BE/CNH: Disparate Scales of Process and Nearshore Fishery Management
OCE-0308440. $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 (University of Washington)
Steve Polasky (University of Minnesota)
Kraig Winters (University of California, San Diego)
Reporting Period: Year 5 (2007-2008)
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 the interaction between chaotic patterns of coastal
circulation, the life cycle of fished organisms, 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. (you repeat the above sentence)
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 three years 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). Also we conducted
collection and analysis of available empirical data sets to assess the validity and skill of
these models (Warner / Gaines).
In the last two years of the F3 project, we further advanced each of the three (Flow, Fish
and Fishing) modeling components, and began to synthesize them at higher levels. For
the flow part, more realistic flow simulations for the Southern California Bight (SCB)
have been employed so that we can apply the developed “Flow, Fish and Fishing”
framework to real-life applications (e.g., ongoing implementation plan of a network of
marine protected areas). The spatially-explicit population dynamics model accounts for
stochastic larval dispersal in a more realistic way, based upon the flow simulation model
products, and has revealed that stochastic larval dispersal is key to species
coexistence. Our economic models have been substantially improved in their efficacy
and applicability, and have been implemented in the simulated flow fields for the SCB.
In the following we summarize the modeling framework developed in the last year of the
F3 project, including their implications.
Results point to several exciting conclusions, including: (1) in the coastal environment,
the statistics of larval dispersal over the course of a single season are spatially
heterogeneous, reflecting interannual and seasonal regional circulation patterns, and
highly variable due to chaotic coastal eddy motions; (2) spatially-heterogeneous,
temporally-intermittent larval dispersal regulates the spatial heterogeneity in fish stock
dynamics; (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.
Modeling of Larval Connectivity in the Coastal Ocean
Scaling of Stochastic Connectivity
Eddy motions predominate in the variability of ocean circulation, and have been
considered to be a major source of stochasticity in settlement and recruitment events.
We clarified intrinsic stochasticity arising from coastal eddy motions in larval dispersal
by introducing simple scaling theory that counts the number of arriving eddies to
suitable habitats for a single spawning season (Siegel et al, 2008), as described below.
This scaling theory suggests that coastal eddy motions set a strong source of
uncertainty in connectivity of coastal marine species for a single spawning season even
when extreme abundance of larval production is available.
Larval connectivity can be modeled as a superposition of arriving larval “packets”
formed by coastal eddies (Siegel et al., 2008). The resulting settlement pattern will
depend on the total number of larval packets arriving for the domain, N ev, and the
spatial extent for each settling packet, δev (normalized by the domain size). A large
number of events, each providing settlers over relatively large spatial scales, will result
in a smooth connectivity pattern; whereas fewer, smaller sized events will result in a
patchy pattern of connectivity. The spatial coefficient of variation in number of
settlement packets, CVset, is a useful measure for assessing heterogeneity. If the
probability that a given packet lands on a particular site is δev and each event is
independent, then the expected number of packets arriving at a site is δev Nev and CVset
can be approximated using binomial sampling theory as.
CVset = [ (1-δev) / (δev Nev ) ]1/2
If Nev or δev decrease, the settlement pattern will become more stochastic. Other factors
can influence δev, such as bathymetry and spatial wind variations. Scale estimates for
Nev can be derived knowing the number of eddies in the domain that can advect larvae
(parameterized as the larval release duration, T, normalized by the eddy time scale, τ,
multiplied by the fraction of the coastline corresponding to the size of each eddy, 1/δev)
and the fraction of possible arriving packets that contain settling larvae, fsv, or
Nev = (T / τ) (1/δev) fsv.
The survivability fraction, fsv, will be controlled by a variety of factors, including the larval
development time course, larval mortality, late stage swimming, etc..
We demonstrated that the scaling theory can accurately account for eddy-induced
stochasticity in simulated dispersal patterns obtained from idealized coastal circulation
simulations of the California Current (Fig. We are currently working on a manuscript to
report the results (Mitarai 2008c).
Figure 1: Comparison of the coefficient of variation for the number of settlements among sites (CV set)
obtained from the idealized circulation simulations (symbols) and predicted by scaling theory (packet
model) as a function of the number of spawning seasons. For the circulation simulations, two seasonal
upwelling conditions are tested: a strong upwelling condition of summer (black symbols) and a weak
upwelling condition of winter (open symbols). Two different scenarios are examined for ontogenetic
vertical migration: staying near the top surface (circles) and migrate 30 m after 5 days from release
(triangles). The horizontal dotted line indicates CVset = 0.13, where connectivity can be approximated by
a smooth and homogeneous diffusion model.
The scaling relationship can be used to develop a “packet model” of larval connectivity
where Nev independent, equally-sized, settlement packets are superimposed to model
larval connectivity (Siegel 2008). The packet modeled connectivity retains the
stochastic character seen in the flow simulations and provides a method for including
transport stochasticity in spatial models of nearshore marine populations. The packet
model provides a means to quantify eddy-induced stochasticity in connectivity, and
gives new insights into the nature of larval dispersal and its potential for regulating
population dynamics of nearshore marine species.
Modeling of larval connectivity in the Southern California Bight
We have made significant progress in modeling larval connectivity in the Southern
California Bight (SCB). Connectivity among nearshore sites via advection of water
parcels is assessed using Regional Ocean Modeling System (ROMS) solutions for the
SCB produced by Dong and McWilliams (2007). Connectivity among 137 sites is
simulated each with a 5-km radius (Fig. 2 a) using a Lagrangian transition probability
density function (PDF) formalism (Mitarai et al, 2008a). The scale for each site is fine
enough to characterize spatial patterns in connectivity while remaining reasonable for
building a network of marine protected areas (MPAs). Connectivity is calculated by
simulating the trajectories of many larvae released from a given site (e.g., Siegel et al.
2008; Mitarai et al. 2008; 2008a; 2008b). Figure 2b shows a sample connectivity matrix
estimated using 30-day trajectories of all particles released from 1996 through 1999.
The simulated connectivity patterns are not homogeneous in space (Fig. 2 b). We
found that some sites are better sources (y-axis) or destinations (x-axis) than others. In
general, the mainland sites are good sources while both the Northern and Southern
Channel Islands (sites 64-97 and 98-137, respectively) are much poorer source sites
though they do receive significant larval fluxes from the mainland sites.
Figure 2: a) Nearshore sites and b) connectivity matrix linking the nearshore sites via advection using the
ROMS simulations. Each site has a 5-km. Here, the connectivity matrix quantifies the degree of inter-site
connectivity for a planktonic larval duration of 30 days. The solid black lines in panel b divide the
nearshore sites into three regions: mainland (i, j = 1, 2, .., 63), Northern Channel Islands (i, j = 64, 65, …,
97) and Southern Channel Islands (i, j = 98, 99, …,137).
The connectivity matrix shown above hold for a particular larval life history coupled with
a long-term view of the SCB circulation. Changes in larval life history (cf., planktonic
larval duration [PLD], spawning season or duration, larval behavior, etc.) or the exact
flow field used (long-term mean, single season, etc.) can alter connectivity pattern
(Siegel et al., 2008; Mitarai et al. 2008; 2008a; 2008c). We have examined changes in
larval connectivity patterns in the SCB caused by changes of spawning season (e.g.,
strong upwelling in summer, etc.), planktonic larval duration (a week to a few months),
climate state (El Niño vs. La Niña years) and larval behavior (surface following vs.
vertically migrating larvae). Larval connectivity changes dramatically from year-to-year
as well as depending on spawning season and PLD (Mitarai et al. 2008a). These
connectivity matrices assume that each site is an equally good source of and
destination for larval productivity. To assess that, information about habitat and egg
productions need to be included, as further described below.
Modeling Spatial Temporal Dynamics of Nearshore Fish Stocks
Fish population dynamics in the Southern California Bight
Figure 3: Spatial distribution of suitable habitat in
the SoCal Bight. Area and color represent a
combination of giant kelp cover where substrate is
less than 100m and the fraction of rock (a value of 1
is the most suitable habitat).
A fundamental question in marine ecology
is "What sets the spatial structure observed
in species distributions?" The spatial
heterogeneity seen in the distribution of
sessile nearshore marine species can be
attributed to two major forces; heterogeneity in suitable habitat and/or heterogeneity in
larval connectivity.
We are currently investigating the relative importance of habitat and connectivity in
setting adult spatial structure (Watson et al., 2008). Using spatial demographic models,
stock abundances can be simulated for a given larval connectivity (Fig. 2b) and habitat
distribution (Fig. 3). Equilibrium distributions of a fish stock (patterned after a rockfish)
are shown in Fig. 4. The left panel of Figure 4 shows the case where the larval
connectivity is spatially uniform and the suitable habitat is varied spatially while in the
right panel shows the case where the larval connectivity is varied spatially but the
habitat is uniform. These results illustrate that both connectivity and habitat can be
important in the spatially structuring fish abundance. Our goal is to understand how fish
life history and demographic characteristics determine whether flow or habitat will
spatially structure fish stocks. These results will be also of great value to the MPA
planning process for the SCB enabling decision support tools such as MARXAN to use
spatial connectivity and habitat information in a much more rigorous manner.
Figure 4: Equilibrium fish stock distributions patterned after a rockfish. The left panel shows the
case where the larval connectivity is spatially uniform while habitat is varied (as Fig. 3). The right
panel is where larval connectivity is varied spatially (as Fig. 2b) and the habitat is assumed to be
uniform. The color indicates the number of adults per 5-km site.
Species Coexistence due to Eddy-induced Variability in Larval Dispersal
Eddy-induced variability in larval dispersal can have important consequences in
structuring nearshore marine populations. One case where coastal eddy motions can
play an important role in marine population dynamics is in the case of interspecific
interactions where competition among larvae may be important for determining post-
settlement recruitment rates. Larvae from different spawning periods can “catch”
different eddies, resulting in different dispersal patterns on a year-to-year or generationto-generation basis. Hence, there is the possibility that larvae from an inferior
competitor will occasionally land in locations that are free of the superior competitor’s
larvae – if it happens often enough, the two species can coexist (Kendall 2008).
We demonstrated that coastal eddy motions can be a dominant mechanism enabling
marine species co-existence in an advective environment (Mitarai 2008b). Imagine two
identical species that disperse in an advective environment with initial conditions in
which one species is distributed upstream from the other. The two species are
demographically identical (e.g., the same mortality, same fecundity, same competency
time windows and post-settlement density dependence factors), and competing for
limited resources (or space). Here, the upstream species has a great advantage
because it can send more larvae to settling sites downstream due to the mean
advection in the system. We assessed the role of eddy-induced stochasticity in
enabling upstream invasions by comparing the two different cases i) where connectivity
matrices are given by diffusion models and ii) when connectivity matrices are given by
the packet model (described
above).
Figure 5: Predictions by a population dynamics
model along a hypothetical straight coastline
modeled after central California. There is a
mean southward (downward) flow in the
domain. Upper panels: spatio-temporal
patterns of two species' (A and B) population
dynamics when a) larval dispersal is described
as a diffusion process and b) larvae are
transported by coastal eddies as a coherent
packet. The color indicates the composition of
the population. The red indicates that 100% of
the population consists of the species A. The
blue, on the other hand, indicates 100% of
population consists of species B. Initially,
species A is introduced in upstream while B in
downstream. Species A and B compete for
recruitment at settling sites. Notice that there is
multi-decadal oscillations in species dominance
(indicated with blue and red ovals) along certain
portions of the model domain.
For the diffusion model case,
species coexistence is not
achievable because there is no mechanism that makes the upstream invasion of the
downstream species possible (Fig. 5 upper panel). The downstream species is nearly
extinct after several decades. For the packet model case, on the other hand, the
population dynamics model show that the two species can coexist (Fig. 5 lower panel).
Downstream species is able to invade upstream because their larvae can be
occasionally transported upstream as a coherent packet, not as a weak diffusion
processes, into areas where upstream species happen to be rare. The number of
successful settlements at a site will show substantial variation year to year, even without
any other sources of uncertainties. The “packet” transport of larvae by coastal eddy
motions, coupled with life history, can structure nearshore marine populations because
it adds strong inter-annual variation in settlements.
Economic Modeling of Optimal Harvest
Marine protected areas and profits
Predictions on the efficacy of marine reserves for benefitting fisheries differ in large part
due to assumptions of models of either intra-cohort or inter-cohort population density
regulating fish recruitment. We considered both processes, and examined using a
bioeconomic model how density dependent recruitment dynamics interact with harvest
costs to influence fishery profit with reserves. Reserves consolidate fishing effort,
favoring fisheries that can profitably harvest low-density stocks of species where adult
density mediates recruitment. Conversely, percentage coastline in reserves that
maximizes profit, and relative improvement in profit from reserves over conventional
management, decline with increasing harvest costs and the relative importance of intracohort density dependence. We quantitatively synthesized diverse results in the
literature, showed disproportionate effects on the economic performance of reserves
from considering only inter- or intra-cohort density dependence, and highlighted fish
population and fishery dynamics predicted to be complimentary to reserve
management.
Figure 6: Mean (surface) ±
maximum/minimum (grids) relative
fishery profit under optimal reserve
versus optimal conventional
management, as influenced by the
stock effect (θ) and inter- relative to
intra-cohort density dependent
recruitment processes (“Density
dependence”, D). Values greater
than one indicate reserves were
optimal and increased fishery profit;
values equal to one indicate
conventional management was
optimal. Grey region indicates
parameter space over which
reserves were never optimal.
Means and ranges summarize
results across all adult productivity
and adult natural mortality
parameter values.
We provided an analytic framework that quantitatively synthesizes disparate
conclusions of the literature on the economic efficacy of reserve-based management
(White 2008). Using a bioeconomic model of nearshore fish population and fishery
dynamics, we considered the stock effect and coupled inter-and intra-cohort density
dependent recruitment processes. We demonstrated how harvest costs and
recruitment dynamics interact nonlinearly to influence relative maximum profits between
reserve and conventional management. We identified past conclusions that result from
considering endpoints of a continuum of density dependent and stock effect conditions,
and related these results to each other and to results generated under interior biological
and economic conditions. We highlighted stock effect and density dependent conditions
– and discussed associated fisheries and species – suggested by the results to be more
favorable to reserve management. Finally, we outlined biological and socio-economic
factors important to the evaluation of fishery management that have yet to be
considered collectively.
Fishermen Travel Behavior and Effort Allocation
Fishing fleet effort and catch distribution
Due to imperfect knowledge of the biogeographical environment and issues of
uncertainty and confidentiality associated with fishery dependent data, fishermen within
a fleet are often considered homogeneous and average values are used to describe fish
and fishing in stock harvest models. We found that heterogeneity exists among
fishermen within a fishing fleet and we address the role of heterogeneity on fish stock
size and fleet catch. Analysis of catch and effort anomalies in California Department of
Fish and Game fish block data reveals fleets with large, tightly clustered portions of
below average effort and catch and smaller but more widely spread portions of far
above average effort and catch.
We found that individuals are consistent over time and that fishermen in different
portions of a fleet have distinctly different characteristics and behaviors. Most
importantly we found noticeable differences in both the spatial and temporal behavior
between those fishermen with consistently high catch per unit of effort (CPUE) and the
rest of the fleet (those with consistently low CPUE and those with highly variable
CPUE). While we do not have data on boat size, engine size, total years of experience,
or similar variables that one would expect to be highly correlated with CPUE, we found
that in many cases a fisherman’s fishing performance is consistent and have thus
developed a method for clustering members of a fishing fleet by their CPUE relative to
the entire fishing fleet (Fig. 7). Furthermore, we found that certain variables, both
environmental and fishery-specific, have a strong influence on expected CPUE. We
found that many of these variables that influence a fisherman’s behavior and
performance are non-linear, requiring flexibility in time trends, seasonal effects, and
environmental and fishery-specific variables and a modeling environment that allows for
a relaxation of these fixed parameters. We therefore choose a setting which allows us
to address a number of shortcomings of linear models, namely nonlinearity of
covariants, correlation of spatial and temporal observations, and heterogeneity among
individuals and segments of the fishing fleet.
When we compared aggregate (pooled) and disaggregate fishing fleet behavior
models we found that these differences between consistently high CPUE
fishermen and the rest of the fleet produce “average” conditions which may not,
in fact, be indicative of any members of the fishing fleet. Oftentimes the behavior
of the consistently high CPUE fishermen, a smaller percentage of the fishing
fleet, is overwhelmed by the behavior of the not consistently high CPUE
fishermen, the majority of the fleet. One significant impact of this heterogeneity
is the conclusion that the fish biomass removed by the below average portions of
the fishing fleets does not balance the consistently above average portions to
generate predicted “average” fleet catches. The imbalance suggests that harvest
models using average values for fishing effort and harvested biomass could
significantly underestimate fleet impacts on fish stocks.
Figure 7: Fleet catch per unit of effort rankings for fishermen with 6 or more years of experience in the
red sea urchin and market squid fleets and 5 or more years of experience in the spiny lobster fleet. Dotted
lines show cluster analysis cutoffs.
Integrating the F3 Biocomplexity Project
We have implemented most of the disparate pieces of “Flow, Fish and Fishing (F3)” and
have many other components under way, and have been working on 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 PIs are involved in public service aspects of the MPA
process (Gaines / Costello / Hilborn / Warner / 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 have been working 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 they 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.
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 (http://www.icess.ucsb.edu/~f3) where all
presentations, working papers, simulation codes, etc. are available. We also hold
annual whole project workshops.
Educational Activities
There are 10 graduate students (3 female) and one postdoc working on the F3
Biocomplexity project. We have been successful in 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 PIs. 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 a 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
Elizabeth Madin
John Lynam
Crow White
Heather Berkley
Brian Kinlan
Michael Robinson
Nicolas Guiterez
Tim Chaffey
James Watson
Andrew Rassweiler
UCSB
UCSB
UCSB
UCSB
UCSB
UCSB
UCSB
UW
UCSB
UCSB
UCSB
Female
Female
Male
Male
Female
Male
Male
Male
Male
Male
Male
US
US
Ireland
US
US
US
US
Uruguay
US
England
US
NSF & UCSB fellowships
NSF & DHS fellowships
F3
F3 + other grant
F3 & UCSB fellowships
Hertz fellow support
F3
Fulbright Fellowship
F3 + other grant
F3
other grant
Postdoc
Satoshi Mitarai
UCSB
Male
Japan
F3
Publications (all attribute F3 support)
2003 – 2006 publications
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.A. 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. Ecological
Applications, 15, 2180-2191.
Kinlan, B., S.D. Gaines, and S. Lester., 2005: Propagule dispersal and the scales of
marine community process. Diversity and Distributions, 11, 139-148.
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, 15,
882-901.
Warner, R.R., S E. Swearer, J.E. Caselle, M. Sheehy, and G. Paradis, 2005: Natal
trace-elemental signatures in the otoliths of an open-coast fish. Limnology and
Oceanography 50, 1529-1542.
Halpern, B., S.D. Gaines, and R.R. Warner, 2005: Habitat size, recruitment, and
longevity as factors limiting population size in stage-structured species. American
Naturalist 165, 82-94.
Ruttenberg, B.I., S.L. Hamilton, M.J.H. Hickford, G.L. Paradis, M.S. Sheehy, J.D.
Standish, O. Ben-Tzvi, and R.R. Warner, 2005: Elevated levels of trace elements in
cores of otoliths and their potential for use as natural tags. Marine Ecology Progress
Series 297, 273-281.
Stoms, D.M., F.W. Davis, S.J. Andelman, M.H. Carr, S.D. Gaines, B.S. Halpern, R.
Hoenicke, S.G. Leibowitz, A. Leydecker, E.M.P. Madin, H. Tallis, and R.R. Warner,
2005: Integrated coastal reserve planning: making the land-sea connection. Frontiers
in Ecology and the Environment 3, 429-436
Hilborn, R., F. Micheli, and G. DeLeo, 2006: Integrating Marine Protected Areas with
catch regulation. Canadian Journal of Fisheries and Aquatic Sciences, 63, 642-649.
Branch, T.A., R. Hilborn, A.C. Haynie, G. Fay, L. Flynn, J. Griffiths, K.N. Marshall, J.K.
Randall, J.M. Scheuerell, E.J. Ward, and M. Young, 2006: Fleet dynamics and
fishermen behavior: lessons for fisheries managers. Canadian Journal of Fisheries
and Aquatic Sciences 63: 1647-1668.
2007 publications
Mitarai, S., D.A. Siegel, and K.B. Winters, 2007: A numerical study of stochastic larval
settlement in California Current System. Journal of Marine Systems, 69, 295.
O'Connor, M.I., J.F. Bruno, S.D. Gaines, B.S. Halpern, S.E. Lester, B.P. Kinlan, and
J.M. Weiss, 2007: Temperature control of larval dispersal and the implications for
marine ecology, evolution, and conservation. Proceedings of the National Academy of
Sciences USA 104(4):1266-1271.
Grafton, R. Q., Kompas, Q., and Hilborn, R. W, 2007: Economics of Over exploitation
Revisited. Science. 318: 1601.
Hilborn, R., 2007: Reinterpreting the state of fisheries and their management.
Ecosystems. DOI: 10.1007/s10021-007-9100-5.
Hilborn, R., 2007: Managing fisheries is managing people: what has been learned?
Fish and Fisheries. 6: 285-296.
Walters, C. J., R. Hilborn, and R. H. Parrish, 2007: An equilibrium model for predicting
the efficacy of marine protected areas in coastal environments. Canadian Journal of
Fisheries and Aquatic Sciences 64:1009-1018.
Hilborn, R., 2007: Moving to Sustainability by Learning from Successful Fisheries.
Ambio. 36: 296-303.
Hilborn, R., 2007: Biodiversity loss in the oceans: how bad is it? Science 316: 12811282.
Hilborn, R., 2007: Defining success in fisheries and conflicts in objectives. Marine
Policy 31: 153-158.
Lester, S.E., S.D. Gaines, and B.P. Kinlan, 2007: Reproduction on the edge: large-scale
patterns of individual performance in a marine invertebrate. Ecology 88(9):2229-2239.
Gaines, S.D., B. Gaylord, L.R. Gerber, A. Hastings, and B.P. Kinlan, 2007: Connecting
places: the ecological consequences of dispersal in the sea. Oceanography 20(3):9099.
Lester, S.E., B.I. Ruttenberg, S.D. Gaines, and B.P. Kinlan., 2007: The relationship
between dispersal ability and geographic range size. Ecology Letters 10:745-758.
2008 publications
Siegel, D.A., S. Mitarai, C.J., Costello, S.D. Gaines, B.E. Kendall, R.R. Warner, and
K.B. Winters, 2008: The stochastic nature of larval connectivity among nearshore
marine populations. Proceedings of National Academy of Science, 105, 8974-8979.
Costello and Polasky. 2008. Optimal harvesting of stochastic spatial resources. Journal
of Environmental Economics and Management. 56(1):1-18.
White, C., B.E. Kendall, S. Gaines, D.A. Siegel, and C. Costello., 2008: Marine Reserve
Effects on Fishery Profit. Ecology Letters 11:370-379.
Gunderson, DR, Parma, AM, Hilborn, R, Cope, JM, Fluharty, DL, Miller, ML, Vetter, RD,
Heppell, SS, and Greene, HG, 2008: The challenge of managing nearshore rocky reef
resources. Fisheries 33: 172-179.
Hard, J. J., Gross, M. R., Heino, M., Hilborn, R., Kope, R. G., Law, R., and Reynolds, J.
D., 2008: Evolutionary consequences of fishing and their implications for salmon.
Evolutionary Applications 1: 388-408.
Grafton, R. Q., Hilborn, R., Ridgeway, L., Squires, D., Williams, M., Garcia, S., Groves,
T., Joseph, J., Kelleher, K., Kompass, T., Libecap, G., Lunden, C. G., Makino, M.,
Matthiasson, T., McLoughlin, R., Parma, A., San Martin, G., Saita, B., Schmidt, C.-C.,
Tait, M., and Zhang, L. X., 2008: Positioning fisheries in a changing world. Marine
Policy 32: 630-634.
de Mutsert, K., Cowan, J. H., Jr, Essington, T., and Hilborn, R., 2008: Reanalyses of
Gulf of Mexico fisheries data: Landings can be misleading in assessments of fisheries
and fisheries ecosystems. Proceedings of the National Academy of Sciences of the
United States of America. 105: 2740-2744.
Sethi, S. A., and Hilborn, R., 2008: Interactions between poaching and management
policy affect marine reserves as conservation tools. Biological Conservation 141: 506516.
In Press
Gaines, S.D., S.E. Lester, G. Eckert, B.P. Kinlan, R. Sagarin and B. Gaylord, In Press:
Dispersal and geographic ranges in the sea. In Marine Macroecology (J. Witman and
K. Roy, eds.), University of Chicago Press, Chicago, IL, in press.
White, C. and B.E. Kendall, In Press: Reassessment of equivalence in yield from
marine reserves. Oikos.
In Review
Walters, C.J., R. Hilborn, and R. Parrish, In Review: An equilibrium model for
predicting efficacyof Marine protected areas in coastal environment. Canadian
Journal of Fisheries and Aquatic Sciences.
McGilliard, C.R., R. Hilborn, In Review: Modeling no-take marine reserves in regulated
fisheries: assessing the role of larval dispersal distance. Canadian Journal of
Fisheries and Aquatic Sciences.
Jurado-Molina J.J., J.S. Palleiro-Nayar, and N.L. Gutirrez, In Review: Developing a
Bayesian stock assessment framework and decision analysis for the red sea urchin
fishery in Baja California, Mexico. ICES Journal of Marine Science.
Suresh A., S. Sethi, and R. Hilborn, In Review: The interaction between poaching and
management policy choice affect marine reserves as conservation tools. Biological
Conservation.
White, C., J. Watson, K.A. Selkoe, D.A. Siegel, D.C. Zacherl and R.J.Toonen, In
Review: Seascape Genetics Reveals Pattern Beneath the Chaos. Proceedings of the
National Academy of Sciences
White C., In Review: Density Dependence, Economics and the Efficacy of Marine
Reserves. Fish and Fisheries
Mitarai, S., Siegel, D.A., Watson J.R., Dong, C., and McWilliams J.C., In Review:
Quantifying connectivity in the coastal ocean with application to the Southern
California Bight, Journal of Geophysical Research - Oceans.
Presentations
2003 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.
2004 presentations
Kinlan, B., D. Siegel, B. Gaylord, and S. 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., B. Kinlan, B. Gaylord, and S. Gaines, 2004: Lagrangian descriptions of
marine larval dispersion. Presented at the 2004 ASLO/TOS Oceans Conference,
Honolulu, HI, February 2004.
Siegel, D., 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., 2004: A Seaweed's Perspective on Marine Reserve Design. Phycological
Society of America. Newport Oregon, 2004.
Siegel, D., 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., 2004: 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., 2004: 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., 2004: The design of marine reserve networks. University of Alaska, Juneau.
December, 2004.
Gaines, S., 2004: A larval biologist's perspective on fisheries management. University of
Alaska, Anchorage. December 2004.
2005 presentations
Siegel, D., 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., 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., Costello, C., Gaines, S., Hilborn, R., Kendall, B., Polasky, S., Warner, R.,
Winters, K., 2005: Flow, Fish And Fishing: A Biocomplexity Project. 2005 ALSO
Meeting Salt Lake City, February 2005.
Mitarai, S., Siegel, D., Winters, K., 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., 2004: Dispersal scales and connectivity among marine populations.
International Coral Reef Congress, Okinawa, 2005.
Warner, R., 2005: Plenary: Dispersal scales and connectivity among marine
populations. IndoPacific Fish Conference, Taiwan, May 2005.
Warner, R., 2005: Dispersal scales and connectivity among marine populations. Marine
Biological Laboratory, Woods Hole, November 2005.
Costello, C., 2005: Can Reserves Increase Profits? Conference “Occasional workshop
on environmental economics” Santa Barbara CA, October 2005.
2006 presentations
Siegel, D., Mitarai, S., White, C., Berkeley, H., Costello, C., Gaines, S., Hilborn, R.,
Kendall, B., Polasky, S., Warner, R., and Winters, R, 2006: Inherent Uncertainties in
Nearshore Fisheries: The Biocomplexity of Flow, Fish and Fishing. 2006 Ocean
Science Meeting, Honolulu, February 2006.
Mitarai, S., Siegel, D., Warner, R., and Winters, K., 2006: Investigation of the role of
larval behavior in determining nearshore habitat connectivity. 2006 Ocean Science
Meeting, Honolulu, February 2006.
Crow, W., Kendall, B., Siegel, D., and Costello, C., 2006: Fishing for Profit, Not Fish: A
Economic Assessment of Marine Reserve Effects on Fisheries. 2006 Ocean Science
Meeting, Honolulu, February 2006.
Berkley, H., Kendall, B., and Siegel, D., 2006: Oceanography Creates Stochastic Larval
Dispersal: Implications for Fishery Dynamics. 2006 Ocean Science Meeting, Honolulu,
February 2006.
McGilliard, C., 2006: Marine Protected Areas used with effort limits: the role of larval
biology and the older female. Quantitative Seminar, School of Aquatic and Fishery
Sciences, 2006.
McGilliard, C.R., and Hilborn, R., 2006. Modeling MPAs in regulated fisheries:
assessing the role of larval dispersal distance. NSF F3 Grant Working Meeting,
University of California Santa Barbara.)
Gutirrez N., 2006: Using the Barefoot Ecologist Program data in sea urchin individual
based models. San Diego Watermen's Association. December 8th 2006, San Diego,
CA.
2007 presentations
Chaffey, T., Mitarai, S., and Siegel, D., 2007: A Description of the Effects of Headland
on Marine Larval Dispersal Using Computational Models. American Society of
Limnology and Oceanography 2007 Annual Meeting, Santa Fe, February 2007.
Watson, J., Siegel, D., Mitarai, S., Oey, L., and Dong, C., 2007: Simulating Larval
Dispersal in the Santa Barbara Channel. American Society of Limnology and
Oceanography 2007 Annual Meeting, Santa Fe, February 2007.
Mitarai, S., Siegel, D., Warner, R., Kendall, B., Gaines, S., and Costello, C., 2007: A
Scaling Tool to Account for Inherent Stochasticity in Larval Dispersal. American
Society of Limnology and Oceanography 2007 Annual Meeting, Santa Fe, February
2007.
Caselle, J., 2007: Revealing patterns of larval connectivity: Bringing multiple tools to the
table. The 31st Larval Fish Conference, St Johns Newfoundland, July 2007
Costello, C., 2007: Optimal spatial fisheries management. American Fisheries Society
Annual Meeting, San Francisco 2007
McGilliard, C., 2007: Space and the state of the fishery in models of no-take marine
reserves. Thesis Defense Seminar; School of Aquatic and Fishery Sciences, 2007.
Gutirrez N, 2007: Analysis of the Barefoot Ecologist Program data and its relevance for
sea urchin stock assessment models. San Diego Watermen's Association. June 14th
2007, San Diego, CA.
White, C., J. Watson, K.A. Selkoe, R.J. Toonen, and D. Zacherl., 2007: Population
Connectivity of an Emerging Coastal Fishery Species and the Influence of ENSO on
Larval Dispersal-Mediated Gene Flow. Western Society of Naturalists. Ventura, CA
Kinlan, B.P., D. McArdle, S.D. Gaines, and K. Emery, 2007: Hierarchical Bayesian
analysis of the Spiny Lobster fishery in California. WSN Annual Meeting, Ventura, CA,
8-11 Nov 2007.
2008 presentations
Costello, C., 2008, Numerous presentations of the F3 model by Costello as he served
as member of Science Advisory Team for Marine Life Protection Act.
Warner, R., 2008, at Bamfield marine lab (in BC, Canada), to a meeting on marine
reserves in Brussels (TPAGE - European Union), and at the International Coral Reef
Symposium.
Robinson, M., 2008: Fishing fleet behavior: Causal structure modeling. Santa Barbara
Channel Long Term Ecological Research annual meeting, June 2008.
Robinson, M., 2008: Heterogeneity and consistency in commercial fishing fleets: Effects
on fleet modeling and fish stock management. Association of American Geographers
annual meeting, Boston, MA, April 2008.
Robinson, M., 2008: Location Choice and Expected Catch: Determining Causal
Structures in Fisherman Travel Behavior. University of California Transportation
Center annual meeting, January 2008.
Kinlan, B.P., 2008: Changes in kelp forest habitats in and around the Channel Islands
Marine Protected Areas." California Islands Symposium, Special Symposium on
Monitoring of the Channel Islands Marine Protected Areas, Oxnard, CA, 9 Feb 2008.
Mitarai, S., Siegel, D.A., Watson, J.R., Dong, C., and McWilliams, J.C., 2008: Simulated
coastal connectivity in the Southern California Bight. California Marine Life Protection
Act Initiative Oceanography Workshop (Department of Fish and Game, State of
California, El Segundo, 2008).
Mitarai, S., Siegel, D.A., Warner, R.R., Gaines, S.D., Kendall, B.E., Costello, C.J., and
Winters, K.B., 2008: Larval dispersal and population dynamics in the turbulent coastal
ocean. Ocean Sciences Meeting 2008 (American Society of Limnology and
Oceanography, Orlando, 2008).
Watson J R., Selkoe K., White C., Siegel D A., Mitarai S., Dong C., McWilliams J., 2008:
Simulating the effect of El nino of the genetic structure of nearshore species in the
Southern California Bight. 2008, Ocean Sciences meeting. Orlando. February 2008