Behavioral Ecology Advance Access originally published online on August 25, 2008
Behavioral Ecology 2008 19(6):1095-1102; doi:10.1093/beheco/arn103
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Flight distance and population trends in European breeding birds
a CNRS, UMR 7103, 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 b UMPC Paris 06, UMR 7103, 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 24 January 2008; revised 30 June 2008; accepted 1 July 2008.
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Flight distance reflects the risk that individual animals are willing to take when approached by a potential predator, as shown by a negative relationship between susceptibility to predation and flight distance. Species with long flight distances should more often suffer from disruption of their activities by potential predators, including humans, than species with short distances, resulting in declining reproductive success and hence declining population size of such species if disturbance happens more often. Long flight distances thus suggest that individuals need large amounts of space for their body size, resulting in the prediction that species with long flight distances should have a higher frequency of declining populations than species with short flight distances, when human impact on natural habitats increases. Bird species with long flight distances had declining population trends across Europe in a comparative study of 56 species, even when controlling statistically for other factors associated with population declines, with effect sizes ranging from 0.36 to 0.58 (Pearson's r). These findings suggest that standardized measures of flight distance provide reliable information about the population consequences of risk-taking behavior by individuals and the susceptibility of different species to increased levels of disturbance.
Key words: bird census, birds, population consequences, risk taking.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Why are some species abundant and widespread while others are rare and locally distributed? These are central questions in ecology since the beginning of ecological research (e.g., Andrewartha and Birch 1954
; Lack 1954; Begon et al. 1972; Krebs 1972; Ricklefs 1975). Body size is an important correlate of both range size and population size (Gaston 2002). However, given that body size correlates with numerous aspects of living organisms including physiology, life history, behavior, and ecology (e.g., Alexander 1971; Peters 1983; Reiss 1989; Roff 2001), such correlation between body size and range size and population size, respectively, are almost impossible to interpret. Therefore, the question remains. How can we identify factors that predict ecological success in terms of range size and population size?
Disruption of the activity of animals can have severe effects on different fitness components. Such disturbance effects can act through physiology, behavior, reproduction and survival, and nonlethal effects of predation risk. First, the most common effect of disturbance on physiology is the corticosterone response that can be elicited by a wide variety of different factors (Wingfield and Ramenofsky 1999). These include proximity of humans, but the response can also be induced by the proximity of predators (Scheuerlein et al. 2001; Fletcher and Boonstra 2006; Ylönen et al. 2006). The corticosterone response affects a wide variety of activities that in combination has the consequence of reducing the damaging effect of the stressor (Wingfield and Ramenofsky 1999). Exposure to potential predators including humans cannot only affect adult birds but also be transferred to their eggs with detrimental 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 the 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). Third, disturbance can also 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), and such effects can potentially affect the energy budget and thus future behavior and reproduction (Feret et al. 2003; Bechet et al. 2004). Fourth, physiological and behavioral consequences of disturbance can have significant effects on reproduction and survival of animals, with effects on long-term trends in population size (Wilson et al. 1991; Woehler et al. 1994; Giese 1996; Cobley and Shears 1999; Arroyo and Razin 2006). In particular, human activities can have dramatic indirect effects on 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 supposedly benign activities like tourism (Madsen 1998a; Arroyo and Razin 2006), sports fishing, sailing, windsurfing, and many others (Rodgers and Smith 1995; Madsen 1998a). Any animal that flees at a long distance when approached by an unarmed human will be at a selective disadvantage, and frequent flight when approached by hunters may also potentially have negative effects on condition and hence reproduction and survival. Finally, nonlethal effects of predation risk can have profound effects on fitness of individuals but also at the population and community levels (e.g., Abrams 1991; Lima 1998). These observations raise the broader question whether interspecific differences in flight distance will affect population trends.
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 (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 their environment. Such 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. Thus, interspecific differences in flight distance may thereby ultimately influence the distribution and abundance of different species (Blumstein and Fernández-Juricic 2004; Blumstein 2006) because species with long flight distances when approached by a potential predator or a human being will spend less time foraging and more time fleeing than a less wary species. This should result in interspecific differences in costs associated with flight and therefore ultimately differences in energy budget, with potential consequences for survival, reproduction, and population size.
If flight distance reflects "space requirement," but also the amount of disturbance tolerated, then species with long flight distance should have greater requirements and hence declining populations in a world where human proximity and hence human disturbance is increasing. Thus, species with long flight distances should generally suffer from declining populations because increases in human population sizes and range of outdoor activities should cause disturbance, with consequences for foraging, net energy balance, and hence for survival of reproduction. Therefore, population trends of European birds should be negatively related to flight distance. Previous studies have indicated that agricultural habitats (Fuller et al. 1995; Chamberlain et al. 2000; Donald et al. 2001; Green et al. 2005), habitat loss and fragmentation (Fahrig 2003; Berthold and Fiedler 2005), migration (Hjort and Lindholm 1978; Baillie and Peach 1992; Bauer and Heine 1992; Berthold 2000; Lemoine and Böhning-Gaese 2003), northern distribution and climate change (Parmesan et al. 2000; Walther et al. 2002; Root et al. 2003, 2005; Julliard et al. 2004; Møller et al. 2004), brain mass (Shultz et al. 2005), and body mass (Bennett and Owens 2002) predicted population trends of common breeding birds. Therefore, I used these variables in addition to flight distance as additional predictors in analyses of population trends of breeding birds.
|
MATERIALS AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Data
Flight distance
During March to August 2006—2007, I estimated flight distances for breeding birds, using a standardized technique developed by Blumstein (2006)
. All estimates were collected blindly with respect to the hypothesis being tested, preventing any conscious or unconscious bias. These recordings were made in Île-de-France and Bretagne, France, and Northern Jutland, Denmark. 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). Focal individuals were entirely chosen based on when an individual was first recorded. The distance at which the individual took flight was recorded as the flight distance, whereas the starting distance 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 was recorded to the nearest meter. While recording these distances, I also recorded date and time of day and the sex of the individual if external characteristics allowed 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). All recordings were made during the breeding season when most individuals are sedentary, thus preventing the same individual from being recorded in different sites. Only adults were included to avoid problems caused by juveniles having short flight distances. If there was doubt about age, an individual was not recorded. I avoided pseudoreplication by only recording individuals of a given sex and species at a given site. I avoided flight response of an individual to my approach affecting the flight response of the next individual chosen by subsequently locating an individual with binoculars ensuring that this individual was unaware of my presence as reflected by its behavior. I recorded a total of 2298 flight distances for 99 species.
Flight distance was consistent for the same species in different studies, as shown by a comparison of the data from the present study and that of Blumstein (2006) (Møller et al. 2008). Furthermore, flight distances estimated by an independent observer were very similar to my estimates (Møller et al. 2008). In addition, flight distances estimated in Denmark were very similar to distances in France (Møller et al. 2008). Finally, flight distances in summer and winter were strongly positively correlated (Møller et al. 2008). This provides evidence for reliability of the estimates.
Coloniality was defined as breeding pairs using territories that only contained nest sites while all other species were considered to be solitarily breeding (following Cramp and Simmons [1977–1994]). There was no significant effect of coloniality on flight distance (partial effect of coloniality in a model with log-transformed flight distance as the dependent variable and coloniality and log-transformed body mass as predictor variables; partial effect of coloniality: F = 0.02, degrees of freedom [df] = 1,96, P = 0.92), suggesting that breeding sociality was not a confounding variable. Therefore, coloniality was not used in subsequent analyses.
Population trends
I obtained estimates of European population trends for the period 1990–2005, based on national breeding bird censuses (Voricek 2003). There were only information available on population trends for 56 of the 99 species for which I had estimates of flight distances, and obviously, any conclusions about flight distance and population trends are restricted to this sample of species. The estimate of population trend is the proportional additive change in population size from an initial index value of one, with the European index being based on national indices weighted by the relative size of different national populations (http://www.ebcc.info/index.php?ID=148).
Migration distance
I determined the global northernmost and southernmost latitude of the breeding and the wintering distributions, respectively, to the nearest tenth of a degree of all species, based on information on breeding and wintering ranges shown on maps in Cramp and Simmons (1977–1994). Migration distance was simply calculated as the mean of the 2 latitudes during breeding minus the mean of the 2 latitudes during winter.
Brain mass
Information on brain mass was obtained from Mlíkovsky (1990), Garamszegi et al. (2002), and Iwaniuk and Nelson (2003).
Farmland habitat
I extracted information on farmland habitat (coded as 0 or 1) from Cramp and Perrins (1977–1994). Coding for habitat was identical to scores for the 48 species that were investigated for population trends across Europe by Voricek (2003).
Northern distribution limit
Information on northern distribution limit was recorded from the maps of the worldwide breeding distribution of all species, as reported by Cramp and Perrins (1977–1994).
Hunting
Species were classified as hunted or not hunted based on knowledge about the local conditions in the areas where flight distances were estimated.
Body mass
Larger species have longer flight distances than small species (Blumstein 2006). Therefore, I used body mass of all species as additional predictor variables 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 Supplementary material (Appendix B).
Statistical analyses
Flight distance, starting distance, brain mass, and body mass were log10 transformed before analyses.
I tested whether flight distance is a species-specific attribute in a nested analysis of variance with flight distance as the response variable and species and starting distance as predictor variables.
All analyses were weighted by sample size 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.
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 uneven branch lengths, assuming a gradual evolution model as implemented in the software, although a second set of analyses based on similar branch length produced qualitatively similar results to those reported here. I tested the statistical and evolutionary assumptions of the comparative analyses (Garland et al. 1992) by regressing absolute standardized contrasts against their standard deviations. 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.
I used farmland habitat as a continuous variable in the analyses despite the fact that it was defined as a dichotomous variable because intermediate states of this variable are biologically meaningful. Using variables as continuous variables in statistical analyses is similar to using a dichotomous variable as a dummy variable in standard regression analyses (Sokal and Rohlf 1995). Because analysis of standardized contrasts is only able to handle one dichotomous variable at a time (Purvis and Rambaut 1995), using these variables as continuous variables also allowed simultaneous statistical control of all potentially confounding variables.
Relative brain size was expressed as the residuals of a linear regression where the slope was obtained from a log–log phylogenetically corrected regression of brain size on body mass of a large set of 567 bird species for which we were able to retrieve information on both phenotypic traits (Mlíkovsky 1990; Garamszegi et al. 2002; Iwaniuk and Nelson 2003) and to code the phylogeny (details not shown). The slope of this regression (based on phylogenetically independent contrasts) was log(brain mass) = 0.581 (standard error [SE] = 0.010) log(body mass) (F = 35.20, df = 1,494, P < 0.0001; contrasts with standardized residuals >|1.96| were excluded from the regression analysis). The same procedure was adopted for contrast analyses. In this case, we first calculated contrasts of log(brain size) on log(body mass) for the set of species included in this study and then calculated the residuals of these contrasts from the above-mentioned phylogenetically corrected regression equation.
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, 2005; 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) (Figure 1 in the Supplementary material). Because information for the composite phylogeny originated from different studies using different molecular and phylogenetic methods, consistent estimates of branch lengths were unavailable. Therefore, branch lengths were transformed assuming a gradual model of evolution with branch lengths being proportional to the number of species contained within a clade. The results from the phylogenetic analyses were also qualitatively similar to those found when making the calculations using the taxonomy of Sibley and Monroe (1990).
Most statistical approaches assume 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). I weighted each observation by sample size in order to use all data in an unbiased fashion, thereby giving each datum a weight that reflects its degree of precision due to sampling effort (Draper and Smith 1981; Neter et al. 1996). The results were qualitatively similar if I used log-transformed sample size as a weighting factor or if I used unweighted analyses. 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 sample size in the analysis 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 2007). Population trend was neither significantly related to sample size or log-transformed sample size for flight distance (sample size: F = 0.06, df = 1,54, P = 0.81; log-transformed sample size: F = 0.79, df = 1,54, P = 0.38) nor log-transformed flight distance strongly related to sample size or log-transformed sample size for flight distance (sample size: F = 1.32, df = 1,54, P = 0.26; log-transformed sample size: F = 4.53, df = 1,54, P = 0.04).
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 1997).
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Intra- and interspecific variation in flight distance
There was significant variation in flight distance among species (F = 22.13, df = 98,1939, r2 = 0.51, P < 0.0001), with more variation among than within species (Sums of squares [among species] = 139.12, Sums of squares [within species] = 135.47). This provides evidence of flight distances being a species-specific attribute. Mean flight for different species ranged from 3.28 to 200 m, with a mean weighted by sample size of 10.39 m (SE = 1.06; after back-transformation from the log10-transformed data), N = 99 species. Inspection of the phylogenetic distribution of flight distance revealed that long flight distances were the ancestral state (Figure 1 in Appendix A). Species that were subject to hunting did not have significantly shorter flight distances for their body size than other species (model that included log-transformed body mass: F = 0.001, df = 1,53, r2 = 0.00, P = 0.93).
Mean flight distance for species increased significantly with body mass (linear regression based on log10-transformed variables, weighted by sample size: F = 65.99, df = 1,97, r2 = 0.40, P < 0.0001, slope [SE] = 0.27 [0.03]). This relationship was much weaker in a phylogenetic regression (F = 4.49, df = 1,97, r2 = 0.04, P = 0.037, slope [SE] = 0.20 [0.10]), implying that body mass was an important confounding variable in the comparative analyses.
Population trends and flight distance
The population trends of 56 species of birds across Europe ranged from –0.0419 to 0.0283, with a mean of –0.0033 (SE = 0.0016), showing no evidence of a general decline (1-sample t-test, t = –1.51, df = 55, P = 0.14). Population trend was explained by flight distance (partial regression: F = 26.19, df = 1,53, r2 = 0.33, P < 0.0001, slope [SE] = –0.054 [0.011], effect size estimated as Pearson's r = 0.58) and body mass (partial regression: F = 8.95, df = 1,53, r2 = 0.14, P = 0.0042, slope [SE] = 0.014 [0.005], effect size = 0.38), with species with long flight distances and smaller species having population declines more frequently than predicted by chance (Figure 1A). An analysis of contrasts only confirmed the effect of flight distance, with an effect size of 0.40 (Figure 1B; F = 10.12, df = 1,53, r2 = 0.16, P = 0.0025, slope [SE] = –0.039 [0.012], effect size = 0.40), whereas the effect of body mass did not reach statistical significance (F = 0.70, df = 1,53, r2 = 0.01, P = 0.41, effect size = 0.11).
|
A full model that included flight distance and all potentially confounding variables showed a significant effect of flight distance and residual brain mass (Table 1; effect size for flight distance = 0.44; effect size for residual brain mass = 0.29). In an analysis of contrasts, only flight distance was retained as a significant predictor (Table 1; effect size = 0.36).
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Flight distance of adult birds when approached by a human being varied significantly among species, with mean distance explaining interspecific variation in population trends in Europe, with a large effect size of 0.36–0.58 depending on the analysis. In general, bird species with long flight distances for their body mass had declining populations compared with species with short flight distances, and this effect was independent of a range of factors previously suggested to account for interspecific variation in population trends.
This study was based on extensive field data on flight distance of adult birds during the breeding season, and I tested the reliability of flight distance estimates in several ways. First, my mean estimates were very similar to 2 independent data sets of mean flight distances obtained by an independent observer in one of my study areas (E. Flensted-Jensen) and obtained elsewhere in Europe by Blumstein (2006). The degree of consistency was very high, implying that flight distance estimates can be reliably estimated as described here. These findings also imply that estimates are repeatable across relatively large geographic scales. The present study was based on the assumption that flight distance from a human approaching an individual bird reflected natural behavior performed in response to a potential risk of predation. Elsewhere, I have tested this assumption by showing that mean flight distance of different species of passerine birds is strongly negatively correlated with risk of predation by the European sparrow hawk Accipiter nisus (Møller et al. 2008). Therefore, mean flight distance as reported here is reflecting not only the response to an approaching human but also the accepted risk to predation by one of the most common predators in the Western Palearctic. 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 (Hedenström and Alerstam 1992; Møller 2008).
Ornithological societies have been conducting bird censuses for more than 40 years, with population trends now being available for the entire European continent (Voricek 2003). The ecological and environmental factors responsible for interspecific differences in population trends have been the subject of close scrutiny during the last decade. Previous studies have indicated that agricultural habitats (Fuller et al. 1995; Chamberlain et al. 2000; Donald et al. 2001; Green et al. 2005), habitat loss and fragmentation (Fahrig 2003; Berthold and Fiedler 2005), migration (Hjort and Lindholm 1978; Baillie and Peach 1992; Bauer and Heine 1992; Berthold 2000; Lemoine and Böhning-Gaese 2003; Sanderson et al. 2006), northern distribution limits and therefore climate change (Parmesan et al. 2000; Walther et al. 2002; Root et al. 2003, 2005; Julliard et al. 2004; Møller et al. 2004), brain mass (Shultz et al. 2005), and body mass (Bennett and Owens 2002) predicted population trends of common breeding birds through effects of environmental change on these variables. I analyzed these factors but also flight distance as predictors of European population trends. Only flight distance was retained as a significant predictor in both species-specific and comparative analyses (Table 1). This finding suggests that mean flight distance and, by inference, the risk that the average individual of different species is willing to take when approached by a potential predator, as shown by relationships between flight distance and risk of predation by sparrow hawk, malarial infection, and innate immune response (Møller et al. 2008), provides the best known predictor of population trends. Whereas many other studies have suggested that other factors account for population trends, I was unable to provide any significant evidence consistent with those previous claims. Here I hypothesize that the population declines of species with long flight distances have come about by increasing levels of disturbance by humans and perhaps their domestic animals (such as dogs and cats) during the last decades.
Because flight distance was defined as the distance when an individual bird took flight when approached by a human, previous experience with humans could have played a role as a mechanism underlying interspecific differences in population trends. If that were the case, this would still raise the question why species that suffered from declining populations did not become habituated to humans. Three pieces of evidence suggests that habituation is not the underlying cause of interspecific differences in flight distance. First, flight distance is significantly negatively related to risk of predation by the European sparrow hawk (Møller et al. 2008), suggesting that risk taking as reflected by flight distance relates directly to risk of mortality. Furthermore, singing males had shorter flight distances when prevalence of malaria was high and when levels of innate immunity were high (Møller et al. 2008). These findings suggest that short flight distances are associated with considerable risks and that species with reduced survival prospects as reflected by parasitism and immunity take greater risks than the average species. Second, the difference in flight distance between urban and rural populations of the same species is positively related to time since urbanization (Møller 2008). Third, individuals are consistent in flight distance when tested repeatedly (Møller AP, unpublished data), suggesting that habituation is not causing flight distance to decrease rapidly with increasing exposure to humans.
The actual mechanisms, underlying the patterns reported here, were not the subject of this study but obviously need to be addressed explicitly. A potential mechanism links flight distance and disturbance to foraging efficiency and resource use (Madsen 1998a; Fernández-Juricic 2002; Feret et al. 2003; Bechet et al. 2004; Blumstein et al. 2005; Rodríguez-Prieto and Fernández-Juricic 2005), body condition (Feret et al. 2003; Bechet et al. 2004), and therefore to reproductive success (Wilson et al. 1991; Woehler et al. 1994; Giese 1996; Cobley and Shears 1999; Arroyo and Razin 2006). This mechanism implies that a high level of responsiveness to disturbance reduces resource use and foraging efficiency under a high and increasing disturbance regime, causing a reduction in body condition and subsequently reduced reproductive success. Such nonlethal effects of disturbance are important in ecology (e.g., Abrams 1991; Lima 1998). 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 potential mechanism may help 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 novel measure of animal behavior that correlates with population trends in birds. This index of space use and disturbance tolerance may assess the susceptibility of different species to perturbations, be they hunting, tourism, or other human activities, but also as a means of assessing general risk aversiveness of different species.
|
SUPPLEMENTARY MATERIAL |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Supplementary material can be found at http://www.beheco.oxfordjournals.org/
|
APPENDIX A |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Composite phylogenetic relationship between bird species with information on flight distance. The main sources were Sibley and Ahlquist (1990)
, combined with other sources listed in Materials and methods. Species with short (white branches) and long flight distances for a given body mass are shown. |
APPENDIX B |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Information on mean flight distance (meters), sample size for flight distance, European population trend, body mass (grams), brain mass (grams), farmland breeding habitat, migration distance (degrees latitude), northernmost breeding distribution limit (degrees N latitude), and hunting (0—not hunted, 1—hunted) for bird species included in the present study. See Materials and methods for further information.
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
ACKNOWLEDGEMENTS |
|---|
E. Flensted-Jensen kindly provided data on flight distances.
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY MATERIALAPPENDIX AAPPENDIX BREFERENCES |
|---|
Abrams PA. Strengths of indirect effects generated by optimal foraging. Oikos (1991) 62:167–176.[CrossRef][Web of Science]
Alexander RM. Size and shape (1971) London: Edward Arnold.
Andrewartha HG, Birch LC. The ecological web (1954) Chicago: University of Chicago Press.
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]
Badyaev AV. Altitudinal variation in sexual dimorphism: a new pattern and alternative hypotheses. Behav Ecol (1997) 8:675–690.
Baillie SR, Peach WJ. Population limitation in Palaearctic-African migrant passerines. Ibis (1992) 134:120–132.
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.
Bauer H-G, Heine G. The development of the breeding birds of Lake Constance: a comparison of grid mapping in 1980/81 and 1990/91. J Ornithol (1992) 133:1–22.[CrossRef]
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]
Begon M, Harper JL, Townsend CR. Ecology (1972) Oxford: Blackwell.
Belanger L, Bédard J. Energetic cost of man-induced disturbance to staging snow geese. J Wildl Manage (1990) 54:36–41.[CrossRef]
Bennett PM, Owens IPF. Evolutionary ecology of birds (2002) Oxford: Oxford University Press.
Berthold P. Vogelzug (2000) Darmstadt (Germany): Wissenschaftliche Buchgesellschaft.
Berthold P, Fiedler W. Changes in the populations of small birds in central Europe, as evidenced by 32 years of trapping data: numbers are mostly declining. Vogelwarte (2005) 43:97–102.
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]
Blumstein DT, Fernández-Juricic E. The emergence of conservation behavior. Conserv Biol (2004) 18:1175–1177.[CrossRef]
Blumstein DT, Fernández-Juricic E, Zollner PA, Garity SC. Inter-specific variation in avian responses to human disturbance. J Appl Ecol (2005) 42:943–953.[CrossRef]
Bregnballe T, Madsen J, Rasmussen PAF. Effects of temporal and spatial hunting control in waterbird reserves. Biol Conserv (2004) 119:93–104.[CrossRef]
Chamberlain DE, Fuller RJ, Bunce RGH, Duckworth JC, Shrubb M. Changes in the abundance of farmland birds in relation to the timing of agricultural intensification in England and Wales. J Appl Ecol (2000) 37:771–788.[CrossRef]
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]
Cramp S, Perrins CM, eds. The birds of the western Palearctic (1977–1994) Oxford: Oxford University Press.
Cramp S, Simmons KEL. The birds of the Western Palearctic (1977–1994) Oxford: Oxford University Press.
Donald PF, Green RE, Heath MF. Agricultural intensification and the collapse of Europe's farmland bird populations. Proc R Soc Lond B Biol Sci (2001) 268:25–29.[Medline]
Draper NR, Smith H. Applied regression analysis (1981) 2nd ed. New York: John Wiley.
Fahrig L. Effects of habitat fragmentation on biodiversity. Ann Rev Ecol Syst (2003) 34:487–515.[CrossRef][Web of Science]
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]
Fernández-Juricic E. Can human disturbance promote nestedness? A case study with birds in an urban fragmented landscape. Oecologia (2002) 131:269–278.[CrossRef][Web of Science]
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]
Fuller RJ, Gregory RD, Gibbons DW, Marchant JH, Wilson JD, Baillie SR, Carter N. Population declines and range contractions among lowland farmland birds in Britain. Conserv Biol (1995) 9:1425–1441.[CrossRef]
Garamszegi LZ, Møller AP, Erritzøe J. Coevolving avian eye size and brain size in relation to prey capture and nocturnality. Proc R Soc Lond B Biol Sci (2002) 269:961–967.[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]
Gaston KJ. The structure and dynamics of geographic ranges (2002) Oxford: Oxford University Press.
Giese M. Effects of human activity on Adelie penguin Pygoscelis adeliae breeding success. Biol Conserv (1996) 75:157–164.[CrossRef]
Green RE, Cornell SJ, Scharlemann JPW, Balmford A. Farming and the fate of wild nature. Science (2005) 307:550–555.
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]
Hjort C, Lindholm C-G. Annual bird ringing totals and population fluctuations. Oikos (1978) 30:387–392.[CrossRef][Web of Science]
Iwaniuk AN, Nelson JE. Developmental differences are correlated with relative brain size in birds: a comparative analysis. Can J Zool (2003) 81:1913–1928.[CrossRef]
Jones KE, Purvis A. An optimum body size for mammals? Comparative evidence from bats. Funct Ecol (1997) 11:751–756.[CrossRef]
Julliard R, Jiguet F, Couvet D. Common birds facing global changes: what makes a species at risk? Glob Chang Biol (2004) 10:148–154.[CrossRef]
Krebs CJ. Ecology (1972) New York: Harper and Row.
Lack D. The natural regulation of animal numbers (1954) Oxford: Clarendon.
Lemoine N, Böhning-Gaese K. Potential impact of global climate change on species richness of long-distance migrants. Conserv Biol (2003) 17:577–586.[CrossRef]
Lima SL. Nonlethal effects in the ecology of predator-prey interactions: what are the ecological effects of anti-predator decision-making? Bioscience (1998) 48:25–34.[CrossRef][Web of Science]
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]
Mlíkovsky J. Brain size in birds: 4. Passeriformes. Acta Soc Zool Bohem (1990) 54:27–37.
Møller AP. Flight distance of urban birds, predation and selection for urban life. Behav Ecol Sociobiol (Forthcoming. 2008).
Møller AP, Fiedler W, Berthold P, eds. Effects of climate change on birds (2004) Amsterdam (The Netherlands): Elsevier.
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, Garamzegi 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.
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]
Parmesan C, Root TL, Willig MR. Impacts of extreme weather and climate on terrestrial biota. Bull Am Meteor Soc (2000) 81:443–450.[CrossRef]
Peters RH. The ecological implications of body size (1983) Cambridge (UK): Cambridge University Press.
Purvis A, Rambaut A. Comparative analysis by independent contrasts (CAIC). Comput Appl Biosci (1995) 11:247–251.
Purvis A, Rambaut A. Comparative analysis by independent contrasts (CAIC): an Apple Macintosh application for analysing comparative data. Comp Appl Biosci (1997) 11:247–251.
Reiss MJ. The allometry of growth and reproduction (1989) Cambridge (UK): Cambridge University Press.
Ricklefs RE. Ecology (1975) New York: W.H. Freeman.
Rodgers JA, Smith HT. Set-back distances to protect nesting bird colonies from human disturbance in Florida. Conserv Biol (1995) 9:89–99.[CrossRef]
Rodríguez-Prieto I, Fernández-Juricic E. Effects of direct human disturbance on the endemic Iberian frog Rana iberica at individual and population levels. Biol Conserv (2005) 123:1–9.[CrossRef]
Roff DA. Life history evolution (2001) New York: Chapman and Hall.
Root TL, MacMynowski DP, Mastrandrea MD, Schneider SH. Human-modified temperatures induce species changes: joint attribution. Proc Natl Acad Sci USA (2005) 102:7465–7469.
Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA. Fingerprints of global warming and wild animals and plants. Nature (2003) 421:57–60.[CrossRef][Web of Science][Medline]
Saino N, Romano M, Ferrari RP, Møller AP. Stressed mothers produce low-quality offspring with poor fitness. J Exp Zool (2005) 303A:998–1006.
Sanderson FJ, Donald PF, Pain DJ, Burfield IJ, van Bommel FPJ. Long-term population declines in Afro-Palearctic migrant birds. Biol Conserv (2006) 131:93–105.[CrossRef]
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]
Shultz S, Bradbury RB, Evans KL, Gregory RD, Blackburn TM. Brain size and resource specialization predict long-term population trends in British birds. Proc R Soc Lond B Biol Sci (2005) 272:2305–2311.[Medline]
Sibley CG, Ahlquist JE. Phylogeny and classification of birds, a study in molecular evolution (1990) New Haven (CT), London: Yale University Press.
Sibley CG, Monroe BL Jr. Distribution and taxonomy of birds of the world (1990) New Haven (CT), London: Yale University Press.
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.
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]
Tamisier A, Bechet A, Jarry G, Lefeuvre JC, Le Maho Y. Effects of hunting disturbance on waterbirds. A review of literature. Terre Vie (2003) 58:435–449.
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]
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]
Voricek P. Population trends of European common birds 2003 (2003) Prague (Czech Republic): Pan-European Common Bird Monitoring.
Walther GR, Post E, Convey P, Menzel A, Parmesan P, Beebee T, Fromentin J-M, Høgh-Guldberg O, Bairlein F. Ecological responses to recent climate change. Nature (2002) 416:389–395.[CrossRef][Web of Science][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]
Ylönen 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]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  A. P. Moller Interspecific variation in fear responses predicts urbanization in birds Behav. Ecol., January 29, 2010; (2010): arp199v1 - arp199. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||