Proposal Summary:
Public confidence in our abilities to manage nearshore fisheries is at an all time low. Along
the U.S. West Coast, nearly all waters deeper than 20 fathoms (~36 m) are closed to all
bottom fishing to protect several species of rockfish from local extinction. Similar, though
actually unrelated, management actions have created no-take marine reserves around the
northern Channel Islands as a means of protecting biodiversity and fish abundances. The
phrase "the oceans are in crisis" once used only by is now being used with increasing
regularity by fishery managers and fishers themselves. The decline of fisheries and
ecological diversity of nearshore waters is driving the "crisis-mode" nature by which
nearshore fish resources are presently being managed. Clearly, it is time to examine the
complexity of nearshore communities and the fisheries that depend upon them.
Nearshore fisheries couple natural (ecological) and human (fishery management) processes
resulting in an emergent dynamic system, rich in complexity. The goal of this proposal is to
develop a process-level description of nearshore fisheries and their management patterned
after California coastal environments. Specifically, we propose to examine the emergent
complexity that arises due to interactions among chaotic coastal circulations, fished
organism life cycles, the productivity and suitability of nearshore habitats, the intensity and
nature of fish harvesting, the economics governing fisheries, fishers and fishery regulations
and the bureaucratic system which implements regulations. Our aim is to assess the
balance points among costs, profits, uncertainties, stock viability and ecological values of
nearshore fished environments.
Central to developing a predictive understanding of the interactions between flow, fish and
fishing (F3) is the notion of time/space scales. The physics, biology, and socio-economic
processes governing this coupled natural/human system operate on inherently different
spatial and temporal scales. Without considering the mismatch in scales explicitly,
mismanagement of fisheries is likely to continue unabated. Fortunately, the components of a
solution to this problem are at hand. Recent advances in modeling coastal ocean
circulations, marine life cycle dynamics, the values of information to fishery management,
and the consequences of management choices in the face of uncertainty have
independently created the pieces necessary to assemble a synthetic approach to nearshore
fisheries management. Moreover, large-scale programs monitoring the dynamics of coastal
ecosystems are finally providing the empirical data necessary to parameterize and test
these models. The individual investigators in this project are leaders in these component
efforts. Collectively, they will link these components into new computational and conceptual
models that examine optimal management choices in the face of unavoidable physical and
biological uncertainty.
Although this project uses the Southern California Bight as a focal ecosystem, the issues
addressed and conceptual frameworks that arise will have broader, global impacts. Declines
in fish stocks and yields are not restricted to any nation or biogeographical region. Indeed,
the core problems from mismatches in spatial and temporal scales are characteristic of
nearly all marine fisheries. In addition, although fisheries management commonly stops at
political borders, ocean flows and fish do not. The ecological scales of coastal ecosystems
are inherently international in scope. The international partnerships between this proposed
effort and ongoing ecological programs in Mexico, Chile, New Zealand and Australia will
expand the regional focus of this effort to global dimensions.
Finally, this project links efforts by physical, natural and social scientists in a system where
understanding all three components and, more importantly, their interactions are critical to
success. The interdisciplinarity of the effort is not just buzzword. Approaches that do not link
across these disciplines are doomed to fail. Hence, this project has rich educational
opportunities across a range of ages. Since most have at one time been a part of a fishery,
either as a harvester or a consumer, the key issues should be appreciated at their core by
nearly everyone. This program integrates several opportunities for interdisciplinary
education from K-12, through undergraduate, graduate and adult programs.
Result from Prior NSF Research:
David Siegel is a professor of oceanography at UC Santa Barbara. His research focuses on the
implications of physical forcing on ocean biological and biogeochemical processes from
individual microbe (m's) to basin scales (1,000 km's). He is funded on a NSF Biocomplexity
project investigating marine N2-fixation and aerosol dust deposition in the global carbon cycles
(OCE-9981398; 1/1/2000-12/31/2004). He is an investigator of the Santa Barbara Coastal-LongTerm Ecological Research (SBC-LTER) site and has been funded by NSF since 1991.
Christopher Costello is an assistant professor of natural resource economics at UC Santa
Barbara. His research focuses on the economics of uncertainty, accounting for uncertainty in
renewable and non-renewable resource management and the value of information in these
systems. He is supported as part of a multidisciplinary team on a Biocomplexity seed grant to
study small-scale fisheries off Baja California, Mexico (OCE-0216637; 9/15/02-1/15/04).
Steven Gaines is a professor of marine ecology at UC Santa Barbara. His research examines
couplings between ocean circulation and marine population dynamics with emphases on larval
dispersal, biogeography, and the design of marine reserves. He is currently a co-PI with the
Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) and the SBC-LTER (OCE9982105; 4/1/00-1/31/06). He has been funded by NSF since 1989.
Ray Hilborn is the Worthington Professor of Fisheries Management at the University of
Washington. He currently serves on the scientific advisory panel for the President's Commission
for Ocean Policy and works with many national and international fisheries management
organizations. In 2001, he (with T. Quinn & D. Schindler) received a NSF support for 5 field
stations in western Alaska.
Bruce Kendall is an assistant professor of population ecology at UC Santa Barbara. He is a
quantitative ecologist with a focus on animal and plant population dynamics. He is interested in
the implications of spatial structure for population dynamics and understanding the effects of
uncertainty and noise in ecological processes. He is also an investigator of the SBC-LTER.
Stephen Polasky is the Fesler-Lampert Chair in Ecological/Environmental Economics at the
University of Minnesota. His research interests include integrating ecological and economic
analysis, game theoretic analysis of natural resource use, common property resources, and
environmental regulation. He was co-PI on a NSF/EPA grant ("Decision-Making Under
Uncertainty in the Conservation of Biological Diversity"; 9/1/1996 - 8/31/1998).
Robert Warner is a professor of marine ecology at UC Santa Barbara. Warner’s work lies in two
areas, both dealing primarily with marine fishes. The first area is in behavioral ecology, focusing
on the evolution of reproductive strategies. The other active area of Warner’s published research
is in recruitment of marine fishes. Both areas of research were supported continuously by NSF
from 1976 to 1996. He is currently a PI with the PISCO project and the SBC-LTER.
Kraig Winters is an associate researcher in coastal oceanography and geophysical fluid
dynamics at the Scripps Institution of Oceanography of UC San Diego. His research focuses on
the numerical modeling of small-scale oceanic flows, especially those with turbulent mixing.
Recent NSF supported work (OCE-9617671; 6/1/97-5/30/02) utilized high-resolution numerical
simulations of ideal & non-deal Lagrangian (or water-following) trajectories in turbulent oceanic
flows to guide in the analysis of data from Lagrangian, ocean measurement systems.
Nearshore Fisheries along the Pacific Coast
Confidence in our abilities to manage nearshore fisheries is at an all time low. The phrase "the
oceans are in crisis" once used by environmentalists and politicians (in election years!!) is being
used with increasing regularity by resource managers and fish harvesters, both commercial and
recreational. The decline of fisheries and ecological diversity for nearshore waters seems to be
driving a "crisis-mode" management of nearshore fish resources.
For example, along the coast of California all waters deeper than 20 fathoms (~36 m) are closed
to bottom fishing, and the profitable spot prawn fishery is likely to be closed state-wide shortly to
reduce by-catch of groundfish. These management actions are aimed at protecting several
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species of rockfish from local extinction. Against this background of increasingly drastic fishing
regulations, no-take marine protected areas (MPA's) are being implemented around the northern
Channel Islands, and more reserves along the California coast are being planned to comply with
the Marine Life Protection Act. While many claims have been made about the ecosystem
management approaches to coastal oceans (e.g., NRC 1999), there is no concrete
understanding of how these management approaches should be implemented or integrated with
traditional fishery management approaches. Clearly, it is time to examine the complexity of
nearshore communities and the fisheries that depend upon them. We need to address as
scientists how local marine ecosystems are assembled, how fishers respond to these
assemblages, and how management schemes can best regulate extraction. There is almost a
"Field of Dreams" belief that if place-based ecosystem or adaptive management approaches are
adopted, good things will happen. Managing resources sustainably is a much more complex
problem than this, requiring the integration of knowledge ranging from physical oceanography to
marine ecology to the economics of information theory to decision-making of fishers.
Here, we propose to develop a mechanistic understanding of the important processes driving
variability in nearshore fisheries and its management. Our goal is to develop the natural and
social scientific background required to successfully implement fishery management instruments.
Emergent Complexity from the Interactions among Flow, Fish & Fishing (F3)
Fisheries dynamics result from the interplay among physical processes, biological interactions,
economic activities, and regulatory decisions (Figure 1). We propose to examine the emergent
complexity due to interactions among coastal circulations, life cycles of fished organisms, the
habitats in which fished adults live, the harvesting of fish, the economics governing fish markets,
fishers and regulation of the fishery and the bureaucratic system that regulates fisheries.
Physical processes (climate, complex ocean flows) and exogenous economic factors represent
extrinsic drivers that impart unpredictability to the
biological and social realms, respectively. The
intrinsic dynamics of fish and of fishers are
reasonably well understood, although they are often
strongly nonlinear.
The role of information in
decisions made by both fishers and regulators is an
area that is just being opened up to study.
Figure 1: An illustration of some of the potential
feedbacks of causation (block arrows) and information
(dotted) possible in the complex system of flow, fish and
fishing. Causative flows have a direct impact on nearshore fish populations (either biomass or dollars) whereas
information flows inform regulators. The market includes
endogenous and exogenous forces.
We propose to examine five areas in which issues of predictability or scale render decisionmaking process difficult. These are the “missing links” in the F3 conceptual model (Figure 1):
1. The physical drivers of larval settlement
2. The spatial scales of nearshore fish populations
3. The role of uncertainty & use of information in fishery management
4. The mismatch in scale between harvesting decisions & regulatory decisions
5. The difficulties associated with multi-species management
We have developed five interlocking hypotheses describing the primary interactions of the F 3
complex system (Figure 1). The resolution of these hypotheses is key for developing a predictive
understanding of biological and economic sustainability of nearshore fisheries.
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H1: Larval Settlement in Complex Coastal Environments - Interactions between coastal
circulations & organism life cycles make larval settlement an intermittent & episodic process.
The coastal ocean is a turbulent environment where flow fields rapidly evolve forced by a myriad
of physical processes. These include wind and tidal forces, remotely forced wave motions (such
as continental trapped waves), and global climatic processes (such as the El Niño/Southern
Oscillation and the Pacific Decadal Oscillation). These forcing phenomena are modulated by the
topographic and bathymetric features of coastal environments through a set of nonlinear
interactions that lead to complicated and highly dynamic flow patterns. As a case in point,
consider the circulation of the Santa Barbara Channel (SBC; e.g., Brink and Muench, 1986;
Dever et al. 1998; Winant et al. 1999; Nishimoto and Washburn, 2002; Cudabeck et al. 2002;
Otero and Siegel, 2002). The SBC is a complex environment with an east-to-west coastline,
complicated by the northern Channel Islands and shadowing of the prevailing northeasterly
winds by the Transverse Range. Shown in figure 2 are four snapshots of surface ocean currents
for the SBC. These dataa show dramatically differing patterns in current magnitude and direction
over just a few days. Peak currents are greater than 25 cm/s which will push passive larvae ~20
km in a single day. Moored current observations indicate decorrelation time scales of 5 to 7 days
for this region (Dever et al. 1999) which is similar to results found for other U.S. West Coast sites
(Winant et al. 1987; Brink et al. 1991; and many others). Hence, the coastal ocean is a highly
variable and energetic environment characterized by a rich set of possible dynamics.
Figure 2 (left): Surface current estimates from high frequency radar measurements for 4 days in July 2002.
Each daily flow field is separated by 4 days. (Libe Washburn, UCSB; www.icess.ucsb.edu/iog/codar.htm).
Figure 3 (right): The four upper panels show drifter trajectories from single day releases in the SBC. In the
lower three panels, the location of where drifters beached ("settled") are shown (solid symbols) when
released from three different locations (open symbols). Figures after based upon Winant et al. (1999).
Surface drifters provide a Lagrangian (water-following) view of coastal ocean circulation relevant
for the modeling of larval transport (e.g., Davis, 1985; Siegel et al. 2002). Drifter trajectories
released from 14 locations in the SBC are shown for four separate release days (upper panels of
Figure 3). There are clear differences in trajectories shown and large differences are found for
simultaneous releases separated by as little as 10 km (see Winant et al. 1999). Decorrelation
time and space scales for drifter trajectories are very short (1 days & 10 km; Dever et al. 1998).
Similar, though slightly larger (2-4 days and 5 to 20 km), Lagrangian scales are found for other
sites (e.g., Davis, 1985; Poulain et al. 1990). Thus, the SBC is a dynamic environment and is
representative of typical U.S. West Coast environments that support nearshore fisheries.
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The life cycle of nearly all harvested nearshore species includes a planktonic developmental
stage (larvae or other propagules; Thorson 1950, Strathmann 1989). Larvae are broadcast into
the plankton where they are advected (for the most part) by the ambient currents while they
develop competency for their next life stage. Larvae that are lucky enough to survive their time
as plankton may settle in favorable habitat and (if fortunate again to avoid predation or other
form of mortality) can recruit to become a reproductive (harvestable) adult. As a first
approximation, planktonic larvae can be considered passive particles, advecting like the drifters
seen in Figure 3. The longer the pre-competency interval, the further larvae will disperse (e.g.,
Jackson and Strathmann, 1981; Roughgarden et al. 1988; Siegel et al. 2002), although larval
behavior may influence dispersal distances and trajectories (Morgan, 1995). Genetic estimates
of larval dispersal as a function of mean planktonic larval duration (PLD) are shown in figure 4 for
32 marine alga, fish and invertebrate species. Dispersal scales are of order 1 km for short PLDs
(< 2 days), but for PLDs > 20 days dispersal scales are on the order of 100 km. Potential larval
dispersal scales can also be diagnosed from examining locations where released drifters beach
(bottom panels Fig. 3). Typically, “settlement” occurs close to the release location (open
symbols) yet some drifters beach after travelling rather long distances (> 50 km).
Genetic dispersal estimates compare well with Markov chain modeling of surface particle
dispersal for idealized flow fields (Siegel et al. 2002). This success suggests that, to first order,
motions of planktonic larvae are well described accounting only for fluid motions. The stochastic
simulations are used to estimate dispersal kernels by simulating the final settlement locations of
1000's of larvae under different flow and PLD regimes (Siegel et al. 2002). Modeled dispersal
kernels are nearly Gaussian in form and can be quantified using PLD and the basic statistics of
the flow field. Dispersal kernels define the spatial probability for settlement from a given location
and thus are useful for spatial fish stock modeling (e.g., Botsford et al. 2001).
Figure 4 (left): The relationship between average dispersal of 32 different marine organisms as a function
of the planktonic larval duration (PLD). From Siegel et al. [2002].
Figure 5 (right): Settlement of selected invertebrate species on "tuffy" kitchen sponges from the SBC (in
units of #/collector/2 day). Settlement is determined every two days for nearly 6 months. The species or
assemblages shown are chosen are ones with large settlement. Data are provided by PISCO (Table 2).
Settlement time series (Fig. 5) show a quasi-random pattern of episodic and sporadic settlement.
Each settlement event is short (no more than 2 days), only a few large settlement events are
observed for each species and there appears to be little correspondence among settlement
events. This stochastic pattern is typically found for high-resolution, settlement time-series
observations (e.g., Caffey, 1985; Underwood and Fairweather, 1989; Dixon et al. 1999).
We hypothesize that the stochastic character of larval settlement is due to the interaction of
organism life cycles with the time and space scales of the coastal ocean. For example, an
organism that continuously releases larvae will produce 100 or so independent trajectories each
year linking the location of its settlement to its parent (~365 days in a year/3 day decorrelation
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time scale). Assuming that from 1 to 10% of these trajectories settle within their competency
time windows, only 1 to 10 independent settlement events will occur each year. This is likely an
overestimate since larval mortality is not considered and most fish do not continuously broadcast
progeny. Hence, larval settlement is likely to be stochastic and intermittent due to the relatively
few independent flow trajectories that drive dispersal and settlement (see Siegel et al. 2002).
H2: Scales of Nearshore Fish Stocks - Nearshore fish stocks are controlled by the nature of
larval settlement and suitable habitats and the migration scale of adults.
Knowledge of the stocks of fishable adults as well as new recruits produced each year is an
essential part of any fishery management plan (e.g., Hilborn and Walters, 1992). However,
uncertainty in fish stock assessments has long plagued fishery managers (e.g., NRC, 1998).
Most of the individual factors that affect fish populations, such as substrate, food availability,
recruitment, adult movement, etc., are well understood. However, each of these has a
potentially unique spatial signature, and fish populations will thrive only where all of these
coincide. Nearshore fish stocks will be controlled by the settlement rate of larvae, the fraction of
those settlers that reach bearing age, the characteristics of the habitat (including the effects of
ecological community interactions & harvest) and the scale of adult home ranges. Stochastic
larval settlement (H1) will imprint a signature on the distribution of adults as modified by
interactions of individuals with the environment. Clearly, adults with extensive home ranges will
not be locked to their initial settling habitat.
Developing a predictive understanding of nearshore fish stocks requires understanding of the
scales of habitat, flow, and migration. Many of these factors are controlled, directly or indirectly,
by the oceanographic flow, which imparts scales of 5 to 20 km. The distribution of hard and soft
substrate is governed by an interaction among regional-scale bathymetric features, movement of
sand and the nature and intensity of harvesting activities (trawling). As discussed in H1, coastal
flow fields controls larval settling patterns in time and space. Together with the locations and
nature of larval release by spawning adults, spatial patterns of potential recruitment occur only
when larvae settle upon suitable habitat. This can create scenarios where certain stocks may
export their larvae to unsuitable habitats (and thereby depending on “upstream” recruitment)
whereas other sites may supply not only their own recruits but will export larvae as well.
Larger scale oceanographic processes, such as El Niño events, will have very dramatic effects
on fish stocks. During an El Niño, primary production is reduced dramatically for the California
Current as a whole (e.g., Chavez et al. 2002; Lavaniegos et al. 2002) influencing the entire food
web. An El Niño signal is clearly seen in catch statistics of many short-lived, harvested fish,
such as market squid (Vojkovich, 1998). For short-lived, sedentary species, climatic changes can
be locally catastrophic, and regional recovery will depend on a sufficiently extensive network of
populations. For long-lived species, such processes may be part of their “normal” environmental
variability and may serve to add another level of intermittency to recruitment.
H3: Uncertainty, Information and Fishery Management - Choice of the most effective
management instrument depends critically on the quantity, quality and cost of information.
If humans harvested only a single species of fish with known abundance and recruitment, fishery
management would be less convoluted. Unfortunately, models used by fishery managers often
assume these conditions. We suggest that multiple sources of uncertainty enter the decision
making of a fishery manager. Uncertainty is likely to take several forms, including:
state variables (spatial distribution of fish stock, harvest yield in biomass & revenue),
parameters (intrinsic growth & mortality rates for species, the local/regional/global economic
environment [price, labor cost, fuel cost, etc.], the state and variability of oceanic flow fields),
processes (relationship between physical oceanic processes & recruitment, response of fishers
to regulation change and/or knowledge of fish stocks, etc.), and
exogenous random effects (climatic shifts, [as measured by El Niño/Southern Oscillation &
Pacific Decadal Oscillation indices], or man-induced catastrophes [e.g., oil spills]).
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We will not focus on uncertainty itself, but rather, on the sources, quantities, reliabilities, and
costs of information that are used to constrain uncertainty for fishery management. We seek the
answer to the question: What types of information have the most value? For example, would
fishery management be improved more by expending effort toward better stock surveys (thus
reducing uncertainty about stock size) or better understanding of oceanographic currents (thus
reducing uncertainty about the spatial distribution of larvae following spawning)? The answer to
these and other questions will help us understand the net values of information for fishery
management and will ultimately inform better management.
H4: Scale Disparities between Harvesting & Management - Fish population dynamics, fisher
decisions and fishery management occurs on disparate space and time scales
Fishers have limited information about the spatial distribution of fish stocks. However, they often
have better local information about fish stocks than do the regulators, and therefore, tend to
react sensitively to temporal and spatial changes in fish stocks. The regulatory system itself is
cumbersome since it needs to maintain transparency of the policy process. Hence, policy
actions are made infrequently after much wrangling and discussion. With this in mind, regulators
are faced with the task of managing large tracts of ocean with highly heterogeneous
oceanographic, biological, and social properties (Figure 6).
Some fishers are opportunistic, while others have more precise, spatial information. Although
this phenomenon is well-known, it remains poorly understood. Socioeconomic models by and
large predict that fishers act to maximize rents. In these models, fishers attempt to best exploit
fish abundances through time. If a fisher in a particular location has poor success, he is
motivated to investigate nearby locations for future exploitation. Despite the recent (albeit
minimal) attention resource managers and economists have paid to modeling spatial decisions of
fishers, little empirical evidence has been collected to test these theories.
Figure 6: The space/time
domain of F3. Moving
counter-clockwise from the
top left, panels show fishing
boat effort around the
Channel Islands, genetic
estimates of larval dispersal
for marine fish (Kinlan &
Gaines, 2002), the recently
approved network of marine
reserves, and the scale of
recent groundfish closures.
The disparate spatial scales
are combined with their
equally disparate time
scales in the central figure.
We propose to identify and evaluate the most efficient management strategies under this
asymmetry in scale of harvest and regulation. By comparison, terrestrial resources are spatially
managed while most marine systems are spatially managed only to the extent that they traverse
politically-defined jurisdictional boundaries. Improvements in technology, such as vessel
monitoring systems (VMS) that use satellite transponders to locate fishing vessels, may facilitate
more fine-grained spatial management of marine resources in the future (Sanchirico and Wilen,
2002). Implementation of no-take marine reserves is another vehicle for implementing spatial
management (e.g., Murray et al. 1999; Lubchenco et al. in press). The costs associated with
fine-grain management may be substantial and these factors need to be considered in the
design of fishery management regimes. To this end, we seek the answer to several questions:
 What kinds of management regulations are appropriate on various spatial or temporal scales?
What kinds and amounts of data (information) do these regulations require?
 What are the costs of implementing these regulations? What technologies would be required?
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 What will be the spatial socioeconomic impacts? Biological impacts? Habitat impacts? Risks?
In answering these and other questions, we aim to develop a predictive understanding of the role
of scale on the management of nearshore fisheries.
H5: Managing Mixed Species Fisheries - The management of mixed fisheries is difficult, as the
status of the slower-maturing species will dictate a successful management strategy.
Different species can be harvested at different rates simply due to differences in their intrinsic
biological factors. For multispecies fisheries, the least productive species (usually the longest
lived) is at the highest risk of being overfished while the more productive species will be
underfished (e.g., Hilborn 1976). Rebuilding plans have effectively closed shelf fisheries to
protect a few overfished species as has occurred for groundfish in the Southern California Bight.
The spatial scales of the fish populations (H2) and the potential for spatial forms of management
(H4) provide an opportunity to maintain viable fisheries by spatially structuring regulations. The
essence would be to identify at a small spatial scale the sites where the least productive species
are particularly abundant or vulnerable, and to close these areas to fishing gears that catch
these species. This form of fine-scale multispecies management would require identification of
the distribution and vulnerability of fish on a fine scale, data growing in availability through
logbook and observer programs. Such systems also require complex spatial patterns of
management that are now possible through VMS and nondestructive fish stock assessments.
Other multispecies regulations can be implemented. For example, the British Columbia trawl
fishery is managed by individual transferable quotas (ITQs) with 100% observer coverage. In
this system, fishers must have unused quota for every species they might catch, otherwise they
cannot fish. This leads to strong incentives to fish on fine spatial scales where they are less
likely to catch rare species (for which there is little quota). There is no discarding of by-catch
because there are observers. The extent to which small-scale management actually works
depends largely on the space-time pattern of stocks and the costs of information.
Given the sources of uncertainty and stochasticity identified above, how can
nearshore fisheries be managed to better achieve balance among stock viability,
ecosystem diversity, and socioeconomic values?
The answer to this question is the ultimate goal of this proposal.
Research Approach:
The F3 Team: The integration of the above five hypotheses gives rise to emergent complex
dynamics that can be examined only through the integrated study of flow, fish and fishing (F3).
An attempt at illustrating proposed interactions of causality and information flows is shown in the
diagram near the beginning of the proposal (Figure 1). The disciplinary interactions of this
system requires us to develop a team of human, natural and physical scientists brought together
for this purpose. The individuals of the F3 team all have demonstrable breadth and vision as
scientists conducting research across disciplinary boundaries. The team consists of physical
oceanographers (Winters & Siegel), marine ecologists (Gaines & Warner), population ecologists
(Kendall, Gaines & Hilborn), a fishery biologist (Hilborn), environmental and resource economists
(Costello & Polasky) and applied mathematicians (Winters, Kendall & Siegel). The PI's are all
excellent numerical modelers with extensive experience coupling physical, ecological and human
processes in their research. The F3 participants have been and are likely to continue to be part of
the process of directing fishery policy. Thus, lessons learned here will influence policymakers
through our direct interactions and through our influences on the directions of academic and
applied research in coupled human/natural systems, fisheries and natural resources.
Approach to Modeling: Numerical models will be central to testing our proposed hypotheses as
it is difficult if not impossible to run controlled experiments on a coupled human-natural system.
We will implement both process models, aimed at the detailed examination of our hypotheses,
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and synthesis models, designed for examining the combination of processes controlling the F 3
system. These models may be idealized models aimed at providing information about the
general case or realized models of a specific region with the goal of providing detailed
comparisons with observations. By creatively combining process vs. synthesis models in
idealized vs. realized spaces, we maximize our abilities to achieve our research goals. We will
confront model results with observations when ever possible. This will provide tests of the robust
nature of our work. Last, we will make model codes available to all interested parties.
A Spatial Model of a Nearshore Fishery: As an example, integral-difference systems can be
used to model the space-time distribution of harvestable adult density (Axt) for a hypothetical
coastal region. Modeling of this type have been used to develop predictions of the impacts of
marine protected areas (MPA) on fishery yields (e.g., Botsford et al. 2001; Siegel, Gaines et al.,
in progress) and can be easily extended to other management regimes.
adult
density
natural
mortality
harvest
mortality
fecundity
dispersal larval post-settlement
kernel mortality recruitment
A xt+Δt = A xt (1 - M xt ) - H xt A xt +  Fx't A x't K x-x'
surviving
adults
harvest
yield
P
Ltx
dx'
fish production
where x is alongshore distance, t is time, t is generation time and the integral is over all space
(dx'). Briefly, the dispersion kernel, Kx-x', determines the fraction of settled larvae at location x
that have come from all locations x'. The other terms in the integral describe the density of
released larvae (Fx't * Ax't) that survive their time in the plankton (P) and recruit to adult stages
(Lxt). The next generation of adults, Axt+1, is equal to the survivor density, recruitment from all
locations (the integral term) minus the harvest yield (Hxt Axt). This is a simple, spatial model of
fish density for a hypothetical coastal region which accounts for organism life cycle (through the
modeling of larval dispersion) and harvest policy.
Figure 7: Steady-state results
from an integral-difference
model of fish density (left)
and harvest yields (right) for a
network of no-take MPA's.
The green line represents
uniform fishing effort (H = 0.8
per generation), the blue line
is the steady solution after the
MPA is implemented (where
Hx = 0) and the red line
reflects the increased fishing
effort due to redirecting fishers from the MPA. A series of 20 km no-take zones are implemented every
100 km. The organism is fairly long-lived (M = 0.2/gen), with a dispersal scale of 50 km (defining K x-x';
Siegel et al. 2002) and Ricker post-settlement density dependence is used. Code is available at
www.icess.ucsb.edu/~davey/MyCodes/MPA_Models.
Here, a network of no-take MPA's (20% set aside) is used to manage a heavily fished species
(see captions for details). For this case, MPA management increases steady state adult
densities by 65% (50% for the case where the effort is increased in proportion to the set aside
fraction) while harvest yields are similar between the two cases (yields are 7% greater when
harvest is increased). This model result assumes a smooth dispersion kernel for average settling
conditions not instantaneous ones needed to model stochastic larval settlement (see below).
Modeling the Value of Information: Information is central to fishery management. The
difficulty is to know how much and what types of information are useful for this problem in the
face of uncertainty. A corollary problem is which management regimes are appropriate under
different amounts and sources of uncertainty. The question can be posed formally by estimating
the value of information; a problem founded in information theory, finance, and economics.
Given a model for fish stocks (xt+1 = f(xt,ht) where t is time, xt is the state vector [stocks] & ht is
8
the control vector [regulations]), a stochastic dynamic programming equation may be written for
the value of a fishery, V(xt;), or
value of
fishery
max over
controls h t
expected value given info θ
V(x t ; θ ) = max E θ [ U(x t ,h t ) + δV(x t+1;θ) ]
info
ht
social payout
of harvesting
discounted
future value
This expression estimates the value of a fishery given a set of control strategies (h t), knowledge
of the social payout for harvest (U(xt,ht)) and the discounted future value of the resource
(V(xt+1;), all viewed through the eyes of the information (). A concrete example of information
is the number of samples in a stock assessment. The solution of this dynamic programming
equation provides an optimal control strategy (i.e., harvest regulation) that maximizes the value
of a fishery based upon the information set,  (see Costello et al. 1998; 2001). The value of
adding incrementally more information or (VOI(,o)) is then V(xt;)-V(xt;).
Figure 8: The expected relationships between value of information,
VOI(,o), and the costs associated with collecting that additional
information, -o. Two cost models are shown; C1 where costs
increase linearly and C2 with significant initialization costs. I1 and I2
illustrate optimal amounts of information to purchase for C1 & C2.
$
C2
VOI(,o)
Information clearly has costs (Figure 8). These costs may
increase linearly (perhaps driven by analysis time), C1, or
C1
there may be steep initialization costs (ship charter or major
equipment purchase), C2. Further, the marginal increase in
the VOI(,o) should decrease as more information is
provided. Based upon the example in figure 8, it makes
sense to purchase information following cost model 1, but
I2
I1

not model 2. In this way, the true value of additional
information for altering management strategies can be determined.
Model Synthesis: The coupling of models of human and natural processes is the obvious way
to develop successful fishery management strategies. Using the two previous examples, the
integral-difference fishery model (which provides spatially explicit fish stocks) can be coupled
with the dynamic programming equations to find optimal harvesting strategies that are now
spatially realized. Experimentation beyond this is where our science begins. For example, stock
estimates can be sampled with varying degrees of error to address the quality of information.
Further experimentation is described below. Our experimental approach to the quantitative
coupling of human and natural system models will provide our synthesis of the F3 problem.
Management Instruments - An important part of our work is determining appropriate fishery
management regimes. Table 1 provides classes of management instruments to be used.
Table 1: Management Instruments
Instrument
Closures (spatial
& temporal)
Quotas
Taxes or Landing
Fees
Gear Restrictions
Limited Entry
Description
MPA’s, seasonal or regional closures, etc.
Implemented on range of scales
Fixes harvest to a given quantity. May
change in time & be tradeable (ITQ’s).
Fishers pay an ex-vessel tax for harvest.
Creates incentive to reduce harvest.
Regulate the technology with which fishers
can fish.
Restrict number of licenses for fishery.
Regulation & Information Costs
Little information required though significant
information is needed to identify closures.
May pose bycatch or high-grading problems.
Significant information required to set quota.
Appropriately setting tax requires significant
amount of information.
Difficult to monitor. Relationship between
gear use & fish mortality is not well known.
Little information required (beyond
monitoring). Politically hard to implement.
Use and Availability of Observational Data: The use of observational data is a critical aspect
of our proposed work. Observational data sets are needed to test hypotheses, derive models
and to assess the skill of predictions. Several field research programs are becoming mature that
9
are available for our use (Table 2). Further, several regulatory initiatives will supplement these
data in the near future. Thus, F3 will benefit greatly from data available at no new costs.
Table 2: Available Field Data Sets for this Work
Project
Santa Barbara
Coastal-Long Term
Ecological Research
site (SBC-LTER)
Partnership for the
Interdisciplinary Study
of Coastal Oceans
Radar Mapping of
Surface Currents
Otolith & statolith
chemistry
Plumes & Blooms
SBC Oil Spill
Response Data
Mesoscale
Meteorology Modeling
Channel Islands Nat.
Park Kelp Forest
Fish block records
Channel Islands Nat.
Marine Sanctuary
Coastal Resource
Assessment of Nearshore Environments
Type of project
Long-term kelp
ecosystem research
Integrated study of
nearshore ecosystems of west coast
HF-Radar array
fish & invert dispersal
research
Ocean color field &
satellite observations
Near real-time ocean
observations
Real-time, regional
meteorology forecasts
Long term kelp forest
habitat monitoring
Catch statistics from
logbooks & observers
Habitat
characterization
Resource assessment
for California Marine
Life Management Act
Resources
Kelp bed fish & invert
stocks & recruitment,
ocean time series,
hydrographic surveys
Large scale monitoring
of stocks & recruitment
of fish & inverts
Hourly surface currents
for SB Channel
Empirical estimates of
dispersal kernels
SST & Chl imagery,
oceanographic obs.
Drifters, current meter
moorings, wave models
Small scale surface
wind & air-sea forcing
Diver surveys for fish,
inverts & algae
Fishing location, catch,
by-catch & effort
Substrate mapping by
sidescan sonar
Large scale monitoring
of fish & invert stocks
Support - PI - Website
NSF Dan Reed [UCSB]
sbc.lternet.edu
Packard Foundation
Gaines/Warner
piscoweb.org
MMS/UC Washburn [UCSB]
www.icess.ucsb.edu/iog
Univ. Cal. Marine Council
Warner
NASA Siegel
www.icess.ucsb.edu/PnB
MMS Winant/Dever [UCSD]
www-ccs.ucsd.edu/oilspill
USFS/NASA Jones [UCSB]
www.icess.ucsb.edu/asr
NPS
www.nature.nps.gov/im/units/chis
NMFS & Calif. Fish & Game
NOAA - CINMS
Channelislands.nos.noaa.gov
CRANE - Calif. Fish & Game
www.dfg.ca.gov/mrd/nfmp
Model Organisms: Three model organisms will be explored: cabezon, red urchin and abalone.
Each have been harvested from the Santa Barbara Channel. We chose these species as they
span the expected range of larval dispersal and life history characteristics and data are available
(both historical and on-going research that is leveraged here) to develop and test models.
Proposed Tests of Hypotheses and the Syntheses of F3:
The five hypotheses describe what we believe to be an emergent complex adaptive system
coupling the dynamics of human and natural systems in nearshore fisheries. In what follows are
short descriptions of how we will test our hypotheses which we envision will lead to a synthetic
understanding of the F3 complex system.
H1: Larval Settlement in Complex Coastal Environments (Winters, Siegel, Gaines, Warner)
The stochastic nature of larval settlement and recruitment in complex coastal environments will
be addressed using a hierarchical modeling approach. We will begin by formulating statistical
descriptions of the dispersal of larva in the coastal environment in the form of “dispersion
kernels” derived from Markov chain statistical simulations as discussed in Siegel et al (2002).
This will enable us to quantitatively relate statistical descriptors of the dispersal process to the
ecological and economic components of the overall modeling framework. Existing data sets in
the form of HF radar-based surface currents and surface drifter tracks for the SBC will be used to
constrain the statistical descriptions.
Our main approach to exploring the stochastic nature of larval settlement will be to use highresolution coastal circulation models to simulate the complex spatial and temporal patterns
inherent in the shallow, wind-driven coastal ocean. We plan to use the MIT Ocean model
(Marshall et al, 1997; 1998) in its non-hydrostatic configuration. Model runs will be made at
spatial resolutions of a few km for periods of a few months. Recent work (DiLorenzo 2003;
Marchesiello et al. 2003, Caldiera & Marchesiello, 2002) has shown that coastal circulation
10
models exhibit rich dynamical structure at small space and time scales provided that the wind
fields driving these motions are prescribed at correspondingly fine scales. A main challenge will
be to obtain realizations of the wind field at appropriate scales. Fortunately, spatial wind fields
are being simulated using high resolution atmospheric models (e.g., Jones et al. 2001; Dorman
& Koracin, 2002). Hence, opportunities exist to incorporate high-resolution surface winds (from
regional meteorological forecasts; Charles Jones, UCSB) and surface ocean currents (from HF
radar; Libe Washburn, UCSB) into the MIT model directly (Table 2).
Circulation experiments will be seeded with large numbers of idealized, water-following "larvae".
We envision experiments in which “larvae” are released both episodically and continuously from
select near-shore locations. Due to the time-dependent nature of the circulation, trajectories from
the same location at different times may trace out very different spatial patterns over short time
intervals. From experiments such as these, we will build a statistical model of larval settlement
consistent with data-driven models of the coastal circulation. The computational framework also
allows biological behaviors (buoyancy regulation, motility, etc.) to be implemented as desired.
We will undertake the circulation modeling in phases, starting with simple configurations of
upwelling favorable winds along straight, sloping coastlines, building toward a data-driven model
of the detailed circulation of the SBC. A numerical modeling study of wind-driven upwelling
focusing on 3-D circulations along an idealized coastline has recently been completed (Winters
et al, 2002). The underlying and unifying principle behind the proposed collection of circulation
experiments is to quantitatively test the hypothesis that time dependent flow processes drive the
stochastic nature of larval settlement patterns. These results will be compared with estimates of
larval dispersal for the 3 model organisms (see otolith & statolith microchemistry; Table 2).
H2: Scales of Nearshore Fish Stocks (Siegel, Kendall, Warner, Gaines, Hilborn, Winters)
The distribution and processes controlling nearshore fish stocks will be explored using observed
data sets and numerical modeling. As listed in Table 2, a variety of data sources can be used to
locate stocks of individual species. These include fish block surveys, NPS kelp forest monitoring
and fishery-independent subtidal monitoring undertaken by the SBC-LTER and PISCO. These
latter efforts will be soon supplemented by extensive state-wide monitoring conducted (CRANE;
Table 2). These data will be used to statistically address variability in fish stocks and recruitment.
Fish habitat information will be derived from a variety of factors (e.g., Boisclair, 2001; Senyk &
Siegel, 2002). These include validated bathymetry (60 m resolution), substrate characterization
from grab samples, daily sea surface temperature and chlorophyll fields from satellite imagery,
model estimates of wave exposure (O'Reilly and Guza, 1993) and kelp forest coverage from
aerial surveys (provided by ISI Alginates). These data are all in hand and are in managed within
a GIS (Senyk and Siegel, 2002). We expect the substrate database to be supplemented through
on-going sidescan sonar work conducted by CINMS (Table 2). Our goal here is to address the
spatial (and temporal) patterns of habitat characteristics in reference to fish stock distributions
and flow fields. By conducting these statistical comparisons, we hope to address the relative
importance of flow-driven settlement patterns vs. habitat characteristics on fish stock
distributions. Adult home ranges (from available data or literature) will also be considered.
Again, modeling will also be used to test this hypothesis. Key will be the development of simple
dispersal kernels that mimic the stochastic nature of larval settlement. These "spiky" kernels will
be used in integral-difference models (like the one shown previously) to simulate the space-time
distributions of fish recruitment and stocks. Determination of a spiky kernel requires (at a
minimum) knowledge of the time scales of larval releases, bounds on space/time scales for the
ocean flows, and the organism's PLD and generation time scales. For illustration sake, we will
estimate spiky kernels for a fish species that broadcasts larvae continuously. The time-varying
flow field will create settlement spatial scales of 5 km, only 100 of these larval trajectories will be
independent from another (the Lagrangian decorrelation time scale is 3 days) and only 10% of
the trajectories settle within the larva's competency window. These assumptions are consistent
with scale arguments presented previously. This results in a spiky kernel constructed from only
10 independent samples operating on a domain with a 5 km resolution. As described above, the
11
proposed circulation modeling (with an understanding of fish stocks & life history information) will
be used to estimate realistic dispersal kernels for the modeling of fish demographics. However,
simple scaling arguments (like the one made here) can be used as a first approximation.
Here, we have repeated the case of MPA management of a heavily fished species (Fig. 7) but
now with a spiky kernel. The results are similar to those shown in figure 9 but now for at a single
moment in time. Implementation of a MPA increases both adult densities (by 74% for this time
step) and harvest yields increase by 16%. However there is now extensive spatial structuring in
both stocks and yields. These changes can also be viewed as a function of time.
Figure 9: A single realization
of the integral-difference model
results for fish density (left)
and harvest yields (right) for a
network of no-take MPA's with
a stochastic dispersal kernel.
All the factors are as in figure 7
except that 10 samples are
taken from a Gaussian kernel.
The spiky kernel is normalized
to insure identical results over
the domain. At this time, fish
stocks are 74% higher for MPA control (61% higher for the increased effort case) while harvest yields are
16% higher (25% more for increased harvest).
The model described here can be extended to address the suitability of habitat & adult mobility.
For example, the effects of habitat may be manifest through the organism's mortality (Mxt),
fecundity (Fxt) or post-settlement recruitment (Lxt) and the space/time structure of these terms
can be parameterized using observed data. A goal here is to develop simulation results that can
be compared in a statistical way with observed data sets. One way would be to develop spatial
population models that predict occupancy of habitat locations, which can be tested against data.
The above model simulations are appropriate for species with sessile adult stages but not when
adults have large home ranges. Unlike larvae, adults can actively choose their movement. We
will extend these models to include adult mobility. Issues to be explored include: what quantifies
a "large" home range, habitat selection, behavioral aspects of home range, habitat- and densitydependent survival, and size-dependent fecundity to the model. This combination of field
analysis and modeling will allow us to test the hypothesis that the combination of flow & habitat
processes regulate the scales over which sessile adult fish are distributed.
H3: Uncertainty, Information & Fishery Management (Costello, Polasky, Hilborn, Siegel, Kendall)
The question of how much and what types of information are most useful for managing a fishery
requires a comprehensive system of models that frame the problem under uncertainty. An
example of the dynamic programming approach has been presented. This is a quantitative tool
used to assess the value of information in fish management. Multiple sources of uncertainty
influence the decision making of a fishery manager. Uncertainty may affect knowledge in many
parts of the F3 system. These include state variables (fish stocks & yields), parameters (market
prices, labor costs, organism mortality rates, etc.), processes accounting for response to
perturbations (links between flow fields & settlement or fish stocks & fecundity) and exogenous
effects (interannual changes due to El Niño). Our focus is not on uncertainty itself, but rather, the
sources, quantities, reliabilities and costs of information used to constrain uncertainty.
We need to know what types of information have the most value. Is it expending more effort
toward better stock surveys (reducing uncertainty about stock size) or is it best to improve our
predictive understanding of the relationship between fish stock characteristics and fecundity
(reducing uncertainty about the number of broadcast larvae)?
The resolution of this issue will take careful analyses and modeling through all stages. The
solution of the dynamic programming equation for the optimal management strategy requires
12
knowledge of 1) the fishery, 2) the types of control strategies over which we want to maximize
the fishery's value and 3) the types of information we have to reach this decision. Tests of the
fish stock scale hypothesis (H2) will provide us with state-of-the-art models of fish stocks in
response to harvest regulations. A variety of regulation strategies can be implemented (Table 1)
but these need to be carefully considered. Last, the important sources of uncertainty need to be
enumerated for the fishery in question. Clearly, the solution of this problem is a difficult and
computationally intensive problem. The modeling procedure itself is an iterative process that
involves the entire team to various degrees. We expect to step through this carefully, starting
with easier scenarios (one regulatory strategy and one type of uncertainty) and working towards
more complicated situations. Following the first round of model development, we will carefully
parameterize the model with the aforementioned data for the nearshore fisheries from the SBC.
H4: Scale Disparities between Harvesting & Management (Kendall, Costello, Hilborn, Polasky,
Gaines, Warner, Siegel)
The resolution of this hypothesis requires us to 1) demonstrate that scale disparities between
harvest & management exist and 2) understand the cause(s?) of these differences. Again, both
empirical and modeling approaches will be employed. Assessments of scales of management
will be done through an analysis of regulatory information for California nearshore fisheries
available from the regulatory agencies (NMFS and CA Fish & Game). We will assess not only
the scale of regulation but we will assess the time scales for implementing these regulations.
Scales of extraction will be addressed through an analysis of logbook and observer data (Table
2) and by interviewing commercial and recreational fishers (one of the graduate students will
dedicate his/her time to this issue). These activities will provide us background empirical
knowledge of the space & time scales of harvest and management.
The hard part will be to develop a predictive understanding of the cause(s?) of these differences.
Analysis of actual behavior of commercial fishermen has shown that effort is allocated on fine
spatial scales to maximize their individual incomes (Hilborn and Ledbetter 1979, 1985). The
hypothesis of an Ideal Free Distribution (Fretwell, 1972) provides a good theoretical basis for
predicting how fishers will allocate their effort (Hilborn 1985b, Prince and Hilborn 1988). Fishers’
incomes will be affected by the regulatory environment and the number of competing users.
Modeling these factors, we can then predict how the fishing fleet, as a whole, will allocate its
effort. We will combine these models of fisher & fishery behavior with fish stock models to
determine the “perfect” management: spatially fine-scale, instantly responding to system
dynamics, and using unlimited information.
We will compare this “gold standard” against various forms of actual management (see Table 1).
Each management approach has characteristic spatial and temporal scales that, if coarser than
that of the fisher behavior, will result in imperfect regulation. So far, this assumes perfect
information. We will characterize the types of information required by each management tool and
use these to calculate the value of information in each scenario. This will allow us to estimate the
optimal information level under each management strategy. This will provide a measure of the
effectiveness of each management strategy.
We expect that in the face of large-scale spatial and temporal uncertainties, regulators may do
best by hedging their bets using large-scale, low information cost management options such as
marine reserves (e.g., Sanchirico and Wilen, 1999). However, the literature shows that for many
cases fishery values can increase under traditional management options (such as limited entry,
harvest quotas, ITQs, gear restrictions, etc). The question becomes one of weighing these
potential gains against the costs of acquiring, modeling, and assimilating the data required to
justify spatially-heterogeneous, fine scale fishery management.
H5: Managing Mixed Species Fisheries (Hilborn, Costello, Gaines, Kendall, Siegel)
The above hypotheses all deal with issues of single fish fisheries. However as has been seen for
the U.S. West Coast groundfish fisheries, mixed species fisheries are especially troublesome.
Hilborn (1985a) shows that only about 10% of the potential yield can be obtained from a mixed
13
fishery while still protecting from overharvest the least productive species. With expanding
knowledge of 80 species presently harvested, it seems that some fisheries may simply cease to
exist because some species will always be overfished and in need of rebuilding.
We propose to address this topic by modeling the interactions among fish stock distributions,
fisher incomes, information costs and ecological consequences for a mixed fishery management
strategy. Our approach will be to develop fish stock models for typical mixed fisheries and to
examine how harvest yields are partitioned among the different species. Key will be an
assessment of the tradeoffs between yield and overfishing under different management systems.
Special focus will be on overfished species and their recovery processes. Ecological interactions
in this hypothetical mixed fishery will also be assessed.
Several mixed fishery management strategies will be examined. For example, one strategy is to
spatially structure regulations so that individual species can be harvested on the small scales
where they may be abundant and overfished species are absent. This requires extensive
knowledge of the distribution and vulnerability of fish stocks on fine scales and we will assess the
costs of this information. Many other mixed fishery management regimes can be implemented,
including trip limits, effort buy-back, MPA’s, spatial patterns of closures, ITQ’s and so on. We will
examine the information needs and costs to manage a multi-species fishery under the various
different regimes. We expect that if the different species are not strongly correlated and
especially if their spatial scales differ, then the best mixed fishery strategy would be different
than that for any one of single species alone. This would require modeling increased costs in
the fishery – both the costs of innovation and the reduced efficiencies of more targeted gear
types and fishing strategies. The extent to which these management plans will actually work
depends on the space-time pattern of stock abundance and the net costs of required information
for the management plan.
F3 Synthesis & Final Products (Siegel & All F3 Team Members)
Our aim is to assess the balance points among costs, profits, uncertainties, stock viability and
ecological values of nearshore fished environments. To do this we propose to develop a set of
interlocking, dynamic models and data analyses to address the F3 complex system. We do not
expect that the modeling and observational work proposed here will result in a "grand synthesis
model" of nearshore fisheries. Rather our goal is to increase the predictive understanding of the
interactions among information & uncertainty for various fishery management strategies and to
push the conceptual envelopes. In particular, we will work to develop “rules of thumb” that can
be used operationally by fishery managers. The development of simple operational tools is likely
to provide the best and furthest reaching impact of our proposed research.
Broader Impacts of the F3 Project:
Our long-term hope is that our work will lead to significant improvements in the scientific
management of nearshore fisheries. The F3 team members are all active in the policy arena and
the greatest impact of our work is likely to come from these interactions (see the F 3 team
description above). In addition to these activities, F3 will interact with the following educational
and global perspective activities.
Educational Components - The F3 project integrates many disciplines to address questions of
key ecological and societal importance. As such, it is programmatically suited to educational
efforts of our home institutions at a variety of levels. A total of six graduate students will be
directly supported by this grant alone (3 at UCSB and 1 each at each subcontracting institution).
Further as all members of the F3 team are active classroom instructors, we naturally integrate
our research into our curricula. In particular, we will integrate F3 with the following efforts:
ORCA (Ocean Research Classroom Adventure) – A new floating laboratory program at UCSB
funded by the State of California and NOAA (Gaines will coordinate). The program takes junior
and senior high school students on half-day research trips. The curriculum immerses students in
14
field research paralleling projects at UCSB. ORCA will develop new research modules on larval
dispersal and fisheries management based on F3. Impact: 2000 to 5000 students per year.
SEASET (Summer Experience And Sabbatical Enrichment for Teachers) – A teacher sabbatical
program at UCSB funded by the Boyd Foundation (Gaines). Up to 10 teachers a year spend
from 3 to 12 months in residence working as part of a marine science research team. They
expand their scientific background, experience the excitement of research and develop
standards-based, K-12 curriculum modules that integrate research. The modules are made
available to teachers throughout California. We will dedicate a minimum of 2 teachers per year to
develop F3 curriculum modules. Impact: 10+ teachers directly and 1000’s of students indirectly.
UCSB Bren School of Environmental Science and Management – The Bren School Master’s
Degree program requires completion of a yearlong group project similar to a master’s thesis.
Proposals for group projects are submitted by various agencies locally, statewide and national.
Many of the issues proposed here are well suited for group student efforts. Costello and Kendall
are Bren School faculty. Impact: Likely two Bren group projects will be conducted (~8 students).
EES-IGERT – The Economics & Environmental Sciences (EES) program is a NSF-IGERT
graduate training program at UCSB aimed at providing rigorous doctoral training in
environmental economics and environmental science (www.ees.ucsb.edu). Siegel is a co-PI and
Costello and Kendall are participating faculty. Although we cannot promise a match from this
program (we expect EES PhD students to select their own dissertation topics), it is likely that
several EES students will earn degrees working as part of the F3 team. Impact: Likely two PhDs
Global Perspectives - As we hope is clear, the issues addressed in this proposal are applicable
to nearly all coastal ecosystems worldwide. To maintain a global perspective and to evaluate our
findings in other geographical settings, we will integrate the F3 project with existing international
collaborations that are currently supported (Gaines will coordinate). These include:
PISCO/Mellon Consortium – a partnership including marine scientists in Chile and New Zealand
funded by the Mellon Foundation that links PISCO with research in other upwelling ecosystems.
Key partners: Dr. Sergio Navarrete, Dr. Juan Carlos Castilla, Dr. David Schiel
PISCO/CICESE – a partnership funded by the UC MEXUS program to coordinate coastal
sampling efforts between Mexican and US scientists. Key partner: Dr. Lydia Ladah
CEQI/Australia – a partnership funded by the state of California to develop new technologies for
directly tracking marine larvae using otolith microchemistry. Key partner: Dr. Stephen Swearer
NCEAS - To further expand the global focus, we will also propose an international working group
to the National Center for Ecological Analysis and Synthesis (NCEAS) in Santa Barbara. This
NSF funded center has previously supported two key working groups on the theory of marine
reserves and scales of larval dispersal. If funded a new working group would include ~20
investigators chosen to extend the global perspectives of the F3 project.
Management Plan:
The composition and qualifications of the F3 team have already been presented ("Prior NSF
Research" and "The F3 Team" sections above). Dave Siegel will act as F3 team leader although
all members are responsible for the success of this work. Individual responsibilities are listed in
the "Hypothesis Tests" and "Broader Impacts" sections above. The F3 team will convene at least
annually at Santa Barbara (or other locale) for several day workshops. A NCEAS working group
will also be proposed (see above). This project is one where one PI's research output is
another's input (although there will be no "lock steps" leading to inactivity).
The F3 team will conduct monthly conference calls with all participants and minutes will be kept
and posted electronically. A detailed calendar, personnel list and research activity outline will
also be kept up to date. This will assist in coordinating investigations as well as making the
reporting to NSF easier and more complete. A website will be developed for use in outreach as
well as helping the team share data, codes and working papers. All results will be freely available
to all interested parties. Siegel and the postdoc will be responsible for the website.
15
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