Behavioral Ecology Advance Access originally published online on August 4, 2008
Behavioral Ecology 2008 19(6):1305-1313; doi:10.1093/beheco/arn074
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Flight distance and blood parasites in birds
CNRS, UMR 7103 and UMPC Paris 06, Laboratoire de Parasitologie Evolutive, Université Pierre et Marie Curie, Bât. A, 7ème étage, 7 quai St Bernard, Case 237, F-75252 Paris Cedex 05, France
Address correspondence to A.P. Møller. E-mail: amoller{at}snv.jussieu.fr.
Received 20 November 2007; revised 22 May 2008; accepted 15 June 2008.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALREFERENCES |
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Flight distance reflects the risk that an individual animal is willing to take when approached by a potential predator. Because residual reproductive value is the average number of offspring that an individual of a given age class is expected to produce after the current reproductive event, individuals with low residual reproductive value should take greater risks than the average individual to make them more likely to gain at least some reproductive success. Therefore, I predicted that individuals belonging to bird species with intense infections with virulent parasites to take greater risks than individuals of species with few or no virulent parasites. In a comparative study of mean flight distance of 133 different bird species, as estimated from the distance at which individuals fled when approached by a human, relative flight distance decreased with the number of blood parasite species and the prevalence of blood parasites, as expected if parasitism reduces residual reproductive value. Birds that take great risks in terms of reduced flight distance run elevated risks of mortality by predators that are allowed to approach potential prey. However, relative flight distance decreased independently for species richness and prevalence of blood parasites and for risk of predation due to the European sparrow hawk Accipiter nisus. These findings suggest that standardized measures of flight distance provide reliable information about risk taking by individuals, with important consequences for life history, parasitism, and risk of predation.
Key words: blood parasites, predation risk, residual reproductive value, risk taking.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALREFERENCES |
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Why are some species easily disturbed while humans and other potential predators readily can approach others? All living beings require food for maintenance and reproduction, but foraging increases the risk of predation (Godin and Smith 1988
), whereas delayed foraging has negative effects on nutritional state (Van der Veen and Sivars 2000) and starvation (Sih 1997). This trade-off causes hungry animals to take more risks because the benefits of risk taking increase with increasing level of hunger (Damsgard and Dill 1998). Previous studies have shown consistent individual differences in risk-taking behavior (Godin and Dugatkin 1996; Coleman and Wilson 1998; Elliot and Thrash 2002). There are huge differences in fearfulness of different species due to differences in age composition, reproductive state, and parasite prevalence (e.g., Koivula et al. 1994; Candolin 1998; Kavaliers and Choleris 2001).
Disruption of activity can have severe effects on physiology, behavior, reproduction, and survival. The most common effect of disturbance on physiology of vertebrates is the corticosterone response that can be elicited by a wide variety of different factors (Wingfield and Ramenofsky 1999), including proximity of humans and predators (Scheuerlein et al. 2001; Fletcher and Boonstra 2006; Ylonen et al. 2006). Corticosterone due to exposure to potential predators can be transferred to eggs with subsequent consequences for offspring development and growth (Saino et al. 2005). In addition, exposure to the proximity of a predator can reduce the efficiency of the immune system, with subsequent increases in prevalence and intensity of parasite infections (Navarro et al. 2004). A second example of disturbance effects with physiological consequences is increases in metabolism when animals are exposed to human proximity (Belanger and Bédard 1990; Nimon et al. 1995, 1996; Feret et al. 2003). Disturbance can have dramatic effects on the behavior of animals by significant reductions in foraging activity and efficiency (Madsen 1998a; Feret et al. 2003; Bechet et al. 2004), potentially affecting the energy budget and thus reproduction (Feret et al. 2003; Bechet et al. 2004). Finally, physiological and behavioral consequences of disturbance can have significant effects on reproduction and survival of animals, with consequences for long-s trends in population size (Wilson et al. 1991; Woehler et al. 1994; Giese 1996; Cobley and Shears 1999; Arroyo and Razin 2006). Human activities can have dramatic effects of animal behavior most notably through hunting (Madsen 1998a, 1998b; Feret et al. 2003; Tamisier et al. 2003; Bregnballe et al. 2004; Thiollay 2005; Arroyo and Razin 2006) but also through benign activities like tourism (Madsen 1998a; Arroyo and Razin 2006), sports fishing, sailing, and windsurfing (Rodgers and Smith 1995; Madsen 1998a). Any animal that flees at a long distance when approached by a human will be at a selective disadvantage in an environment where such disturbance is common because short flights are energetically very expensive compared with long or sustained flights (Tatner and Bryant 1986).
An important requirement by all living organisms is the amount of space needed for successful survival and reproduction. Flight distance from a potential predator like a human being will mirror the risks that an individual is willing to take during its daily routine to fulfill its requirements for space need (Blumstein 2006), reflecting the trade-off between the benefits acquired by current activity and the costs of fleeing during the approach of a potential predator. Therefore, analysis of interspecific differences in risk taking as reflected by flight distance will provide important information about the extent to which individuals of different species perceive and react to their environment (Blumstein 2006). Such interspecific differences in flight distance will affect the susceptibility of different species to perturbations of the environment and the general disturbance regime caused by other species including human beings. Individuals should respond to risks in relation to their residual reproductive value, which is defined as the average number of offspring that an individual of a given age class is expected to produce after the current reproductive event (Williams 1957, 1966). The reason for this prediction about risk taking and residual reproductive value is that the potential fitness costs of daily routine should be traded against the fitness benefits. Risk taking will mainly affect the immediate probability of mortality and therefore only secondarily future reproduction. The factors that contribute to residual reproductive value include the future costs of current reproduction, the costs of current behavioral decisions in terms of future survival and reproduction, and the costs of interactions with conspecifics and heterospecifics that may have an impact on future survival and reproduction. The most prominent factors belonging to the latter category are predators and parasites.
The objectives of this study were to assess to which extent risk aversiveness as reflected by flight distance could be predicted by parasitism, using a comparative approach in birds. More specifically, I predicted that because parasitism is an important cause of morbidity (e.g., Lehman 1993; Møller 1997), severely reducing survival and fecundity, such reductions in 2 important fitness components should reduce residual reproductive value, with consequences for risk aversiveness and hence flight distance. Therefore, I predicted that relative flight distance after adjusting for body mass should be reduced in hosts with high levels of parasitism. Because virulence increases when hosts suffer from multiple infections (Bull 1994; Frank 1996), I predicted negative relationships between relative flight distance and the number of co-occurring parasite species. I also predicted negative relationships between relative flight distance and prevalence of parasites because a high prevalence would imply a greater risk of mortality than a low prevalence, everything else being equal. Finally, risk of predation by a common avian predator, the European sparrow hawk Accipiter nisus, should increase with decreasing flight distance if short flight distances indeed reflected an increased risk taken by an individual. If the effects of blood parasites on flight distance mediated the effects of predation risk (Møller and Nielsen 2007), then we should only expect predation risk to be a significant predictor of flight distance. However, if a significant number of individuals died from infection with blood parasites, without being eaten by predators, we should expect both infection with blood parasites and risk of predation to independently predict flight distance. I tested these predictions using standardized estimates of flight distance of birds obtained in the field, combined with information on level of parasitism by blood parasites, Hematozoa, relying on extensive data collected in the field and from the literature.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALREFERENCES |
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Flight distance
During March to August 2006—2007, I estimated flight distances for birds in Badajoz, Spain; Ile-de-France and Bretagne, France; and Northern Jutland, Denmark, using a standard technique developed by Blumstein (2006)
. In brief, when an individual bird had been located while resting, foraging, preening, or singing, I moved at a normal walking speed toward the individual while recording the number of steps (which approximately equals the number of meters). The distance at which the individual took flight was defined as the flight distance, whereas in accordance with Blumstein (2006), I defined distance walked was the distance from where the observer started walking until the position of the bird. If the individual was positioned in the vegetation, the height above ground, when I started walking toward it, was recorded to the nearest meter. While recording these distances, I also recorded date and time of day, age, and the sex of the individual if external characteristics allowed aging and sexing with binoculars. Flight distance was estimated as the square root of the sum of the squared horizontal distance and the squared height (Blumstein 2006). For further details, see Møller et al. (2007). All recordings were made blindly with respect to information on predation risk and parasitism. All recordings were made during the breeding season, when most individuals are sedentary, thus preventing the same individual from being recorded in different sites, with each site being more than 100 m apart (equaling a territory of 3.1 ha), although for larger species such as herons and corvids, I used a distance of 500 m (equaling a territory of 78.5 ha) because of their larger territories. Using only breeding birds may cause another problem if territorial individuals are less likely to have long flight distances because they are reluctant to cross territory boundaries. An analysis of 24 species (based on 2018 observations) assessed during winter (December 2006 to February 2007) and summer revealed a strong positive relationship between estimates (weighted regression: F = 41.50, degrees of freedom [df] = 1, 23, r2 = 0.64, P < 0.0001, slope [standard error {SE}] = 0.98 [0.15]). The intercept did not differ from zero (–0.02 [SE = 0.12], t = –0.15, P = 0.88), and the slope did not differ from one (t = –0.10, P = 0.92), implying that estimates were very similar and proportional during the 2 periods. I recorded a total of 2776 flight distances for 133 species for the present study.
I assessed the spatial and temporal consistency in estimates of mean flight distances in the following 4 ways. First, I compared the estimated mean flight distances with those from 2 other data sets. Blumstein (2006) reported mean flight distances of 13 species that were also included in the present data set (Carduelis carduelis, Carduelis chloris, Certhia brachydactyla, Corvus monedula, Erithacus rubecula, Fulica atra, Motacilla alba, Parus ater, Parus caeruleus, Parus major, Passer montanus, Phylloscopus collybita, Picus viridis, and Sturnus vulgaris), providing a test for consistency in estimates among observers and among sites. During spring 2006, Einar Flensted-Jensen recorded flight distances in my Danish study area for 39 species that were also included in the present data set, testing for consistency among observers in the same study site. Second, I compared my mean estimates from France and Denmark as a test for consistency in estimates of a single observer among sites. Finally, I compared my mean estimates from France from 2006 to 2007 as a test for temporal consistency in estimates.
Blood parasites
I used information on prevalence of 4 genera of blood parasites (Leucocytozoon, Hemoproteus, Plasmodium, and Trypanosoma) and all blood parasites combined from Europe by relying on Peirce (1981) and Scheuerlein and Ricklefs (2004), combined with information from Merilä et al. (1995), Sol et al. (2000), Merino et al. (2002), Marzal et al. (2004), and Marzal A, Merino S, Møller AP (unpublished data). I also extracted information on the number of individuals examined for each of the host species. In total, this study was based on examination of infection level of 16 995 individual juvenile and adult hosts based on blood smears, with a range from 2 to 1539 individuals each for the species for which there were data on blood parasites. Most of the blood parasite information derives from Northern Europe, where the study of predation was also conducted. Hence, there was a high degree of overlap between the geographical location of study sites for parasites and flight distance. Finally, Scheuerlein and Ricklefs (2004) have shown that prevalence estimates are repeatable across study sites.
Susceptibility to predation by sparrow hawks
Nielsen (2004a, 2004b) studied European sparrow hawks during 21 years in a study area of 2417 km2 in Northern Denmark while attempting to find all nests within the study area. Prey remains of the European sparrow hawk were systematically collected near 1709 nests during April to August 1977–1997 (Nielsen 2004a, 2004b, unpublished data). Only prey that was judged to have been left in the field less than 1 month when found was included in the study in order to avoid the possibility that prey of other predator species inadvertently had been included. A total of 31 745 prey items of 64 species of birds were used for the sparrow hawk, whereas 3178 prey items were excluded because they were mammals, cage birds, or migrants. All nest sites were visited 3–4 times during the breeding season, and sampling effort can therefore be considered to remain similar across sites.
I calculated the expected number of prey by using information on density of breeding birds (Grell 1998). Maps of the density of breeding birds have been made based on systematic point counts of breeding birds carried out by hundreds of amateurs, and the mean density of breeding prey species of birds in the study areas of Nielsen (2004a, 2004b) was extracted from the maps in Grell (1998). These point counts provide reliable estimates of breeding bird density as shown by extensive analyses of potential sources of error and bias and by cross-validation with other census methods (see summary in Grell 1998). I used this estimate of population density in the subsequent analyses.
I estimated a logarithmic index of prey vulnerability as the observed log10-transformed number of prey minus the log10-transformed expected number of prey. This index has a value of zero when prey is taken according to their abundance, with a value of +1 indicating an overrepresentation by a factor 10 with respect to abundance, whereas a value of –1 indicates an underrepresentation by a factor 10. The expected number of prey according to abundance was estimated as the proportion of prey individuals of each species multiplied by the total number of prey individuals less than 1 month old, according to the results of point counts reported by Grell (1998).
The prey vulnerability index for the sparrow hawk had a mean value of –0.08 (SE = 0.10, range = –2.077 to +2.241, N = 64) that did not differ significantly from zero (1-sample t-test, t = –0.80, df = 63, P = 0.33). The frequency distribution of the prey vulnerability index for the sparrow hawk did not differ significantly from a normal distribution (Shapiro–Wilk W test, sparrow hawk: W = 0.97, P = 0.37).
Body mass
Larger species have longer flight distances than small species (Blumstein 2006). Therefore, I used body mass of all species as an additional predictor variable based on my own field measurements or in the absence of data as reported by Cramp and Perrins (1977–1994). All data are reported in the Appendix (Supplementary material online).
Statistical analyses
Flight distance, number of blood parasite species, distance walked, and body mass were log10 transformed and prevalence was square root arcsine transformed before analyses.
I tested whether flight distance was a species-specific attribute in an analysis of variance with flight distance as the response variable and species and distance walked as predictor variables.
All analyses were weighted by log10-transformed sample size of blood parasite data to adjust for uneven sampling effort among species, under the assumption that estimates based on larger sample sizes were closer to the true population estimate.
I tested for repeatability of flight distance estimates for species, using multiple sampling as described above. Repeatability (R) is a standard estimate of consistency in phenotypic traits among measurements that allows partitioning of the phenotypic variance within and among individuals, ranging from 0 (no consistency) to 1 (all values remain constant) (Falconer and Mackay 1996). Repeatability also has the important property that repeatability sets an upper limit to heritability (Falconer and Mackay 1996). We estimated repeatability and its SE using the equations in Becker (1984).
Comparative analyses
Closely related species are more likely to have similar phenotypes than species that are more distantly related. Therefore, species cannot be treated as statistically independent observations in comparative analyses because apparent phenotypic correlations among species may result from species sharing a common ancestor rather than convergent evolution.
I controlled for similarity in phenotype among species due to common phylogenetic descent by calculating standardized independent linear contrasts (Felsenstein 1985), using the software CAIC (Purvis and Rambaut 1995). All branches of the phylogenies were assigned even branch lengths, assuming a punctuated model of evolution as implemented in the software, although a second set of analyses based on unequal branch lengths produced qualitatively similar results to those reported here, with no changes in the statistical significance. I tested the statistical and evolutionary assumptions of the comparative analyses (Garland et al. 1992) by regressing absolute standardized contrasts against their standard deviations, and these were met in most cases. In order to test for effects of problems of heterogeneity in variance, 1) I excluded outliers (contrasts with Studentized residuals >3) in a second series of analyses (Jones and Purvis 1997) and 2) analyses were repeated with the independent variable expressed in ranks. These analyses are conservative tests of the null hypothesis, explicitly investigating the robustness of the conclusions. In neither case did these new analyses change any of the conclusions, and they are therefore not reported here.
The composite phylogeny used in the comparative analyses was based on Sibley and Ahlquist (1990), combined with information from other sources (Sheldon et al. 1992; Suhonen et al. 1994; Blondel et al. 1996; Slikas et al. 1996; Badyaev 1997; Cibois and Pasquet 1999; Helbig and Seibold 1999; Voelker 1999; Barker et al. 2001, 2004; Yuri and Mindell 2002; Thomas et al. 2004; Voelker and Spellman 2004; Sheldon et al. 2005; Jønsson and Fjeldså 2006). The results from the phylogenetic analyses were qualitatively similar to those found when making the calculations using the taxonomy of Sibley and Monroe (1990).
A common underlying assumption of most statistical approaches is that each data point provides equally precise information about the deterministic part of total process variation, that is, the standard deviation of the error term is constant over all values of the predictor variables (Sokal and Rohlf 1995). The standard solution to violations of this assumption is to weight each observation by sampling effort in order to use all data by giving each datum a weight that reflects its degree of precision due to sampling effort (Draper and Smith 1981; Neter et al. 1996). Comparative analyses may be confounded by sample size if sampling effort is important and if sample size varies considerably among taxa. In order to weight regressions by log10-transformed sample size for blood parasites in the analyses of contrasts, I calculated weights for each contrast by calculating the mean sample size for the taxa immediately subtended by that node (Møller and Nielsen 2006).
I used multiple regression to find the best-fit model, using the software JMP (2000). The best-fit model was determined using Akaike's information criterion as an estimate of the improvement in fit for addition of variables (Burnham and Anderson 2002). There was no evidence of collinearity between variables because the variance inflation factor was less than 5 in all cases (Tabachnick and Fidell 1996).
I used MacClade 4.03 (Maddison WP and Maddison DR 2001) to reconstruct ancestral state for relative flight distance, after adjusting for the effects of body mass. This was done using strict parsimony by dichotomizing flight distance by assigning species with values above the median a value of 1 and all other species a value of 0. The ancestral state of flight distance and the minimum number of transitions from the ancestral state to the other state were subsequently recorded, using the phylogenetic hypothesis listed above.
Regressions of standardized linear contrasts were forced through the origin because the comparative analyses assume that there has been no evolutionary change in a character when the predictor variable has not changed (Purvis and Rambaut 1995).
I calculated as an estimate of effect size Pearson's product–moment correlation coefficient, using the equations in Rosenthal (1991, p. 73–74), relying on Cohen's (1988) conventions, where r = 0.1 equals a small effect, r = 0.3 an intermediate effect, and r = 0.5 equals a large effect.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALREFERENCES |
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Intra- and interspecific variation in flight distance
There was significant variation in flight distance among species (F = 22.45, df = 135, 2746, r2 = 0.52, P < 0.0001), with more variation among than within species (R = 0.50 [SE = 0.05]). This provides evidence of flight distances being a species-specific attribute. Mean flight distance for 133 different species ranged from 3.28 to 200 m, with a mean weighted by sample size of 10.60 m (SE = 1.06; after back transformation from log10-transformed data).
Flight distance was consistent for the same species in different studies, as shown by comparison of the data from the present study and that of Blumstein (2006). For 14 species that were recorded in both data sets, I found a positive relationship between the 2 series of mean estimates (F = 3.77, df = 15, 16, r2 = 0.78, P = 0.0061, R = 0.58 [SE = 0.24]). Second, I compared my mean estimates with those collected by an independent observer (Einar Flensted-Jensen) in my Danish study area. The independent observer had been instructed how to estimate flight distances but otherwise worked completely independently. Again, I found a positive relationship between the 2 series of mean estimates (F = 3.88, df = 40, 41, r2 = 0.79, P < 0.0001, R = 0.59 [SE = 0.15]). This provides evidence for reliability of the estimates across observers. Furthermore, I tested if mean estimates from 2 different years (2006 and 2007) were consistent and found a strong positive relationship (F = 9.55, df = 56, 57, r2 = 0.90, P < 0.0001, R = 0.81 [SE = 0.06]), providing evidence for temporal consistency in estimates. Finally, I tested for consistency of mean estimates of different species between the study sites in Denmark and France. Again, there was a positive relationship (F = 4.68, df = 12, 13, r2 = 0.81, P = 0.0048, R = 0.65 [0.24]), showing evidence of consistency among sites.
Mean flight distance for species increased significantly with body mass (linear regression based on log10-transformed variables weighted by sample size: F = 89.14, df = 1, 115, r2 = 0.44, P < 0.0001, slope [SE] = 0.31 [0.03]). There was a weak positive correlation between flight distance and distance walked, in a model that included species as a random factor (distance walked: F = 14.64, df = 1, 2636, P < 0.0001, slope [SE] = 0.10 [0.02]). However, given that this factor explained less than 0.6% of the variance, I did not include distance walked in the subsequent analyses.
Next, I tested whether flight distance was affected by hunting or persecution by entering hunting as a factor. The effect of hunting was not significant after accounting for the effect of body mass in a model weighted by sample size (partial effect of hunting: F = 0.59, df = 1, 128, r2 = 0.005, P = 0.45).
Mean flight distance after adjusting for the allometric effects of body mass showed considerable variation among species, with a phylogenetic signal as shown by sister taxa being more likely to have a similar relative flight distance than expected by chance (Figure 1). Furthermore, the distribution of relative flight distance in the phylogeny indicated that long flight distances (i.e., species with a flight distance above the median) were the ancestral state among the 133 species investigated, and thus, short flight distances (i.e., species with a flight distance below the median) were a derived state.
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Blood parasites and flight distance
There was evidence of competition among blood parasites because mean prevalence of the different species of parasites decreased significantly with increasing parasite species richness (model weighted by sample size: F = 4.76, df = 1, 94, r2 = 0.05, P = 0.032, slope [SE] = –0.005 [0.002]). Therefore, parasites should become more virulent as species richness increased.
Mean flight distance was negatively related to the number of blood parasite species accounting for 53% of the variance (Table 1 [number of blood parasite species]). This negative relationship implied that birds with many different blood parasites had shorter flight distances for their body mass than species with few species of blood parasites (Figure 2A). However, an analysis of contrasts did not confirm this result, suggesting that the relationship for species-specific data was due to species richness of blood parasites and flight distance having an aggregated distribution in specific taxa (Table 1 [number of blood parasite species]).
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Mean flight distance was negatively related to prevalence of all blood parasites accounting for 53% of the variance (Table 1 [prevalence of all blood parasites]). Bird species with high prevalence of blood parasites had short flight distances for their body mass (Table 1 [prevalence of all blood parasites]). An analysis of contrasts produced a similar conclusion, although this relationship did not reach statistical significance (Table 1 [prevalence of all blood parasites]).
Finally, I investigated the independent effects of the 4 different genera of blood parasites on relative flight distance in a multiple regression analysis. The best-fit model accounted for 53% of the variance (Table 1 [prevalence of different genera of blood parasites]). The prevalence of Hemoproteus predicted mean flight distance, whereas prevalence of the other 3 genera did not (Table 1). An analysis of contrasts also revealed a significant relationship for Hemoproteus only (Table 1 [prevalence of different genera of blood parasites]).
Blood parasites, predation risk, and flight distance
Blood parasites may affect risk of predation, and I therefore investigated the independent effect of blood parasites and risk of predation in determining flight distance. The number of blood parasite species and risk of predation by the sparrow hawk independently explained variation in flight distance, with the effect of blood parasites being greater than the effect of sparrow hawks (Table 2 [number blood parasite species]). An analysis of contrasts only showed a significant effect of number of blood parasite species, but not of prey vulnerability (Table 2 [number blood parasite species]). Prevalence of blood parasites but not risk of predation by the sparrow hawk explained variation in flight distance (Table 2 [prevalence of all blood parasites]). An analysis of contrasts only revealed significant effects of prevalence and body mass (Table 2 [prevalence of all blood parasites]). Finally, I investigated the independent associations between prevalence of different genera of blood parasites and risk of predation by sparrow hawks, respectively, and relative flight distance (Table 2 [prevalence of different genera of blood parasites]). Prevalence of Plasmodium was negatively related to flight distance, whereas the risk of predation by sparrow hawks did not reach significance (Table 2 [prevalence of different genera of blood parasites]). An analysis of contrasts showed a significant negative effect of Hemoproteus and significant positive effects of Trypanosoma and body mass (Table 1 [prevalence of different genera of blood parasites]). Therefore, blood parasites predicted flight distance independently of risk of sparrow hawk predation.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALREFERENCES |
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Flight distance of adult birds when approached by a human being varied significantly among species. In addition, interspecific variation in flight distance was explained by species richness and prevalence of blood parasites.
Parasitism is a major cause of mortality in free-living animals, with important consequences for life-history evolution. Parasites reduce the life expectancy of their hosts directly (e.g., Lehman 1993; Møller 1997) but also by increasing the risk of predation (Temple 1986). For example, blood parasites including malaria caused by Plasmodium reduce host survival, particularly among fledglings that are immunologically naive compared with older individuals (Van Riper et al. 1986; Atkinson and Van Riper 1991; Bennett et al. 1993; Miller et al. 2002). Furthermore, blood parasites significantly increase susceptibility of small birds to avian predators (Møller and Nielsen 2006). Therefore, animals that suffer from parasitism should take greater risks than healthy individuals because they have reduced residual reproductive value. Alternatively, parasites have direct negative effects on their hosts with consequences for their flight distance. The distinguishing feature of these 2 alternative hypotheses is that the former predicts that flight distance will decrease with residual reproductive value even in uninfected individuals because certain individuals run a high risk of being infected in the future. In contrast, the second hypothesis predicts that flight distance will depend directly on infection status. Blood parasitism has physiological consequences that include induction of immune responses, heat shock proteins, and fever (Wakelin 1996; Merino et al. 1998; Chen et al. 2001). Such effects may also have consequences for flight distance. Fever is characterized by elevated body temperature and inactivity (e.g., Hart 1990, 1997), and such inactivity may directly affect the ability of individuals to detect an approaching predator. Whenever I approached a bird, I made sure of being seen because I approached with a clear field of vision, because of the direction of gaze by the birds, and because of changes in movement before the bird actually flew off. This makes the hypothesis based on direct effects of parasitism less likely. There is also a theoretical reason for rejecting this latter hypothesis. Risk taking by hosts as shown by flight distance should tend to reduce virulence because individual hosts infected by virulent parasites would have short flight distances and hence elevated risks of predation, with reductions in parasite transmission as a consequence (cf., Williams and Day 2001).
There was evidence of competition among blood parasites because mean prevalence of different species of parasites decreased significantly with increasing parasite species richness. This decrease in mean prevalence is what should be expected in case of competition for limiting resources. If there was no competition among parasite strains, mean prevalence should be independent of species richness of blood parasites. Such competition among unrelated strains of parasites for limiting resources is the basis for models of evolution of virulence due to multiple infections (Bull 1994; Frank 1996).
Bird species with relatively short flight distances for their body size have an elevated risk of falling prey to the European sparrow hawk, increasing with a factor 18 across the range of flight distances analyzed (Møller et al. 2007). Here I extended these previous analyses by including both blood parasite infections and risk of predation by sparrow hawks as predictors of relative flight distance, showing that these 2 factors independently predicted relative flight distance. This implies that the cost of reduction in flight distance could not only be measured in terms of increased risk of predation due to the European sparrow hawk caused by blood parasitism (Møller and Nielsen 2007). I hypothesize that this additional reduction in flight distance may be costly in terms of risk of predation due to other predators. Alternatively, it might simply reflect the elevated risk of mortality due to blood parasitism on its own (i.e., without an infected host falling prey to a predator).
Both risk of predation and parasitism may be linked through the effect of plumage brightness. The risk of predation to the European sparrow hawk increased in sexually dichromatic species compared with monochromatic species (Møller and Nielsen 2006). Blood parasitism has also been suggested to be linked to sexual coloration in birds (Hamilton and Zuk 1982), although subsequent analyses that controlled statistically for similarity among taxa due to common phylogenetic descent could not replicate that finding (Read and Harvey 1989). The latter finding suggests that plumage brightness is an unlikely intermediary variable accounting for the results presented here.
The significant increase in flight distance of birds with increasing body mass may arise from large bird species needing longer distances for takeoff and climb rates decreasing with increasing body mass from a maximum of 1.63 m s–1 in the 43 g dunlin Calidris alpina to 0.32 m s–1 in the 10 750 g mute swan Cygnus olor (Hedenström and Alerstam 1992). These findings may partly explain why flight distance increases with body mass (see also Blumstein 2006).
I did not study the actual mechanisms underlying the patterns reported here, but they obviously need to be addressed explicitly. A potential mechanism is that flight distance and disturbance affect foraging efficiency (Madsen 1998a; Feret et al. 2003; Bechet et al. 2004) and body condition (Feret et al. 2003; Bechet et al. 2004) and therefore reproductive success (Wilson et al. 1991; Woehler et al. 1994; Giese 1996; Cobley and Shears 1999; Arroyo and Razin 2006). If future potential reproduction is reduced, this should affect residual reproductive value and hence the risk that an individual should be willing to take. This mechanism implies that a high level of responsiveness to disturbance reduces foraging efficiency under a disturbance regime, causing a reduction in body condition and subsequently reduced reproductive success. Alternatively, flight distance can be considered to represent the risk that an individual of a given species is willing to take when engaged in a given activity (Blumstein 2006). Therefore, individuals with large residual reproductive value should take small risks, providing a link between a fast life history and short flight distances. This mechanism helps explain the coexistence of species with short and long flight distances because such species would represent different life-history strategies.
In conclusion, I have described a measure of animal behavior that correlates with blood parasitism in birds. Bird species with large species richness of blood parasites had short flight distances for their body size, and prevalence of blood parasites was independently related to relative flight distance. Short flight distances were independently related to risk of predation by the sparrow hawk and blood parasitism, implying that the relationship between predation risk and relative flight distance was not only mediated by blood parasites.
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SUPPLEMENTARY MATERIAL |
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Supplementary material can be found at http://www.beheco.oxfordjournals.org/
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ACKNOWLEDGEMENTS |
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Einar Flensted-Jensen kindly provided data on flight distances. The study met national French requirements for experimentation.
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REFERENCES |
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Arroyo B, Razin M. Effect of human activities on bearded vulture behaviour and breeding success in the French Pyrenees. Biol Conserv (2006) 128:276–284.[CrossRef]
Atkinson C, Van Riper C 3rd. Pathogenicity and epizootiology of avian haematozoa: Plasmodium, Leucocytozoon and Haemoproteus. In: Bird-parasite interactions—Zuk M, ed. (1991) Oxford: Oxford University Press. 19–48.
Badyaev AV. Altitudinal variation in sexual dimorphism: a new pattern and alternative hypotheses. Behav Ecol (1997) 8:675–690.
Barker FK, Barrowclough GF, Groth JG. A phylogenetic hypothesis for passerine birds: taxonomic and biogeographic implications of an analysis of nuclear DNA sequence data. Proc R Soc Lond B Biol Sci (2001) 269:295–308.
Barker FK, Cibois A, Schikler P, Feinstein J, Cracraft J. Phylogeny and diversification of the largest avian radiation. Proc Natl Acad Sci USA (2004) 101:11040–11045.
Bechet A, Giroux JF, Gauthier G. The effects of disturbance on behaviour, habitat use and energy of spring staging snow geese. J Appl Ecol (2004) 41:689–700.[CrossRef]
Becker WA. Manual of quantitative genetics (1984) Pullman (WA): Academic Enterprises.
Belanger L, Bédard J. Energetic cost of man-induced disturbance to staging snow geese. J Wildl Manage (1990) 54:36–41.[CrossRef]
Bennett GF, Peirce MA, Ashford RW. Avian haematozoa: mortality and pathogenicity. J Nat Hist (1993) 26:993–1001.
Blondel J, Catzeflis F, Perret P. Molecular phylogeny and the historical biogeography of the warblers of the genus Sylvia (Aves). J Evol Biol (1996) 9:871–891.[CrossRef][Web of Science]
Blumstein DT. Developing an evolutionary ecology of fear: how life history and natural history traits affect disturbance tolerance in birds. Anim Behav (2006) 71:389–399.[CrossRef][Web of Science]
Bregnballe T, Madsen J, Rasmussen PAF. Effects of temporal and spatial hunting control in waterbird reserves. Biol Conserv (2004) 119:93–104.[CrossRef]
Bull JJ. Virulence. Evolution (1994) 48:1423–1437.[CrossRef][Web of Science]
Burnham KP, Anderson DR. Model selection and inference (2002) 2nd ed. New York: Springer.
Candolin U. Reproduction under predation risk and the trade-off between current and future reproduction in the three-spined stickleback. Proc R Soc Lond B Biol Sci (1998) 265:1171–1175.[CrossRef]
Chen M, Shi L, Sullivan D Jr. Haemoproteus and Schistosoma synthesize heme polymers similar to Plasmodium hemozoin and β-hematin. Mol Biochem Parasitol (2001) 113:1–8.[CrossRef][Web of Science][Medline]
Cibois A, Pasquet E. Molecular analysis of the phylogeny of 11 genera of the Corvidae. Ibis (1999) 141:297–306.[CrossRef]
Cobley ND, Shears JR. Breeding performance of gentoo penguins (Pygoscelis papua) at a colony exposed to high levels of human disturbance. Polar Biol (1999) 21:355–360.[CrossRef][Web of Science]
Cohen J. Statistical power analysis for the behavioral sciences (1988) 2nd ed. Hillsdale (NJ): Lawrence Erlbaum.
Coleman K, Wilson DS. Shyness and boldness in pumpkinseed sunfish: individual differences are context specific. Anim Behav (1998) 56:927–936.[CrossRef][Web of Science][Medline]
Cramp S, Perrins CM, eds. The birds of the Western Palearctic (1977–1994) Oxford: Oxford University Press.
Damsgard B, Dill LM. Risk-taking behavior in weight-compensating coho salmon, Oncorhynchus kisutch. Behav Ecol (1998) 9:26–32.
Draper NR, Smith H. Applied regression analysis (1981) 2nd ed. New York: John Wiley.
Elliot AJ, Thrash TM. Approach-avoidance motivation in personality: approach and avoidance temperaments and goals. J Pers Soc Psychol (2002) 82:804–818.[CrossRef][Web of Science][Medline]
Falconer DS, Mackay TFC. Introduction to quantitative genetics (1996) 4th ed. New York: Longman.
Felsenstein J. Phylogenies and the comparative method. Am Nat (1985) 125:1–15.[CrossRef][Web of Science]
Feret M, Gauthier G, Bechet A, Giroux JF, Hobson KA. Effect of a spring hunt on nutrient storage by greater snow geese in southern Quebec. J Wildl Manage (2003) 67:796–807.[CrossRef]
Fletcher QE, Boonstra R. Do captive male meadow voles experience acute stress in response to weasel odour? Can J Zool (2006) 84:583–588.[CrossRef]
Frank S. Models of parasite virulence. Q Rev Biol (1996) 71:37–78.[CrossRef][Medline]
Garland T Jr, Harvey PH, Ives AR. Procedures for the analysis of comparative data using phylogenetically independent contrasts. Syst Biol (1992) 41:18–32.[Abstract]
Giese M. Effects of human activity on Adelie penguin Pygoscelis adeliae breeding success. Biol Conserve (1996) 75:157–164.[CrossRef]
Godin JJG, Dugatkin LA. Female mating preference for bold males in the guppy, Poecilia reticulata. Proc Natl Acad Sci USA (1996) 93:10262–10267.
Godin JJG, Smith SA. A fitness cost of foraging in the guppy. Nature (1988) 333:69–71.[CrossRef][Web of Science]
Grell MB. 1998. Fuglenes Danmark. Copenhagen (Denmark): Gad.
Hamilton WD, Zuk M. Heritable true fitness and bright birds: a role for parasites? Science (1982) 218:384–387.
Hart BJ. Behavioral adaptations to pathogens and parasites: five strategies. Neurosci Biobehav Rev (1990) 14:273–294.[CrossRef][Web of Science][Medline]
Hart BJ. Behavioural defence. In: Host-parasite evolution: general principles and avian models—Clayton DH, Moore J, eds. (1997) Oxford: Oxford Univervisty Press. 59–77.
Hedenström A, Alerstam T. Climbing performance of migrating birds as a basis for estimating limits for fuel-carrying capacity and muscle work. J Theor Biol (1992) 164:19–38.
Helbig AJ, Seibold I. Molecular phylogeny of Palearctic-African Acrocephalus and Hippolais (Aves: sylviidae). Mol Phylogenet Evol (1999) 11:246–260.[CrossRef][Web of Science][Medline]
JMP. 2000. JMP Cary (NC): SAS Institute.
Jones KE, Purvis A. An optimum body size for mammals? Comparative evidence from bats. Funct Ecol (1997) 11:751–756.[CrossRef]
Jønsson KA, Fjeldså J. A phylogenetic supertree of oscine passerine birds (Aves: Passeri). Zool Scripta (2006) 35:149–186.[CrossRef][Web of Science]
Kavaliers M, Choleris E. Antipredator responses and defensive behavior: ecological and ethological approaches for the neurosciences. Neurosci Biobehav Rev (2001) 25:577–586.[CrossRef][Web of Science][Medline]
Koivula K, Lahti K, Rytkönen S, Orell M. Do subordinates expose themselves to predation? Field experiments on feeding site selection by willow tits. J Avian Biol (1994) 25:178–183.[CrossRef]
Lehman T. Ectoparasites: direct impact on host fitness. Parasitol Today (1993) 9:8–13.[Medline]
Maddison WP, Maddison DR. MacClade (2001) Sunderland (MA): Sinauer. Version 4.03.
Madsen J. Experimental refuges for migratory waterfowl in Danish wetlands I: Baseline assessment of the disturbance effects of recreational activities. J Appl Ecol (1998a) 35:386–397.[CrossRef]
Madsen J. Experimental refuges for migratory waterfowl in Danish wetlands II: Tests of hunting disturbance effects. J Appl Ecol (1998b) 35:398–417.[CrossRef]
Marzal A, de Lope F, Navarro C, Møller AP. Malarial parasites decrease reproductive success: an experimental study in a passerine bird. Oecologia (2005) 142:541–545.[CrossRef][Web of Science][Medline]
Merilä J, Björklund M, Bennett GF. Geographic and individual variation in haematozoan infections in the greenfinch, Carduelis chloris. Can J Zool (1995) 73:1798–1804.[CrossRef]
Merino S, Martínez J, Barbosa A, Møller AP, de Lope F, Pérez J, Rodríguez-Caabeiro F. Increase in heat-shock protein from blood cells in response of nestling house martins (Delichon urbica) to parasitism: An experimental approach. Oecologia (1998) 116:343–347.[CrossRef][Web of Science]
Merino S, Martinez J, Møller AP, Barbosa A, de Lope F, Rodriguez-Caabeiro F. Blood stress protein levels in relation to sex and parasitism of barn swallows (Hirundo rustica). Ecoscience (2002) 9:300–305.[Web of Science]
Miller LH, Baruch DR, Marsh K, Doumbo OK. The pathogenic nature of malaria. Nature (2002) 415:67–679.
Møller AP. Parasitism and the evolution of host life history. In: Host-parasite evolution: general principles and avian models—Clayton DH, Moore J, eds. (1997) Oxford: Oxford University Press. 105–127.
Møller AP, Nielsen JT. Prey vulnerability in relation to sexual coloration of prey. Behav Ecol Sociobiol (2006) 60:227–233.[CrossRef][Web of Science]
Møller AP, Nielsen JT. Malaria and risk of predation: a comparative study of birds. Ecology (2007) 88:871–881.[CrossRef][Web of Science][Medline]
Møller AP, Nielsen JT, Garamszegi LZ. Risk taking by singing males. Behav Ecol (2008) 19:41–53.
Navarro C, de Lope F, Marzal A, Møller AP. Predation risk, host immune response and parasitism. Behav Ecol (2004) 15:629–635.
Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied linear statistical models (1996) Chicago: Irwin.
Nielsen JT. A population study of sparrowhawks accipiter nisus in Vendsyssel, Denmark, 1977-1997 [Danish with English summary]. Dansk Ornithol Foren Tidsskr (2004a) 98:147–162.
Nielsen JT. Prey selection of sparrowhawks in Vendsyssel, Denmark [Danish with English summary]. Dansk Ornithol Foren Tidsskr (2004b) 98:164–173.
Nimon AJ, Schroter RC, Oxenham RKC. Artificial eggs: measuring heart rate and effects of disturbance in nesting penguins. Physiol Behav (1996) 60:1019–1022.[Medline]
Nimon AJ, Schroter RC, Stonehouse B. Heart-rate of disturbed penguins. Nature (1995) 374:415.[CrossRef][Web of Science]
Peirce MA. Distribution and host parasite checklist of the hematozoa of birds in Western Europe. J Nat Hist (1981) 15:419–458.[CrossRef]
Purvis A, Rambaut A. Comparative analysis by independent contrasts (CAIC). Comp Appl Biosci (1995) 11:247–251.[Medline]
Read AF, Harvey PH. Reassessment of comparative evidence for Hamilton and Zuk theory on the evolution of secondary sexual characters. Nature (1989) 339:618–620.[CrossRef][Web of Science]
Rodgers JA, Smith HT. Set-back distances to protect nesting bird colonies from human disturbance in Florida. Conserv Biol (1995) 9:89–99.[CrossRef]
Rosenthal R. Meta-analytic procedures for social research (1991) New York: Sage.
Saino N, Romano M, Ferrari RP, Møller AP. Stressed mothers produce low-quality offspring with poor fitness. J Exp Zool (2005) 303A:998–1006.
Scheuerlein A, Ricklefs RE. Prevalence of blood parasites in European passeriform birds. Proc R Soc Lond B Biol Sci (2004) 271:1363–1370.[Medline]
Scheuerlein A, Van't Hof TJ, Gwinner E. Predators as stressors? Physiological and reproductive consequences of predation risk in tropical stonechats (Saxicola torquata axillaries). Proc R Soc Lond B Biol Sci (2001) 268:1575–1582.[Medline]
Sheldon FH, Slikas B, Kinnarney M, Gill FB, Zhao E, Silverin B. DNA-DNA hybridization evidence of phylogenetic relationships among major lineages of Parus. Auk (1992) 109:173–185.[Web of Science]
Sheldon FH, Wittingham LA, Moyle RG, Slikas B, Winkler DW. Phylogeny of swallows (Aves: Hirundinidae) estimated from nuclear and mitochondrial DNA sequences. Mol Phylogenet Evol (2005) 35:254–270.[CrossRef][Web of Science][Medline]
Sibley CG, Ahlquist JE. Phylogeny and classification of birds, a study in molecular evolution (1990) New Haven (CT): Yale University Press.
Sibley CG, Monroe BL Jr. Distribution and taxonomy of birds of the World (1990) New Haven (CT): Yale University Press.
Sih A. Prey uncertainty and the balance of antipredator and feeding needs. Am Nat (1997) 139:1052–1069.[CrossRef]
Slikas B, Sheldon FH, Gill FB. Phylogeny of titmice (Paridae) I: Estimate of relationships among subgenera based on DNA-DNA hybridization. J Avian Biol (1996) 27:70–82.[CrossRef]
Sokal RR, Rohlf FJ. Biometry (1995) 3rd ed. New York: Freeman.
Sol D, Jovani R, Torres J. Geographical variation in blood parasites in feral pigeons: the role of vectors. Ecography (2000) 23:307–314.
Suhonen J, Alatalo RV, Gustafsson L. Evolution of foraging ecology in Fennoscandian tits (Parus sp.). Proc R Soc Lond B Biol Sci (1994) 258:127–131.[CrossRef]
Tabachnick BG, Fidell LS. Using multivariate statistics (1996) New York: Harper Collins.
Tamisier A, Bechet A, Jarry GG, Lefeuvre JC, Le Maho Y. Effects of hunting disturbance on waterbirds. A review of literature. Terre Vie (2003) 58:435–449.
Tatner P, Bryant DM. Flight cost of a small passerine measured using doubly labeled water: implications for energetic studies. Auk (1986) 103:169–180.[Web of Science]
Temple SA. Do predators always capture substandard individuals disproportionately from prey populations? Ecology (1986) 68:669–674.[CrossRef][Web of Science]
Thiollay JM. Effects of hunting on Guianan forest game birds. Biodivers Conserv (2005) 14:1121–1135.[CrossRef]
Thomas GH, Wills MA, Székely T. A supertree approach to shorebird phylogeny. BMC Evol Biol (2004) 4:28.[CrossRef][Medline]
Van der Veen IT, Sivars LE. Causes and consequences of mass loss upon predator encounter: feeding interruption, stress or fit-for-flight? Funct Ecol (2000) 14:638–644.[CrossRef]
Van Riper C 3rd, Van Riper SG, Goff ML, Laird M. Epizootiology and ecological significance of malaria in Hawaiian land birds. Ecol Monogr (1986) 56:327–344.[CrossRef][Web of Science]
Voelker G. Dispersal, vicariance, and clocks: historical biogeography and speciation in a cosmopolitan passerine genus (Anthus; Motacillidae). Evolution (1999) 53:1536–1552.[CrossRef][Web of Science]
Voelker G, Spellman GM. Nuclear and mitochondrial DNA evidence of polyphyly in the avian superfamily Muscicapoidea. Mol Phylogenet Evol (2004) 30:386–394.[CrossRef][Web of Science][Medline]
Wakelin D. Immunity to parasites: how parasitic infections are controlled (1996) Cambridge: Cambridge University Press.
Williams GC. Pleiotropy, natural selection, and the evolution of senescence. Evolution (1957) 11:398–411.[CrossRef][Web of Science]
Williams GC. Natural selection, the cost of reproduction, and a refinement of Lack's principle. Am Nat (1966) 100:687–690.[CrossRef][Web of Science]
Williams PD, Day T. Interactions between sources of mortality and the evolution of parasite virulence. Proc R Soc Lond B Biol Sci (2001) 268:2331–2337.[Medline]
Wilson RP, Culik B, Danfeld R, Adelung D. People in Antarctica—how much do Adelie penguins Pygoscelis adeliae care. Polar Biol (1991) 11:363–370.[Web of Science]
Wingfield JC, Ramenofsky M. Hormones and the behavioral ecology of stress. In: Stress physiology of animals—Baum PMH, ed. (1999) Sheffield (UK): Sheffield Academic Press. 1–51.
Woehler EJ, Penney RL, Creet SM, Burton HR. Impacts of human visitors on breeding success and long-term population trends in Adelie penguins at Casey, Antarctica. Polar Biol (1994) 14:269–274.[Web of Science]
Ylonen H, Eccard JA, Jokinen I, Sundell J. Is the antipredatory response in behaviour reflected in stress measured in faecal corticosteroids in a small rodent? Behav Ecol Sociobiol (2006) 60:350–358.[CrossRef][Web of Science]
Yuri T, Mindell DP. Molecular phylogenetic analysis of Fringillidae, "New World nine-primaried oscines" (Aves: Passeriformes). Mol Phylogenet Evol (2002) 23:229–243.[CrossRef][Web of Science][Medline]
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