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Intra-Guild Predation and Cannibalism in Pelagic Predators: Implications for the Dynamics, Assessment and Management of Pacific Tuna Populations

Progress reports (PDF): FY 2010, FY 2009, FY 2008, FY 2007

The proposed work seeks to formally assess the hypothesis that the production of economically important tuna stocks has been enhanced by the depletion of large-bodied predators. If this hypothesis is correct, then fisheries policies must consider the direct and indirect effects of fishing and their ultimate impacts on management measures and objectives. For instance, if the production of the highly-valued tuna fisheries has indeed been enhanced through the depletion of sharks, marlins, and large-bodied tunas, then the objective of maximizing fisheries yield and profits is best met through continued suppression of large-bodied predator abundance. If this hypothesis is deemed implausible, then recent claims that tuna stocks have declined to ~10% of their pre-fishing levels are not consistent with general principles of population biology and compensatory responses in food webs.

Researchers propose to evaluate the hypothesis that tuna productivity has been enhanced by predator depletion through an approach that combines a research synthesis of data on the feeding habits of large predators with simulation models of tuna populations. These models will be used to analyze alternative fishing strategies by first defining plausible representations of intra-guild predation and cannibalism, and then exploring the implications of these interactions on management strategies developed to meet fisheries objectives. The specific objectives are to:

(1) Quantify the magnitude of feeding on skipjack, yellowfin and bigeye tunas by conspecifics and heterospecifics throughout the Pacific Ocean
(2) Explore how that feeding varies temporally and regionally
(3) Identify tuna life history stages vulnerable to each predator by constructing prey-size-spectra for each predator species
(4) Couple age-based modeling approaches with bioenergetics models to estimate predation mortality for each stage of each tuna species
(5) Explore the implications of predation and possible predator depletion for policy-relevant reference points.

Proposed Activities

Research synthesis of pelagic food habits data
The goal is to describe the sources, magnitude, and variability in predation on bigeye, yellowfin and skipjack tunas, and to identify the life history stages when predation impacts are most important. Project researchers will use the existing large body of research conducted over the past half-century on the food habits of tunas, sharks and billfishes via a formal research synthesis (Cooper and Hedges 1994). It is anticipated that the existing body of research potentially suffers from confounding effects caused by differences in sampling methodology, data reporting and analysis across studies, as well as the absence of simultaneous sampling in different ocean regions. Yet, the advantage of the research synthesis approach is that by thoughtful statistical treatment of these data, which attempts to identify and remove confounding factors, it is possible to derive benefits from data already collected a trivial fraction of the cost of conducting novel research (Zeller et al. 2005). Moreover, because the literature on food habits studies date back to the 1950's, it is possible to test for long-term shifts in food habits that might have accompanied shifts in food web structure caused by fishing. The focus will be on the volumetric or mass contribution of skipjack, yellowfin, and bigeye tuna in the diets of potential predators. These predators include marlins (blue, black, white, striped), pelagic sharks (bigeye thresher, oceanic whitetip, silky, white, shortfin mako, blue) and tunas (skipjack, yellowfin, bigeye, albacore, bluefin).

The first stage of the analysis is to collect and digitize all information that is available on the food habits of these fishes. Project researchers have already identified 24 data sources. The second stage is to code each data source based on the breadth and detail of available information. Researchers propose an initial organizational framework that recognizes the hierarchical nature of the available data and permits a standardized approach to evaluate each data source.

Once data are entered and coded, two separate types of analyses are proposed. The first is to describe the mean contribution of each tuna (by life history stage) to the diets of each predator species, and to explore how this contribution varies by region, season, and across time periods. Classification and Regression Trees (CART) will be used as an initial exploratory tool to partition the variance in predation attributable to these effects. Subsequent analysis may include, but not be limited to, generalized additive models (GAMs) (Hastie and Tibshirani 1990) and generalized linear mixed models (GLMs).

The second analysis will use the detailed data available in contemporary studies to describe the prey-size spectrum of each predator species. The prey size spectrum describes the relationship between predator body sizes and the size range of food items eaten (Cohen et al. 1993). This relationship is thought to reflect a combination of morphological constraints on feeding (Magnuson and Heitz 1971) as well as foraging decisions presumably made to maximize the trade-offs between energy gain and handling time (Charnov 1976).

Population Modeling
Researchers propose to develop simulation models of skipjack, yellowfin, and bigeye tuna populations to assess the range of impacts that tuna, shark, and marlin predation may have on stock productivity and on the values of key fisheries reference points. A simulation modeling framework over an estimation modeling framework was chosen for several reasons. Foremost is the immense data requirements of estimation models that attempt to simultaneously estimate tuna and predator stock dynamics and their interaction terms, all in a size-specific manner. Such a modeling effort may indeed prove ultimately useful, but a simulation modeling approach such as the proposee here can be used to quickly screen alternative plausible representations of tuna dynamics, and thereby reveal whether a more intensive estimation modeling exercise is warranted.

This modeling approach combines two well developed quantitative tools - age-based population models and bioenergetics modeling. The former provides an ideal framework for this work, because it contains the minimum degree of model complexity (i.e., age- and size-structure) needed to address the hypothesis, and it also uses the same parameters and assumptions as those used in tuna stock-assessment models. This therefore permits an efficient and careful transfer of information about tuna demographic rates, and it allows presentation of the simulation modeling results in a context already familiar to stock assessment modelers. Bioenergetics modeling represents the best-developed tool for quantifying predator demand, having a long history in fisheries ecology (Stewart et al. 1981), and is increasingly being applied to explore large-scale shifts in marine communities (Essington et al. 2002, Williams et al. 2004).

Once plausible parameterizations of each species' population model is complete, researchers will perform Monte-Carlo simulation runs of the models where they estimate key biological and policy-relevant parameters for multiple possible model parameterizations. Because the historical status of tuna predators is a key uncertainty, they will avoid assigning probabilities to either end of the continuum, but instead consider alternative scenarios of "high historical biomass" and "low historical biomass". Policy-relevant variables include FMSY, BMSY, and the optimal allocation of effort across gears and purse-seine methods. Biologically relevant variables include maximum reproductive rate at low and high predator abundance, and ranges of biologically plausible historical and contemporary tuna biomasses.

Year 1 funding for this 2-year project estimated to be available mid-2006.

Charnov, E. L. 1976. Optimal Foraging: attack strategy of a mantid. American Naturalist 110:141-151.
Cohen, J. E., S. L. Pimm, P. Yodzis, and J. Saldaña. 1993. Body sizes of animal predators and animal prey in food webs. Journal of Animal Ecology 62:67-78.
Cooper, H., and L. V. Hedges, editors. 1994. The handbook of research synthesis. Russell Sage Foundation, New York, NY.
Essington, T. E., D. E. Schindler, R. J. Olson, J. F. Kitchell, C. Boggs, and R. Hilborn. 2002. Alternative fisheries and the predation rate of yellowfin tuna in the Eastern Pacific Ocean. Ecological Applications 12:724-734.
Hastie, T., and R. Tibshirani. 1990. Generalized Additive Models. Chapman Hall, London.
Magnuson, J. J., and J. G. Heitz. 1971. Gill raker apparatus and food selectivity among mackerels, tunas and dolphins. Fisheries Bulletin 69:361-370.
Stewart, D. J., J. F. Kitchell, and L. B. Crowder. 1981. Forage Fishes and Their Salmonid Predators in Lake-Michigan. Transactions of the American Fisheries Society 110:751-763.
Williams, T. M., J. A. Estes, D. F. Doak, and A. M. Springer. 2004. Killer appetites: assessing the role of predators in ecological communities. Ecology 85:3373-3384.
Zeller, D., R. Froese, and D. Pauly. 2005. On losing and recovering fisheries and marine science data. Marine Policy 29:69-73.

Project Investigators:

Dr. Timothy Essington
School of Aquatic & Fishery Sciences
University of Washington
P.O. Box 355020
Seattle, WA 98195 USA
Phone (206) 616-3698
FAX (206) 685-7471

Dr. Mark Maunder
Inter-American Tropical Tuna Commission
8604 La Jolla Shores Drive
La Jolla, CA 92037-1508 USA
Phone (858) 546-7027
FAX (858) 546-7133
email: mmaunder@iattc.org

Dr. Robert Olson
Inter-American Tropical Tuna Commission
8604 La Jolla Shores Drive
La Jolla, CA 92037-1508 USA
Phone (858) 546-7160
FAX (858) 546-7133
email: rolson@iattc.org

Dr. Enric Cortes
National Marine Fisheries Service
Southeast Fisheries Science Center
3500 Delwood Beach Road
Panama City, FL 32408 USA
Phone (850) 234-6541 ext. 220
FAX (850) 235-3559
email: Enric.Cortes@noaa.gov

Dr. James Kitchell
Center for Limnology
University of Wisconsin
680 N. Park Street
Madison, WI 53706-1492 USA
Phone (608) 262-2840
FAX (608) 265-2340
email: kitchell@wisc.edu
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This page updated October 4, 2010