Behavioral Ecology Advance Access originally published online on February 19, 2008
Behavioral Ecology 2008 19(3):539-545; doi:10.1093/beheco/arn005
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Competitor density cues for habitat quality facilitating habitat selection and investment decisions
Department of Ecology and Evolution/Animal Ecology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18 D, SE-75236 Uppsala, Sweden
Address correspondence to J.T. Forsman, who is now at the Department of Biology, University of Oulu, PO Box 3000, FI-90014 Oulu, Finland. E-mail: jukka.forsman{at}baanamail.fi.
Received 28 July 2007; revised 20 November 2007; accepted 20 December 2007.
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ABSTRACT |
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The theory of species coexistence predicts avoidance between species that compete for similar resources. Recent studies, however, have suggested that facilitation is also possible if competitor density provides information about resources. Optimal solution to trade-off between competition and facilitation is predicted to occur at intermediate competitor densities. We tested this hypothesis by experimentally creating a density range of resident tit species (Parus spp.), and measured the response of a competitively subordinate migratory bird, the collared flycatcher (Ficedula albicollis) in terms of habitat preference (settlement order and density), offspring investment (clutch size and primary sex ratio of offspring), and reproductive success (number and condition of nestlings). We show that most habitat choice and investment decisions of flycatchers were unimodally related to tit density, whereas reproductive success decreased linearly with increasing density. Flycatchers thus made mismatched investment decisions at the artificial tit densities because manipulation disassociated the natural correlation between habitat quality and population density. Apparently low and high tit densities were perceived as indication of poor quality habitat in terms of low amount or quality of resources/high mortality risk and high costs of competition, respectively. This demonstrates that competitor density can be used in assessing overall habitat quality in habitat selection and offspring investment decisions, integrating information on resources and competition.
Key words: facilitation, habitat selection, interspecific competition, offspring investment, public information, social information, species interactions.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONFUNDINGREFERENCES |
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Most organisms face a heterogeneous and to a certain extent unpredictable world, where crucial decisions affecting fitness have to be made. Two such important decisions are where to reproduce and how much to invest in progeny. Optimal habitat choices and investment decisions are complicated by spatial and temporal variation in factors affecting the quality of habitats, such as the amount and quality of food resources, intensity of predation and competition, and microclimate conditions (e.g., Gustafsson 1987
; Robinson et al. 1995; Sinervo and DeNardo 1996; Forsman et al. 1998; Martin 2001; Shima and Osenberg 2003; Marshall and Cooper 2004). The theory of habitat selection assumes that individuals are aware of the relative differences in quality among potential habitat patches and choose the best possible option (Fretwell and Lucas 1970; Morris 2003). Yet, very little is known how well animals are informed about the relative quality of habitat patches, how they gather information, and how extensively information is used in habitat selection and reproductive investment decisions.
Recent studies have revealed that animals in various taxa indeed actively gather information about environmental conditions to better match their behavior with the prevailing conditions. Accumulating evidence indicates that individuals use proximate cues derived from the presence, behavior, or performance of conspecific individuals in assessing habitat quality (social information; see Danchin et al. 2004; Dall et al. 2005 for reviews).
Even though most studies on social information use have focused on conspecifics, individuals of other species also can provide information if they at least partly share the same resource needs or mortality factors. Thus, paradoxically, the larger the overlap in niches between species, the better opportunities there are for interspecific information use and the strongest competitors are expected to provide the most accurate information (Parejo et al. 2005; Seppänen et al. 2007). Compared with information derived from conspecifics, ecological and evolutionary differences between species may decrease the value of interspecific information. However, heterospecifics may also reveal more diverse and updated information because they usually experience and interact with the environment in a slightly different way or time. There is a rapidly growing body of evidence that heterospecifics indeed are used as a source of information concerning habitat quality, predation risk, and food location in birds, mammals, reptiles, fish, and insects (see Seppänen et al. 2007 for a review).
Population density is potentially a good source of information. Theoretical and empirical studies show that due to habitat choice, productivity, and mortality, population density is generally positively correlated with habitat quality (e.g., Fretwell and Lucas 1970; Fretwell 1972; Shima and Osenberg 2003; Donahue 2006; Johnson et al. 2006). Thus, competitor density may provide a cue to available habitat, or more precise information on habitat quality, facilitating habitat selection. Recent theoretical studies predict that optimum conditions relative to competitor density occur at some intermediate density (Mönkkönen et al. 1999; Fletcher 2006; see also Forsman et al. 2002). This is because costs of competition also increase with density (e.g., Fretwell and Lucas 1970; Rosenzweig 1981; Morris 2003; Shima and Osenberg 2003) and eventually outweigh the benefits of finding higher habitat quality, whereas low densities suggest such poor quality habitat or high mortality risk that even the low costs of competition cannot compensate for. Thus, the preference for, the investment in and the outcome of reproduction is expected to be a unimodal function of competitor density.
In temperate and boreal breeding bird communities, resident tits (Parus spp.) and migratory birds provide a promising system to examine interspecific information use. Due to their resident lifestyle, tit density presumably reflects habitat quality, which then can be used as a cue by migratory birds that share similar resource needs with tits. Especially flycatchers (Ficedula spp.), which are tropical migrants, have overlapping mortality factors and resource needs with tits, in terms of nest sites and food resources. Gustafsson (1987) showed that tits are dominant competitors over collared flycatchers (Ficedula albicollis) that suffer lowered nesting success when nesting in high tit density areas. Further, many migratory birds have been shown to be attracted to the presence of tits (Mönkkönen et al. 1990, 1997; Forsman et al. 1998; Thomson et al. 2003), and flycatchers gain fitness benefits (Forsman et al. 2002) and can even blindly acquire behavioral traits associated with nest-site characteristics from tits (Seppänen and Forsman 2007). Flycatchers also have been shown to visit tit nests at the time when tits are feeding their nestlings, possibly gathering interspecific performance-based information (Forsman and Thomson 2008).
We conducted an experiment to test whether a subordinate species—the collared flycatcher—is integrating information on resources and competition from dominant competitor—the resident tits—density to make breeding habitat and reproductive investment decisions. Because population density and habitat quality are positively correlated, potential information use and its impact on habitat selection and investment decisions is difficult to detect observationally. We therefore experimentally created a continuum of tit densities at habitat patches that uncoupled the positive relationship between tit densities (and cost of competition) and quality of resources (e.g., food, nest sites), and local conditions (e.g., microclimate conditions, mortality risk), whereas the negative effects of tit density remained. Thus, if flycatchers are using the local tit density as a proxy for habitat quality (net effect of anticipated resource quality and costs of competition), we predicted that flycatchers' 1) habitat preference (settlement order measured as onset of egg laying and population density) and 2) investment in reproduction (clutch size and primary sex ratio of offspring) is a nonlinear, unimodal function of manipulated tit density. In contrast, if they use tit density only as a measure for costs of competition, a linear and negative association between habitat preference and offspring investment and tit density is expected. If flycatchers personally assess resource quality, we expect no association between tit density and habitat preference and reproductive investment because experimental design disassociated naturally occurring positive relationship between density (and costs of competition) and resource quality. Further, because of the experimental design, only the costs of competition covaried systematically with density and the reproductive outcome is therefore expected to have a negative linear relationship with tit density.
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METHODS |
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Study populations and experimental design
The experiment was conducted on the island of Gotland in the Baltic Sea, Sweden, in 2005, using 13 woodlots (3.0–7.0 ha in size) that were randomly assigned to different tit density manipulation regimes. All study plots were newly established in winter 2004/2005 ensuring that flycatchers did not have any prior information that could have affected their habitat selection or investment decisions (Doligez et al. 2002
). We experimentally created a range of breeding densities of resident tits (great and blue tits; Parus major and Cyanistes caeruleus) within the natural range (Gustafsson 1987) from low (close to 0 pairs/ha) to high (close to 4 pairs/ha) to test whether collared flycatchers are using the density of tits as a cue for habitat quality. Experimental tit densities were mainly reached by manipulating the number of available nest-boxes (0–8 boxes/ha) in late winter before the arrival of flycatchers. In the study plots randomly assigned to have the lowest densities of tits (4 plots), no open nest-boxes were available and the number of tits breeding in natural cavities was decreased by catching them with mist nets and transferring them elsewhere. In the other end of the continuum, 8 nest-boxes per hectare were provided for tits and some of the removed tits were also released there in attempt to augment tit density. A continuum of tit densities was used instead of replicated treatments of fixed density because without a priori knowledge of optimal tit density for flycatchers, a unimodal pattern can be easily missed (Forsman et al. 2002). After the aimed tit densities were reached on study plots and before the arrival of flycatchers, the number of boxes available for flycatchers was set equal in all study plots (3 boxes/ha). Procedures were approved by the Swedish National Board for Laboratory Animals.
Tit densities were inferred from great and blue tits breeding in nest-boxes and by standard territory mapping surveys, which give the most accurate density estimates (Robbins 1970). Surveys were done during the peak breeding season between May 14 and June 10 in fair weather at 5 different occasions during different times in the morning (04.30–09.30 h). Surveys were done by J.T.F. and M.B.H.
Measurements
We used occupancy order (initiation of egg laying) and population density of flycatchers as estimates of habitat preference. Habitat preference is known to correlate with arrival time, as high-quality habitats and territories are occupied first (Fretwell 1972; Alatalo et al. 1986) and they also are assumed to reach highest densities (Fretwell 1972). Clutch size and the primary sex ratio of offspring were used to measure reproductive investment. We predicted that the clutch size is greatest at intermediate tit densities if flycatchers are using tits as a source of information and for the same reason we expect more male offspring in such areas and more females in low and high tit density areas. A recent meta-analysis showed that the strongest pattern for facultative sex ratio adjustment was related to habitat quality (Ewen et al. 2004). Like in most bird species, male collared flycatcher is the philopatric sex (Pärt and Gustafsson 1989). Further, the quality of woodlots is temporally autocorrelated in our study area (Doligez et al. 1999). Therefore, males will give the highest fitness output because they more likely return to breed to high-quality areas in the preceding breeding season (Julliard 2000). The sex of chicks and embryos was determined using molecular methods, using the primers P2 and P8 amplifying 2 homologous genes (Griffiths et al. 1998), using the amplification procedures as in Sheldon et al. (1999). The products, 2 different-sized products for females and 1 for males, were visualized using silver staining (Bassam et al. 1991).
To assess reproductive outcome, the number, body mass and length of tarsi of nestlings at the age of 13 days were recorded. These fitness-related variables (Lindström 1999) are commonly used in avian studies. Mass was measured using a digital balance (accuracy ± 0.1 g) and tarsus by a digital caliper (±0.01 mm). In total, data included 109 flycatcher pairs of which 15 were excluded from the primary sex ratio analyses due to missing samples from nestlings or eggs. In the breeding success analyses, we included only broods that had both parents nurturing the chicks and 12 additional broods were therefore excluded from the analyses. In analyses of reproductive success, only broods with at least one living nestling at the age of 13 days were included.
Statistical analyses
The effect of manipulation on tit densities was examined by testing the relationship between manipulation effort (available nest-box density) and tit density using each study plot as sampling unit. To see if the manipulation of tit densities had an effect on the flycatcher density, a regression analysis was conducted using plotwise density as sampling unit. In the rest of the analyses, brood was used as a sampling unit and study plot as random factor to account for its effect on parameter estimates. The primary sex ratio of the broods was analyzed using generalized linear mixed models (GLMM) with study plot as random factor, with binomial errors, a logit link function, using males in a brood as response variable as well as controlling for overdispersion (McCullagh and Nelder 1989). The rest of the response variables were analyzed with general linear mixed models with study plot as a random factor. The normal distribution of response variables was verified by inspecting normal quantile plots (Sokal and Rohlf 1995). Homogeneity of error variances was verified by inspecting residual plots (Quinn and Keough 2002).
We principally compared 2 a priori–determined competing models when analyzing the effect of treatment on response variables to test our predictions. These 2 models test whether the response variables are linearly or unimodally related to manipulated tit density. The linear model included only tit density, whereas the nonlinear model included also a squared tit density term. We also included additional explanatory factors as covariates to remove their effects on response variables. Date of the first egg partly reflects adult quality and correlates negatively with offspring investment (Klomp 1970) and was therefore included in analyses of offspring investment. Clutch size was included in analyses of reproductive success because it potentially affects average brood condition. In addition, to better evaluate the effect of tit density (linear and nonlinear) on the response variables, we also employed, if possible, a third model that only included covariates. This was done for clutch size and all variables of reproductive success. The best model was selected using Akaike's information criterion (AIC, Burnham and Anderson 2002). However, Venables and Ripley (2002) and Pinheiro and Bates (2000) emphasize that in GLMM resulting log-likelihoods are not accurate and, hence, AIC values for models cannot be constructed either. In the analysis for the sex ratio of nestlings, we therefore compared linear and nonlinear tit density models employing conventional P-value examination. Analyses were done by SPSS 11.5. software and in R environment using GLMMPQL function.
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RESULTS |
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There was more than a 10-fold difference in the manipulated tit density among study plots (0.33–3.87 pairs/ha), and the density of available nest-boxes in woodlots significantly explained tit density (regression, F1,12 = 24.42, P < 0.0001) showing that the tit density manipulation was successful. The age (proportion of young females) or body characteristics (body mass and tarsus length of males and females and forehead patch size of the male) of adult flycatchers were not related to tit density linearly or unimodally (results not shown in detail) suggesting that the observed differences were not due to phenotypic differences of adults.
Linear tit density model fitted better than nonlinear model to the observed flycatcher density (Table 1), but explained very little of the observed variation and the effect of tit density was not statistically significant (F2,10 = 0.197, P = 0.824, R2 = 0.5%). However, flycatchers' occupancy order of study plots and investment in clutch size were better explained by the nonlinear than the linear model in terms of the AIC value (Table 1). Onset of egg laying was clearly unimodally related to tit density (tit density: effect size [SE] = –4.34 [1.64], F1,101 = 6.98, P = 0.010, R2 = 6.45%; tit density2: effect size [SE] = 0.91 (0.41), F1,101 = 5.02, P = 0.027, R2 = 4.73%). Flycatchers breeding at intermediate tit densities initiated egg laying on average about 2.5 and 4.0 days earlier than at high and low tit densities, respectively (Figure 1A).
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Tit density also affected investment in offspring number. Highest clutch sizes were observed at intermediate tit densities, whereas flycatchers breeding at the highest and lowest densities laid about 0.6 and 0.3 eggs less, respectively (Figure 1B; tit density: effect size [SE] = 0.45 [0.28], F1,100 = 2.66, P = 0.106, R2 = 2.58%; tit density2: effect size [SE] = –0.13 [0.07], F1,100 = 3.51, P = 0.064, R2 = 3.38%; laying date: effect size [SE] = –0.07 [0.02], F1,100 = 3.51, P < 0.000, R2 = 15.49%). This result suggests that manipulated tit density affected clutch size in 2 ways: first, delayed onset of egg laying at low and high tit densities had a negative effect on clutch size and, second, after accounting for the effect of onset of egg laying, tit density also had a discernible unimodal though statistically marginally nonsignificant effect on clutch size (Figure 1B). Strong effect of onset of egg laying on clutch size was reflected to the results of model comparisons whereby the third model, including only the onset of egg laying, was the best (Table 1). Low
AIC and AIC weights between the nonlinear and linear heterospecific models suggest that these models are both as likely. However, this is again due to the strong effect of laying date alone, as in the linear model tit density had no significant effect (laying date: F1,102 = 22.78, P < 0.000, R2 = 18.26%; tit density: F1,102 = 0.71, P = 0.401, R2 = 0.70%). Also in line with our predictions, the nonlinear model was clearly better than the linear model explaining the observed primary sex ratios of offspring. The highest proportion of males was observed at intermediate tit densities (ca. 60% of the brood) and lowest (ca. 40% of the brood) at the low and high ends of the tit density range (Figure 1C; n = 93; tit density: effect size [SE] = 1.13 [0.42], t = 2.67, P = 0.024, R2 = 7.12%; tit density2: effect size [SE] = –0.27 [0.10], t = 2.66, P = 0.024, R2 = 7.10%; laying date: effect size [SE] = 0.07 [0.15], t = 2.36, P = 0.021, R2 = 5.63%; clutch size: effect size [SE] = –0.07 [0.15], t = 0.52, P = 0.602, R2 = 0.29%). In the linear model, the tit density term was not statistically significant (effect size [SE] = 0.034 [0.09], P = 0.727, t = 0.36, R2 = 0.13%).
In contrast with the investment decisions, linear tit density model explained best most variables reflecting reproductive output and offspring condition and they were negatively related to tit density (Table 2). The third model, including only clutch size, fitted poorly with the data emphasizing the importance of tit density on reproductive success of flycatchers. Highest number of nestlings (tit density: effect size [SE] = –0.38 [0.16], F1,79 = 5.95, P = 0.017, R2 = 6.99%, clutch: effect size [SE] = 0.87 [0.20], F1,79 = 18.88, P < 0.000, R2 = 19.29%) and their body mass (tit density: effect size [SE] = –0.38 [0.13], F1,79 = 7.48, P = 0.008, R2 = 8.64%, clutch: effect size [SE] = 0.28 [0.18], F1,79 = 2.43, P = 0.123, R2 = 2.98%) were observed at low tit densities and they monotonically decreased with increasing tit density (Figure 2A,B). There was again relatively high uncertainty about the best model, as indicated by the low (<2.0) AIC values and weights between the linear and nonlinear models (Table 2). However, in the nonlinear models for the number of nestlings and their body mass, all linear and nonlinear tit density terms were statistically insignificant with minute explanatory power (P > 0.301, R2 = 0.01–1.36%). The exception was tarsus length in which the nonlinear heterospecific model best fitted the observed data according to the AIC value (Figure 2C; tit density: effect size [SE] = 0.24 [0.22], F1,78 = 1.34, P = 0.290, R2 = 1.45%; tit density2: effect size [SE] = –0.12 [0.06], F1,78 = 4.52, P = 0.037, R2 = 5.51%; clutch: effect size [SE] = 0.04 [0.07], F1,87 = 0.38, P = 0.540, R2 = 0.48%). However, low AIC and evidence ratio between the linear and the nonlinear heterospecific models suggests that these models are both as likely.
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DISCUSSION |
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Our results on the habitat preference, reproductive investment, and reproductive success of collared flycatchers relative to manipulated tit density suggest that competitively subordinate flycatchers used the density of dominant resident tits in settlement decisions and in assessing overall habitat quality. Flycatchers' settlement order measured as onset of egg laying was unimodally related to tit density, highest preference observed at intermediate tit densities. Further, investment in offspring in terms of clutch size and the proportion of males in broods was unimodally related to tit density. Effect on clutch size was mostly due to expedited or delayed onset of egg laying that was affected by the manipulation as suggested by multimodel selection based on AIC. However, tit density also had an independent, though statistically only marginal effect on clutch size explaining about 6% of the observed variation. This effect is biologically significant, considering the low variation in clutch size (mean 6.1, standard deviation 0.67 in these data) implying that flycatchers also used tit density as integrated information on resource quality and costs of competition. Had flycatchers used only personally acquired information about resources as a basis of settlement and investment decisions, there should have been no effect of tit density because manipulations were assigned randomly (Fretwell 1972
). If flycatchers would have perceived tit density only as a proxy for costs of competition, their settlement and investment decisions should have been negatively associated with manipulated tit density.
In contrast to investment decisions, most variables reflecting reproductive success were highest at low tit densities and decreased with increasing tit density. Models including tit density terms were clearly better than the third model with covariate only demonstrating the importance of tit density as a vital part of habitat quality for flycatchers and effect of competition on reproductive success. Because tit density manipulations were randomly assigned, natural variation in habitat quality among study plots should not have caused the observed patterns suggesting that flycatchers made mismatched investment decisions. At low densities, the actual resource quality was probably higher and at intermediate and high densities, it was lower than resident densities would suggest in natural setting, resulting in nonoptimal breeding investment relative to costs of competition (Gustafsson 1987). Because cost of competition was the only factor varying systematically along the gradient of manipulated tit density, the breeding success of flycatchers was negatively associated with tit density. It therefore is most likely that flycatchers used heterospecific competitor density as a proxy for habitat quality: high and low densities anticipate low habitat quality in terms of high costs of competition and poor quality resources or high mortality risk, respectively, whereas intermediate tit densities provided optimal balance between habitat quality and costs of competition.
For the number and tarsus length of nestlings, the multimodel selection procedure based on AIC showed ambiguity between nonlinear and linear tit density models. These results, however, do not necessary reflect a weak response to the manipulation. The manipulation of tit density had a unimodal effect on flycatcher's reproductive investment that can explain why nonlinear tit density model was almost as good as linear tit density model for the number of nestlings. Probably the cost of competition did not cause such high nestling mortality that it would have switched the originally unimodal relationship in clutch size to a negative linear for the number of nestlings. Negative effects of competition were better expressed in traits reflecting the condition of nestlings. The only exception of linear effects of density on breeding success was tarsus length of flycatcher nestlings, where tit density had a significant unimodal effect (Table 2; Figure 2C). This result may actually reflect females' investment decisions through maternal effects. Schwabl (1996) experimentally demonstrated that testosterone enhances nestlings' tarsus growth. If females have adjusted the hormonal input in egg yolks in response to the perceived tit density, effects are more discernible in tarsus length because it develops faster than mass, before the negative effects of interspecific competition over food accumulate during nestling period (see Gustafsson 1987). Because flycatchers' breeding cycle lags about 2 weeks behind than that of tits', their nestlings' food demand peaks at the time when large tit broods and fledglings have already harvested shared food resources. This probably explains why effects of competition are expressed more strongly in characteristics developed later during nestling period.
Interspecific information use has important direct and indirect effects on where and when individuals settle and on individual fitness. On average, manipulated tit density delayed or advanced flycatchers' settlement decision up to 4 days. This is a conservative estimate because we used onset of egg laying as a measure of settlement preference as later arriving individuals can complete nest building and start laying in a few days, whereas for early arriving birds it often takes longer (Forsman JT, personal observation). Swift settlement has been shown to have substantial fitness effects in time-limited breeders and there is directional selection for earlier initiation of egg laying (Sheldon et al. 2003). Here, timing of breeding affected flycatchers' investment decisions (clutch size and primary sex ratio of offspring), which then also reflects to the final reproductive output. Fletcher (2007) also has experimentally demonstrated interspecific information use between 2 North American migratory birds in which the information user showed preference, revealed in arrival dates and density, for intermediate competitor densities. Fletcher (2007) suggested that the result was mainly due to social stimulus during habitat selection phase that facilitates finding suitable habitat patches in the landscape. Our results suggest more extensive information use in which competitor density is used at habitat selection phase, but in addition, the unimodal effect of tit density on decisions (independently of the laying date) show that competitor density integrates information about resource quality and competition.
In contrast with the results by Fletcher (2007), neither this study nor the study of Forsman et al. (2002) shows a density response of flycatchers to tit density manipulation. However, density of many other migratory birds, mostly species belonging to the same foraging guild with tits (foliage gleaners), has been shown to be positively associated with tit density (Thomson et al. 2003; Forsman JT, Hjernquist MB, Gustafsson L, unpublished manuscript). These and the present results further support the conclusion that the density of resident tits is used as a proxy for the amount and quality of resources by migrant birds and in adjusting investment in offspring accordingly. Flycatchers' divergent density response is probably explained by shortage of suitable nest sites. As a secondary cavity nesting species, available and high-quality nest sites are valuable and attraction to nest-boxes may hinder emergence of density differences among habitat patches.
Even though interspecific information use has been mostly studied in avian systems, such information acquisition strategy is applicable in any system where 1) information available for the 2 species differs (due to, e.g., residency or cognitive limitations), 2) information is publicly observable in the actions of its holder (because of, e.g., earlier onset of breeding), and 3) ecological overlap between species ensures sufficient relevance of information. Species pairs potentially fulfilling these requirements are easily identified in most ecosystems and interspecific information use therefore has important ecological and evolutionary implications. First, it clearly affects settlement order of habitat patches and distribution and density of individuals in the landscape (Mönkkönen et al. 1990; Forsman et al. 1998, 2002, 2007; Thomson et al. 2003; Parejo et al. 2005; Fletcher 2007, this study). Such effects are not considered in traditional models of habitat selection (Morris 2003) or metapopulation dynamics (Hanski and Simberloff 1997). Corresponding phenomenon within species (conspecific attraction and social information use; e.g., Stamps 1988; Doligez et al. 2004; Nocera et al. 2006; Fletcher 2007) affects population dynamics (e.g., Stephens and Sutherland 1999; Reed and Levine 2005). Interspecific information use can be predicted to have similar effects, but with the added complexity of coupled dynamics of separate species. Second, as is already done with conspecific attraction (Ward and Schlossberg 2004), manipulation of density of demonstrator species could be used in conservation purposes aiming to either attract or repel focal species and affect its investment decisions. Third, earlier studies (Forsman et al. 2002; Thomson et al. 2003; Fletcher 2007) and the present results indicate that the presence of competitor does not automatically mean costs but can actually facilitate even subordinant species via information acquisition. Such indirect positive effects are not considered in traditional models of species coexistence (e.g., MacArthur and Levins 1967; Tilman 1982). Depending on the conditions, inclusion of information use in the theory of coexistence of species results in a continuum of possible outcomes from mutualistic to exploitative interactions that may even lead to evolutionary arms race in hiding and acquiring information (Fletcher 2007; Forsman et al. 2007).
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FUNDING |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONFUNDINGREFERENCES |
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Academy of Finland (project no. 202388); European Commission (Marie Curie Intra-European Fellowship, project MEIF-CT-2003-500554) to J.T.F.; the Swedish Research Council Formas to M.B.H. and L.G.; Stiftelsen för zooekologisk forskning to J.T.F. and M.B.H.
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ACKNOWLEDGEMENTS |
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We thank B. Doligez, J.-T. Seppänen, and 2 anonymous referees for comments on the manuscript, J. Barke for help in the field, and T. Morrow for improving the English. We also thank J.-T. Seppänen for aid in statistical analyses.
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