Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site
1
Science
December 2011, vol. 334 (6063), pp. 1703-1706
http://dx.doi.org/10.1126/science.1212928
© 2012 American Association for the Advancement of
Science. All Rights Reserved.
Archimer
http://archimer.ifremer.fr
Global Seabird Response to Forage Fish Depletion—One-Third for the
Birds
Philippe M. Cury
1,*
, Ian L. Boyd
2,*
, Sylvain Bonhommeau
3
, Tycho Anker-Nilssen
4
,
Robert J. M. Crawford
5
, Robert W. Furness
6
, James A. Mills
7
, Eugene J. Murphy
8
, Henrik Österblom
9
,
Michelle Paleczny
10
, John F. Piatt
11
, Jean-Paul Roux
12,13
, Lynne Shannon
14
, William J. Sydeman
15
1
Institut de Recherche pour le Développement, UMR EME-212, Centre de Recherche Halieutique
Méditerranéenne et Tropicale, Avenue Jean Monnet, BP 171, 34203 Sète Cedex, France.
2
Scottish Oceans Institute, University of St Andrews, St Andrews KY16 8LB, UK.
3
Ifremer, UMR EME 212, Centre de Recherche Halieutique Méditerranéenne et Tropicale, Avenue Jean Monnet,
BP 171, 34203 Sète Cedex, France.
4
Norwegian Institute for Nature Research, Post Office Box 5685 Sluppen, NO-7485 Trondheim, Norway.
5
Branch Oceans and Coasts, Department of Environmental Affairs, Private Bag X2, Rogge Bay 8012, South
Africa.
6
College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK.
7
10527 A Skyline Drive, Corning, NY 14830, USA.
8
British Antarctic Survey, High Cross, Madingley Road, Cambridge CB3 0ET, UK.
9
Baltic Nest Institute, Stockholm Resilience Centre, Stockholm University, SE-106 91 Stockholm, Sweden.
10
Fisheries Centre, Aquatic Ecosystems Research Laboratory (AERL), 2202 Main Mall, The University of British
Columbia, Vancouver, BC, Canada V6T 1Z4.
11
U.S. Geological Survey, Alaska Science Center, 4210 University Drive, Anchorage, AK 99508, USA.
12
Ecosystem Analysis Section, Ministry of Fisheries and Marine Resources, Lüderitz Marine Research, Post
Office Box 394, Lüderitz, Namibia.
13
Animal Demography Unit, Zoology Department, University of Cape Town, Private Bag X3, Rondebosch, Cape
Town 7701, South Africa.
14
Marine Research Institute and Zoology Department, University of Cape Town, Private Bag X3, Rondebosch,
Cape Town 7701, South Africa.
15
Farallon Institute for Advanced Ecosystem Research, Post Office Box 750756 Petaluma, CA 94952, USA.
*: Corresponding authors : Philippe M. Cury, email address : philippe.cury@ird.fr ; Ian L. Boyd, email address :
ilb@st-andrews.ac.uk
Abstract :
Determining the form of key predator-prey relationships is critical for understanding marine ecosystem
dynamics. Using a comprehensive global database, we quantified the effect of fluctuations in food
abundance on seabird breeding success. We identified a threshold in prey (fish and krill, termed
“forage fish”) abundance below which seabirds experience consistently reduced and more variable
productivity. This response was common to all seven ecosystems and 14 bird species examined within
the Atlantic, Pacific, and Southern Oceans. The threshold approximated one-third of the maximum
prey biomass observed in long-term studies. This provides an indicator of the minimal forage fish
biomass needed to sustain seabird productivity over the long term.
Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site
2
Introduction
Public and scientific appreciation for the role of top predators in marine ecosystems has
grown considerably, yet many upper trophic level (UTL) species, including seabirds, marine
mammals and large predatory fish, remain depleted owing to human activities (1-4).
Fisheries impacts include direct mortality of exploited species and the more subtle effects of
altering trophic pathways and the functioning of marine ecosystem (5). Specifically, fisheries
for lower trophic level (LTL) species, primarily small coastal pelagic fish (e.g. anchovies and
sardines), euphausiid crustaceans (krill) and squid (hereafter referred to as “forage fish”),
threaten the future sustainability of UTL predators in marine ecosystems (6, 7). An increasing
global demand for protein and marine oils contributes pressure to catch more LTL species
(8). Thus, fisheries for LTL species are likely to increase while the consequences of such
activity remain largely unknown at ecosystem scale. It remains challenging, however, to
assess fishing impacts on food webs because numerical relationships between predators
and prey are often unknown, even for commercially valuable fish (9, 10). Ecosystem models
and ecosystem-based fisheries management, for which maintaining predator populations is
an objective (2, 11, 12), will remain controversial until these relationships are more fully
quantified.
To improve our understanding of the effects of LTL fisheries on marine ecosystems, more
information on predator-prey relationships across a range of species and ecosystems is
required (6). Seabirds are conspicuous members of global marine ecosystems. Many
aspects of seabird ecology have been measured consistently for decades, encompassing
ecosystem change at multiple scales (13). Though substantial long-term datasets on seabird
breeding success have been compiled for many taxa in several marine
4
ecosystems around the world (14-16), for relatively few has independent
information on prey availability been concurrently obtained. For those where
prey data are available, temporal covariance in predator and prey parameters
suggests that seabirds can be used as indicators of forage fish population
fluctuations (7, 16, 17). In the present study we use data collected
contemporaneously over multiple decades from seabirds and forage fish to
test the hypothesis that the form of the numerical response between seabird
breeding success and forage fish abundance is consistent across species and
ecosystems. Data were selected based on the duration of the time series for
both seabirds and forage fish, and high spatial and temporal congruence
between the seabird data and the fish population data. Seabirds with strong
dependencies on the monitored forage fish population were selected. We
compiled data from 19 time-series covering 7 marine ecosystems, 9 sites and
14 seabird species and their major prey (Fig. 1, Table S1). The dataset
included 438 data points spanning 15-47 colony-years per breeding site (Table
S1). The abundance of principal prey for each seabird species was estimated
independently of the data collected for the birds, usually as part of population
assessments conducted in support of fisheries management (Table S1).
5
Fig. 1. Map of the distribution of seabird and prey species considered in our
analysis.
To examine empirical relationships between seabird breeding success and
prey abundance, we used non-parametric statistical methods that facilitate
non-linear modeling by making no a priori assumptions about the form of the
relationships (Generalized Additive Models or GAMs). Initially, each time-
series (seabird breeding success and prey abundance) was normalized by
expressing the measurements as the number of standard deviations from the
mean; this enables robust comparisons across species and ecosystems. Once
the numerical relationship was established, we used a change-point analysis
(sequential t-tests that find the most likely point at which the slope of breeding
success changes in relation to prey abundance) to identify thresholds within
non-linear relationships (18, Fig. 2A). A bootstrap analysis was used to
calculate confidence intervals of the threshold and the variance in seabird
6
breeding success was calculated for each prey abundance class. Last, a
selection of a priori parametric models ranging from linear, sigmoid,
asymptotic to hierarchical (Table S2) was fitted to the general relationship. The
most parsimonious model was then used to fit the relationship between
seabird breeding success and forage fish population size for each ecosystem
(pooling all species) and each seabird species (pooling all ecosystems).
Across ecosystems, seabird breeding success showed a non-linear response
to changes in prey abundance (Fig. 2A). The threshold at which breeding
success began to decline from the asymptote was not significantly different
from the long-term mean of prey abundance (range -0.30 and +0.13 standard
deviation of the mean, Fig. 2A). The threshold was 34.6% (95% confidence
interval 31% to 39%), or approximately one-third of the maximum observed
prey abundance. The coefficient of variation between the different thresholds
among species and ecosystems was 28% (Table S1). All time series were of
sufficient duration to identify the threshold (detection is possible after 13 years
of observation, Fig. S1) and the maximum biomass (detection is possible after
11 years, Fig. S2). Variance in breeding success increased significantly (F-
test, p<10
-4
) below the threshold of prey abundance (Fig. 2B). Fitting
parametric models to individual responses showed that there was a similar
inflection point and asymptotic values across ecosystems and species (Fig.
2C, Fig. 2D, Fig. 3), indicating that the functional form was a general feature of
the seabird–forage fish relationship.
