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Running Head: The Ecology of Information
THE ECOLOGY OF INFORMATION: AN OVERVIEW ON THE
ECOLOGICAL SIGNIFICANCE OF MAKING INFORMED DECISIONS
Kenneth A Schmidt1,*, Sasha R. X. Dall2, Jan A. van Gils3
1 Department of Biological Sciences, Texas Tech University, MS 3131, Lubbock, TX, 79424, USA
2 Center for Ecology & Conservation, School of Biosciences, University of Exeter, Tremough, Penryn,
TR10 9EZ, UK TR10 9EZ
3 Department of Marine Ecology (MEE), NIOZ Royal Netherlands Institute for Sea Research, P.O. Box
59, 1790 AB Den Burg, Texel, and Department of Plant-Animal Interactions (PAI), Centre for Limnology,
Netherlands Institute of Ecology (NIOO-KNAW), Rijksstraatweg 6, AC Nieuwersluis, The Netherlands
* Corresponding Author:
Kenneth Schmidt [email protected] FAX
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ABSTRACT
Information is characterized as the reduction of uncertainty and by a change in the state of a receiving
organism. Thus, organisms can acquire information about their environment (or future environment) that
reduces uncertainty and increases their likelihood of choosing a best-matching strategy. We define the
Ecology of Information as the study of how organisms acquire and use information in decision-making
and its significance for populations, communities, landscapes, and ecosystems. As a whole, it
encompasses the reception and processing of information, decision-making, and the ecological
consequences of making informed decisions. The first two stages constitute the domains of, e.g., sensory
ecology and behavioral ecology. The exploration of the consequences of information use at larger spatial
and temporal scales in ecology has generally lagged behind the success of these other disciplines. In our
overview we characterize information, review statistical decision theory as a quantitative framework to
analyze information and decision-making, and discuss some potential ecological ramifications. Rather
than attempt a superficial review of the enormity of the scope of information we highlight information use
in three areas: breeding habitat selection, interceptive eavesdropping and alarm calls, and information
webs. Through these topics we discuss specific examples of ecological information use and the emerging
ecological consequences. We emphasize recurring themes: information is collected fromthrough multiple
sources, over varying temporal and spatial scales, and in many cases links heterospecifics to one another.
This leads to questions where further development is needed: (1) how are information sources are
integrated and prioritized, (2) how does the spatial and temporal correlation between when and where
information is obtained and acted upon affect behavioral strategies, population processes, and ecological
interactions, (3) how best to integrate interaction webs and information webs between organisms.
Keywords: alarm calling, Bayesian updating, breeding habitat selection, eavesdropping, information,
predation risk, statistical decision theory
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INTRODUCTION
The Ecology of Information is the study of how organisms acquire and use information in decision-
making to manage their lives of, e.g., finding food, selecting habitats, avoiding predators, and allocating
effort to current and future reproductive success, and its significance for populations, communities,
landscapes, and ecosystems. It is a burgeoning and integrative field that melds together the various
disciplines that deal with the reception and processing of information on the one hand and the ecological
and evolutionary consequences of making informed decisions on the other hand (Fig. 1). Information is
considered one of the central biological concepts of the twentieth century (Maynard Smith 2000,
Jablonska 2002) and is critical to the adaptive process (Plotkin 1997, Dall et al. 2005). The concept of
information (or its various subdivisions) as related to the fields of animal behavior and ecology has been
reviewed at least six times since 2004 (Danchin et al. 2004, Dall et al. 2005, Vos et al. 2006, Seppänen et
al. 2007, Bonnie and Earley 2007, and Valone 2007), and as a more general concept in biology by
Maynard-Smith (2000) and Jablonka (2002).
This set of excellent review and synthesis papers has exposed the concept of information to many
ecologists, and has taken the first important step of presenting, defining, and circumscribing the role(s) of
information in ecology as well as illustrating a diverse set of ecological contexts and organisms that
utilize information. They have been extremely successful in this regard; however, the limits of a single
review article leaves little room for exploring ecological processes or the relevance of information for
processes and patterns within populations, communities, and ecosystems – i.e., the spatial and temporal
scales that form the bulk of the ecological studies (Fig. 1). Thus, with a strong backdrop of information
in ecology already in place, there still remains a very real and urgent need to bridge the gap between the
behavioral, ecological, and conservation sciences (Fryxell and Lundberg 1998, Sutherland and Norris
2002) with information at its core. By way of this overview, we hope to highlight some areas of
successful incorporation of information as well as areas where further development is needed. However,
the scope of information in evolution and ecology is enormous and cannot be covered in any reasonable
manner in a single paper (or indeed a featured set of papers); likewise, the upsurge of interest as catalyst
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for recent reviews means that a certain amount of redundancy is unavoidable. We have taken the strategy
of selecting a narrow set of topics to discuss the implications that are more relevant to populations,
communities, landscapes, and ecosystems that have not necessarily been the main subject material for
past reviews. But this also means we barely scratched the surface of the functional role of information in
ecology.
Characterizing information
An abstract property of events and entities that make their characteristics predictable to
individuals… [It] enables…individuals to make choices, select their activities …appropriately for
their needs and opportunities. (Smith 1977:193)
This passage of Smith (1977) expresses two elements that are typically used to characterize information:
(1) the reduction of uncertainty, i.e., information is that which makes the world more predictable
(syntactic view of information: Shannon and Weaver 1949, Danchin et al. 2004) and (2) change in the
state of a receiver in a functional way (semantic view: Blumstein and Bouskila 1996, Jablonka 2002; Dall
2005). Heterogeneity and variability limit an organism’s ability to possess complete knowledge of the
state of its current world or anticipate future conditions, and hence choose an appropriate strategy for the
actual set of conditions it will encounter. In the face of this uncertainty organisms can acquire
information about their physical and biotic environment (or future environment) that reduce uncertainty
and increase their likelihood of choosing a best-matching strategy. Implicit in this description is that for
information to exist and to have fitness consequences there must be both variation in environmental
conditions (e.g., states) and in phenotypic strategies (Stephens 1989). Moreover, it assumes organisms
make adaptive choices under the existence of constraints, a central premise of the field of behavioral
ecology (Mitchell and Valone 1990). While this last premise is demonstrably false in many cases, it is
often so because the genome or phenotypic plasticity fails to precisely track a changing environment (or
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correctly perceive a constant environment). Ecological traps and information disruption are two
anthropogenic causes for these events that we discuss throughout the paper.
Terminology: Box 1 provides a glossary for types of information discussed throughout that is
based on and expanded by Wagner and Danchin (this issue). Information can come from almost any
source: an organism’s abiotic environment (e.g., physical cues) biotic environment (e.g., signals and
biological cues from con- and heterospecifics), perception of its own internal state (e.g., hunger,
motivation), social learning, and trial and error experience. Furthermore, even when sources of
information are private, such as state or motivation, information can become public through indirect
means following the pathway described by Seppänen et al. (2007; Box 1). For example, Wong et al.
(2005) demonstrated that sand fiddler crabs (Uca pugilator) use observations of threat-induced responses
of neighbors (actions, stage 2 in Seppänen et al. 2007; Box 1) to guide their own refuge-seeking behavior.
Breeding birds may preferentially settle on territories where conspecific reproductive success
(consequences, stage 3 in Seppänen et al. 2007; Box 1) was high in prior years. Public information based
on the actions or consequences of other individuals (i.e., social information; Box 1) may be actively
sought out by both conspecifics and heterospecifics. It is possible that the quality of information degrades
through this sequence (Giraldeau et al. 2002, van Bergen et al. 2004, Seppänen et al. 2007).
Alternatively, observations of the primary observer’s actions and consequences may provide higher
quality information (i.e., more reliable cues) since the primary observer confronts ambiguity (e.g., errors
in signal detection; Bradbury and Vehrencamp 1998) or uncertainty (Stephens 1989), and its actions may
be influenced by gambles (e.g., risk-sensitive behavior; Stephens and Krebs 1973) or seeking insurances
(Dall and Johnstone 2002) that secondary observers may wish to avoid.
Lastly, it is important to distinguish between the source of information and its content; our
terminology refers only to the former. It is entirely possible that a source of information that is social in
origin (e.g. the feeding performance of flockmates) can inform about non-social issues (e.g. the amount of
food in the environment). Identifying sources is important as, e.g., social information has unique
implications for ecology and evolution, such as cultural evolution (Danchin et al. 2004) or information
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webs and the consequences of their deterioration (Vos et al. 2006, Holt 2007; Seppänen et al. this issue;
see Concluding Discussion). However, in other contexts the type of information can be safely ignored
relative to its content. In the later sections that discuss ecological processes we often do just that: focus
more on the content than the source of information.
Information, uncertainty, and phenotypic variation: Phenotypic variation is an outgrowth of
spatiotemporal variation in the environment and its predictability, i.e., information (Levins 1968,
Donaldson-Matasci et al. 2008, Lachman et al. this issue). This is especially true of phenotypic plasticity;
however the genome itself is a record of the past success of heritable strategies. Thus phenotypic
variation is influenced by (1) past information stored in the form of allelic (genetic) variation (2)
maternally-acquired cues (Massot and Clobert 2000, Mathis et al. 2008) capable of producing epigenetic
effects in one or more future generations (Gilbert and Epel 2008), and (3) current signals or cues that
produced variation through norm of reaction, polyphenism, or behavioral plasticity. A discussion of the
evolution of plasticity (and the related concept of bet-hedging as a response to uncertainty) would go far
beyond our overview. But we note that the form of plasticity will be influenced, in part, by the degree to
which the environment varies (spatiotemporal correlation) and the relative cost of incorrectly matching
your strategy to the environment. If change is slow (e.g., ponds tend be fishless or not during a
cladoceran’s lifetime) then irreversible developmental shifts (protective crest development) may be
favored over a sophisticated perceptual system that is costly to maintain. On the other hand, behavioral
flexibility may be favored if changes are rapid and frequent within an individual’s lifetime, e.g., nest
defense or broken wing display in response to predators that frequently move in and out of the vicinity of
a plover’s nest. We largely restrict our overview to behavioral plasticity. Behavior has attracted far
greater attention from the field of statistical decision theory (next section), and is perceived as a more
important proximate causal agent of higher level ecological processes. However, this perception may be
more of a statement about how the field of biology is fractionalized.
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Statistical decision theory - In their review, Dall et al. (2005) promote statistical decision theory (SDT)
as a quantitative framework from which to analyze the use of information by organisms (also see
McNamara and Houston 1980, McNamara et al. 2006, and Oikos v.112, issue 2). At its heart is the use of
Bayesian methods (specifically Bayes’ theorem for calculating conditional probabilities) to explore how
organisms integrate prior expectations based on personal experience and evolutionary history (i.e., genetic
information) with new information to arrive at a revised, posterior expectation. Take the example of mate
choice: choosy females are assumed to have (perfect) knowledge of the distribution of male quality (e.g.,
parenting skills, parasite loads) in a population, whereas the quality of an individual male is uncertain and
must be sampled through observation of song, display, etc. (Getty 1996, Luttbeg 1996). The value of
sampling information lies in the formation of a revised posterior expectation of the individual male’s
quality that reflects reduced uncertainty associated with possible outcomes (Dall et al. 2005). We direct
our readers to reviews on the SDT framework (Dall et al. 2005) and empirical studies of Bayesian
behavior (Valone 2006; half the issue is in fact devoted to Bayesian foraging) for recent updates in this
field and it’s important to the ecology of information. We make reference to SDT throughout, but for
brevity we do not duplicate the material in these reviews.
Two salient questions not directly addressed in the reviews are: (1) what is the relationship
between information updating and information use and (2) how do alternatives to Bayesian updating
compare? To address the former, it has been suggested (to us) that SDT tackles only the question of how
information is updated and not how it is used (and thus its relevance for ecological processes). This may
be a reaction to how SDT has been used in the past, for the statement is certainly not true. For example,
van Gils (this issue) demonstrates that the Bayesian Potential Value Rule (Olsson and Brown 1996)
predicts the pattern of area-restricted search when foraging in a spatially correlated environment. For the
first time, we have a realistic theoretical representation of this behavior. Schmidt and Whelan (this issue)
used SDT to predict optimal renesting behavior in single-brooded birds. One prediction of their model
they called the Renester’s Paradox: habitats with greater nest failure that require more nest attempts, on
average, to successfully raise a brood are the very same that are selected for fewer nests attempted.
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Furthermore, they show how uncertainty surrounding habitat quality and the process of information
updating links changes in the quality or proportion of one habitat type to behavior in the other habitat.
Kokko and Sutherland (2001) modeled breeding habitat selection combined with habitat
degradation without changing preferences (i.e., an ecological trap scenario). Notably, they demonstrated
that variation in how priors are governed can greatly alter the threat of extinction and place very different
demands on management or intervention. Their model incorporated rules-of-thumb (e.g., imprinting or
learned preferences) rather than take an explicit Bayesian approach. Behavioral rules may be common
(McNamara and Houston 1980, Bouskila and Blumstein 1992; see next section), and often perform close
to optimal Bayesian solutions when the two have been compared (e.g., Beauchamp 2000, Welton et al.
2003). However, this may not be true when the circumstances under which the rule evolved have
changed thus generating evolutionary traps (Schlaepfer et al. 2002; and see Conservation biology and
ecological traps).
There is little doubt that SDT (especially Bayesian approaches) need to be expanded beyond a
handful of contexts. To date, Bayesian methods have mostly found their way in “simple” short-term tasks
such as foraging or predation-avoidance (e.g., Rodríguez-Gironés and Vásquez 1997, Olsson and
Holmgren 2000, van Gils et al. 2003). But even here one may ask to what extent do solutions to problems
such as patch departure rules influence populations, communities, landscapes, and ecosystems? Olsson
and Brown (this issue) give us glimpse into the future by examining how information states (e.g.,
Bayesian vs. fixed-time foragers) sculpt the resource distribution in their environment in ways that may
promote or prevent species coexistence. Following Olsson and Brown’s lead in incorporating Bayesian
approaches and behavioral strategies in population and community models (e.g., Fryxell and Lundberg
1998, Sutherland and Norris 2002) will begin to close the gap between information updating and
ecological processes at higher scales.
From information to an Ecology of Information – That information use can have significant population
consequences is demonstrated in this section using a quintessential ecological question: population
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persistence and the distribution and abundance of individuals (we focus only on persistence here). We
start with a model of metapopulation persistence in anwithin information-free world (Bascompte et al.
2004). We do not fault the authors for this; rather they developed an elegant and useful model to
illustrate the relationship between the persistence of a stochastic patchy population network and the
number of habitat patches connected through dispersal. Under the assumptions 1) that patches experience
periods of density independent population growth ( > 1; good years) or decline ( < 1; bad years) with
equal probability, 2) patches are decoupled with respect to temporal variability (i.e., good and bad years
are assigned to patches independently), and 3) patches are spatially coupled through even dispersal
events, the geometric growth rate of a population network, GEOM, composed of n patches can be
approximated as the spatial-arithmetic mean growth rate, ARITH, minus a sampling error for small n. If for
any habit patch good and bad years are equally likely and uncorrelated there is no information available to
choose a patch. However, if patch quality is temporally correlated (regardless if good and bad years
remain equally likely in the long term) then previous experience informs an individual of the likelihood
the current conditions will persist. Correlation makes information available; however the organism still
requires the means of detecting, processing, and using the information. Thus, even in a temporally
correlated world even dispersal is uninformed behavior. The result is that regardless of the number of
patches (or the presence of temporal correlation), GEOM will never exceed ARITH such that if ARITH < 1.0
the metapopulation quickly becomes extinct (Fig. 2). An alternative is to adjust patch fidelity based on
prior experience: return to (or stay at) patches that experienced high productivity the prior year and vacate
patches that had poor productivity the prior year. This win-stay:lose-switch (WSLS) rule performs no
better than even dispersal in a world lacking temporal correlation and soin which prior experience is not
informative. However, the combination of the WSLS rule and temporal correlation (experience is
informative) has dramatic consequences on the persistence time of the metapopulation (Schmidt 2004;
Fig. 2). While we detail this single example, other excellent studies drive home the point that models of
ecological processes built around partiallyimperfectly informed organisms often behave dramatically
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different from information-free or perfect information scenarios (e.g., Brown et al. 1999, Vos et al. 2001,
Donahue 2006), neither of which is realistic or likely to be common.
INFORMATION AND ECOLOGICAL PROCESSES
In this section we expand on information use in three areas: (1) Ecological developmental biology briefly
considers environmental cues that direct phenotypic variation in morphology and physiology, (2)
Breeding habitat selection, which makes use of multiple sources of information collected over varying
temporal and spatial scales, and (3) Alarm calling and heterospecific information transfer within a
landscape context.
Ecological developmental biology – Some decisions in an organism’s life are made only once and are
irreversible. These include selecting among alternative developmental endpoints (polyphenism) and life-
cycle progressions timing (e.g., diapause, metamorphosis) that directly or indirectly affect morphology
and physiology in addition to behavior. These decisions are guided in part by environment cues (e.g.,
photoperiod, temperature, nutrition, predation risk, social proximity) and concern the emerging field of
ecological developmental biology (Gilbert 2001, Gilbert and Epel 2009). Examples include predator-
induced shell morphology in the barnacle Thais lamellosa (Palmer 1985), nutrition-induced polyphenism
in Nemoria arizonaria caterpillars that develop a cuticle resembling either an oak catkin or oak twig
depending on the season in which they hatch (Greene 1989), and vibrational assessment of predation risk
and premature hatching in embryo red-eyed tree frogs, Agalychnis callidryas (Warkentin et al. 2007); for
many other examples see reviews in (Pechenik et al. 1998, Gilbert 2001, Relyea 2007).
From an information perspective there may be little fundamental difference between these single,
irreversible decisions and rapidly repeatable and reversible behavioral decisions: the framework of SDT
can apply to either. For instance, Warkentin et al. (2007) couched their study of premature hatching in
red-eyed tree frogs as a signal detection problem: balancing the costs to frog embryos of missed cues
(snake predation on embryos) and false alarms (greater susceptibility of premature hatchlings to aquatic
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predators. At the same time, developmental decisions have somewhat unique circumstances. In many
cases there is no direct assessment of the future environment (e.g., aquatic ↔ terrestrial) and thus limited
opportunity for learning. On the ecological side, there may be large latent effects stemming from
choosing an alternative fixed phenotype or altering developmental timing (Pechenik 2006). These effects
may capable of producing large population and community consequences. Lastly, global climate change
and other anthropogenic effects are rapidly altering the probabilistic linkagesconditional probabilities
between (future) state and proximate cue, and the reliability of chemical cues, which often direct
development progression, is being disrupted by anthropogenic substances.
Breeding habitat selection -Spatial heterogeneity and temporal variability in the underlying factors that
contribute to breeding productivity (e.g., food abundance, predation risk) are widespread (e.g., Lewis &
Murray 1993, Schmidt et al. 2006, Simpson et al. 2008). Choice of breeding location has high fitness
consequences, and it is not surprising that organisms acquire information to guide their decisions.
Although selecting a breeding location within a hierarchy of landscapes, habitats, territories, and breeding
sites (e.g., nest sites, dens) differs considerably among taxa, broadly speaking the potential sources of
information and their use are likely to generalize (Box 2). For instance, social information, based on
presence (conspecific attraction; Stamps 1988, Fletcher 2006) or performance (habitat copying; Clobert et
al. 2001) may be advantageous because they are integrative measures, provide greater sampling power
(i.e., more independent sources), and, in the case of post-reproductive cues may reveal the consequences
of conspecifics’ decisions. Thus, prospecting (i.e., gathering local information on, e.g., reproductive
success; Reed et al. 1999, Ward 2005) is likely to be an important behavioral strategy that is implemented
throughout the year or at least those critical periods when information is least costly and most readily
available. Recent reviews have highlighted the apparent ubiquity of prospecting in birds where it has
been best studied (Reed et al. 1999). Among vertebrates, studies have shown they use conspecifics
presence, density (Cote and Clobert 2007), and reproductive productivity or its correlates (e.g., post-
breeding singing rates, Betts et al. 2008; quality/quantity of fledglings produced, Doligez et al. 2002,
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Parejo et a. 2007) when choosing breeding sites or to find suitable habitat using phonotoaxis and
orientation (Diego-Rassila 2004). These cues may provide high quality information on habitat or site
quality and may be used to reduce search and settlement costs (Stamps 2001, Fletcher 2006). In addition,
proximate cues, e.g., presence versus density, may provide unique or complementary information on
different components of habitat (e.g., suitability versus intraspecific competition). Moreover, these
strategies are not limited to cues from conspecifics; cues from heterospecifics may also be used for these
same or alternative types of information or as cost saving strategies (Mönkkönen and Forsman 2002,
Diego-Rassila et al. 2004, Seppänen et al. 2007). Prospecting may also include assessment of habitat
components, such as predator activity and food availability, and breeders may eavesdrop on inadvertent
public cues, such as vocalizations of predators (Emmering and Schmidt in review) prey, (Simpson et al.
2008), or competitors (Fletcher 2008).
Adaptive information use depends on the level of spatial and temporal correlation which places
bounds on the quality or amountreliability of information available (Doligez et al. 2003, Schmidt 2004,
Donahue 2006; see Box 2). High quality information sources will vary widely with spatiotemporal
correlation relative to the timing of prospecting and constraints that limit it. For example, in the kittiwake
(Rissa tridactyla) Boulinier et al. (1996) observed a ~30 day window over which the proportion of
successful nests at a given date reflects the productivity of a breeding patch, and a peak in the number of
prospectors in this window. Betts et al. (2008) provide example of a window of opportunity, but with
declining reliability over time, to use post-breeding singing as a cue to reproductive productivity (also see
Fletcher and Miller (2008) for timing of social information in the cactus bug (Chelinidea vittiger)).
Further documentation of these relationships is important because it establishes the relationship between
cue and consequence, quantifies a level of cue reliability, and may document the existence of spatial and
temporal constraints on prospecting and information use.
The multitude of putative information sources within and among perceptual modalities (visual
auditory, chemical), ecologies (hetero- and conspecifics), spatial (personal versus public) and temporal
(prior and versus current) domains presents ecologists with challenges and opportunities. How are these
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inputs combined? Are they redundant, complementary, or reinforcing? If data from two or more sources
contradict, which should take priority to maximize information acquisition? Only a handful of empirical
studies have examined how multiple measures of breeding success combine. In both kittiwakes (Danchin
et al. 1998) and collared flycatchers (Ficedula albicollis; Doligez et al. 2002) individuals differ in their
relative use of personal and patch (social) breeding success, revealing contextual sources of information
(prior success, age, sex difference) or possibly phenotypic differences among individuals (e.g., behavioral
syndromes). More such studies are desperately needed as well as experimental manipulations designed to
produce conflicting information as have been applied to foraging contexts (Kendal et al. 2004, van
Bergen et al. 2004, Coolen et al. 2005). In concert with this, we need further theoretical development that
incorporates the multitude of putative information sources seen in empirical studies, and under varying
scenarios of spatial and temporal predictability.
Ecological implications: population dynamics: Personal or public information on breeding
productivity may provide the information that leads to site dependent (SD) regulation, a potentially
widespread form of density dependence produced by the pattern in site (e.g., territory) settlement in
spatially heterogeneous environments (Pulliam and Danielson 1991, Rodenhouse et al.1997).
Information from prior success (WSLS-rule) can lead to prolonged persistence of metapopulations within
patchy landscapes (Fig. 2), whereas information from social cues (e.g., conspecific attraction) can deter
dispersal to new, high quality habitats (Ray et al. 1991, Forbes and Kaiser 1994). Nonetheless, SD
models assume perfect information and are phenomenological, whereas the WSLS-rule assumes prior
success trumps all other sources of information and is applied absolutely. A more reasonable alternative
is to make the WSLS rule probabilistic and conditional on context and other sources of information (e.g.,
Boulinier et al. 2008). Site dependent models, on the other hand, should become more mechanistic – in
the absence of mechanism they have no connection to the proximate source of information or its
spatiotemporal context, which limits their predictive power and insight into changing environments
(Sutherland and Norris 2002) or conservation strategies. For instance, understanding of whether a target
species uses information from con- or heterospecifics and the cues they use could benefit restoration of
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locally extinct populations and suggest strategies (e.g., staggered release over several years) and numbers
of animals for reintroduction (Mihoub et al. 2009).
Community ecology: Species differences in the acquisition or use of information is evident when
comparing the few studies that have examined simultaneous responses of multiple species to manipulated
information pertinent to breeding habitat (or site) selection indicate (Nocera et al. 2006, Emmering and
Schmidt in review; also a comparison of Parejo et al. 2007 and Doligez et al. 2002). Likewise, the
identical cue may vary among species in its reliability, i.e., ability to forecast future reproductive success,
due to the interspecific variation in spatiotemporal correlation (Parejo et al. 2005, Schmidt et al. 2006).
Informed individuals have greater fitness than uninformed (see papers in this feature by van Gils, Olsson
and Brown, and McNamara and Dall), hence those species better at assessing information or whose
fitness is more closely linked to temporally and spatially correlated environmental parameters may (1)
have a competitive advantage (Olsson and Brown, this issue), (2) ameliorate rapid ecological change, but
also (3) be more susceptible to ecological traps (Nocera et al. 2006). Broadening the ecological context
beyond breeding habitat selection, acquiring information may tradeoff with other fitness enhancing
activities (e.g., Dukas 2002, Schmidt et al. 2008) and could lead to mechanisms of coexistence (Olsson
and Brown, this issue). Coexistence or competitive displacement mediated through information may
complement purely performance-based mechanisms (e.g., Vincent et al. 1996), and are ripe for ecological
investigation. Lastly, heterospecific ‘informants’ may play a role in community assembly (e.g., Elmberg
et al. 1997, Mönkkönen et al. 1990, Fletcher 2008) when their presence provides performance or
productivity-based information to other species. Migrants may especially rely on the presence of
residents to gauge habitat quality (Mönkkönen and Forsman 2002, Thomson et al. 2003, Forsman et al.
2008), and as shown by Fletcher (2008) experimental vocal cues alone were sufficient to generate
differences in community structure.
Conservation biology and ecological traps: Ecological traps are defined as the result of
anthropogenic processes that decouple a formerly reliable cue from habitat quality resulting in
maladaptive habitat choice (Schlaepfer et al. 2002, Robertson and Hutto 2006). For instance, by
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orientating toward polarized light, odonates mistake asphalt surfaces, a cue ‘mimic’, for ponds and lay
their eggs on an unsuitable surface (Kriska et al. 1998). Statistical decision theory (Bradbury and
Vehrencamp 1998) provides a useful framework for examining an organism’s decision after receiving a
cue (or signal) based on (1) the correlation between cue and state (2) the fitness consequences of its
decision in those states (i.e., value of information), and (3) the commonness of the states (in Bayesian
terms, the animal’s prior). Under these considerations, an organism has an optimal cutoff probability
(i.e., minimizes the ratio of errors to correct choices weighed by their fitness value) that favors alternative
actions (select habitat A or B) on opposite sides of the cutoff. SDT has not been used in the context of
examining ecological traps, perhaps because it oversimplifies habitat selection, e.g., ignoring density
dependence. But SDT may be valuable because it illustrates unique pathways to a trap (1- 3 above).
Alternatively, adaptive behavior (the organism’s cutoff is optimal given the information available) can
lead to an increase in the proportion of individuals settling in the poorer habitat under any of the three
paths. But without an understanding of the decision process these would be incorrectly labeled as
ecological traps. Moreover, we do not expect to see an evolutionary response and conservation
management may be required.
Organisms may alternatively based habitat choice or settlement on behavioral rules-of-thumb
where priors, for instance, are based on learning. These rules may be more flexible and result in fewer
incorrect settlement decisions as shown by a theoretical analysis of Kokko and Sutherland (2001). When
priors were based on natal imprinting (being born is informative - philopatric preference strategy in
Kokko and Sutherland 2001) or the WSLS rule (learned preference strategy in Kokko and Sutherland
2001), individuals adjusted their habitat preferences to reduce the impact of ecological traps (modeled as
reduced quality of preferred habitat). Habitat preferences changed most rapidly under imprinting, but the
rate of preference change under WSLS still out paced the change of genetically fixed habitat preferences
when genetic variation was low. These analyses suggest that if cues are learned, then even if the
reliability of a cue is compromised organisms may rapidly readjust their cutoff threshold (Kokko and
Sutherland 2001), whereas if the cue use has a strong genetic component (Kriska et al. 1998) traps may
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persist. It is interesting that the WSLS rule may create a trap when changes in the reliability of cues
occurs (WSLS base on qualitative nest success; Schmidt 2001), but also rescue a population when traps
were created through a decrease in the quality of preferred habitat (Kokko and Sutherland 2001). The
lessons here are (1) depending on the ecological context the use of cues may both initiate and rescue a
population from an ecological trap and (2) how priors are formed can be the difference between requiring
management to save population or not.
Alarm calling in a community and landscape context – Alarm signals are a ubiquitous, largely public
strategy of informing others (intentionally or as a by-product) of dangers (most often predation risk) in
the environment (reviewed in Caro 2005). Regardless of how the interaction is characterized (i.e.,
altruistic or selfish), signals that carry information about danger or predator presence confer an advantage
to potential prey within perceptual range. We focus on heterospecific receivers and consider two
ecological implications: First eavesdropping on alarm calls to manage activity in time and space and
avoid predators may be common and of significant value (survival and foraging efficiency). Short-term
benefits include reacting with an appropriate anti-predator behavior and adjusting time allocation to
scanning for predators or to safer activities. It is difficult to extrapolate ecological consequences from
short-term benefits, so we consider the topic from the perspective of the presence of heterospecific alarm
callers. Second, the presence some …..of alter landscape connectivity and the resistance of habitat
elements (e.g., habitat edges) to facilitate movement through and within landscapes.
Alarm calls, eavesdropping, and predation risk: Birds in the Family Paridae (Parus, Baeolophus,
Poecile) are known to have high vigilance, aggressive mobbing behavior, and a sophisticated alarm
communication (Templeton et al. 2005) that extends to a large heterospecific audience (Langham et al.
2006). In the presence of black-capped chickadees (Poecile atricapilla), downy woodpeckers (Picoides
pubescens) decrease vigilance by 70% thereby increasing foraging rates (Sullivan 1984; also see Telleria
et al. 2001 for similar patterns among blue and great tits). Likewise, white-breasted nuthatches (Sitta
carolinensis) visit food patches more frequently in the presence of titmice (Dolby and Grubb 2000).
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Quantifying long-term advantages are difficult and far less common, but potentially far-reaching. For
example, Dolby and Grubb (1998) demonstrated long-term consequences for nuthatches occupying forest
fragments in which parids were removed for the winter, i.e., the time of year when they lead mixed-
species flocks. Both energetic state and survivorship declined (all mortality events were in parid-removal
fragments), although mortality events were rare and the difference was not significant. A similar
exclusion experiment, but during the breeding season, by Forsman et al. (2002) demonstrated decreased
reproductive productivity in pied flycatchers (Ficedula hypoleuca) in the absence of parids. These studies
minimally demonstrate that there is an effect of the presence of informants that has fitness consequences,
ostensibly through the production of alarm calls. However, it may be more likely that it is that the
absence of alarm signals and presence of non-alarm vocalizations that indicates safety (e.g., Sullivan
1984, Moller 1992), thereby reducing stress, increasing foraging efficiency, and avoiding unnecessary
activity and energetic expenditure (Vitousek et al. 2007). The latest study by Hetrick et al. (this issue-a)
found that changes in the performance and structure of alarm calls in the Eastern Tufted Titmouse (which
reference predator type and magnitude of risk; also see Templeton et al. 2005) are mirrored by changes in
contact calls. This suggests that alarm calls themselves are not necessary to communicate perceived
predation risk. Certainly more research is needed to determine how fitness benefits arise in these species.
Predation, information, and landscape connectivity: As the preceding section suggests,
perception of predation risk is modified by the availability of information, such as publicly broadcast
alarm signals or contact calls (and also other behavioral acts such as looking upward or fleeing or moving
toward cover; e.g., Wong et al. 2005). It stands to reason that this information combines with the physical
environment, perceptual aptitudes of the organism, and the costs and benefits associated with decision-
making to influence (facilitate or impede) movement among resource or habitat patches (Taylor et al.
1993); that is, to influence the functional connectivity of landscapes (Lima and Zollner 1996, Bélisle
2005). Information may influence the resistance of some organisms to cross patches boundaries
(Desrochers and Fortin 2000, Sieving et al. 2004, Tubelis et al. 2006) and habitat gaps (Bélisle and
Desrochers 2002), variation that has been linked to patterns of extinction in birds (Moore et al. 2008).
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Sieving et al. (2004) observed greater frequency of boundary patch crossings among songbirds when in
the presence of titmice. Tubelis et al. (2006) observed greater use of adjacent savannah habitat by mixed-
species flocks that form around sentinel species in relation to the level of predation risk (Ragusa-Netto
2002). Lastly, Wolters and Zuberbühler (2003) observed greater travel and broadening of niche space
(increased use of mid-canopy layer) in associating Campbell’s and Diana monkeys relative to isolated
species groups. These studies suggest the presence or absence of risk-base information may be akin to
landscape models that vary the quality of the matrix (e.g., Fahrig 2007). The effects may be especially
important in selecting migratory stopover sites (Nocera et al. 2008) where, because of lack of experience,
organisms are more vulnerable and less accurate at estimating predation risk (Pomeroy 2006, Pomeroy et
al. 2006, van den Hout et al. 2008).
As a consequence of the value of social information regarding predation risk, interspecific
sociality – from ‘loose’ attraction among heterospecifics to stable polyspecific associations - may be
attributed as much to the value of informants as to other ecological variables (Terborgh 1990, Goodale
and Kotagama 2005); however, demonstrating an effect of information per se, rather than simply selfish
herd, confusion, or dilution effects is difficult. Nonetheless, mixed-species groups of (most notably) birds
and monkeys form around specific nuclear sentinel species that signal a predator’s presence more often
and more reliably than others (Gaddis 1983, Bshary and Noë 1997, Goodale and Kotagama 2005)
suggesting that non-informational effects are insufficient to explain the phenomena (Wolters and
Zuberbühler 2003). Within these mixed-species groups, the risk-based information from sentinel species
extends to wider audiences (Goodale and Kotagama 2005, Langham et al. 2007) and leads to fitness
consequences (Dolby and Grubb 1998, Forsman et al. 2002) prompting their recognition as keystone
signalers (Hetrick et al. this issue-b). Moreover, an exchange of risk-based information in these systems
may represent an example of a stable resource exchange mutualism between species (Schwartz and
Hoeksema 1998).
CONCLUDING DISCUSSION
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In this section we break away from featuring specific ecological contexts to discuss some implications of
information at larger ecological scales and as a central organizing principle. Because little research has
been conducted at these levels to date our discussion is necessarily brief and more conjectural.
Nonetheless, in advancing our case we highlight the need to understand the possible consequences of
information at these scales.
Information Webs: Breeding habitat selection and alarm calling are themes of the larger
phenomena of using proximate cues or alternative sources of information to locate areas of high resource
abundance and low mortality risk; decisions which often dominate the daily lives of individuals and the
ecological interactions among organisms (Stephens et al. 2007). The responses affect the strength of
species interactions (Vos et al. 2006), generate non-lethal effects of predation (Brown et al. 1999), and
when cues are from heterospecifics, generate trait-mediated indirect interactions (Peacor 2003). It should
be evident therefore that there exists an information web that complements and greatly increases the
complexity of food webs and interaction webs (Dicke and Vet 1999, Vos et al. 2006, Holt 2007). Of what
consequence then is this information for food or interaction webs, i.e., beyond an individuals’ (or
strategy’s) own fitness? Predator-prey models suggest that adaptive behavior tends to destabilize
predator-prey dynamics (i.e., simple food-web modules) unless it is based on imperfect information
(Brown et al. 1999, Luttbeg and Schmitz 2000). Of what consequence is food web structure for
information? We are not aware that this question has been properly framed before. Vos et al.’s (2001)
work on infochemical mimicry (increasing noise) and confusion effects in tri-trophic interactions shows
that information can be a decreasing function of species diversity, specifically host specialization. In high
diversity systems infochemicals produced from herbivore-damaged leaves attracted parasitoids to plants
containing many individuals of non-host species. In theoretical analyses, effects in high diversity systems
weaken species interactions and lead to stabilized dynamics at intermediate species richness (Vos et al.
2001). Hence, community structure affects information that feedbacks to lower diversity.
Information as a third niche axis: We contend that information share center stage with abiotic
conditions and biotic resources as a third set of niche axes. To take an example, sunlight is an abiotic
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factor that is converted through photosynthesis and metabolic processes into a biotic resource for
heterotrophs. Sunlight also produces warmth and light (by definition) for activities such as foraging.
However, imagine a world where the daily mean number of hours of sunlight was equivalent to Earth’s
but randomly distributed throughout the year. Outside of H2S reduction as a source of energy, would life
even be possible? Perhaps, we but suggest it is the pattern (information) of sunlight in the form of
circadian rhythm that makes ecological systems what they are today. In a recent review, Resco et al.
(2009) discuss the ecological implications of plants’ ability to tell time noting that “[T]he circadian clock
affects gas exchange by ‘anticipating’ cycles of dawn and dusk” (Resco et al. 2009: 4; the anticipation
hypothesis). For instance, mutant, arrhythmic Arabidopsis show a 40% decrease in net carbon fixation
compared to wild-type (Dodd et al. 2005). Depending on the organism, sunlight is a consumable resource
or an abiotic condition; circadian rhythm is information.
Circadian rhythm of sunlight presents an extreme example, so consider something more mundane
but still exciting to most ecologists: species interactions. Predators kill or exert non-lethal effects such as
fear on their prey, but predators may also produce inadvertent social cues to their location in time and
space (i.e., information). Such information can enable prey to find spatial or temporal refugia from
predators; it is no wonder that predators (and prey) may behave with ‘purposeful unpredictability’ (sensu
Roth and Lima 2007; also see Gripenberg et al. 2007). Within the framework of consumer-resource
interactions, prey that acquire information to use to avoid predation can sustain zero-net population
growth at a higher density of predators, i.e., higher P* (Holt 1994). This in turn increases predators’ R*
(minimum prey abundance to achieve zero-net population growth) since informed prey are more difficult
to capture (Brown et al. 1999). The process can also start the other way around: information about prey
lowers a predator’s R*. If any of these feedback loops lowers, say the predator’s population density,
there may be reduced value to acquire information by the prey and their vulnerability goes up. The idea
that limiting factors (R* and P*, resources and predator, respectively) influence coexistence places
information as a critical element influencing community structure through population processes such as
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competitive exclusion (Tilman 1980, Holt et al. 1994); a process itself considered as a fundamental
property of the ecological niche (Leibold 1995).
Information as an Ecosystem Process: In the preceding section we pointed out that arrhythmic
Arabidopsis shows a decrease in net carbon fixation. Resco et al. (2009) would have us scale up these
effects suggesting that the circadian clock in plants drives gas exchange at the level of biosphere-
atmosphere interactions. If we accept this premise, if only to explore the consequences, then (1) Vos et
al. (2001) demonstrates that information can drive (in non-linear fashion) diversity and (2) Resco et al.
(2009) suggests information can drive ecosystem process. The point we wish to make is that ecologists
currently take a fairly rigid casual interpretation of ecosystem process-diversity relationships: diversity
drives the former. Yet these ignore the potential role of information as direct and indirect (e.g.,
information diversity ecosystem process) driver.
Noise and Info-disruption: Modern anthropogenic processes are greatly accelerating the loss of
species richness and diversity (Pimm et al. 2006, Bradshaw et al. 2009). Less appreciated is the loss of
information, including information processing (i.e., disruption) and transmission (e.g., low urban signal-
to-noise ratios; Rabin et al. 2006, Slabbekoorn and Ripmeester 2008). For example, info-disruption
(Lürling and Scheffer 2007) is the disturbance to chemical information transfer caused by pollutants, such
as heavy metals, surfactants, and pesticides. [Info-disruption is analogous to endocrine-disruption, which
itself is essentially a signal detection problem at the level of chemical recognition]. In aquatic systems
these substances are known to negatively affect anti-predator responses to chemical alarm signals in fish
(impaired avoidance), algae (reduced protective colony formation), and cladocerans (inhibited protective
crest development); see Lürling and Scheffer (2007). The noise associated with wind turbines interferes
with acoustic alarm calls among California ground squirrels (Spermophilus beecheyi) and subsequently
affects vigilance patterns and flight to burrow behavior (Rabin et al. 2006). Urban noise similarly affects
song (signal) efficiency in birds in turn affecting foraging-vigilance tradeoffs (Quinn et al. 2006) as well
as lowered abundance and reproductive success near highways (Slabbekoorn and Ripmeester 2008).
Species that communicate at frequencies above urban noise are little affected, and may come to dominate
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urban communities leading to faunal homogenization (Slabbekoorn and Ripmeester 2008). Invasive
species can also alter information flows, such as in the acoustic-orienting parasitoid fly (Ormia ochracea)
selecting for a song-less morph of the field cricket (Teleogryllus oceanicus); see Zuk et al. (2006).
Lastly, phenological mismatching due to global climate change is a consequence of information
disruption that has received considerable attention (e.g., Both et al. 2009, Brooks 2009). Mismatching
occurs when the timing of developmental or behavioral processes, such as hibernation, migration, or
reproduction, is altered triggered directly (temperature) or indirect from climatic cues (e.g., flowering
phenology)
Examples such as these are unfortunately too common, and the prospect of deteriorating
information webs led Holt (2007) to ask whether this was the “next depressing frontier in conservation?”
The sum of these effects can be large indeed and need prompt attention by conservation biologists. Not
just for conserving species but also the preservation of animal cultures (Laiolo and Tella 2007). We
recognize the value of genetic information by preserving genomes; it is time now to expand this
conservation priority to non-genetic biological information.
Conclusions: With one or more major reviews published each year since 2000 (see Introduction) one
may ask whether information is a passing fad or a general theme (we’re not arguing the only theme)
around which to organize empirical and theoretical research and conservation priorities in ecology and
evolution. We believe the latter, but the answer will only come with further development of an Ecology
of Information Framework, one that integrates all the sub-disciplines in Figure 1. Through our overview
and the papers that follow in this special feature on the Ecology of Information, we hope to have …….
ACKNOWLEDGEMENTS (566)
KAS’s research in the ecology of information is supported by a grant from the National Science
Foundation (DEB 0746985). JAvG was supported by the Netherlands Organization for Scientific
Research (NWO) and by the Royal Netherlands Academy of Arts and Sciences (KNAW). This is
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publication xxxx of the Netherlands Institute of Ecology (NIOO-KNAW) and xxx of the Centre for
Wetland Ecology. This paper was improved by comments of Luc-Alain Giraldeau and an anonymous
reviewer. Lastly, we are grateful to Per Lundberg and Linus Svensson for their support of this special
feature.
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FIGURE LEGENDS
Figure 1: Domains of the disciplines that investigate information in biology at the organism-level. The
figure is adapted in part from Koops (1998).
Figure 2: Consider information as a change in the probability (; or probability distribution) of an event
or state, e.g., might be a prey’s estimate of the probability a predator is present in a forest patch
occupied by the prey or male quality as judged by a female’s observations of male display. An individual
has a prior estimate, PR, which is altered (PO; posterior) as information is processed from an observation
(e.g., an alarm call). PO decays over time (this functional form is illustrated as D) as information is
temporally discounted, and eventually settles back to the prior value (PR). The form of the decay curve
will be highly variable and specific to context. Information may be used to permanently assign a state
(e.g., an individual’s gender or a fishless pond) that may result in a permanent developmental switch
(polyphenism) or time its development . Alternatively, states repeatedly cycle between states in
time or space (behavioral). Lastly, norm of reaction
Evolutionary traps are produced when one or more of these three responses (PR, PO, and D) no longer
matches its environment because the type or reliability of the cue or the state it ‘refers’ to has changed.
Figure 3: Mean persistence time ( SE) as a function of the number of component patches in the network
in a temporal autocorrelated landscape ( = 0.7). Dispersal is even (black line) or using the WSLS rule
(for more details see Schmidt 2004).
35
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
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FIGURE 1
Information Source
Reception
Perception
Decision, i.e., response of the receiver
Transmission
Translation
Evaluation
Behavioral , developmental and life history changes
Populations
Communities
Ecosystems
Consequence(public info)
Evolutionary Ecology
Sensory ecology,Cognition, Psychology
Evolution(genetic info)
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10641065
1066106710681069107010711072
36
FIGURE 2
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1073107410751076
1077107810791080
37
FIGURE 3
Even dispersal, rho = 0.7
2 3 4 5 6 7 8
Number of patches
0
500
1000
1500
2000
WSLS, rho = 0.7
Mea
n po
pula
tion
pers
isten
ce (y
rs)
38
1081108210831084
108510861087108810891090109110921093
38
Box 1: Glossary of terms: Note, these definitions closely follow Wagner and Danchin (this issue)
Cues – A detectable fact that is non-intentionally produced. Includes facts produced by physical agents
or inadvertently produced by biological agents.
Signals – A trait or behavior of a signaler evolved specifically to alter the behavior of the receiver in a
way to benefit the signaler. The change in receiver behavior should also have evolved to enhanced
receiver fitness.
Public information – Information that is in the public domain and potentially available to any organism.
Private information - Information that is undetectable to other organisms through direct means. Private
information may become public through indirect means. This follows a sequence (using the model by
Seppänen et al. 2007): 1) observation of an event or state by a primary observer; 2) a decision that is
manifest via change in behavior (i.e., an action) of the primary observer; 3) the consequence of the
action. For example, Wong et al. (2005) demonstrated that sand fiddler crabs (Uca pugilator) use
observations of threat-induced responses of neighbors (stage 2 in the sequence above) to guide their
own refuge-seeking behavior. Birds settling on territories where conspecific reproductive success was
high in prior years (see Breeding habitat selection) is an example of observing the consequences (stage
3) of past decisions by conspecifics.
Socially acquired Information – Information extracted from other individuals (con- or heterospecific) be
they signals or (inadvertent) cues (including actions and consequences). Note: all social information
must be public.
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1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
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Interceptive and Social Eavesdropping (Peake 2005) – A mechanism of acquiring social information from
signals (i.e., communication) between two (or more) individuals. In interceptive eavesdropping
individuals acquire information about their environment (e.g., an alarm call gives information about the
presence of a predator), whereas in social eavesdropping individuals acquire information regarding the
social relationship between the communicating parties (e.g., dominance hierarchy, kinship).
40
1118
1119
1120
1121
1122
1123
40
Box 2: Breeding habitat selection/settlement strategies as a function of spatial heterogeneity and
temporal predictability. Four regions are identified across what is in reality a continuum. In the top
row, high variation in individual (fine scale) site quality exists, whereas patch quality, averaged over the
many individual sites it contains, is low. Fidelity to successful sites is favored provided temporal
predictability is high (top right). Likewise, dispersal from unsuccessful sites is favored and individuals
should prospect for future sites not patches since patch reproductive success has little spatial variation.
We call this the win-stay, lose-prospect strategy (WSLP). When spatial variation is higher between
patches than sites (bottom row) individuals should prospect for information on patch reproductive
success provided temporal predictability is high (bottom right). Fidelity or dispersal should be linked
closely to patch reproductive success rather than an individual’s own success. When temporal
predictability is low an individual should prospect during the pre-breeding season for current, proximate
cues of reproductive potential at the scale of greatest spatial: at sites (top left) or patches (bottom left).
Conspecific attraction is only favored when spatial variation is higher between patches than sites,
assuming individuals preempt sites by occupation. It further requires that some individuals (call them
prospectors) use patch reproductive success as a settlement cue. In other words, conspecific attraction
is an information-scrounger strategy that requires information collected by the information-producer
strategy of prospectors (Dall et al. 2005).
41
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
114111421143
41
BOX 2
Spati
al
hete
roge
neity
temporal predictability
Fine:site > patch
Coarse:patch > site
low high
(1)
(3)
(2)
(4)
PRS high
PRS
Fidelity: to siteInfo: pers. repro. successConspecific attraction: noProspect: pre- or post-breeding; prospect at sites
Fidelity: noneInfo: noneConspecific attraction: noProspect: pre-breeding; prospect at sites
Fidelity: to patch Info: patch repro. successConspecific attraction: yesProspect: pre- or post-breeding; prospect at patches
Fidelity: noneInfo: noneConspecific attraction: ?Prospect: pre-breeding; Prospect at patches
Site
Patch
42
1144114511461147
114811491150115111521153115411551156115711581159
42