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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
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Center for Ecology & Conservation, School of Biosciences, University of Exeter, Tremough, Penryn,
TR10 9EZ, UK
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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]
806-742-2723
806-742-2693 FAX
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ABSTRACT
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Information is characterized as the reduction of uncertainty and by a change in the state of a receiving
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organism. Thus, organisms can acquire information about their environment that reduces uncertainty and
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increases their likelihood of choosing a best-matching strategy. We define the Ecology of Information as
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the study of how organisms acquire and use information in decision-making and its significance for
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populations, communities, landscapes, and ecosystems. As a whole, it encompasses the reception and
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processing of information, decision-making, and the ecological consequences of making informed
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decisions. The first two stages constitute the domains of, e.g., sensory ecology and behavioral ecology.
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The exploration of the consequences of information use at larger spatial and temporal scales in ecology
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has generally lagged behind the success of these other disciplines. In our overview we characterize
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information, review statistical decision theory as a quantitative framework to analyze information and
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decision-making, and discuss some potential ecological ramifications. Rather than attempt a superficial
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review of the enormity of the scope of information we highlight information use in three areas: breeding
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habitat selection, interceptive eavesdropping and alarm calls, and information webs. Through these topics
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we discuss specific examples of ecological information use and the emerging ecological consequences.
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We emphasize recurring themes: information is collected from multiple sources, over varying temporal
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and spatial scales, and in many cases links heterospecifics to one another. This leads to questions where
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further development is needed: (1) how information sources are integrated and prioritized, (2) how does
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the spatial and temporal correlation between when and where information is obtained and acted upon
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affect behavioral strategies, population processes, and ecological interactions, (3) how best to integrate
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interaction and information webs between organisms.
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Keywords: alarm calling, Bayesian updating, breeding habitat selection, eavesdropping, information,
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predation risk, statistical decision theory
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INTRODUCTION
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The Ecology of Information is the study of how organisms acquire and use information in decision-
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making to manage their lives of, e.g., finding food, selecting habitats, avoiding predators, and allocating
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effort to current and future reproductive success, and its significance for populations, communities,
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landscapes, and ecosystems. It is a burgeoning and integrative field that melds together the various
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disciplines that deal with the reception and processing of information on the one hand and the ecological
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and evolutionary consequences of making informed decisions on the other hand (Fig. 1). Information is
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considered one of the central biological concepts of the twentieth century (Maynard Smith 2000,
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Jablonska 2002) and is critical to the adaptive process (Plotkin 1997, Dall et al. 2005). The concept of
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information (or its various subdivisions) as related to the fields of animal behavior and ecology has been
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reviewed at least six times since 2004 (Danchin et al. 2004, Dall et al. 2005, Vos et al. 2006, Seppänen et
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al. 2007, Bonnie and Earley 2007, and Valone 2007), and as a more general concept in biology by
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Maynard-Smith (2000) and Jablonka (2002).
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This set of excellent review and synthesis papers has exposed the concept of information to many
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ecologists, and has taken the first important step of presenting, defining, and circumscribing the role(s) of
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information in ecology as well as illustrating a diverse set of ecological contexts and organisms that
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utilize information. They have been extremely successful in this regard; however, the limits of a single
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review article leaves little room for exploring ecological processes or the relevance of information for
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processes and patterns within populations, communities, and ecosystems – i.e., the spatial and temporal
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scales that form the bulk of the ecological studies (Fig. 1). Thus, with a strong backdrop of information
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in ecology already in place, there still remains a very real and urgent need to bridge the gap between the
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behavioral, ecological, and conservation sciences (Fryxell and Lundberg 1998, Sutherland and Norris
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2002) with information at its core. By way of this overview, we hope to highlight some areas of
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successful incorporation of information as well as areas where further development is needed. However,
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the scope of information in evolution and ecology is enormous and cannot be covered in any reasonable
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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
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of selecting a narrow set of topics to discuss the implications that are more relevant to populations,
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communities, landscapes, and ecosystems that have not necessarily been the main subject material for
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past reviews. But this also means we barely scratched the surface of the functional role of information in
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ecology.
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Characterizing information
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An abstract property of events and entities that make their characteristics predictable to
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individuals… [It] enables…individuals to make choices, select their activities …appropriately for
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their needs and opportunities. (Smith 1977:193)
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This passage of Smith (1977) expresses two elements that are typically used to characterize information:
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(1) the reduction of uncertainty, i.e., information is that which makes the world more predictable
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(syntactic view of information: Shannon and Weaver 1949, Danchin et al. 2004) and (2) change in the
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state of a receiver in a functional way (semantic view: Blumstein and Bouskila 1996, Jablonka 2002; Dall
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2005). Heterogeneity and variability limit an organism’s ability to possess complete knowledge of the
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state of its current world or anticipate future conditions, and hence choose an appropriate strategy for the
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actual set of conditions it will encounter. In the face of this uncertainty organisms can acquire
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information about their physical and biotic environment (or future environment) that reduce uncertainty
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and increase their likelihood of choosing a best-matching strategy. Implicit in this description is that for
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information to exist and to have fitness consequences there must be both variation in environmental
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conditions (e.g., states) and in phenotypic strategies (Stephens 1989). Moreover, it assumes organisms
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make adaptive choices under the existence of constraints, a central premise of the field of behavioral
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ecology (Mitchell and Valone 1990). While this last premise is demonstrably false in many cases, it is
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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
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anthropogenic causes for these events that we discuss throughout the paper.
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Terminology: Box 1 provides a glossary for types of information discussed throughout that is
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based on and expanded by Wagner and Danchin (this issue). Information can come from almost any
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source: an organism’s abiotic environment (e.g., physical cues) biotic environment (e.g., signals and
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biological cues from con- and heterospecifics), perception of its own internal state (e.g., hunger,
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motivation), social learning, and trial and error experience. Furthermore, even when sources of
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information are private, such as state or motivation, information can become public through indirect
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means following the pathway described by Seppänen et al. (2007; Box 1). For example, Wong et al.
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(2005) demonstrated that sand fiddler crabs (Uca pugilator) use observations of threat-induced responses
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of neighbors (actions, stage 2 in Seppänen et al. 2007; Box 1) to guide their own refuge-seeking behavior.
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Breeding birds may preferentially settle on territories where conspecific reproductive success
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(consequences, stage 3 in Seppänen et al. 2007; Box 1) was high in prior years. Public information based
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on the actions or consequences of other individuals (i.e., social information; Box 1) may be actively
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sought out by both conspecifics and heterospecifics. It is possible that the quality of information degrades
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through this sequence (Giraldeau et al. 2002, van Bergen et al. 2004, Seppänen et al. 2007).
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Alternatively, observations of the primary observer’s actions and consequences may provide higher
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quality information (i.e., more reliable cues) since the primary observer confronts ambiguity (e.g., errors
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in signal detection; Bradbury and Vehrencamp 1998) or uncertainty (Stephens 1989), and its actions may
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be influenced by gambles (e.g., risk-sensitive behavior; Stephens and Krebs 1973) or seeking insurances
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(Dall and Johnstone 2002) that secondary observers may wish to avoid.
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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
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origin (e.g. the feeding performance of flockmates) can inform about non-social issues (e.g. the amount of
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food in the environment). Identifying sources is important as, e.g., social information has unique
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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;
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see Concluding Discussion). However, in other contexts the type of information can be safely ignored
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relative to its content. In the later sections that discuss ecological processes we often do just that: focus
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more on the content than the source of information.
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Information, uncertainty, and phenotypic variation: Phenotypic variation is an outgrowth of
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spatiotemporal variation in the environment and its predictability, i.e., information (Levins 1968,
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Donaldson-Matasci et al. 2008, Lachman et al. this issue). This is especially true of phenotypic plasticity;
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however the genome itself is a record of the past success of heritable strategies. Thus phenotypic
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variation is influenced by (1) past information stored in the form of allelic (genetic) variation (2)
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maternally-acquired cues (Massot and Clobert 2000, Mathis et al. 2008) capable of producing epigenetic
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effects in one or more future generations (Gilbert and Epel 2008), and (3) current signals or cues that
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produced variation through norm of reaction, polyphenism, or behavioral plasticity. A discussion of the
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evolution of plasticity (and the related concept of bet-hedging as a response to uncertainty) would go far
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beyond our overview. But we note that the form of plasticity will be influenced, in part, by the degree to
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which the environment varies (spatiotemporal correlation) and the relative cost of incorrectly matching
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your strategy to the environment. If change is slow (e.g., ponds tend be fishless or not during a
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cladoceran’s lifetime) then irreversible developmental shifts (protective crest development) may be
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favored over a sophisticated perceptual system that is costly to maintain. On the other hand, behavioral
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flexibility may be favored if changes are rapid and frequent within an individual’s lifetime, e.g., nest
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defense or broken wing display in response to predators that frequently move in and out of the vicinity of
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a plover’s nest. We largely restrict our overview to behavioral plasticity. Behavior has attracted far
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greater attention from the field of statistical decision theory (next section), and is perceived as a more
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important proximate causal agent of higher level ecological processes. However, this perception may be
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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)
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as a quantitative framework from which to analyze the use of information by organisms (also see
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McNamara and Houston 1980, McNamara et al. 2006, and Oikos v.112, issue 2). At its heart is the use of
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Bayesian methods (specifically Bayes’ theorem for calculating conditional probabilities) to explore how
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organisms integrate prior expectations based on personal experience and evolutionary history (i.e., genetic
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information) with new information to arrive at a revised, posterior expectation. Take the example of mate
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choice: choosy females are assumed to have (perfect) knowledge of the distribution of male quality (e.g.,
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parenting skills, parasite loads) in a population, whereas the quality of an individual male is uncertain and
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must be sampled through observation of song, display, etc. (Getty 1996, Luttbeg 1996). The value of
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sampling information lies in the formation of a revised posterior expectation of the individual male’s
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quality that reflects reduced uncertainty associated with possible outcomes (Dall et al. 2005). We direct
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our readers to reviews on the SDT framework (Dall et al. 2005) and empirical studies of Bayesian
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behavior (Valone 2006; half the issue is in fact devoted to Bayesian foraging) for recent updates in this
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field and it’s important to the ecology of information. We make reference to SDT throughout, but for
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brevity we do not duplicate the material in these reviews.
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Two salient questions not directly addressed in the reviews are: (1) what is the relationship
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between information updating and information use and (2) how do alternatives to Bayesian updating
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compare? To address the former, it has been suggested (to us) that SDT tackles only the question of how
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information is updated and not how it is used (and thus its relevance for ecological processes). This may
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be a reaction to how SDT has been used in the past, for the statement is certainly not true. For example,
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van Gils (this issue) demonstrates that the Bayesian Potential Value Rule (Olsson and Brown 1996)
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predicts the pattern of area-restricted search when foraging in a spatially correlated environment. For the
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first time, we have a realistic theoretical representation of this behavior. Schmidt and Whelan (this issue)
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used SDT to predict optimal renesting behavior in single-brooded birds. One prediction of their model
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they called the Renester’s Paradox: habitats with greater nest failure that require more nest attempts, on
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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
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updating links changes in the quality or proportion of one habitat type to behavior in the other habitat.
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Kokko and Sutherland (2001) modeled breeding habitat selection combined with habitat
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degradation without changing preferences (i.e., an ecological trap scenario). Notably, they demonstrated
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that variation in how priors are governed can greatly alter the threat of extinction and place very different
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demands on management or intervention. Their model incorporated rules-of-thumb (e.g., imprinting or
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learned preferences) rather than take an explicit Bayesian approach. Behavioral rules may be common
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(McNamara and Houston 1980, Bouskila and Blumstein 1992; see next section), and often perform close
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to optimal Bayesian solutions when the two have been compared (e.g., Beauchamp 2000, Welton et al.
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2003). However, this may not be true when the circumstances under which the rule evolved have
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changed thus generating evolutionary traps (Schlaepfer et al. 2002; and see Conservation biology and
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ecological traps).
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There is little doubt that SDT (especially Bayesian approaches) need to be expanded beyond a
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handful of contexts. To date, Bayesian methods have mostly found their way in “simple” short-term tasks
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such as foraging or predation-avoidance (e.g., Rodríguez-Gironés and Vásquez 1997, Olsson and
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Holmgren 2000, van Gils et al. 2003). But even here one may ask to what extent do solutions to problems
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such as patch departure rules influence populations, communities, landscapes, and ecosystems? Olsson
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and Brown (this issue) give us glimpse into the future by examining how information states (e.g.,
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Bayesian vs. fixed-time foragers) sculpt the resource distribution in their environment in ways that may
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promote or prevent species coexistence. Following Olsson and Brown’s lead in incorporating Bayesian
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approaches and behavioral strategies in population and community models (e.g., Fryxell and Lundberg
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1998, Sutherland and Norris 2002) will begin to close the gap between information updating and
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ecological processes at higher scales.
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From information to an Ecology of Information – That information use can have significant population
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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
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start with a model of metapopulation persistence in an information-free world (Bascompte et al. 2004).
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We do not fault the authors for this; rather they developed an elegant and useful model to illustrate the
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relationship between the persistence of a stochastic patchy population network and the number of habitat
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patches connected through dispersal. Under the assumptions 1) that patches experience periods of density
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independent population growth ( > 1; good years) or decline ( < 1; bad years) with equal probability, 2)
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patches are decoupled with respect to temporal variability (i.e., good and bad years are assigned to
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patches independently), and 3) patches are spatially coupled through even dispersal events, the geometric
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growth rate of a population network, GEOM, composed of n patches can be approximated as the spatial-
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arithmetic mean growth rate, ARITH, minus a sampling error for small n. If for any habit patch good and
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bad years are equally likely and uncorrelated there is no information available to choose a patch.
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However, if patch quality is temporally correlated (regardless if good and bad years remain equally likely
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in the long term) then previous experience informs an individual of the likelihood the current conditions
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will persist. Correlation makes information available; however the organism still requires the means of
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detecting, processing, and using the information. Thus, even in a temporally correlated world even
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dispersal is uninformed behavior. The result is that regardless of the number of patches (or the presence
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of temporal correlation), GEOM will never exceed ARITH such that if ARITH < 1.0 the metapopulation
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quickly becomes extinct (Fig. 2). An alternative is to adjust patch fidelity based on prior experience:
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return to (or stay at) patches that experienced high productivity the prior year and vacate patches that had
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poor productivity the prior year. This win-stay:lose-switch (WSLS) rule performs no better than even
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dispersal in a world lacking temporal correlation and so prior experience is not informative. However, the
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combination of the WSLS rule and temporal correlation (experience is informative) has dramatic
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consequences on the persistence time of the metapopulation (Schmidt 2004; Fig. 2). While we detail this
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single example, other excellent studies drive home the point that models of ecological processes built
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around partially informed organisms often behave dramatically different from information-free or perfect
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information scenarios (e.g., Brown et al. 1999, Vos et al. 2001, Donahue 2006), neither of which is
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realistic or likely to be common.
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INFORMATION AND ECOLOGICAL PROCESSES
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In this section we expand on information use in three areas: (1) Ecological developmental biology briefly
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considers environmental cues that direct phenotypic variation in morphology and physiology, (2)
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Breeding habitat selection, which makes use of multiple sources of information collected over varying
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temporal and spatial scales, and (3) Alarm calling and heterospecific information transfer within a
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landscape context.
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Ecological developmental biology – Some decisions in an organism’s life are made only once and are
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irreversible. These include selecting among alternative developmental endpoints (polyphenism) and life-
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cycle progressions timing (e.g., diapause, metamorphosis) that directly or indirectly affect morphology
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and physiology in addition to behavior. These decisions are guided in part by environment cues (e.g.,
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photoperiod, temperature, nutrition, predation risk, social proximity) and concern the emerging field of
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ecological developmental biology (Gilbert 2001, Gilbert and Epel 2009). Examples include predator-
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induced shell morphology in the barnacle Thais lamellosa (Palmer 1985), nutrition-induced polyphenism
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in Nemoria arizonaria caterpillars that develop a cuticle resembling either an oak catkin or oak twig
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depending on the season in which they hatch (Greene 1989), and vibrational assessment of predation risk
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and premature hatching in embryo red-eyed tree frogs, Agalychnis callidryas (Warkentin et al. 2007); for
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many other examples see reviews in (Pechenik et al. 1998, Gilbert 2001, Relyea 2007).
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From an information perspective there may be little fundamental difference between these single,
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irreversible decisions and rapidly repeatable and reversible behavioral decisions: the framework of SDT
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can apply to either. For instance, Warkentin et al. (2007) couched their study of premature hatching in
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red-eyed tree frogs as a signal detection problem: balancing the costs to frog embryos of missed cues
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(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
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cases there is no direct assessment of the future environment (e.g., aquatic ↔ terrestrial) and thus limited
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opportunity for learning. On the ecological side, there may be large latent effects stemming from
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choosing an alternative fixed phenotype or altering developmental timing (Pechenik 2006). These effects
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may capable of producing large population and community consequences. Lastly, global climate change
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and other anthropogenic effects are rapidly altering the probabilistic linkages between (future) state and
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proximate cue, and the reliability of chemical cues, which often direct development progression, is being
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disrupted by anthropogenic substances.
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Breeding habitat selection -Spatial heterogeneity and temporal variability in the underlying factors that
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contribute to breeding productivity (e.g., food abundance, predation risk) are widespread (e.g., Lewis &
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Murray 1993, Schmidt et al. 2006, Simpson et al. 2008). Choice of breeding location has high fitness
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consequences, and it is not surprising that organisms acquire information to guide their decisions.
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Although selecting a breeding location within a hierarchy of landscapes, habitats, territories, and breeding
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sites (e.g., nest sites, dens) differs considerably among taxa, broadly speaking the potential sources of
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information and their use are likely to generalize (Box 2). For instance, social information, based on
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presence (conspecific attraction; Stamps 1988, Fletcher 2006) or performance (habitat copying; Clobert et
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al. 2001) may be advantageous because they are integrative measures, provide greater sampling power
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(i.e., more independent sources), and, in the case of post-reproductive cues may reveal the consequences
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of conspecifics’ decisions. Thus, prospecting (i.e., gathering local information on, e.g., reproductive
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success; Reed et al. 1999, Ward 2005) is likely to be an important behavioral strategy that is implemented
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throughout the year or at least those critical periods when information is least costly and most readily
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available. Recent reviews have highlighted the apparent ubiquity of prospecting in birds where it has
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been best studied (Reed et al. 1999). Among vertebrates, studies have shown they use conspecifics
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presence, density (Cote and Clobert 2007), and reproductive productivity or its correlates (e.g., post-
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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
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orientation (Diego-Rassila 2004). These cues may provide high quality information on habitat or site
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quality and may be used to reduce search and settlement costs (Stamps 2001, Fletcher 2006). In addition,
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proximate cues, e.g., presence versus density, may provide unique or complementary information on
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different components of habitat (e.g., suitability versus intraspecific competition). Moreover, these
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strategies are not limited to cues from conspecifics; cues from heterospecifics may also be used for these
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same or alternative types of information or as cost saving strategies (Mönkkönen and Forsman 2002,
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Diego-Rassila et al. 2004, Seppänen et al. 2007). Prospecting may also include assessment of habitat
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components, such as predator activity and food availability, and breeders may eavesdrop on inadvertent
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public cues, such as vocalizations of predators (Emmering and Schmidt in review) prey, (Simpson et al.
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2008), or competitors (Fletcher 2008).
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Adaptive information use depends on the level of spatial and temporal correlation which places
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bounds on the quality or amount of information available (Doligez et al. 2003, Schmidt 2004, Donahue
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2006; see Box 2). High quality information sources will vary widely with spatiotemporal correlation
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relative to the timing of prospecting and constraints that limit it. For example, in the kittiwake (Rissa
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tridactyla) Boulinier et al. (1996) observed a ~30 day window over which the proportion of successful
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nests at a given date reflects the productivity of a breeding patch, and a peak in the number of prospectors
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in this window. Betts et al. (2008) provide example of a window of opportunity, but with declining
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reliability over time, to use post-breeding singing as a cue to reproductive productivity (also see Fletcher
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and Miller (2008) for timing of social information in the cactus bug (Chelinidea vittiger)). Further
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documentation of these relationships is important because it establishes the relationship between cue and
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consequence, quantifies a level of cue reliability, and may document the existence of spatial and temporal
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constraints on prospecting and information use.
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The multitude of putative information sources within and among perceptual modalities (visual
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auditory, chemical), ecologies (hetero- and conspecifics), spatial (personal versus public) and temporal
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(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
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contradict, which should take priority to maximize information acquisition? Only a handful of empirical
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studies have examined how multiple measures of breeding success combine. In both kittiwakes (Danchin
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et al. 1998) and collared flycatchers (Ficedula albicollis; Doligez et al. 2002) individuals differ in their
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relative use of personal and patch (social) breeding success, revealing contextual sources of information
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(prior success, age, sex difference) or possibly phenotypic differences among individuals (e.g., behavioral
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syndromes). More such studies are desperately needed as well as experimental manipulations designed to
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produce conflicting information as have been applied to foraging contexts (Kendal et al. 2004, van
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Bergen et al. 2004, Coolen et al. 2005). In concert with this, we need further theoretical development that
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incorporates the multitude of putative information sources seen in empirical studies, and under varying
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scenarios of spatial and temporal predictability.
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Ecological implications: population dynamics: Personal or public information on breeding
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productivity may provide the information that leads to site dependent (SD) regulation, a potentially
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widespread form of density dependence produced by the pattern in site (e.g., territory) settlement in
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spatially heterogeneous environments (Pulliam and Danielson 1991, Rodenhouse et al.1997).
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Information from prior success (WSLS-rule) can lead to prolonged persistence of metapopulations within
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patchy landscapes (Fig. 2), whereas information from social cues (e.g., conspecific attraction) can deter
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dispersal to new, high quality habitats (Ray et al. 1991, Forbes and Kaiser 1994). Nonetheless, SD
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models assume perfect information and are phenomenological, whereas the WSLS-rule assumes prior
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success trumps all other sources of information and is applied absolutely. A more reasonable alternative
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is to make the WSLS rule probabilistic and conditional on context and other sources of information (e.g.,
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Boulinier et al. 2008). Site dependent models, on the other hand, should become more mechanistic – in
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the absence of mechanism they have no connection to the proximate source of information or its
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spatiotemporal context, which limits their predictive power and insight into changing environments
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(Sutherland and Norris 2002) or conservation strategies. For instance, understanding of whether a target
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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
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of animals for reintroduction (Mihoub et al. 2009).
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Community ecology: Species differences in the acquisition or use of information is evident when
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comparing the few studies that have examined simultaneous responses of multiple species to manipulated
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information pertinent to breeding habitat (or site) selection indicate (Nocera et al. 2006, Emmering and
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Schmidt in review; also a comparison of Parejo et al. 2007 and Doligez et al. 2002). Likewise, the
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identical cue may vary among species in its reliability, i.e., ability to forecast future reproductive success,
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due to the interspecific variation in spatiotemporal correlation (Parejo et al. 2005, Schmidt et al. 2006).
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Informed individuals have greater fitness than uninformed (see papers in this feature by van Gils, Olsson
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and Brown, and McNamara and Dall), hence those species better at assessing information or whose
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fitness is more closely linked to temporally and spatially correlated environmental parameters may (1)
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have a competitive advantage (Olsson and Brown, this issue), (2) ameliorate rapid ecological change, but
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also (3) be more susceptible to ecological traps (Nocera et al. 2006). Broadening the ecological context
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beyond breeding habitat selection, acquiring information may tradeoff with other fitness enhancing
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activities (e.g., Dukas 2002, Schmidt et al. 2008) and could lead to mechanisms of coexistence (Olsson
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and Brown, this issue). Coexistence or competitive displacement mediated through information may
326
complement purely performance-based mechanisms (e.g., Vincent et al. 1996), and are ripe for ecological
327
investigation. Lastly, heterospecific ‘informants’ may play a role in community assembly (e.g., Elmberg
328
et al. 1997, Mönkkönen et al. 1990, Fletcher 2008) when their presence provides performance or
329
productivity-based information to other species. Migrants may especially rely on the presence of
330
residents to gauge habitat quality (Mönkkönen and Forsman 2002, Thomson et al. 2003, Forsman et al.
331
2008), and as shown by Fletcher (2008) experimental vocal cues alone were sufficient to generate
332
differences in community structure.
333
Conservation biology and ecological traps: Ecological traps are defined as the result of
334
anthropogenic processes that decouple a formerly reliable cue from habitat quality resulting in
335
maladaptive habitat choice (Schlaepfer et al. 2002, Robertson and Hutto 2006). For instance, by
14
336
orientating toward polarized light, odonates mistake asphalt surfaces, a cue ‘mimic’, for ponds and lay
337
their eggs on an unsuitable surface (Kriska et al. 1998). Statistical decision theory (Bradbury and
338
Vehrencamp 1998) provides a useful framework for examining an organism’s decision after receiving a
339
cue (or signal) based on (1) the correlation between cue and state (2) the fitness consequences of its
340
decision in those states (i.e., value of information), and (3) the commonness of the states (in Bayesian
341
terms, the animal’s prior). Under these considerations, an organism has an optimal cutoff probability
342
(i.e., minimizes the ratio of errors to correct choices weighed by their fitness value) that favors alternative
343
actions (select habitat A or B) on opposite sides of the cutoff. SDT has not been used in the context of
344
examining ecological traps, perhaps because it oversimplifies habitat selection, e.g., ignoring density
345
dependence. But SDT may be valuable because it illustrates unique pathways to a trap (1- 3 above).
346
Alternatively, adaptive behavior (the organism’s cutoff is optimal given the information available) can
347
lead to an increase in the proportion of individuals settling in the poorer habitat under any of the three
348
paths. But without an understanding of the decision process these would be incorrectly labeled as
349
ecological traps. Moreover, we do not expect to see an evolutionary response and conservation
350
management may be required.
351
Organisms may alternatively based habitat choice or settlement on behavioral rules-of-thumb
352
where priors, for instance, are based on learning. These rules may be more flexible and result in fewer
353
incorrect settlement decisions as shown by a theoretical analysis of Kokko and Sutherland (2001). When
354
priors were based on natal imprinting (being born is informative - philopatric preference strategy in
355
Kokko and Sutherland 2001) or the WSLS rule (learned preference strategy in Kokko and Sutherland
356
2001), individuals adjusted their habitat preferences to reduce the impact of ecological traps (modeled as
357
reduced quality of preferred habitat). Habitat preferences changed most rapidly under imprinting, but the
358
rate of preference change under WSLS still out paced the change of genetically fixed habitat preferences
359
when genetic variation was low. These analyses suggest that if cues are learned, then even if the
360
reliability of a cue is compromised organisms may rapidly readjust their cutoff threshold (Kokko and
361
Sutherland 2001), whereas if the cue use has a strong genetic component (Kriska et al. 1998) traps may
15
362
persist. It is interesting that the WSLS rule may create a trap when changes in the reliability of cues
363
occurs (WSLS base on qualitative nest success; Schmidt 2001), but also rescue a population when traps
364
were created through a decrease in the quality of preferred habitat (Kokko and Sutherland 2001). The
365
lessons here are (1) depending on the ecological context the use of cues may both initiate and rescue a
366
population from an ecological trap and (2) how priors are formed can be the difference between requiring
367
management to save population or not.
368
369
Alarm calling in a community and landscape context – Alarm signals are a ubiquitous, largely public
370
strategy of informing others (intentionally or as a by-product) of dangers (most often predation risk) in the
371
environment (reviewed in Caro 2005). Regardless of how the interaction is characterized (i.e., altruistic
372
or selfish), signals that carry information about danger or predator presence confer an advantage to
373
potential prey within perceptual range. We focus on heterospecific receivers and consider two ecological
374
implications: First eavesdropping on alarm calls to manage activity in time and space and avoid predators
375
may be common and of significant value (survival and foraging efficiency). Short-term benefits include
376
reacting with an appropriate anti-predator behavior and adjusting time allocation to scanning for predators
377
or to safer activities. It is difficult to extrapolate ecological consequences from short-term benefits, so we
378
consider the topic from the perspective of the presence of heterospecific alarm callers. Second, the
379
presence some …..of alter landscape connectivity and the resistance of habitat elements (e.g., habitat
380
edges) to facilitate movement through and within landscapes.
381
Alarm calls, eavesdropping, and predation risk: Birds in the Family Paridae (Parus, Baeolophus,
382
Poecile) are known to have high vigilance, aggressive mobbing behavior, and a sophisticated alarm
383
communication (Templeton et al. 2005) that extends to a large heterospecific audience (Langham et al.
384
2006). In the presence of black-capped chickadees (Poecile atricapilla), downy woodpeckers (Picoides
385
pubescens) decrease vigilance by 70% thereby increasing foraging rates (Sullivan 1984; also see Telleria
386
et al. 2001 for similar patterns among blue and great tits). Likewise, white-breasted nuthatches (Sitta
387
carolinensis) visit food patches more frequently in the presence of titmice (Dolby and Grubb 2000).
16
388
Quantifying long-term advantages are difficult and far less common, but potentially far-reaching. For
389
example, Dolby and Grubb (1998) demonstrated long-term consequences for nuthatches occupying forest
390
fragments in which parids were removed for the winter, i.e., the time of year when they lead mixed-
391
species flocks. Both energetic state and survivorship declined (all mortality events were in parid-removal
392
fragments), although mortality events were rare and the difference was not significant. A similar
393
exclusion experiment, but during the breeding season, by Forsman et al. (2002) demonstrated decreased
394
reproductive productivity in pied flycatchers (Ficedula hypoleuca) in the absence of parids. These studies
395
minimally demonstrate that there is an effect of the presence of informants that has fitness consequences,
396
ostensibly through the production of alarm calls. However, it may be more likely that it is that the
397
absence of alarm signals and presence of non-alarm vocalizations that indicates safety (e.g., Sullivan
398
1984, Moller 1992), thereby reducing stress, increasing foraging efficiency, and avoiding unnecessary
399
activity and energetic expenditure (Vitousek et al. 2007). The latest study by Hetrick et al. (this issue-a)
400
found that changes in the performance and structure of alarm calls in the Eastern Tufted Titmouse (which
401
reference predator type and magnitude of risk; also see Templeton et al. 2005) are mirrored by changes in
402
contact calls. This suggests that alarm calls themselves are not necessary to communicate perceived
403
predation risk. Certainly more research is needed to determine how fitness benefits arise in these species.
404
Predation, information, and landscape connectivity: As the preceding section suggests,
405
perception of predation risk is modified by the availability of information, such as publicly broadcast
406
alarm signals or contact calls (and also other behavioral acts such as looking upward or fleeing or moving
407
toward cover; e.g., Wong et al. 2005). It stands to reason that this information combines with the physical
408
environment, perceptual aptitudes of the organism, and the costs and benefits associated with decision-
409
making to influence (facilitate or impede) movement among resource or habitat patches (Taylor et al.
410
1993); that is, to influence the functional connectivity of landscapes (Lima and Zollner 1996, Bélisle
411
2005). Information may influence the resistance of some organisms to cross patches boundaries
412
(Desrochers and Fortin 2000, Sieving et al. 2004, Tubelis et al. 2006) and habitat gaps (Bélisle and
413
Desrochers 2002), variation that has been linked to patterns of extinction in birds (Moore et al. 2008).
17
414
Sieving et al. (2004) observed greater frequency of boundary patch crossings among songbirds when in
415
the presence of titmice. Tubelis et al. (2006) observed greater use of adjacent savannah habitat by mixed-
416
species flocks that form around sentinel species in relation to the level of predation risk (Ragusa-Netto
417
2002). Lastly, Wolters and Zuberbühler (2003) observed greater travel and broadening of niche space
418
(increased use of mid-canopy layer) in associating Campbell’s and Diana monkeys relative to isolated
419
species groups. These studies suggest the presence or absence of risk-base information may be akin to
420
landscape models that vary the quality of the matrix (e.g., Fahrig 2007). The effects may be especially
421
important in selecting migratory stopover sites (Nocera et al. 2008) where, because of lack of experience,
422
organisms are more vulnerable and less accurate at estimating predation risk (Pomeroy 2006, Pomeroy et
423
al. 2006, van den Hout et al. 2008).
424
As a consequence of the value of social information regarding predation risk, interspecific
425
sociality – from ‘loose’ attraction among heterospecifics to stable polyspecific associations - may be
426
attributed as much to the value of informants as to other ecological variables (Terborgh 1990, Goodale
427
and Kotagama 2005); however, demonstrating an effect of information per se, rather than simply selfish
428
herd, confusion, or dilution effects is difficult. Nonetheless, mixed-species groups of (most notably) birds
429
and monkeys form around specific nuclear sentinel species that signal a predator’s presence more often
430
and more reliably than others (Gaddis 1983, Bshary and Noë 1997, Goodale and Kotagama 2005)
431
suggesting that non-informational effects are insufficient to explain the phenomena (Wolters and
432
Zuberbühler 2003). Within these mixed-species groups, the risk-based information from sentinel species
433
extends to wider audiences (Goodale and Kotagama 2005, Langham et al. 2007) and leads to fitness
434
consequences (Dolby and Grubb 1998, Forsman et al. 2002) prompting their recognition as keystone
435
signalers (Hetrick et al. this issue-b). Moreover, an exchange of risk-based information in these systems
436
may represent an example of a stable resource exchange mutualism between species (Schwartz and
437
Hoeksema 1998).
438
439
CONCLUDING DISCUSSION
18
440
In this section we break away from featuring specific ecological contexts to discuss some implications of
441
information at larger ecological scales and as a central organizing principle. Because little research has
442
been conducted at these levels to date our discussion is necessarily brief and more conjectural.
443
Nonetheless, in advancing our case we highlight the need to understand the possible consequences of
444
information at these scales.
445
Information Webs: Breeding habitat selection and alarm calling are themes of the larger
446
phenomena of using proximate cues or alternative sources of information to locate areas of high resource
447
abundance and low mortality risk; decisions which often dominate the daily lives of individuals and the
448
ecological interactions among organisms (Stephens et al. 2007). The responses affect the strength of
449
species interactions (Vos et al. 2006), generate non-lethal effects of predation (Brown et al. 1999), and
450
when cues are from heterospecifics, generate trait-mediated indirect interactions (Peacor 2003). It should
451
be evident therefore that there exists an information web that complements and greatly increases the
452
complexity of food webs and interaction webs (Dicke and Vet 1999, Vos et al. 2006, Holt 2007). Of what
453
consequence then is this information for food or interaction webs, i.e., beyond an individuals’ (or
454
strategy’s) own fitness? Predator-prey models suggest that adaptive behavior tends to destabilize
455
predator-prey dynamics (i.e., simple food-web modules) unless it is based on imperfect information
456
(Brown et al. 1999, Luttbeg and Schmitz 2000). Of what consequence is food web structure for
457
information? We are not aware that this question has been properly framed before. Vos et al.’s (2001)
458
work on infochemical mimicry (increasing noise) and confusion effects in tri-trophic interactions shows
459
that information can be a decreasing function of species diversity, specifically host specialization. In high
460
diversity systems infochemicals produced from herbivore-damaged leaves attracted parasitoids to plants
461
containing many individuals of non-host species. In theoretical analyses, effects in high diversity systems
462
weaken species interactions and lead to stabilized dynamics at intermediate species richness (Vos et al.
463
2001). Hence, community structure affects information that feedbacks to lower diversity.
464
Information as a third niche axis: We contend that information share center stage with abiotic
465
conditions and biotic resources as a third set of niche axes. To take an example, sunlight is an abiotic
19
466
factor that is converted through photosynthesis and metabolic processes into a biotic resource for
467
heterotrophs. Sunlight also produces warmth and light (by definition) for activities such as foraging.
468
However, imagine a world where the daily mean number of hours of sunlight was equivalent to Earth’s
469
but randomly distributed throughout the year. Outside of H2S reduction as a source of energy, would life
470
even be possible? Perhaps, we but suggest it is the pattern (information) of sunlight in the form of
471
circadian rhythm that makes ecological systems what they are today. In a recent review, Resco et al.
472
(2009) discuss the ecological implications of plants’ ability to tell time noting that “[T]he circadian clock
473
affects gas exchange by ‘anticipating’ cycles of dawn and dusk” (Resco et al. 2009: 4; the anticipation
474
hypothesis). For instance, mutant, arrhythmic Arabidopsis show a 40% decrease in net carbon fixation
475
compared to wild-type (Dodd et al. 2005). Depending on the organism, sunlight is a consumable resource
476
or an abiotic condition; circadian rhythm is information.
477
Circadian rhythm of sunlight presents an extreme example, so consider something more mundane
478
but still exciting to most ecologists: species interactions. Predators kill or exert non-lethal effects such as
479
fear on their prey, but predators may also produce inadvertent social cues to their location in time and
480
space (i.e., information). Such information can enable prey to find spatial or temporal refugia from
481
predators; it is no wonder that predators (and prey) may behave with ‘purposeful unpredictability’ (sensu
482
Roth and Lima 2007; also see Gripenberg et al. 2007). Within the framework of consumer-resource
483
interactions, prey that acquire information to use to avoid predation can sustain zero-net population
484
growth at a higher density of predators, i.e., higher P* (Holt 1994). This in turn increases predators’ R*
485
(minimum prey abundance to achieve zero-net population growth) since informed prey are more difficult
486
to capture (Brown et al. 1999). The process can also start the other way around: information about prey
487
lowers a predator’s R*. If any of these feedback loops lowers, say the predator’s population density, there
488
may be reduced value to acquire information by the prey and their vulnerability goes up. The idea that
489
limiting factors (R* and P*, resources and predator, respectively) influence coexistence places
490
information as a critical element influencing community structure through population processes such as
20
491
competitive exclusion (Tilman 1980, Holt et al. 1994); a process itself considered as a fundamental
492
property of the ecological niche (Leibold 1995).
493
Information as an Ecosystem Process: In the preceding section we pointed out that arrhythmic
494
Arabidopsis shows a decrease in net carbon fixation. Resco et al. (2009) would have us scale up these
495
effects suggesting that the circadian clock in plants drives gas exchange at the level of biosphere-
496
atmosphere interactions. If we accept this premise, if only to explore the consequences, then (1) Vos et
497
al. (2001) demonstrates that information can drive (in non-linear fashion) diversity and (2) Resco et al.
498
(2009) suggests information can drive ecosystem process. The point we wish to make is that ecologists
499
currently take a fairly rigid casual interpretation of ecosystem process-diversity relationships: diversity
500
drives the former. Yet these ignore the potential role of information as direct and indirect (e.g.,
501
information  diversity  ecosystem process) driver.
502
Noise and Info-disruption: Modern anthropogenic processes are greatly accelerating the loss of
503
species richness and diversity (Pimm et al. 2006, Bradshaw et al. 2009). Less appreciated is the loss of
504
information, including information processing (i.e., disruption) and transmission (e.g., low urban signal-
505
to-noise ratios; Rabin et al. 2006, Slabbekoorn and Ripmeester 2008). For example, info-disruption
506
(Lürling and Scheffer 2007) is the disturbance to chemical information transfer caused by pollutants, such
507
as heavy metals, surfactants, and pesticides. [Info-disruption is analogous to endocrine-disruption, which
508
itself is essentially a signal detection problem at the level of chemical recognition]. In aquatic systems
509
these substances are known to negatively affect anti-predator responses to chemical alarm signals in fish
510
(impaired avoidance), algae (reduced protective colony formation), and cladocerans (inhibited protective
511
crest development); see Lürling and Scheffer (2007). The noise associated with wind turbines interferes
512
with acoustic alarm calls among California ground squirrels (Spermophilus beecheyi) and subsequently
513
affects vigilance patterns and flight to burrow behavior (Rabin et al. 2006). Urban noise similarly affects
514
song (signal) efficiency in birds in turn affecting foraging-vigilance tradeoffs (Quinn et al. 2006) as well
515
as lowered abundance and reproductive success near highways (Slabbekoorn and Ripmeester 2008).
516
Species that communicate at frequencies above urban noise are little affected, and may come to dominate
21
517
urban communities leading to faunal homogenization (Slabbekoorn and Ripmeester 2008). Invasive
518
species can also alter information flows, such as in the acoustic-orienting parasitoid fly (Ormia ochracea)
519
selecting for a song-less morph of the field cricket (Teleogryllus oceanicus); see Zuk et al. (2006). Lastly,
520
phenological mismatching due to global climate change is a consequence of information disruption that
521
has received considerable attention (e.g., Both et al. 2009, Brooks 2009). Mismatching occurs when the
522
timing of developmental or behavioral processes, such as hibernation, migration, or reproduction, is
523
altered triggered directly (temperature) or indirect from climatic cues (e.g., flowering phenology)
524
Examples such as these are unfortunately too common, and the prospect of deteriorating
525
information webs led Holt (2007) to ask whether this was the “next depressing frontier in conservation?”
526
The sum of these effects can be large indeed and need prompt attention by conservation biologists. Not
527
just for conserving species but also the preservation of animal cultures (Laiolo and Tella 2007). We
528
recognize the value of genetic information by preserving genomes; it is time now to expand this
529
conservation priority to non-genetic biological information.
530
531
Conclusions: With one or more major reviews published each year since 2000 (see Introduction) one
532
may ask whether information is a passing fad or a general theme (we’re not arguing the only theme)
533
around which to organize empirical and theoretical research and conservation priorities in ecology and
534
evolution. We believe the latter, but the answer will only come with further development of an Ecology
535
of Information Framework, one that integrates all the sub-disciplines in Figure 1. Through our overview
536
and the papers that follow in this special feature on the Ecology of Information, we hope to have …….
537
538
ACKNOWLEDGEMENTS (566)
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KAS’s research in the ecology of information is supported by a grant from the National Science
540
Foundation (DEB 0746985). JAvG was supported by the Netherlands Organization for Scientific
541
Research (NWO) and by the Royal Netherlands Academy of Arts and Sciences (KNAW). This is
542
publication xxxx of the Netherlands Institute of Ecology (NIOO-KNAW) and xxx of the Centre for
22
543
Wetland Ecology. This paper was improved by comments of Luc-Alain Giraldeau and an anonymous
544
reviewer. Lastly, we are grateful to Per Lundberg and Linus Svensson for their support of this special
545
feature.
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23
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34
1019
FIGURE LEGENDS
1020
Figure 1: Domains of the disciplines that investigate information in biology at the organism-level. The
1021
figure is adapted in part from Koops (1998).
1022
1023
Figure 2: Consider information as a change in the probability (; or probability distribution) of an event
1024
or state, e.g.,  might be a prey’s estimate of the probability a predator is present in a forest patch
1025
occupied by the prey or male quality as judged by a female’s observations of male display. An individual
1026
has a prior estimate, PR, which is altered (PO; posterior) as information is processed from an observation
1027
(e.g., an alarm call). PO decays over time (this functional form is illustrated as D) as information is
1028
temporally discounted, and eventually settles back to the prior value (PR). The form of the decay curve
1029
will be highly variable and specific to context. Information may be used to permanently assign a state
1030
(e.g., an individual’s gender or a fishless pond) that may result in a permanent developmental switch
1031
(polyphenism) or time its development
1032
time or space (behavioral). Lastly, norm of reaction
1033
Evolutionary traps are produced when one or more of these three responses (PR, PO, and D) no longer
1034
matches its environment because the type or reliability of the cue or the state it ‘refers’ to has changed.
. Alternatively, states repeatedly cycle between states in
1035
1036
Figure 3: Mean persistence time ( SE) as a function of the number of component patches in the network
1037
in a temporal autocorrelated landscape ( = 0.7). Dispersal is even (black line) or using the WSLS rule
1038
(for more details see Schmidt 2004).
1039
1040
1041
1042
35
1043
1044
FIGURE 1
Information Source
Sensory ecology,
Cognition,
Psychology
Transmission
Reception
Translation
Perception
Evaluation
Decision, i.e., response
of the receiver

Behavioral , developmental
and life history changes
Consequence
Populations
(public info)
Evolution
(genetic info)
Communities
Evolutionary Ecology
Ecosystems
1045
1046
1047
1048
1049
1050
1051
1052
36
1053
1054
1055
1056
FIGURE 2
PO

Polyphenism or Norm of Reaction
D
behavior
Norm of reaction
PR
cue or
signal
1057
1058
1059
1060
1061
Time (or space)
37
1062
1063
1064
1065
FIGURE 3
Mean population persistence (yrs)
2000
WSLS, rho = 0.7
1500
1000
500
Even dispersal, rho = 0.7
0
2
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
3
4
5
6
7
Number of patches
38
8
1076
1077
Box 1: Glossary of terms: Note, these definitions closely follow Wagner and Danchin (this issue)
1078
Cues – A detectable fact that is non-intentionally produced. Includes facts produced by physical agents
1079
or inadvertently produced by biological agents.
1080
1081
Signals – A trait or behavior of a signaler evolved specifically to alter the behavior of the receiver in a
1082
way to benefit the signaler. The change in receiver behavior should also have evolved to enhanced
1083
receiver fitness.
1084
1085
Public information – Information that is in the public domain and potentially available to any organism.
1086
1087
Private information - Information that is undetectable to other organisms through direct means. Private
1088
information may become public through indirect means. This follows a sequence (using the model by
1089
Seppänen et al. 2007): 1) observation of an event or state by a primary observer; 2) a decision that is
1090
manifest via change in behavior (i.e., an action) of the primary observer; 3) the consequence of the
1091
action. For example, Wong et al. (2005) demonstrated that sand fiddler crabs (Uca pugilator) use
1092
observations of threat-induced responses of neighbors (stage 2 in the sequence above) to guide their
1093
own refuge-seeking behavior. Birds settling on territories where conspecific reproductive success was
1094
high in prior years (see Breeding habitat selection) is an example of observing the consequences (stage
1095
3) of past decisions by conspecifics.
1096
1097
Socially acquired Information – Information extracted from other individuals (con- or heterospecific) be
1098
they signals or (inadvertent) cues (including actions and consequences). Note: all social information
1099
must be public.
1100
39
1101
Interceptive and Social Eavesdropping (Peake 2005) – A mechanism of acquiring social information from
1102
signals (i.e., communication) between two (or more) individuals. In interceptive eavesdropping
1103
individuals acquire information about their environment (e.g., an alarm call gives information about the
1104
presence of a predator), whereas in social eavesdropping individuals acquire information regarding the
1105
social relationship between the communicating parties (e.g., dominance hierarchy, kinship).
1106
40
1107
Box 2: Breeding habitat selection/settlement strategies as a function of spatial heterogeneity and
1108
temporal predictability. Four regions are identified across what is in reality a continuum. In the top
1109
row, high variation in individual (fine scale) site quality exists, whereas patch quality, averaged over the
1110
many individual sites it contains, is low. Fidelity to successful sites is favored provided temporal
1111
predictability is high (top right). Likewise, dispersal from unsuccessful sites is favored and individuals
1112
should prospect for future sites not patches since patch reproductive success has little spatial variation.
1113
We call this the win-stay, lose-prospect strategy (WSLP). When spatial variation is higher between
1114
patches than sites (bottom row) individuals should prospect for information on patch reproductive
1115
success provided temporal predictability is high (bottom right). Fidelity or dispersal should be linked
1116
closely to patch reproductive success rather than an individual’s own success. When temporal
1117
predictability is low an individual should prospect during the pre-breeding season for current, proximate
1118
cues of reproductive potential at the scale of greatest spatial: at sites (top left) or patches (bottom left).
1119
Conspecific attraction is only favored when spatial variation is higher between patches than sites,
1120
assuming individuals preempt sites by occupation. It further requires that some individuals (call them
1121
prospectors) use patch reproductive success as a settlement cue. In other words, conspecific attraction
1122
is an information-scrounger strategy that requires information collected by the information-producer
1123
strategy of prospectors (Dall et al. 2005).
1124
1125
1126
1127
41
1128
1129
1130
1131
BOX 2
Spatial
heterogeneity
Site
Fine:
site > patch
Patch
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
Coarse:
patch > site
(1)
(2)
Fidelity: none
Info: none
Conspecific attraction: no
Prospect: pre-breeding;
prospect at sites
Fidelity: to site
Info: pers. repro. success
Conspecific attraction: no
Prospect: pre- or postbreeding; prospect at sites
(3)
(4)
Fidelity: none
Info:
none
PRS
high
Conspecific attraction: ?
Prospect: pre-breeding;
Prospect at patches
Fidelity: to patch
Info: patch repro. success
Conspecific attraction: yes
PRS pre- or postProspect:
breeding; prospect at
patches
low
high
temporal predictability
42