequently, PLD
is estimated from laboratory studies on larval growth rates
A. METAXAS AND M. SAUNDERS
(Paulay et al., 1985; Hart and Scheibling, 1988; Graham et
al., 2008), or by using indirect approaches, such as size-
frequency analyses of repeatedly sampled larval cohorts
(Lamare and Barker, 1999; Tapia and Pineda, 2007). The
advantage of the latter approach is that in situ measurements
are obtained; however, it is based on the assumption that the
sampled cohort does not mix with other populations during
the sampling period. Tracking labeled cohorts of larvae in
the field could also prove useful (Levin, 1990; Thorrold et
al., 2002), but has not yet been used successfully to measure
growth rates for invertebrates. For larval fish, PLD is rou-
tinely estimated by counting the daily increments in otoliths
(e.g., Danilowicz, 1997; Shima and Findlay, 2002). This
method could potentially be used in larval invertebrates
with hard shells or statoliths, such as molluscs or some
bryozoans, if the appropriate relationships between mor-
phology, environmental parameters, and time are devel-
Several environmental factors can affect larval growth
rates, but temperature is the most important; warmer tem-
peratures result in faster growth rates and shorter develop-
ment times (Hart and Scheibling, 1988; Hoegh-Guldberg
and Pearse, 1995). However, at temperatures much warmer
than typically experienced in the field, development and
survival are reduced (e.g., Chen and Chen, 1992). In a
modeling study, Reitzel et al. (2004) demonstrated that the
shape of the functional relationship with temperature could
have significant implications for the development times of
marine invertebrates, depending on the timing of spawning.
O’Connor et al. (2007) determined that the functional rela-
tionship between PLD and temperature was the same for 72
species of invertebrate and fish. Variations among species
existed in the magnitude of PLD at a given temperature, but
not in the relationship between PLD and temperature, which
was mainly influenced by developmental mode (lecitrophic
vs. planktotrophic), size, and geographic region (global
scale) (O’Connor et al., 2007). In the laboratory, larval
growth rate and development time are measured at a range
of fixed temperatures, and most studies use mean tempera-
ture to predict larval development period. However, for
ectothermic organisms, the thermal integral, or growing-
degree-day (GDD), is a much better predictor of growth
than is mean temperature (Neuheimer and Taggart, 2007)
because it incorporates the variability in temperature, as
well as the thermal history the organism experienced in the
past. Thermal time has been used extensively to predict
growth in insects and plants (Trudgill et al., 2005), and
recently, size-at-age of fish (Neuheimer and Taggart, 2007)
and settlement in marine invertebrates (Saunders and
Metaxas, 2007). The metric is particularly relevant for the
accurate prediction of development time of larvae in the
water column, where they can spend up to several weeks
experiencing fluctuating temperatures. For example, the
size-at-age of a larval species could be determined using
either a number of relationships at different temperatures or
a single relationship for GDD (see Neuheimer and Taggart,
2007, for examples using fish).
Food availability can limit development (Lucas, 1982;
Paulay et al., 1985; Olson and Olson, 1989) of planktotro-
phic larvae, but its effect is considered less important than
that of temperature (Hoegh-Guldberg and Pearse, 1995).
Larval natural diets are presumably quite diverse and may
include bacteria, microalgae, and possibly dissolved organic
matter (Boidron-Me ´tairon, 1995). Consequently, larvae
may be able to avoid food limitation by taking advantage of
patches of different types of food (Boidron-Me ´tairon, 1995;
Metaxas and Young, 1998a). The combined effects of tem-
perature and food availability on larval development can
have a significant impact on transport distances and con-
nectivity, since longer development time can result in
longer dispersal distances (Shanks et al., 2003). If PLD is
strongly dependent on food, the patchiness of food in the
field will need to be resolved (e.g., Pepin et al., 2003, for
Larvae must be competent to settle before they can move
from the pelagic to the benthic habitat. For some inverte-
brate species, including corals (Wilson and Harrison, 1998),
asteroids (Hoegh-Guldberg and Pearse, 1995), and molluscs
(Pechenik and Lima, 1984; Coon et al., 1990), larvae can
prolong the period of settlement competence for days or
weeks. The ability to maintain competence for a period of
time increases the probability that larvae will encounter
appropriate settlement substrata. However, larvae that delay
settlement will have an increased probability of planktonic
mortality and may have reduced post-settlement growth or
survival (Pechenik et al., 1993; Maldonado and Young,
1999). Delayed competence is more prevalent among inver-
tebrates than among fish, probably because, in contrast to
fish, most adult invertebrates have limited or no ability to
move to preferable habitat (Bradbury and Snelgrove, 2001).
For some species (e.g., gastropods; Pechenik and Lima,
1984), the ability to delay settlement after competence is
related to temperature, with a longer delay being associated
with slower growth rates in colder temperatures. If warmer
temperatures cause both shorter larval durations (thus,
shorter dispersal distance) and shorter periods of delayed
competence (thus, lower probability of encountering suit-
able settlement substrate), increases in temperature can have
significant impacts on larval transport.
In biophysical models, PLD usually ranges from a few
days to weeks and is parameterized to broadly reflect de-
velopment periods from the literature (e.g., Siegel et al.,
2003; Ellien et al., 2004; Paris and Cowen, 2004; Aiken et
al., 2007). To our knowledge, PLDs longer than 100 days
have not been included in any models, even though they are
common for many species (Shanks and Eckert, 2005; Gra-
ham et al., 2008). In a few studies, functions describing
growth and development in terms of the temperature or food
BIOPHYSICAL MODELS OF LARVAL TRANSPORT
regime encountered during dispersal have been included in
models of larval lobsters, oysters, and barnacles (Incze and
Naimie, 2000; Dekshenieks et al., 1996, 1997; Pfeiffer-
Herbert et al., 2007). These functions were based on labo-
ratory experiments and on the temperature or phytoplankton
concentration corresponding to each time step of the model.
Coupled bio-physical models of invertebrate larval transport
that incorporate rate-dependent processes, such as stage devel-
opment, ingestion, and excretion, are less developed for
benthic invertebrates than for fish (Daewel et al., 2008) and
copepods (e.g., Lynch et al., 1998; Tittensor et al., 2003).
While Nutrients-Phytoplankton-Zooplankton-Detritus (NPZD)
models have been coupled to physical models (e.g., Franks
and Chen, 2001), they do not typically incorporate or ex-
amine the transport of individual larvae (but see Daewel
et al., 2008).
Judging from the outcomes of the few biophysical models
that have tested for it, the length of PLD can have significant
effects on larval transport (Edwards et al., 2007). For ex-
ample, a reduction in the PLD of scallop larvae on the
Scotian Shelf substantially decreased their displacement
distance (Tremblay et al., 1994). Rates of larval retention in
the natal region and of colonization of distant populations
decreased with increasing PLD for populations of the brittle
star Ophiothrix fragilis in the English Channel (Lefebvre et
al., 2003). Importantly, when examining the effect of PLD
on dispersal, mortality (M) must be incorporated into mod-
els (see below), because increasing PLD significantly in-
creases the probability of mortality and decreases the num-
ber of larvae settling at distant sites (Lefebvre et al., 2003;
for fish, Paris et al., 2007). Shanks (2009) found a bimodal
relationship between PLD and dispersal distance. A third of
the species with long PLD (?1 day and ?1 month) dis-
persed less than 1 km, the same distance as those with short
(e.g., ?1 h) PLD (Shanks, 2009). To date, most efforts to
model invertebrate larval transport have used a fixed PLD
(e.g., Barnay et al., 2003) and have not accounted for
variability in the periods of pre-competency and compe-
tency. Given the potentially significant effect of PLD (in-
cluding prolonged competency) on dispersal distance, it is
important that the estimates used in the models are robust.
To achieve this, temperature- or food-dependent (rather
than mean) larval growth rates should be used (e.g., Incze
and Naimie, 2000), because the amount of variation in these
two environmental parameters can have pronounced effects
on PLD and, consequently, on larval trajectories (Pfeiffer-
Herbert et al., 2007). The resolution of the temperature and
phytoplankton distributions in the model domains is typi-
cally high enough to allow for a linked function that adjusts
growth rate on the basis of the regime encountered at the
end of each time step. Additionally, because the develop-
ment rate can vary even among larvae that have experienced
the same temperature and food regime (Pechenik and Lima,
1984), variability in the duration of pre- and post-compe-
tency periods should also be included in the models. This
could be accomplished by assigning different development
periods to different individuals in the population in individ-
ual-based models. Delayed competence can be incorporated
into models by allowing larvae to settle at any point within
the competency window during which they encounter suit-
able substrate (as for corals: Baums et al., 2006; and larval
fish: Mitarai et al., 2008, Siegel et al., 2008). Given (1) the
extensive information on the effects of different factors on
larval growth, development, and competency period that has
been collected in laboratory studies, and (2) the high reso-
lution of those factors in the physical models, the use of
accurate estimates of PLD in the biophysical models should
be within reach.
The magnitude of larval natural mortality in the plankton
is not well-quantified for benthic invertebrates, although it
is presumed to be great (?90%; Rumrill, 1990). Different
approaches have been used or proposed to quantify mortal-
ity in the laboratory and in the field, but only a few studies
have directly addressed this difficult problem. The main
source of mortality is assumed to be predation (Young and
Chia, 1987; Rumrill, 1990; Morgan, 1995), but other factors
include starvation and physiological stress due to tempera-
ture or salinity (Young and Chia, 1987; Morgan, 1995). The
relative importance of each of these factors also is not
known (Morgan, 1995; Pechenik, 1999). Additionally, mor-
tality can vary with developmental stage or size (Pennington
et al., 1986).
Larval predators can be both planktonic and benthic
suspension-feeders from a wide range of taxa, such as
cnidarians, ctenophores, polychaetes, chaetoganths, and
crustaceans, including decapod larvae and megalopae
(Young and Chia, 1987), but studies that have quantified
predation rates are limited. In the laboratory, experiments
have mostly measured survival rates (expressed as percent-
age of introduced larvae) of echinoderms, bivalves,
polychaetes, and crustaceans, using small containers
(125-ml to 19-l containers) and concentrations of prey much
higher than those found in the field (Cowden et al., 1984;
Pennington et al., 1986; Morgan, 1992; Johnson and Brink,
1998). Some experiments have examined ingestion, rejec-
tion, and excretion rates of prey items by the predators
(Mileikovski, 1974; Purcell et al., 1991). Measurements of
predation rates in the field are even more limited, and they
range in approach from tracking individual ascidian tad-
poles (Olson and McPherson, 1987) and tethering of lobster
postlarvae (Acosta and Butler, 1999) and crab megalopae
(Allen and McAlister, 2007) to using large enclosures (123
liter) with natural concentrations of echinoplutei and snail
veligers, as well as other plankton (Johnson and Shanks,
2003). Predation rates in the field appear to be much lower
A. METAXAS AND M. SAUNDERS
than those measured in the laboratory, most likely because
both predators and prey are less abundant (Johnson and
Shanks, 1997) and the container effects are smaller (in the
case of enclosures) or nonexistent.
Rates of larval mortality (including, but not limited to,
that from predation) can be estimated in situ if cohorts or
patches of larvae can be identified and tracked. However,
because spawning events that can produce large enough
numbers of larvae over short periods of time are generally
unpredictable for most benthic invertebrates, measures of
mortality rates are sorely lacking. When larval cohorts can
be tracked, mortality is estimated using changes in larval
abundance (total or stage-specific) between temporally con-
secutive data points or by comparing larval production at
spawning with post-settlement recruitment (Rumrill, 1990).
A mass spawning event of the sea urchin Evechinus chlo-
roticus in a New Zealand fjord provided a unique opportu-
nity to track a single larval cohort for 7 weeks (Lamare and
Barker, 1999). In that study, mortality estimates were sim-
ilar when using changes in two measures of abundance
(total: 0.164 per day, stage-specific: 0.173 per day), but
much lower when using the relationship of production to
recruitment (0.092 per day). In general, estimates of mor-
tality that are based on the difference between production
and recruitment tend to be lower than those based on
changes in larval abundance in the plankton, because the
former combine periods of potentially different levels of
mortality (higher in the plankton than during and after
settlement) (Rumrill, 1990). It must be noted that a number
of cohorts or spawning events would be required to generate
statistically meaningful estimates of mortality. Interestingly,
biophysical transport models have been used to estimate
larval mortality in the field. Pepin et al. (2003) tracked a
water parcel using a biophysical model with a particle-
tracking subroutine (no mortality included), and sampled a
group of larval fish within the parcel at consecutive times.
They then estimated mortality in the larval patch by com-
paring the larval abundance as predicted by the model to
that sampled in the field.
The main assumption in the analyses described above is
that the same group of larvae is being tracked over the study
period (e.g., Natunewicz et al., 2001). However, larvae that
are lost from or added to the patch from other populations
between consecutive points also contribute to the estimates
of “mortality” in the patch. Concurrent measures of the flow
regime and the size/age structure of the larval population
within the sampling domain may minimize under- or over-
estimation of mortality. Aksnes and Ohman (1996) pro-
posed an alternative approach, which uses the relative abun-
dance of all developmental stages at one point in time to
calculate mortality. Tapia and Pineda (2007) used this ap-
proach by sampling multiple cohorts of the barnacles Bala-
nus glandula and Chthamalus spp. in the nearshore waters
at La Jolla, California, over 7 days during the reproductive
period of this species. Mortality rates varied between days
by up to an order of magnitude, ranging from 0.043 to 0.693
Mortality is not explicitly included in all biophysical
models, despite its potentially significant role in limiting
larval dispersal at different times during the planktonic
periods (different locations or at different developmental
stages). By definition, advection-diffusion-mortality models
incorporate mortality as a term (Jackson and Strathmann,
1981; Hill, 1990; Cowen et al., 2000; Lefebvre et al., 2003;
Ellien et al., 2004). These studies have shown that larval
mortality in the plankton can have a pronounced effect on
larval dispersal. Most notably, retention near the release site
increases (or, conversely, dispersal away from the source
decreases) with increasing mortality, for larvae with differ-
ent swimming abilities—for example, the Norway lobster
Nephrops norvegicus (Hill, 1990) and the brittle star Ophio-
thrix fragilis (Lefebvre et al., 2003). For the polychaete
Pectinaria koreni on the eastern Bay of Seine, mortality was
shown to have a greater effect on larval loss (quantified as
proportion of released larvae that settle) than advection,
particularly at low current speeds (Ellien et al., 2004).
Similarly, Jackson and Strathmann (1981) showed that, for
a “model larval population,” increasing larval mortality in
the plankton during pre-competence decreases success at
larval settlement. For virtual larvae released over a period of
30 days off the west coast of Barbados, a mortality rate of
18% per day resulted in insufficient numbers of larvae
reaching downstream locations to maintain populations
there (Cowen et al., 2000). Dekshenieks et al. (1997) in-
corporated a term for predation-induced larval mortality in
a model that examined larval vertical distribution of the
oyster Crassostrea virginica. In a series of simulations, they
examined larval supply using different relationships of mor-
tality with larval size (constant, increasing, decreasing) and
with depth (surface, pycnocline, benthos). Larval survival at
the end of the planktonic period was extremely low
(?2.5%) when mortality was size-dependent, and was much
greater when predation was limited to the surface (?88%)
than the bottom (14%) (Dekshenieks et al., 1997).
The mortality rates used in the biophysical models are
based on laboratory and field estimates provided in the
literature. However, the accuracy of the parameterizations is
unknown, given the limitations in (1) obtaining accurate
estimates in small containers and still water in the labora-
tory; (2) the small number of estimates from the field; and
(3) the inconsistency in the estimates between settings.
Rumrill (1990) reviewed mortality rates of benthic inverte-
brates from most studies (field and laboratory) available at
that time, and is frequently the source of the rates used in the
models (Jackson and Strathmann, 1981; Dekshenieks et al.,
1997; Lefebvre et al., 2003; Ellien et al., 2004). It must be
noted, however, that the number of taxa in Rumrill (1990)
was not exhaustive, and the values used in the models are
BIOPHYSICAL MODELS OF LARVAL TRANSPORT
often for the same genus or family as in Rumrill, but not
necessarily for the same species. In some cases, the mortal-
ity values that are used have been derived specifically for
the population being modeled (e.g., Hill, 1990).
We propose that larval mortality rates that have been
obtained by sampling larval populations over time or across
space in the field provide the best estimates for parameter-
izing biophysical models for several reasons. (1) Perhaps
most importantly, these estimates incorporate all sources of
mortality (or loss) without the need to understand the rela-
tive contributions of different sources. (2) Mortality rates
can be estimated for the species and population of choice,
rather than relying on extrapolations from other species or
areas. (3) Estimates of both population-wide and stage-
specific mortality are possible. (4) Approaches have been
developed that allow the estimation of mortality rates with-
out tracking larval cohorts or patches, as long as the spatial
coverage of the sampling is greater than the scale of advec-
tion. To provide accurate measures of mortality, field sam-
pling requires an understanding of the appropriate spatial
coverage (i.e., spatial extent of the larval population; spatial
scale of advective transport) and temporal resolution (i.e.,
timing of larval release; developmental periods), but is not
Larval behavior in the water column
Larvae of marine invertebrates are weak swimmers
(Young and Chia, 1987; Young, 1995). Locomotion is
achieved by mechanisms ranging from ciliary to muscular
activity, and swimming speeds are in the order of millime-
ters to centimeters per second (Chia et al., 1984; Young,
1995; Metaxas, 2001). It has been suggested that larvae
have the ability to sense their environment, and effects on
swimming speed (acceleration, deceleration, or cessation)
and directionality have been recorded in the presence of
chemical and physical stimuli (Kingsford et al., 2002).
However, with the exception of some large, strong-swim-
ming larvae (ascidian tadpoles, crab zoea, larval lobsters),
these measurements have been taken in the laboratory, and
mostly with no flow. Consequently, the conditions in the
field under which larvae can sustain their ability to maneu-
ver relative to perceived stimuli are not known.
Meroplanktonic invertebrate larvae have been shown to
exhibit behavioral responses to cues in the water column
(Young, 1995; Metaxas, 2001; Kingsford et al., 2002),
although some of the mechanisms for cue detection remain
unknown (Kingsford et al., 2002). The expression of some
of these behaviors (most notably vertical migration) can
vary on a diel cycle or ontogenetically (e.g., Carriker, 1951;
Shanks, 1986; Metaxas and Young, 1998b). Most studies
have addressed responses to cues by recording effects on
vertical (or horizontal) distribution or swimming speed in
the laboratory under static flow conditions (Metaxas, 2001;
Kingsford et al., 2002). Because of our inability to success-
fully track larvae (individuals or cohorts; but see Cobb et
al., 1989; Bingham and Young, 1991; Shanks, 1985, 1995;
Shanks and Wright, 1987; Annis, 2005), studies done in the
field have mainly focused on larval vertical distributions
relative to features in the water column, such as pycno-
clines, fronts, or layers of chlorophyll maxima (Metaxas,
The most commonly quantified larval behavior in the
field is vertical migration. For example, it is well established
that crustaceans (e.g., the barnacle Balanus improvisus, the
blue crab Callinectes sapidus, the lobster Homarus ameri-
canus) regulate their vertical position in the water column
over the period of larval development, some stages being
found near or at the sea surface while others near the bottom
at depths of tens of meters (Harding et al., 1987; Epifanio
and Garvine, 2001). Several species (e.g., the oyster Cras-
sostrea virginica, the scallop Placopecten magellanicus,
and the green crab Carcinus maenas) are also known to
show diel or tidal vertical migration across tens of meters
(Forward, 1988; Manuel et al., 1997; Queiroga and Blanton,
2004). Most often, light has been inferred as the cue that
drives vertical migration: the stages that migrate toward the
surface show positive phototaxis, while the ones that move
away from the surface or toward the ocean floor show
negative phototaxis (Lang et al., 1979; Forward, 1988).
Changes in salinity and pressure have also been implicated
as cues for tidal vertical migration (Sulkin, 1984; Tanker-
sley et al., 1995). Several of the studies that have addressed
vertical migration have emphasized its importance to larval
horizontal transport (Sulkin, 1984; Zeng and Naylor, 1996;
Manuel et al., 1997; Kingsford et al., 2002; Shanks and
Larvae of many taxa respond to physical characteristics
of the water column, such as the salinity and temperature
structure, both in the field and in the laboratory (Metaxas,
2001). Additionally, larval behavioral response to biological
features of the water column, such as the presence of food
(phytoplankton) or predators, has been inferred from distri-
butional studies in the field (Metaxas 2001), as well as from
manipulative experiments in the laboratory (Metaxas, 2001;
Metaxas and Burdett-Coutts, 2006; Woodson and McMa-
nus, 2007; Sameoto and Metaxas, 2008a). The response can
be manifested as either aggregation or avoidance of the
feature. A few studies have recorded measurable effects of
flow characteristics (shear, turbulence) on larval vertical
distribution or swimming patterns (Luckenbach and Orth,
1992; Welch et al., 1999; Fuchs et al., 2004; Metaxas et al.,
2009; Sameoto et al., Dalhousie University, unpubl. data).
Although these studies have alluded to the potential signif-
icance of the detected larval responses in larval dispersal,
the realized role of larval behavior during larval transport
Larval behavior has been incorporated in some studies
A. METAXAS AND M. SAUNDERS
using biophysical models to predict larval transport, al-
though not consistently (e.g., Lefebvre et al., 2003; Aiken et
al., 2007). Most frequently, the incorporated behavior is
vertical migration. Ontogenetic vertical migration has been
incorporated for a number of species with different swim-
ming abilities (e.g., lobsters, crabs, scallops, as well as fish)
by modifying the vertical position of the tracked particles at
different times after their release (i.e.. according to their
“age”). For example, modeling studies of the lobster H.
americanus and the rock crab Cancer irroratus have re-
leased and tracked particles in the top few meters of the
water column (Katz et al., 1984; Clancy and Cobb, 1997;
Incze and Naimie, 2000), whereas depth of ascent of the sea
scallop P. magellanicus (Tremblay et al., 1994) and larval
depth for the barnacle B. glandula (Pfeiffer-Herbert et al.,
2007) were varied with age. Diel vertical migration has been
incorporated in biophysical models that predict larval trans-
port of the green crab C. maenas by modifying the vertical
position of individuals depending on time of day (Marta-
Almeida et al., 2006; Peliz et al., 2007). Ontogenetic ver-
tical migration is one of the most frequently incorporated
behavioral components in models of larval fish transport
(Hare et al., 1999; Cowen et al., 2000; James et al., 2002;
Paris and Cowen, 2004; Paris et al., 2005, 2007; Fiksen et
The larval response to salinity stratification in the water
column has been incorporated into biophysical models in a
more complex manner. Dekshenieks et al. (1996) used a
size-structured advection-diffusion model to specifically ex-
amine the effects of the salinity structure of the water
column (well-mixed, partially stratified, and strongly strat-
ified) on the larval vertical distribution of the oyster Cras-
sostrea virginica. In this model, the total vertical advective
velocity included a size-dependent component of vertical
movement (larger larvae swam faster but also sank deeper),
and the percentage of time larvae spent swimming was
varied with the rate change in salinity (Dekshenieks et al.,
1996). North et al. (2008) examined the effect of differences
between two oysters (C. virginica and C. ariakensis) in their
responses to haloclines on their horizontal larval transport in
Chesapeake Bay (a partially mixed estuarine system). At
each time step, they used a threshold gradient in salinity to
induce a species-dependent larval response, which was to
either move upward toward the surface or downward toward
the bottom, in both cases away from the halocline (North et
Although biophysical models may incorporate larval be-
havior, they do not always directly evaluate its role in larval
transport (e.g., James et al., 2002; Pfeiffer-Herbert et al.,
2007); in those studies that do, larval behavior is shown to
have an impact on larval transport in both weak- and strong-
swimming larvae. For example, the depth of ascent for
larval scallops had a significant effect on the distribution of
potential settlement locations on Georges Bank (Tremblay
et al., 1994). Similarly, larval swimming needed to be
included in the models that successfully delivered larval
lobsters to known sites of settlement; particle transport by
currents alone resulted in larval loss from the system (Katz
et al., 1994). Including a larval response to the salinity
structure of the water column significantly affected both the
vertical distribution (Dekshenieks et al., 1996) and horizon-
tal transport (North et al., 2008) of oyster larvae. The
importance of diel/tidal vertical migration on larval export/
retention has been demonstrated for decapods on a range of
scales from the continental shelf of Portugal (Marta-
Almeida et al., 2006; Peliz et al., 2007) to low-inflow
estuaries (DiBacco et al., 2001). Paris et al. (2005) per-
formed a sensitivity analysis of different scenarios of larval
behavior (onset of active behavior and sensing distance to
suitable habitat) for Cuban snapper (Lutjanus spp.) and
found variable effects of these factors on dispersal distance.
A commonly documented effect of vertical migration in fish
larvae is increased retention near natal sites (Cowen et al.,
2000; James et al., 2002).
It must be noted, however, that not all studies have
confirmed that larval behavior can affect dispersal. In a
modeling study that examined factors affecting dispersal
and connectivity of populations on the southeastern US
continental shelf, Edwards et al. (2007) showed that larval
vertical position in the water column (surface-fixed, depth-
fixed, mid-depth passive) had little effect on the resulting
dispersal kernels. For the blue crab Callinectes sapidus, the
original hypothesis that vertical migration of megalopae
allows their return to settlement sites has been challenged;
flow alone has been postulated to regulate megalopal trans-
port (Epifanio and Garvine, 2001).
To quantify the influence of larval behavior on larval
transport, biophysical models must parameterize this behav-
ior within realistic boundaries. Currently, these parameter-
izations are obtained from experimental studies done in the
laboratory or from field studies that have measured larval
distributions. In some studies, field or laboratory observa-
tions are precisely reproduced in the parameterizations,
such as larval swimming speeds (Tremblay et al., 1994;
Dekshenieks et al., 1996) or direction of response to a cue
(e.g., avoidance or aggregation to haloclines) (North et al.,
2008). In other studies, larval behavior is broadly based on
our understanding from the literature, but the details that are
incorporated into models (e.g., precise timing or distance
traveled for vertical migration; threshold salinity gradients
that induce an avoidance response) are relatively accurate
representations (same order of magnitude) rather than pre-
cise parameterizations (Tremblay et al., 1994; James et al.,
2002; Paris et al., 2005, 2007; Marta-Almeida et al., 2006;
Pfeiffer-Herbert et al., 2007; North et al., 2008). The re-
quired level of precision in the parameterization of behav-
ioral components of the models is currently unknown, but
BIOPHYSICAL MODELS OF LARVAL TRANSPORT
can only be determined through validation of model output
and sensitivity analyses (see MODEL VALIDATION).
Several complementary approaches are needed if we are
to obtain realistic parameterizations of larval behavior.
Measures of behavior are relatively easy to obtain in the
laboratory, and have been used either as first-order estimates
or to bound the ranges used in the biological components in
the models. For example, relationships between tempera-
ture, salinity, and food concentration with swimming speeds
can be easily measured for a range of larval taxa. Similarly,
we can expand the number of experiments that examine the
effects of physicochemical and biological cues on swim-
ming performance or population distributions. As in North
et al. (2008), these experiments can provide threshold val-
ues that induce a response. We do caution that these values
can be species-specific (e.g., salinity; Sameoto and Metaxas,
2008b) or population-specific (e.g., vertical migration;
Tremblay and Sinclair, 1990; Manuel et al., 1996), and need
to be derived for each species and system of interest. At a
more advanced stage, trade-offs between potential behav-
ioral responses given the multiple cues that larvae will
sample concurrently can be incorporated in the models
using dynamic programming, such as individual-based neu-
ral networks (Werner et al., 2007). A similar approach has
recently been applied to the dispersal of larvae of the cod
Gadus morhua in northern Norway (Fiksen et al., 2007).
As discussed above, validating larval behaviors in the
field, although logistically challenging, is possible. Distri-
butional changes are relatively easy to measure; however,
measurements must be taken at the appropriate resolution to
avoid aliasing. For example, high temporal resolution
(hours) of changes in vertical distribution is needed, and has
been used (e.g., DiBacco et al., 2001) to evaluate vertical
migration on tidal and diel scales, and high spatial resolu-
tion (meters) is needed to evaluate vertical migration in
response to physical or biological features in the water
To date, larval behavior has only been examined in situ at
the individual level, by releasing and tracking small num-
bers of individuals in a small number of studies (see above).
These observations have not been made at the population
level using controlled larval releases; however, it may be
feasible in certain systems to track released larval patches or
cohorts over short periods of time (days) (e.g., Arnold et al.,
2005). Enclosed bays with strong (stable) stratification
(temperature or salinity) and predictable, advective circula-
tion (e.g., fjord-like) are such physical systems, and certain
taxa of larvae that are easy to culture in large numbers in the
laboratory (e.g., echinoids, bivalves) may be appropriate
biological systems. Small passive drifters, released concur-
rently with and exposed to the same scales of the flow
regime as the larvae, can provide estimates of transport in
the absence of larval behavior (e.g., Taggart and Ruddick,
2006; Gawarkiewicz et al., 2007). During these releases,
sampling resolution would have to be high, both temporally
and spatially, to reflect the processes and structures of
interest. The use of experimental mesocosms can provide an
initial, more tractable approach, at scales intermediate be-
tween laboratory experiments and in situ releases. Advances
in available technologies may facilitate larval tracking of
many individuals over short time scales (tens of minutes)
(e.g., Genin et al., 2005).
In sum, different approaches can be used to accurately
parameterize larval behaviors that are relevant to the spe-
cific biological and physical systems of interest. Recently,
Leis (2007) reviewed factors that must be considered when
integrating behavior in biophysical models of dispersal for
demersal fish. Many of his recommendations are similar to
ours. In the end, only accurate estimates will allow us to test
the relative importance of larval behavior in larval transport
and supply to potentially suitable habitats for settlement.
Larval behavior during settlement
Without successful larval settlement and recruitment into
the adult population, physical movement of individuals
among populations (and thus connectivity) of benthic inver-
tebrates is limited. In turn, settlement and recruitment can-
not be successful unless competent larvae encounter appro-
priate substratum for settlement. The precise mechanisms
used by larvae to locate appropriate settlement substrata are
not completely understood, but larvae of many taxa respond
behaviorally to a variety of biotic (chemical) and abiotic
(light, water movement, pressure, salinity, temperature, and
gravity) stimuli (Kingsford et al., 2002). Biotic stimuli, such
as chemicals produced by conspecifics or prey, can act as
strong cues for larval behavior at settlement for many
benthic species (Hadfield and Paul, 2001). For example, the
veligers of the nudibrach Phestilla sibogae cease swim-
ming, sink, and undergo metamorphosis on the benthos
when they encounter plumes of a chemical cue emitted by
its prey, the coral Porites compressa (Hadfield and Koehl,
2004). Larvae of the sponge Luffariella variabilis respond
faster to the presence of conspecifics and a chemical cue
produced by conspecifics than to seawater (Ettinger-Epstein
et al., 2008). Abiotic stimuli, such as light levels, can also
act as settlement cues; for example, the proportion of set-
tling larvae of L. variabilis decreased with increasing light
intensity (Ettinger-Epstein et al., 2008). It has been shown
that above a threshold of turbulent kinetic energy, larvae of
the mud snail Ilyanassa obsoleta stop swimming and sink to
the benthos (Fuchs et al., 2004). Sound, particularly that
associated with reefs, has been considered a strong cue for
location of settlement substrata by larval fish (Tolimieri et
al., 2000; Simpson et al., 2004). Recently, larvae of some
invertebrate species of decapods have also been shown to
display directional movement in response to sound (Radford
et al., 2007). As for reefs, distinctive sounds have been
A. METAXAS AND M. SAUNDERS
associated with particular habitats dominated by inverte-
brates, such as urchin barrens (Radford et al., 2008), under-
scoring the potential for sound as a navigation cue for
It has been suggested that larval behavioral responses to
sensory cues can significantly affect spatial patterns in set-
tlement and recruitment (Kingsford et al., 2002), and ulti-
mately contribute to spatial patterns in adult abundance;
however, most evidence has been collected for relatively
small (centimeters to meters) spatial scales. For example,
settlers of the bryozoan Membranipora membranacea are
found primarily at the proximal ends of laminarian algae
(Seed, 1976), a pattern on spatial scales of centimeters to
meters that has been attributed to preferential larval settle-
ment in response to chemical or tactile (level of damage of
the host kelp related to age) cues (Brumbaugh et al., 1994).
Larvae that settle gregariously and in darkness, such as the
sponge L. variabilis, are distributed patchily in cryptic hab-
itats in the field (Ettinger-Epstein et al., 2008). In the Gulf
of California, larvae of the intertidal barnacle Cthamalus
anisopoma settled in response to chemical cues both from
conspecifics and from other cohabitants, including preda-
tors, in the upper intertidal zone (Raimondi, 1988). In com-
bination, these settlement cues contributed to the vertical
zonation in distribution exhibited by adults (Raimondi,
1988). Cyprids of the barnacle Balanus improvisus do not
settle when water velocities are higher than those in which
juveniles can feed effectively, and this may contribute to
spatial patterns in adult abundance (Larsson and Jonsson,
2006), depending on spatial and temporal patterns in hydro-
dynamics. A suite of larval behaviors, including upward
swimming, settlement on the undersides of overhangs, and
settlement in response to molecules produced by crustose
red algae, drives the adult distribution of the coral Agaricia
humilis (Raimondi and Morse, 2000).
When settlement behavior is incorporated into large-scale
transport models, it is only at an elementary level: particles
representing larvae are allowed to settle only within a de-
fined “sensory zone” of suitable substrata (Paris et al.,
2007). For example, North et al. (2008) allowed oyster
larvae to settle only in grids containing culch, a known
inducer of oyster larval settlement (Tamburri et al., 1996).
Similarly, in a simulation of larval transport in the English
Channel, Barnay et al. (2003) allowed particles representing
the polychaete Owenia fusiformis to settle only when the
appropriate substrata were present. In a simulation of dis-
persal of larvae of the coral Acropora palmata in the Ca-
ribbean, larvae were allowed to settle only at a maximum
distance of 9 km from a coral reef (Baums et al., 2006). At
these large scales and coarse resolutions, only a few studies
have explicitly tested the role of settlement behavior on
larval transport. For example, Tremblay et al. (1994) found
significant effects of both the depth of downward vertical
migration of competent larvae and the length of search
period for suitable substrate on the patterns of larval trans-
port for the sea scallop Placopecten magellanicus.
On smaller (millimeter to meter) scales, modeling exper-
iments have demonstrated that larval behavioral responses
to settlement cues can significantly affect transport to the
benthos. Using a one-dimension (vertical) model, Eckman
et al. (1994) showed that the rate of larval transport to the
benthos can be significantly enhanced if settling velocity
increases in response to a concentration gradient of a chem-
ical cue. However, chemicals released into a turbulent water
column (or benthic boundary layer) form filamentous
streams of high concentrations of the cue interspersed with
“clean” water, rather than continuous concentration gradi-
ents (Koehl et al., 2007). An individual-based numerical
model of the nudibranch Phestilla sibogae that incorporated
net flow, monochromatic waves, and turbulence showed
that the rate at which larvae were transported to the benthos
was reduced if a behavioral response to stop swimming only
in the presence of cue, rather than continuously sinking, was
included (Koehl et al., 2007). Inclusion of a behavioral
response of competent larvae of Ilyanassa obsoleta to tur-
bulence into an advection-diffusion model significantly in-
creased their transport to the benthos in highly energetic
(e.g., tidal) environments (Fuchs et al., 2007). On the basis
of these studies, we conclude that settlement behavior can
only be incorporated into biophysical models if they can be
resolved at these fine scales of millimeters to meters.
For settlement behavior to be incorporated in biophysical
transport models, both their biological and physical compo-
nents must be improved. Most importantly, the behavioral
mechanisms that regulate settlement to the benthos must
become better known. Firstly, the relationship between spa-
tial and temporal patterns in larval (including ontogenetic
changes in distribution) and settler abundance should be
quantified for a given species in a given region, particularly
if settler abundance is used to validate model outcomes. If
post-settlement processes such as predation have a strong
(and variable) influence on the abundance of settlers, then
using measures of settlement to validate transport models is
not advisable. Secondly, the cues that trigger behavioral
changes leading to settlement should be identified, and the
concentrations that induce a response measured, under con-
trolled conditions in the laboratory. Although many studies
have measured the outcome of the response to cues (typi-
cally as percentage of larvae that settle) in laboratory ex-
periments, less is known about the behaviors (cessation of
swimming or directional swimming) that give rise to the
observed patterns. The distributional patterns of the cues
(e.g., chemical, hydrodynamic, light, sound) that are shown
to induce movement of larvae from the water column to the
benthos must be measured in the field (e.g., Hadfield and
Koehl, 2004). These distributions can set the spatial scales
over which behavior at settlement may be relevant.
Because settlement and recruitment can vary on small
BIOPHYSICAL MODELS OF LARVAL TRANSPORT
(tens to hundreds of meters) horizontal spatial scales (Ladah
et al., 2005; Porri et al., 2006), there is a significant mis-
match between the spatial (millimeter to meter) scales over
which settlement behavior occurs and the spatial resolution
of oceanic circulation models (hundreds of meters to kilo-
meters). The constraint of mismatched scales can be par-
tially addressed by nesting successively smaller grids near
the shoreline within the coarser grids of circulation (but see
MODEL VALIDATION for trade-offs). Alternatively, a
first approach can be to parameterize settlement behavior
and incorporate it in existing models. For example, particles
representing larvae could “sink” at a rate approximating
observed settlement rates when located within the model
grids nearest to the coast. Since larval transport occurs in the
water column and before settlement, the incorporation of
settlement behavior into larval transport models would be
relevant only if (1) larval descent into the benthos in re-
sponse to a cue alters their horizontal distance by moving
them into a different water layer; and (2) model validation
is based on comparing spatiotemporal patterns of settling
larvae to those of settlers or recruits (see MODEL VALI-
DATION). Whether settlement behavior has a significant
effect on the outcome of larval transport models remains to
The predictive power of biophysical models of larval
transport can be ascertained only through proper validation,
a nontrivial problem because of the difficulty in measuring
larval dispersal. In fact, it is this difficulty that led to the
development of predictive biophysical models as a tool in
the first place. Some modeling studies have been used to
address theoretical considerations of the relative importance
of different factors in larval transport and did not attempt
validation of the model outcome (Jackson and Strathmann,
1981; Hill, 1990; Marta-Almeida et al., 2006; Aiken et al.,
2007; Paris et al., 2007; Mitarai et al., 2008; North et al.,
2008); however, others have attempted validation of either
the model outcome or of different components of the model
(see below). The major constraints in model validation are
that the response variables are not easily quantified, and the
scales of the different components of the model can be
A significant challenge of model validation is that the
response variables, such as the dispersal kernel (Siegel et
al., 2003; Aiken et al., 2007; Edwards et al., 2007), larval
trajectories and “dispersal paths” (Pfeiffer-Herbert et al.,
2007), and mean dispersal distance (Edwards et al., 2007;
North et al., 2008) focus on a transitionary phase between
two life-history stages that occupy different habitats (the
plankton and the substratum) and are sampled by different
approaches—temporally discrete for larvae and time-inte-
grative for settlers (Pineda, 2000). Specifically, these vari-
ables are calculated for settling larvae, which have under-
gone dispersal, survived the planktonic period, and been
delivered to the point of settlement, but have not yet settled
and metamorphosed. Larval transport ends at the onset of
Using the number of settlers or recruits to validate model
projections of the number of settling larvae is generally
inappropriate unless a known relationship exists between
larval supply and settlement, on the basis of which general
trends can be extrapolated and first-order predictions (e.g.,
general spatial patterns) may be possible. A correlation
between larval abundance and settlement has been recorded
(Minchinton and Scheibling, 1991; Tapia and Pineda,
2007), but not consistently (Porri et al., 2006; Rilov et al.,
2008). For example, larvae of intertidal species may settle
on suitable subtidal substrate (Rilov et al., 2008), resulting
in decreased settlement in the intertidal zone. Conversely,
when available substrate is limited, there may be an “inten-
sification” of settlement rate (Pineda, 2000). Biological or
physical processes occurring in the surf zone may also
decouple the relationship between larval abundance at hun-
dreds of meters from shore and settlement on the shore
(Rilov et al., 2008). Larval and benthic stages are affected
by a different suite of biological and physical processes that
operate on different scales, the most obvious example being
that of advection. The magnitude of these differences may
be smaller for fish, the benthic stages of which also inhabit
the water column, than for benthic invertebrates, which
spend their adult life “attached” to the two-dimensional
ocean floor. For some species and geographic locations, the
settlement period can last several days, during which factors
such as food and space availability, predation, and resus-
pension can significantly influence settler mortality (Hunt
and Scheibling, 1997). Settlement success can vary greatly
both spatially and temporally, and there may be density-
dependent effects of recruitment on the rate of settlement
(Hunt and Scheibling, 1997). The degree of motility of the
benthic stage will introduce another source of error in the
estimate of dispersal distance: for more sedentary species
(such as barnacles, molluscs, and gastropods), the locations
of larval delivery and settlement may vary by meters,
whereas greater discrepancies may arise with increasing
settler or recruit motility (e.g., for decapods and fish).
Different approaches of varying accuracy have been used
to validate the outcomes of model simulations. In several
studies, the accuracy of model predictions was confirmed
where larvae were delivered to geographic locations of
known adult populations (i.e., at scales of tens to hundreds
of kilometers; Katz et al., 1994; Johnson and Perry, 1999;
Incze et al., 2000; James et al., 2002; Paris et al., 2005) or
when spatial patterns in larval transport corresponded to the
observed spatial patterns of recruits along the coastline
(Pfeiffer-Herbert et al., 2007). A few studies have made
direct comparisons between simulated and observed hori-
A. METAXAS AND M. SAUNDERS
zontal or vertical patterns of larval abundance. For example,
the model used to simulate patterns of oyster larvae relative
to a halocline in Dekshenieks et al. (1996) was used to
predict “reasonably well” the vertical distribution of oyster
larvae in an earlier study in New Jersey (Carriker, 1951).
Ellien et al. (2004) and Peliz et al. (2007) evaluated the role
of different factors in larval dispersal pathways by directly
comparing simulated with observed larval distributions of
polychaetes and decapods, respectively. For larval fish,
Paris and Cowen (2004) observed similar patterns in the
vertical distributions of virtual and sampled early-stage lar-
vae of Stegastes partitus off the west coast of Barbados.
Perhaps the most comprehensive approach to model valida-
tion was achieved for a cohort of larval Mercenaria, re-
leased in a well-defined, semi-enclosed, microtidal lagoon
in Florida (Arnold et al., 2005). The tractability of the
system allowed for the comparison of three different ap-
proaches applied concurrently to measure transport: (1)
measurement of advective transport using subsurface drift-
ers and of diffusive transport using released SF6; (2) mod-
eling transport with a circulation model coupled with a
Langrangian particle-trajectory model; and (3) sampling of
released larvae. Unfortunately, although this approach al-
lows for a convincing validation, it is also extremely re-
source-demanding and its use may not be amenable to
highly advective/diffusive systems, such as open coastlines.
An alternative approach for model validation, which,
however, does not allow for mechanistic inferences about
observed patterns, is larval “tagging” (geochemical or ge-
netic), used to assign natal origin to dispersing or settling
larvae. Although only applicable to larval taxa with “hard”
body parts, elemental fingerprinting of larval shells and
statoliths has been developed and used successfully to as-
sign origin in a handful of studies with benthic invertebrates
(DiBacco and Levin, 2000; Zacherl et al., 2003; Becker et
al., 2007). This technique, although promising, also has
some limitations: (1) it requires detectable differences in the
trace element composition of seawater between natal sites;
(2) trace-element deposition can vary with other environ-
mental conditions at the natal site, such as temperature and
salinity, as well as growth rate; and (3) sample processing is
quite costly and its effectiveness is inconsistent (Strasser et
al., 2007, 2008; Lloyd et al., 2008). In contrast, elemental
fingerprinting of otoliths to assess natal origin is well es-
tablished for fish (Campana, 1999; Thorrold et al., 2001,
2007). Genetic fingerprinting for assignment of population
origin is an alternative approach that, however, can also
involve several challenges (Manel et al., 2005; Cowen and
Sponaugle, 2009). For example, the potential source popu-
lations must be known and distinguishable by detectable
genetic differentiation (Hedgecock et al., 2007).
Another constraint placed on the validation of biophysi-
cal models is the mismatch in relevant scales and realized
resolution of parameter estimation between biological and
physical processes. In the biophysical models described
above, the grid resolution for physical processes is greater
than 1 km (Ellien et al., 2004; Baums et al., 2006; Peliz et
al., 2007; Pfeiffer-Herbet et al., 2007; North et al., 2008),
although the relevant scales may be much smaller—that is,
centimeters to meters for diffusion, much smaller than for
advection (Largier, 2003). Nested grids of higher resolution
(hundreds of meters to 1 kilometer) can be incorporated to
focus on areas of interest at the expense of a broader
horizontal coverage (Peliz et al., 2007; Pfeiffert-Herbet et
al., 2007). The smaller the sampling area of interest, the
higher the grid resolution can be (e.g., Arnold et al., 2005).
In contrast, larval abundance can vary over scales of tens to
hundreds of meters, and sampling can incorporate this level
of resolution (Di Bacco et al., 2001; Natunewicz and Epi-
fanio, 2001; Ellien et al., 2004; Arnold et al., 2005; Tapia
and Pineda, 2007), particularly in semi-enclosed bays. The
physical and biological components of the models can be
better matched vertically: most circulation models can be
resolved to scales of meters to tens of meters, a degree of
resolution that can also be achieved in larval sampling.
However, this may not be the case if settlement behavior is
included in the models. The temporal resolutions of the
biological and physical processes are mismatched in the
opposite direction relative to the spatial resolutions. The
circulation and particle-trajectory models in the above stud-
ies are resolved at frequencies of tens of seconds to minutes,
whereas larval sampling typically occurs at minimum fre-
quencies of hours to days. Although higher frequencies may
provide more appropriate scales for biological processes, it
is currently not possible to achieve them. The interpolation
required to match the spatial and temporal scales between
processes inevitably reduces the accuracy of the estimates
taken at the coarser resolution, and the relationship of the
interpolated to the real values is not known.
Adequate validation of the overall performance of bio-
physical models, as well as that of the individual compo-
nents, at the appropriate spatial and temporal scales, is
absolutely necessary. However, the considerable challenges
that must be overcome to achieve validation may make it
impractical except in certain biological and physical sys-
tems where the obstacles are more tractable.
Biophysical models are increasingly being used to predict
larval transport, assess population connectivity, and evalu-
ate the role of different biological and physical factors on
larval dispersal of marine benthic invertebrates. Because of
increased computing power, general circulation models
(ROMS, POMS, CANDIE, etc.) are becoming accessible
and, in turn, are being coupled with particle-tracking sub-
routines that “simulate” larval transport. There is little doubt
that the use of these models has greatly accelerated (in some
BIOPHYSICAL MODELS OF LARVAL TRANSPORT
instances, made possible) the gains in our understanding of
larval transport, and these gains have, in turn, garnered
considerable support for this approach. However, we must
also be aware of the potential limitations of these models
and avoid misinterpretation of their results. In particular, the
predictive power of biophysical models has not been as-
sessed extensively for several reasons, including low trac-
tability of the modeled systems, inaccuracy in parameter
estimation, difficulties in measuring the response variables
and conducting model validation, and mismatch in scales of
the biological and physical processes. It must be noted that
larval fish biologists face similar challenges, particularly the
relevance and parameterization of larval behavior, in their
attempts to use models to predict larval transport (Fiksen et
al., 2007; Leis, 2007; Werner et al., 2007), and cross-
fertilization of approaches may prove productive.
Biophysical models have shown potentially significant
effects of the duration of the larval period, mortality rates,
and larval behavior in the plankton on larval transport. It is
likely, although not extensively tested, that behavior of
settling larvae can show similar influence. Because of the
potential significance of the biological components, their
parameterization requires more accurate estimates that are
specific to both both species and habitat (or location). For
parameters where first-order estimates do not exist, they
should be obtained either in the laboratory or the field. For
parameters for which such estimates do exist, we must turn
our attention to obtaining field-generated measures of
Advances in the estimation of the response variables used
in the models must also be made, to provide us with the
ability to validate model performance. To avoid the pitfalls
associated with the common current practice of validating
larval abundance using settler or recruit abundance, possible
approaches include (1) improving our techniques for mea-
suring larval abundance after larval delivery to the settle-
ment habitat, but immediately prior to settlement; and (2)
incorporating components for settlement into the models.
Although the latter approach may not be immediately fea-
sible, it may be achievable in a shorter term than the former.
Some simple models of behavior at settlement already exist
(see Larval behavior during settlement) and can be linked to
the large-scale biophysical models. The approach would
most likely require greater spatial resolution of the models
at least near the coastal benthos, and consequently more
computational power. However, given the rapid advances in
computing, greater power at a lower cost will most likely be
achievable within a few years.
The significant challenge of the mismatch of scales be-
tween the biological and physical processes may be more
difficult to overcome in the near future. It is unlikely that an
increase in the temporal resolution of the measurements of
biological processes in the field is feasible, but an increase
in the spatial resolution of the physical processes is likely
achievable (also by increased computing power). We be-
lieve this to be particularly important, because it is unknown
whether the scales of the physical processes currently being
used are even relevant to the scales that larvae experience.
Similarly, drifters that are frequently used to validate the
physical processes in models are also not exposed to fluid
motion at the same scales as larvae.
Biophysical models show great promise as predictive
tools of larval transport. Their development entails an in-
terdisciplinary approach that combines larval biology and
physical oceanography, two disciplines with different sam-
pling approaches, at different spatial and temporal resolu-
tions. To maximize the power of the approach, we must be
cautious about these differences and the limitations that may
arise as a result.
Research on larval biology by A. Metaxas and her stu-
dents has been supported by grants from NSERC, CFI, and
DFO. M. Saunders has been funded by scholarships from
NSERC and the Killam Trusts at Dalhousie University.
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