BE/CNH: Disparate Scales of Process and Nearshore Fishery Management (OCE0308440). $1,995,951 to David A. Siegel, Bruce E. Kendall, Christopher Costello,
Robert R. Warner, and Steven D. Gaines (UC Santa Barbara). 09/01/2003 –
08/31/2008.
Subcontractors:
Ray Hilborn – Univ. Washington
Steve Polasky – Univ. Minnesota
Kraig Winters – UC San Diego
Reporting Period: Year 2 (2004-2005)
The Flow, Fish and Fishing (F3) Biocomplexity Project:
The goal of the Flow, Fish and Fishing (F3) biocomplexity project is to develop a
process-level description of nearshore fisheries and their management patterned after
California coastal environments. Specifically, our aim is 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, fishermen and fishery
regulations and the bureaucratic system which implements regulations with the aim of
assessing the balance points among costs, profits, uncertainties, stock viability and
ecological values of nearshore fished environments. Our aim is to assess the balance
points among costs, profits, uncertainties, stock viability and ecological values of
nearshore fished environments.
A Component View of the F3 Biocomplexity Project:
Our initial efforts have been spent on developing the modeling tools required to address
the questions and hypotheses posed by the Flow, Fish and Fishing biocomplexity
project. In our first year of the F3 project, we have implemented 1) idealized ocean
circulation models to explore the statistical properties of larval dispersal (Siegel /
Winters), 2) a spatially explicit fish population dynamic model that allows us to explore
the consequences of stochastic dispersal and heterogeneous fishing effort (Kendall /
Siegel) and 3) economic models to evaluate the optimal management strategy when
there is complete information of the fishery (Costello / Polasky). These three thrust
areas will be the focus of this reporting. Also note that we have also begun the
collection and analysis of available empirical data sets to assess the validity and skill of
these models (Warner / Gaines). Preliminary results point to several exciting
conclusions, including: (1) even in a homogeneous physical environment, the statistics
of larval dispersal over the course of a single season are spatially heterogeneous; (2)
with stochastic dispersal, harvesting at maximum sustainable yield maximizes the
spatial heterogeneity in fish stocks; (3) if the primary source of density dependence is
post-settlement, then marine protected areas can increase fisheries profits, and there is
a wide range of nearly equivalent solutions in terms of both total area and reserve
configuration; and (4) in the absence of temporal environmental fluctuations, the optimal
management strategy is a location-specific temporally constant escapement level.
These manuscripts are under preparation. In the following we will introduce the
modeling tools developed in the first year of the F3 project.
It should be mentioned that there other activities that are on-going in the F3 project that
will have important bearing on future research integration of this problem. These
include the development of models of fishing fleet dynamics (Hilborn) and fisherman
behavior and choice (Costello / Hilborn / Polasky), implementing regional circulation
models for “real” environments (Winters), developing the information flow model for
optimal fishery management (Costello / Polasky), modeling mixed fisheries and bycatch
(Hilborn / Gaines), modeling the efficiency of marine protected areas as a fishery
management tool (Gaines / Hilborn / Costello / Siegel / Warner / Kendall) and
application of the F3 stock variability modeling approach for assessing genetic
properties of nearshore populations (Gaines). These components of the F3 project will
be subject of future reports.
Idealized modeling of larval transport (Siegel / Winters – lead)
We have developed a data-driven, idealized model of a coastal environment to examine
larval dispersal and settlement for typical flow conditions in the California Current. The
model is built upon the Regional Ocean Model System (ROMS) and is freely available
for research use. The model domain is patterned after sites in Central California
(CalCOFI lines 70) although it is highly idealized with uniform alongshore variations in
bathymetry, periodic boundary conditions in the alongshore direction, and an open
boundary condition for the offshore region. The model is forced by statistical wind
stress on the top surface and an alongshore pressure gradient obtained from in-situ
observation data. Model runs are conducted for perpetual months of January and July
and are run to a quasi-steady state. Once steady-state conditions are achieved,
synthetic larvae are released from nearshore regions following a set schedule. This
larval release schedule is meant to model the processes controlling larval production
including duration of releases, time scale between individual release events, length
scale between release locations, and how / where settlement occurs. This modeling of
larval release and settlement is critical as it encapsulates both fished organism
characteristics, including behavior, and fluid mechanical processes of coastal ocean
circulations. The larval release schedule is modeled after typical rocky reef fish.
Nearshore habitat is defined as waters inside 20 km from the coast; larvae are released
for a month (90 days); competency time window is set to one month (20 to 40 days after
release); and settlement occurs when larvae are found in the nearshore habitat during
their competency time window. Larvae are passively transported in the horizontal
directions, while they can control their vertical positioning.
Example trajectories (Figure 1; see also movie in the online supplemental section) show
the advection of larvae by the simulated currents (roughly along lines of constant sea
level) and these patterns evolve rapidly as the sea level patterns change in response to
the stochastic wind forcing. Importantly, eddies sweep newly released larvae together
into “packets” which stay coherent through much of their pelagic stage. The stochastic
nature of arriving settlers is immediately apparent: larvae settle in infrequent pulses and
come from both nearby and distant source locations (Figure 2). Settlement pulses last
from 5 to 30 days and originate from source distances from 0 to >500 km upstream.
Sometimes arrival events occur coincident with reversals in the alongshore winds (see
after day 90), which would advect surface water parcels onshore23. However, onshore
Ekman transport is clearly not the only process by which larval settlement occurs. More
often than not, successful settlement occurs because eddies advect larvae to suitable
habitats. Larval settlement is episodic and spatially localized with a characteristic time
scale of 13 days and alongshore spatial scales of 30 km (Figure 2).
The stochastic nature of the larval transport can be visualized for a given season by
constructing source-destination matrices as a function of planktonic larval duration.
Connectivity matrices, which are often used to illustrate source-destination relationships
for larval transport, show that connectivity is highly heterogeneous in space even for a
uniform coastline (Figure 3a). Some sites receive only a few settlers, while others
receive large pulses from a wide array of source locations. These connectivity matrices
do not look like those predicted by simple larval diffusion (Figure 3b); rather, they are
made up of a few “hot spots” that connect sites in an otherwise unstructured domain.
Settlement hot spots can also occur on the “self-settlement” line suggesting that eddies
alone can lead to self settlement. Different realizations (simulated using different initial
random seeds) produce connections among nearshore habitats that are still spatially
heterogeneous but the locations and intensities of the “hot spots” have changed
(Figures 3c and 3d). Thus, larval settlement patterns will not be consistent from one
time year to the next even under identical climate regimes. Ocean stirring makes larval
connections among nearshore sites a stochastic process that is both spatially
heterogeneous and temporally intermittent.
In Figure 3e, the spawning season is reduced from 90 to 30 days and this shows a
more heterogeneous connectivity matrix (fewer hot spots). Moderate changes of the
spatial scale between adjacent releases or times between releases have a moderate
role in altering connectivity (Figures 3f and 3g). This is because adjacent larval releases
are swept together into packets by the eddy field (Figure 1). However, dramatic
reductions in the frequency or density of larval releases dramatically increase the
stochastic nature of connectivity (Figure 3h). This is because the time between
releases is now much larger than the characteristic time scale for the eddy field.
Altering the larval settlement competency window can also have important effects.
Reducing this from 20 to 40 days down to 5 to 10 days (i.e., shortening the average
pelagic duration and the period during which settlement can occur) makes connectivity
more regular (Figure 3i). This occurs because a greater fraction of larvae “survive” (are
in the nearshore environment during their settlement competency window). Further,
different connectivity matrices arise from the same flow field if life history characteristics
(settlement competency periods) differ.
This work is on-going. The initial manuscript has been accepted for publication by
Journal of Marine Systems. Another manuscript is to be submitted to the Proceeding of
the National Academy shortly. And, two more manuscripts are being drafted for
publication. We would like to note the contributions made by Dr. Satoshi Mitarai, F3
postdoc at UCSB, to this work.
Figure 1: Depictions of the sea level distribution (color contours in cm) and the trajectories of
larvae for simulated days 60, 80 and 100. Here, the number of the released larvae is reduced;
larvae are released every 8 km and every 4 days. The simulated domain is 256 km in the
alongshore direction and 288 km in the cross shore direction (only the inner 200 km are shown
in the cross-shore direction). The circles show the location of the larvae while the white trails
behind each show their previous 2 day trajectories. The vertical dashed red line indicates the
boundary for the nearshore habitat from which larvae are released and where settlement can
occur. The flow field is modeled to represent conditions found in the central coast of California
(CalCOFI line 70) during a typical July (high upwelling conditions). Low sea level features
correspond to cyclones and support counter-clockwise geostrophic currents. Anti-cyclones, high
sea level features, create clockwise circulations. An animated time-lapsed depiction of this flow
field and associated larval transport is provided in the online supplemental information.
Figure 2: Time series showing a) the density, alongshore source locations and timing of larvae
that successfully settle in the domain (i.e., the departure density), b) the density and arrival
locations of settlers from all source locations (i.e., the arrival density) and c) the alongshore
wind speed forcing the model (- is upwelling favorable). Larvae can settle within their
competency time window (20 to 40 days) if they are in suitable habitat (the inner 20 km). The
periodic interval in the alongshore direction is stretched to account for larval source regions
which are many periodic distances upstream from their arrival locations (a feature of using
periodic boundary conditions in the alongshore direction). The timing of larval releases
simulated is as follows (see methods section); the circulation model is driven to a quasi-steady
state and larvae are released starting on day 0. The first larvae are able to settle on day 20.
Larval releases stop at day 90 and settlement continues until day 130. Densities are normalized
by the total number of settlers so that the same color bar can be used in a) and b). Length and
time scales for settlement are calculated using the variogram range31 of the arrival density with
arrival time (or location) held constant. Resulting time scales from Figure 2b are 13 days and
alongshore spatial scales are 30 km.
Figure 3: Connectivity matrices for larval dispersal for A) the base case simulation shown in
Figures 1 and 2, B) a diffusion model assuming the same statistics of the base case simulation,
C) a second realization of base case simulation, and D) a third realization of base case
simulation. These depictions are single realizations of the probability density function of the
relationship between source and destination locations along a coastline. Source locations
(vertical axis) and destination locations (horizontal axis) are identified by their alongshore
location. The connectivity matrices are normalized so that the summed probabilities equal one.
The dashed slanted line represents self-settlement (where source and destination locations
coincide). The broad extent of the source locations in the above diagrams takes advantage of
the periodic boundary conditions in the alongshore direction. The three realizations of the base
case simulation show different connectivity patterns illustrating intermittency of larval transport.
The diffusion example (panel b) is constructed from statistics from the base case simulation
(panel a) using a mean offset of 133 km and a spread of the Gaussian distribution of 88 km.
Modeling spatial temporal dynamics of nearshore fish stocks (Kendall /
Siegel – lead)
The second modeling component of the F3 project to be presented is a spatially explicit
fish population dynamic model that allows us to explore the consequences of stochastic
dispersal and heterogeneous fishing effort. This model simulates the changes of
nearshore populations using an integral-difference approach, or
A nx1  A nx  Hnx  Mnx (A nx  Hnx )   (A nx '  Hnx ' )Fxn'LnK nx x 'Rnx dx '
(1)
where A nx = adult abundance at location x in year n (# / km), Hnx  is the harvest rate (# /
km), Mnx  natural mortality for adults(fraction of adults/year), K nx-x' = larval dispersal
kernel from location x’ to x in year n (1 / km), Fx'n = fecundity of adults at location x’ (#
larval releases per fecund adult), Ln = larval survival (from release through settlement),
Rnx = post-settlement recruitment of settled larvae to adults at location x
and the integration is over all sites x’ (fraction). For simplicity, the terms for fecundity,
larval survival and post-settlement recruitment are multiplied together and are described
using a single constant to quantify fish productivity, Po (# recruits per spawning adult).
Density dependence is described using the Ricker equation and can be acting on postsettlement recruitment based on adult densities or larval densities, on fecundity based
on adult densities or on adult mortality. There are important differences that arise in the
dynamics of a population in how density dependence is applied on the system which
are especially important when stochastic dispersal is considered. We are in the process
of drafting a manuscript on this particular issue now. Stochastic settlement is modeled
assuming by selecting a few draws from a probability distribution of dispersal which is a
function of PLD and ocean current conditions (Siegel et al. 2003). We refer to this as
the “spiky” model. The number of draws is a function of the release duration and
character as well as the PLD of the organism.
We used circulation models that to add realistic spatial structure to the larval release
and settlement. Figure 1A shows simulations from the circulation model over several
spawning seasons. We added the source-destination relationships from the circulation
model to the population model, referred to as the “packet” model (see Figure 1B). Now,
larvae within a set of adjacent sources are released together to for “packets” that are
advected by the currents and settle together, across adjacent settlement locations. The
“packet” model functions similarly to the “spiky” model but adds spatial correlation to the
larval release and settlement sites.
Figure 1. Connectivity matrices for larval dispersal examining the role of spawning
season (each season is 90 days). (A) Connectivity matrices obtained from the
simulations of ROMS model. (B) Prediction by a simple advection-diffusion model. (C)
Prediction by the settlement-pulse model. For more than 1 season, this represents the
average connectivity over multiple spawning seasons.
Figure 2: Modeled population density and recruitment with and without stochastic settlement for
a nearshore fish stock. (a) and (b) spatial distribution for the 50th generation (after statistical
steady state is achieved). The time space history larval settlement (# per year) when statistical
steady state occurs (generations 20 to 50) for the “spiky” model (c) and the “packet” model (d).
Model parameters are: M = 0.05, Po = 1100, post-settlement density dependence, no harvest,
absorbing boundaries (at +/- 250 km – x = 5 km), Kernel parameters – PLD = 50 days.
Stochastic settlement is modeled using 60 draws each with a 5% probability of success.
Harvest is also applied in this system through the implementation of a fishing policy.
Polices implemented to date include both spatial and non-spatial policies and can be set
knowing a final escapement level, the total allowable catch or a prescribed fishing
mortality rate.
The temporal order of processes follows the life cycle introduced previously,
 Fished individuals are removed from system, Hnx , based upon a prescribed
fishing policy.
 Settlers are released into the plankton as a function of adult fecundity at a
spawning location and the surviving fish densities (  Fxn' (A nx '  Hnx ' ) ). This can be
a density dependent process (as expected for sea urchin).
 A fraction of these spawned larvae survive (based upon the larval survival
probability, Ln) to settle at a location determined by the dispersal kernel, K nx-x'
 A fraction of the settlers recruit to adult stages (density dependence is often
important at this stage of the life cycle for many groundfish).
 The adult population is reduced by natural mortality ( Mnx ) which again may be
density dependent.
 New recruits are added to remaining adult population in order to generate the
census available to the next time step.
This model is written in Matlab and is freely available via the Flow, Fish and Fishing
webpage (www.icess.ucsb.edu/~satoshi/f3). A full description of the modeling system is
available there along with a webpage where you can try out the model. We are now at
the point of performing science with this model. We have mentioned several activities
going on with our fish stock / harvest model and are beginning to draft manuscripts.
These papers are on a wide variety of problems. First we find that even in a
homogeneous physical environment, the statistics of larval dispersal over the course of
a single season are spatially heterogeneous and temporally intermittent, for both the
“spiky” and “packet” versions of the model. This shows that stochastic larval dispersal
will have an important role on the intergeneration changes in fish stocks and will provide
difficulties in informing the fishery management process. Second with stochastic
dispersal, harvesting at maximum sustainable yield maximizes the spatial heterogeneity
in fish stocks. This is not intuitive but arises due to the type and strength of the density
dependence in stock dynamics. Last, if the primary source of density dependence is
post-settlement, then marine protected areas can increase fisheries profits, and there is
a wide range of nearly equivalent solutions in terms of both total area and reserve
configuration. Manuscripts describing all of these issues are presently underway at this
time. We plan to incorporate age structure into the population model as well as
age/size-dependent harvest.
Economic modeling of optimal harvest under spatial & temporal variability
(Costello / Polasky – lead)
The last modeling component we wish to present is the economic modeling of the
optimal management strategy when there is complete information of the fishery. We
employ a stochastic bioeconomic framework to derive analytically and explore
empirically an optimal spatial harvest strategy in an uncertain environment. Our focal
resource is a near-shore marine fishery in which adults are sessile but larvae disperse,
up to thousands of kilometers, via ocean currents. Optimal harvest within patch i will
depend not only on the biological dynamics within that patch, but on the growth
potential, stock size, economic conditions, and indeed management, in all patches to
which larvae spawned in patch i might disperse. Motivated by state-of-the-art biological
models which identify multiple sources of uncertainty and variability in such systems, we
incorporate (1) spatial heterogeneity among an arbitrary number (N) of patches, (2)
stochastic larval dispersal (due to stochastic ocean currents), (3) random shocks to the
biological fish production function, which may be spatially autocorrelated, and (4)
random shocks to economic variables of the system (e.g. price). These features are
incorporated into a spatial version of the bioeconomic model of fishery management
introduced by Reed (1979). Using the dynamic optimization technique called stochastic
dynamic programming, we are able to identify an optimal feedback control rule to
maximize the expected net present value of the fishery over any time horizon. The
analytical result has a number of salient features. First, the solution reveals that the
optimal control rule is to identify a target escapement level in each patch (which will
depend, in general, on characteristics of that patch and other patches with which it
communicates), and to harvest down to that level every year. These escapement
targets are unique for each patch and, importantly, are time-independent. A second set
of results concerns the use of area closures as a management instrument. We find that
if the target escapement level cannot be reached (i.e. the harvestable biomass in that
patch is smaller than the target escapement level), the optimal policy is indeed a closure
of that patch. In such cases, we find that harvest outside that patch should be
decreased. On the contrary, we find that if a marine reserve is sub-optimally cited, then
harvest outside that patch should actually increase. Biological and economic intuition is
provided for these, and other results.
The main innovation this year has been to refine our spatial bioeconomic model with a
particular emphasis on the conditions under which reserves are optimal. The
economics literature to date is very pessimistic about the ability of reserves to actually
increase the profits from harvesting. This is the first paper to show that profits will
increase with reserves, but only when those reserves are placed in optimal locations.
Our framework also allows us to examine how harvest outside the reserve should be
managed given that a reserve is put in place. This paper will be submitted for
publication within the next month. Building on these results, we have started working on
a model of information and reserve placement. In the absence of information about
larval dispersal, it is difficult to know where to place reserves. In fact, in that case,
reserves may not be optimal. With improved scientific information about larval drift
those reserves can be optimally placed. The resulting increase in value of the fishery is
the value of the scientific information. We expect to have a paper submitted on this
within the next year.
Empirical data acquisition and analysis (Warner / Gaines – lead)
To support the F3 modeling components we have begun collecting and analyzing
available empirical data for the California coastal region. This has been made easier by
the existence of field programs that several of the PIs are participate in. These include
the PISCO (www.piscoweb.org), the Santa Barbara Coastal-LTER (sbc.lternet.edu) and
the monitoring program for the new marine protected areas placed around the Northern
Channel Islands (http://www.dfg.ca.gov/mrd/channel_islands/monitoringplan0204.pdf).
We have also compiled data from the peer reviewed literature, theses, dissertations and
reports on a number of issues. These include life history variation in mixed species
fisheries to parameterize models of multispecies management, patch scales in
oceanographic features along the west coasts of the US and Chile, population genetic
data for marine taxa to estimate scales of larval dispersal, rates of invasion of marine
exotic species to estimate scales of larval and spore dispersal, and paired data on
marine dispersal distances and geographic ranges to examine the roles of dispersal on
geographic distributions.
In particular, we have assembled the literature on larval behavior, with particular
attention paid to the development of sensory and swimming abilities (this will be used to
generate realistic "spheres of attraction" of proper settlement habitat) and on depth
distribution changes with ontogeny (to generate realistic larval behavior scenarios). We
have also assembled the literature on large-scale (in space and time) studies of
recruitment patterns. This will be used to gauge whether the modeled spikiness (in
space and time) is actually found in nature.
The Role of Information in Fishermen Search (Costello - Lead)
There are three components to this research in the F3 project: a theoretical model, an
empirical study and a survey/laboratory validation.
Models in fisheries economics tend to assume that fishermen (i) know where fish stocks
are located and (ii) that this information is common knowledge. The first component of
the research develops a model that relaxes these assumptions and builds on a
framework more in line with how actual fishermen describe their searching and
information sharing behavior. In particular, the model asks, “How does the degree of
information-sharing amongst fishermen affect the allocation of search effort and overall
catch rates?” We find, unsurprisingly, that expected catch increases with the sharing of
information. A preliminary and counter-intuitive result is that, as the underlying spatial
and temporal uncertainty of the system increases, competitive open access begins to
approximate optimal management of the fishery. The next step in integrating this model
with the other components of F3 is to ask, “What degree of stochasticity in larval
transport and population dynamics would be required to produce this counter-intuitive
outcome and can we reasonably expect a natural system to approximate it?”
The second component is an empirical study of fishermen behavior in the Northern
California red urchin fishery during the period 1988-1997. Using individual diver data,
we are able to test a number of hypotheses: (i) more experienced divers extract more of
the resource (ii) divers often ignore their own catch rates and make choices based on
the choices of other divers. Tests of the fist hypothesis have produced interesting and
robust results; tests of the second remain inconclusive.
The third component is a study of how fishermen actually share information in a number
of Californian fisheries. Interviews with fishermen have revealed that the most common
method is to form an information sharing coalition, referred to as a “code group”. We
formulate this decision-making process analytically and solve for the optimal code group
size. Code group size inherently depends on the oceanographic and biological factors
being explored by other members of the F3 team (for example, fisheries with pelagic
adults or highly stochastic larval settlement patterns tend to have larger code groups).
We plan to test these predictions using surveys of fishermen and laboratory
experiments using human subjects.
Fishermen Travel Behavior and Effort Allocation (Siegel – Lead)
Fishing effort is unevenly distributed in space and time as well as among fishermen.
These distributions depend on five dynamic factors:
1) fish presence/abundance
2) physical environment (location, substrate, depth, etc),
3) weather (wind speed, wind direction, wave height, etc),
4) economics (domestic and international markets, fuel costs, etc), and,
5) fisherman characteristics and behavioral tendencies.
Much of the existing fisherman travel behavior literature either assumes complete
knowledge of the environment and focuses on profit maximization, or assumes
complete ignorance of the environment and assigns successful fishing effort to random
chance. Reality is somewhere between these two extremes and varies considerably
from fisherman to fisherman.
This research develops a geographic (spatial) transportation model that describes and
predicts the behavior of a fishing fleet by altering behavior metrics to determine realistic
fisherman behavior. The resulting model will address the question, “How do fishermen
decide when and where to fish?” This will predict how the fishing fleet, as a whole,
allocates its efforts based on the physical environment, fish presence, weather,
economics, and human travel behavior. A final goal of this research is to determine the
effects these fishing efforts have on stock abundance and sustainability.
Steps to Integrate the F3 Biocomplexity Project:
As we hope is apparent, we have implemented several of the disparate pieces of Flow,
Fish and Fishing and have many other components under way. However, building a
successful Biocomplexity project will require the careful integration of these pieces into
a whole bigger than the parts. We still have component research objectives we are
working on and need to publish these results.
There are several activities that we are working on now that will insure the integration of
the F3 components. The first is the work being conducted by F3 PIs and their students
on the modeling of marine protected area (MPA) design and effectiveness. Some of
this was addressed previously in the report. Our MPA work is being done in support of
on-going state and federal processes on designating and evaluating MPA deployed in
California waters. Several of the PI’s are involved in public service aspects of the MPA
process (Gaines / Warner / Hilborn / Siegel). The MPA problem is also a good one for
getting students to start modeling as it provides a spatial restriction on fishing access
which helps students develop intuition to this problem.
We are beginning to work with several California fishing industries. For example, Ray
Hilborn has met with representatives of the California Sea Urchin fishing industry and
California state researchers and managers several times and have developed an initial
population dynamics and fleet model of the fishery in San Diego (a reasonably simple
fishery). We will use this experience as a prototype for the Santa Barbara/Channel
Islands fishery and have started to work with a wider range of industry groups in
cooperative data mining activities (we expect this activity to grow dramatically in the
next year).
Last, we have gone to great lengths to insure there is effective project communication
among its participants. The UCSB group meets biweekly and detailed discussions of
research projects and papers which blend economists and ecologists, students and
professors. We have developed mailing lists where people regularly post work, papers
to read, etc. We have a website (www.icess.ucsb.edu/~satoshi/f3) where all
presentations, working papers, simulation codes, etc. are available. We also hold
annual whole project workshops.
Educational Activities
There are 9 graduate students (4 female) and two postdocs working on the F3
Biocomplexity project. We have done a great job leveraging other sources to expand
the pool of students contributing to F3. Although only one-half of these students will
focus their dissertations directly on F3, all will gain skills and direction based upon the
F3 project. In some ways, F3 is becoming the analytical heart of the marine ecological
work happening at UCSB. The UCSB group meets biweekly for two hours as an
extended group meeting for students and PI’s. A similar activity is occurring at UW.
This past year, three graduate seminars were convened at UCSB to support the
learning required for students involved in F3. In addition, Kendall and Costello are both
faculty in the Bren school and faculty there have supervised group master thesis (3 to 5
masters students working on a yearlong group project) on the spiny lobster fishery and
this interest will grow throughout the project. Last, we continue our work with the
commercial sea urchin divers in California and California Department of Fish and Game.
We held two meetings with them during the year and have moved forward our model of
the urchin-fleet interaction.
Graduate Students
Robin Pelc
Liz Madin
Thom Young
John Lynam
Crow White
Heather Berkeley
Michael Robinson
Carey McGilliard
Nicolas Guiterez
UCSB
UCSB
UCSB
UCSB
UCSB
UCSB
UCSB
UW
UW
Female
Female
Male
Male
Male
Female
Male
Female
Male
US
US
US
Ireland
US
US
US
US
Uruguay
NSF & UCSB fellowship
NSF fellowship
?
F3
F3 + other grant
F3 & UCSB fellowship
F3
UW fellowship
Fulbright Fellowship
UCSB
UW
Male
Male
Japan
US
F3
F3
Postdocs
Satoshi Mitarai
Brandon Chasco
Publications (all attribute F3 support)
Siegel, D.A., B.P. Kinlan, B. Gaylord and S.D. Gaines, 2003: Lagrangian descriptions
of marine larval dispersion. Marine Ecology Progress Series, 260, 83-96.
Guichard, F.R., S. Levin, A. Hastings and D. Siegel, 2004: Toward a metacommunity
approach to marine reserve theory. Bioscience, 54, 1003-1011.
Gaylord, B., S.D. Gaines, D.A. Siegel and M. Carr, 2005: Marine reserves can exploit
life history and population structure to enable higher fisheries yields. In press,
Ecological Applications.
Kinlan, B., S. D. Gaines, and S. Lester. 2005, Propagule dispersal and the scales of
marine community process. Diversity and Distributions. In press.
Baskett, M. L., S. A. Levin, S. D. Gaines, and J. Dushoff. 2005: Marine reserve design
and the evolution of size at maturation in harvested fish. Ecological Applications. in
press.
Hilborn, R. Micheli, F. and DeLeo, G., 2006: Integrating Marine Protected Areas with
catch regulation. Canadian Journal of Fisheries and Aquatic Sciences, 63, 642-649.
Warner, R.R., S. E. Swearer, J. E. Caselle, M. Sheehy, and G. Paradis. Natal traceelemental signatures in the otoliths of an open-coast fish. In press, Limnology and
Oceanography.
Presentations
Siegel, D., C. Costello, S. Gaines, R. Hilborn, B. Kendall, S. Polasky, R. Warner, K.
Winters, 2003: Flow, Fish and Fishing: Sources and Implications of Uncertainty in
Nearshore Fishery Management. Presented at the 50th Eastern Pacific Ocean
Conference, Catalina Island, Sept. 2003.
Hilborn, R., 2003: The conflict between science and advocacy, Invited Presentation,
Western Society of Naturalists, Long Beach CA. November 2003.
Hilborn, R., 2003: Achieving sustainable fisheries, Invited Presentation – IFEMER
Laboratory Montpellier France, December 2003.
Hilborn, R., 2003: Achieving sustainable fisheries, Invited Presentation – London
Zoological Society, December 2003.
Kinlan, B P., D.A. Siegel, B. Gaylord, and S.D. Gaines, 2004: Marine Larval Dispersion
and Prediction in Coastal Fisheries Science. Presented at the 2004 AGU Ocean
Sciences Meeting, Portland OR. January 2004.
Siegel, D.A., B.P. Kinlan, B. Gaylord, and S.D. Gaines, 2004: Lagrangian descriptions
of marine larval dispersion. Presented at the 2004 ASLO/TOS Oceans Conference,
Honolulu, HI, February 2004.
Siegel, D.A. 2004: Applying LTER principals to the establishment of marine reserves in
coastal systems. Presented at the 4th NSF-LTER Symposium at the National Science
Foundation, Arlington VA, February 26, 2004.
Gaines,, S. D. A Seaweed's Perspective on Marine Reserve Design. Phycological
Society of America. Newport Oregon.
Siegel, D.A., 2004: Flow, Fish and Fishing. Seminar presented to the Biological
Sciences Department of the University of Southern California. March 9, 2004.
Costello, C., 2004: Spatial management of renewable resources under uncertainty.
Invited presentation at the Spatial-dynamic Models of Economics and Ecosystems
meeting, Trieste Italy, April 2004.
Gaines, S. D. Large Scale Patterns in Marine Ecosystems. University of Maryland. April
2004.
Hilborn, R., 2004: Achieving sustainable fisheries, Invited Seminar – Bren School of
Environmental Studies, U.C. Santa Barbara, May 2004.
Kendall, B., D. Siegel, C. Costello, S. Gaines, R. Hilborn, R. Warner, K. Winters, 2004:
Population Dynamics in a Stirred, not Mixed, Ocean. Ecological Society of America
meeting, Portland OR, August 2-6, 2004.
White, C., B. Kendall, D. Siegel, and C. Costello, 2004: Marine reserve spacing and
fishery yield: practical designs offer optimal solutions. Ecological Society of America
meeting, Portland OR, August 2-6, 2004.
Berkley, H., B. Kendall, D. Siegel, C. Costello, 2004: Fishery in a stirred ocean
sustainable harvest can increase spatial variation in fish populations. Ecological
Society of America meeting, Portland OR, August 2-6, 2004.
Gaines, S. D. The design of marine reserve networks. Association of Pacific Rim
Universities. August 2004.
White, C., B. Kendall, D. Siegel, and C. Costello, 2004: Marine reserve spacing and
fishery profit: practical designs offer optimal solutions. Western Society of Naturalists
meeting. November 11-14, 2004
Mitarai, S., D. Siegel, and K. Winters, 2004: Stochastic larval settlement in nearshore
marine system. AGU Fall Meeting, San Francisco CA, December, 2004.
Gaines, S. D. The design of marine reserve networks. University of Alaska, Juneau.
December, 2004.
Gaines, S. D. A larval biologist's perspective on fisheries management. University of
Alaska, Anchorage. December 2004.
Siegel, D.A., 2005: It’s Stirred, Not Mixed!! Role of Fluid Stirring in Aquatic Ecosystems.
Seminar presented to the Marine Sciences PhD program at UC Santa Barbara,
February 2005.
Siegel, D.A., 2005: It’s Stirred, Not Mixed!! Role of Fluid Stirring in Aquatic Ecosystems.
Plenary talk presented at the 2005 ALSO Meeting Salt Lake City, February 2005.
Siegel, D.A., Costello, C., Gaines, S.D., Hilborn, R.W., Kendall, Polasky, S., Warner,
R.R., Winters, K.B., 2005: Flow, Fish And Fishing: A Biocomplexity Project. 2005
ALSO Meeting Salt Lake City, February 2005.
Mitarai, S., Siegel, D. A., Winters, K. B., 2005: A numerical study of stochastic larval
settlement in nearshore environments. 2005 ALSO Meeting Salt Lake City, February
2005.
McGilliard, C., and Hilborn, R., 2005: Effects of larval dispersal at the interface of
Marine Protected Areas and traditional management regimes. American Fisheries
Society Annual Meeting, 2005.
Costello, C., 2005: Spatial Bioeconomics Under Uncertainty. Conference on “Spatial
Models in Economics and Ecology” Trieste Italy, April 2005.
Warner, R., 2005: Plenary. IndoPacific Fish Conference, Taiwan, May 2005
Costello, C., 2005: Can Reserves Increase Profits? Conference “Occasional workshop
on environmental economics” Santa Barbara CA, October 2005.
Acknowledgement
This material is based upon work supported by the National Science Foundation under
Grant No. 0308440.