Abstract

Breeding habitat selection is expected to be adaptive. Animals should respond to strong agents of natural selection, such as expected offspring mortality due to nest predators, in their settlement decisions. In birds, mammalian nest predators are a significant mortality source and birds are known to respond to their presence. However, the mechanism used by birds to perceive mammalian nest predators and estimate the nest predation risk remains unknown, in particular at larger spatial scales while comparing potential breeding habitat patches. We experimentally tested whether the farmland bird community can detect and perceive cues of a mammalian nest predator (urine and feces), and how this perception affected the habitat selection and community structure of birds. The experiment was conducted at a large habitat patch scale by simulating a high abundance of nest predator by spraying on the ground water with dissolved mink excrements, whereas water was sprayed in the control treatment. Birds avoided settling in the plots treated with nest predator excrement. The number of migratory passerine species and their total density were lower in the simulated predation risk treatment than in the control treatment. Our results revealed a novel antipredator behavioral mechanism in birds; passerine birds can detect the excrements of mammalian nest predators and thereby assess the relative nest predation risk among potential breeding habitat patches. This mechanism has direct impacts on the structure of avian breeding bird community.

Introduction

Accumulating empirical and theoretical evidence suggests that animals make important decisions, such as where to produce offspring, forage, or with whom to mate, after actively gathering information from various sources about their environment (see Danchin et al. 2004 ; Dall et al. 2005 ; Seppänen et al. 2007 for reviews). Because information gathering usually entails costs ( Dall et al. 2005 ; Stamps et al. 2005 ) one can expect that information acquisition is particularly important for decisions that potentially entail large fitness effects. The selection of a site to reproduce and rear offspring is such a decision, because conditions in the chosen site (e.g., abiotic conditions, food resources, and agents of mortality) directly affect crucial components of fitness, the number and quality of offspring. Consequently, the selection of habitat for producing offspring has a genetic background ( Jaenike and Holt 1991 ), which is adaptive and under natural selection ( Martin 1998 ).

Offspring mortality caused by predators often comprises a significant proportion of the total mortality ( Martin 1995 ; Kessler and Baldwin 2002 ; Rieger et al. 2004 ). Natural selection should select for information acquisition strategies that increase the probability of detecting cues about offspring mortality risk. Results suggest that animals can make adjustments within habitat patches about where to place a nest or oviposit ( Kessler and Baldwin 2002 ; Forstmeier and Weiss 2004 ; Tschanz et al. 2005 ; Eggers et al. 2006 ; Mönkkönen et al. 2009 ; Trnka et al. 2011 ), and after site selection, in offspring investment decisions ( Eggers et al. 2006 ; Fontaine and Martin 2006a ; Mönkkönen et al. 2009 ; see also the review by Lima 2009 ). However, offspring mortality risk often varies over large spatial scales (among habitat patches, Robinson et al. 1995 ; Resetarits 2005 ; Chalfoun and Martin 2010 ) and theory predicts that animals should take it into account in habitat choices ( Fretwell 1972 ). Only a few studies have addressed this question and their results suggest that animals can avoid settling in high-risk habitats in the first place by comparing the mortality risk of potential habitats ( Resetarits 2005 ; Fontaine and Martin 2006b ; Mönkkönen et al. 2007 ; Forsman and Martin 2009 ; Morosinotto et al. 2010 ).

In birds, nest predation is the major cause of nestling mortality ( Martin 1995 ). Yet Fontaine and Martin (2006b) only recently experimentally demonstrated that the risk of offspring mortality affects breeding habitat selection decisions of birds at larger spatial scales, among habitat patches. But how birds can perceive nest predators, estimate the local nest predation risk and avoid high-risk sites? Lima (2009) emphasized this as one of the major questions in birds’ antipredator strategies that is unclear and lacks experimental evidence. The nest predator guild consists of predators that differ in their ecology and perceptivity by their victims. Avian nest predators are diurnal and often vocal, and thus quite easily perceived. Most avian studies have focused on how birds perceive and react to visual and vocal stimulus of predators ( Doligez and Clobert 2003 ; Eggers et al. 2006 ; Emmering and Schmidt 2011 ). However, many mammalian nest predators, such as mustelids and omnivorous rodents, are difficult to perceive directly because they are nocturnal and nonvocal. But because these mammalian nest predators are destructive nest predators and significant agents of natural selection ( Martin 1993 ; Söderström et al. 1998 , Valkama et al. 1999 ; Norrdahl and Korpimäki 2000 ), one could expect that birds can assess their relative risk at larger scales. Yet, no study has examined the possible mechanism(s) how birds could perceive and estimate relative mammalian nest predation risk among habitat patches and make adaptive habitat choices.

One potential cue that birds may use to assess mammalian nest predator density is excrement (feces and urine). Plausible perception mechanisms could either be visual, by observing the ultraviolet (UV) light reflectance (cf. Viitala et al. 1995 ), or olfactory, by smelling ( Amo et al. 2008 ; Roth et al. 2008 ) the excrement.

We experimentally tested whether cues of presence of a mammalian nest predator affected the habitat selection and community structure of birds at a large habitat patch scale. We simulated high nest predator abundance by spraying the ground of treatment plots with American mink ( Neovision vision ) excrement dissolved in water, while water alone was sprayed on control plots. We predicted that if birds perceive the feces of the nest predator and use it as a cue for nest predation risk, then birds will avoid settling in the nest predator treatment plots and their density, and also plausibly species richness of the community, will be reduced.

Materials and Methods

We conducted the experiment in 2011 in Kauhava, central-western Finland on agricultural fields where hay, cereals, and root crops are cultivated. The study area is open and intensively cultivated. For agricultural avian population density and community structure, banks along ditches form a crucial habitat ( Suhonen et al. 1994 ; Norrdahl and Korpimäki 1998 ), because they provide the only low-disturbance (i.e., agricultural activities do not directly affect ditches) breeding environment in the agricultural fields. The most discernible component of ditch vegetation is different shrub species ( Salix and Betula spp.) whereas the ground vegetation consists of grass species ( Poaceae spp.).

The experiment included 2 treatments: the simulated mammalian nest predation risk treatment and the control treatment. The sampling unit (study plot) of the experiment was a ditch that included a strip of vegetation (range 170–310 m) between 2 fields and a 40 m wide belt of field on both sides of the ditch. We measured the response of birds to the treatment by estimating their densities with bird surveys (see later). Our study plots correspond to habitat patches because each of them encompassed a multispecies breeding bird community that is surrounded by nonhabitable agricultural fields. To control for erroneous site effects that may influence avian habitat decisions, such as microclimate and avian and mammalian predator community (cf. Suhonen et al. 1994 ), we paired closely situated ditches of similar widths and vegetative structure, and randomly assigned each as either a treatment or a control. Although in proximity, all plots were separated by a minimum of 300 m to ensure independence. Altogether, we had 11 matched pairs of plots within an area of circa 200 km 2 . In the simulated nest predation risk treatment the total and average length of plots were 2543 and 231 m, respectively, while the corresponding values in the control treatment were 2590 and 235 m.

We used the American mink as a model predatory species because it belongs to the family of Mustelidae that are wide-spread generalist terrestrial predators and known to depredate avian nests ( Dunstone and Birks 1987 ; Söderström et al. 1998 ; Valkama et al. 1999 ; Jedrzejewska et al. 2001 ; Salo et al. 2010 ), and therefore likely to induce adaptive behavioral responses in nesting birds. In addition to mustelid predators (the stoat and the least weasel), the other common mammalian nest predators in the area include the raccoon dog ( Nyctereutes procyonoides ) and the red fox ( Vulpes vulpes ) ( Norrdahl and Korpimäki 2000 ). The most common avian predator in agricultural habitat is the European kestrel ( Falco tinnunculus ; see Norrdahl and Korpimäki 1998 ).

To prepare mink treatment liquid, fresh mink feces and urine were acquired from a mink fur farm. The source population included only females that were either pregnant or had just produced pups. We mixed feces/urine and water in relation to 1:3, let it settle for 12–24h after which it was sieved and used within 24h.

In the simulated increased mammalian nest predation risk treatment, we increased the apparent nest predator abundance, and consequent perceived nest predation risk, by spraying a liquid made of American mink urine and feces and placing small solid pieces of feces along the experimental ditches. We sprayed 3 dl of treatment liquids in 1 spot every 20 m and placed a small piece of solid feces every 10 m. In the control treatment, we sprayed water and put pieces of mud resembling mink feces along the ditches. Treatment liquids were sprayed not only on the ground but also on high visible objects, such as big rocks, if available. Treatments were started on 27 April, prior to the arrival of most migratory birds and the start of the breeding activities, and were repeated every third day, at the same locations, during the whole arrival period of migratory birds until 30 May. Beyond the specific differences in the contents of the treatments, human activities between treatments and controls did not differ.

We measured the response of birds to the treatments by estimating their densities with breeding bird surveys (modified line transect method by Järvinen and Väisänen 1976 ) on the study plots. Two surveys were made, the first survey was conducted in mid-May (settlement phase survey) and the second during the first week of June when all migratory birds had arrived (breeding phase survey). Birds were surveyed by walking along the ditch at fixed speed and monitoring and marking all birds and their behavior onto schematic plot maps from which pairs were later interpreted. Surveys were conducted in fair weather between 04.00 and 08.00h. In the settlement phase survey each plot was surveyed once and in the breeding phase survey 2 times.

Of the observed species, only the yellowhammer ( Emberiza citrinella ) was a resident species and plausibly most of them had selected territories before the experiment was started. The yellowhammer was by far the most abundant bird and it was observed in 90% of the plots. Because of the ubiquity of the yellowhammer, its density may give cues about the similarity of habitat structure among plots. The other species were less common and were observed in 5–50% of the plots. Because most migratory species were rare, which makes species level analyses unfeasible, we analyzed the response at the community level. We created 2 groups: the density of the resident (the yellowhammer) and the migratory species. For the migratory species, we summed the densities of all observed migratory species in each study plot into a single density estimate.

Because ditch lengths varied, observed pair numbers were transformed to densities (number of species and pairs/100 m) and the effect of treatment on response variables was examined within matched ditch pairs using paired tests. Species richness and bird densities obtained in the settlement phase surveys were not normally distributed and the treatment effect was analyzed using a sign test. The response variables obtained from the breeding phase bird surveys were normally distributed (species richness of the migratory birds after log 10x + 0.5 transformation) and they were analyzed with paired t -tests. An α = 0.05 was used to reject H 0 -hypothesis. Analyses were conducted using the SPSS 16.0 software.

Results

We observed altogether 17 migratory passerine species ( Table 1 ). In the settlement phase bird surveys, some migrants (9 species) were already on site but neither their relative species richness (number of species/100 m) (sign test P = 1.0, Figure 1 ) nor their density (sign test P = 1.0, Figure 2a ) differed between the treatment and control plots. The density of the resident bird, the yellowhammer, did not differ between treatments and controls (sign test P = 0.508, Figure 2a ) implying that the vegetation structure of the plots affecting species densities and community structure was similar within matched pairs.

Table 1

Average densities (pairs/100 m) and their error estimates (standard error of the mean; SEM) of observed birds (calculated across all plots) and density difference between the predator treatment and the control (simulated nest predator treatment – control treatment)

Species Average density SEM Density difference 
Emberiza citrinella 0.59 0.08 −0.17 
Alauda arvensis 0.36 0.09 −0.12 
Saxicola rubetra 0.27 0.07 −0.04 
Emberiza schoeniclus 0.22 0.08 −0.21 
Acrocephalus schoenobanus 0.22 0.06 −0.21 
Carduelis chloris 0.21 0.05 −0.05 
Motacilla alba 0.14 0.05 −0.21 
Fringilla coelebs 0.13 0.04 −0.09 
Carpodacus erythrinus 0.09 0.05  0.04 
Emberiza hortulana 0.07 0.03  0.00 
Phylloscopus trochilus 0.07 0.03 −0.08 
Sylvia curruca 0.07 0.04  0.13 
Lanius collurio 0.06 0.03  0.04 
Muscicapa striata 0.04 0.03 −0.08 
Turdus iliacus 0.03 0.03  0.00 
Carduelis cannabina 0.03 0.03  0.05 
Sylvia borin 0.02 0.02  0.03 
Sylvia communis 0.01 0.01 −0.03 
Species Average density SEM Density difference 
Emberiza citrinella 0.59 0.08 −0.17 
Alauda arvensis 0.36 0.09 −0.12 
Saxicola rubetra 0.27 0.07 −0.04 
Emberiza schoeniclus 0.22 0.08 −0.21 
Acrocephalus schoenobanus 0.22 0.06 −0.21 
Carduelis chloris 0.21 0.05 −0.05 
Motacilla alba 0.14 0.05 −0.21 
Fringilla coelebs 0.13 0.04 −0.09 
Carpodacus erythrinus 0.09 0.05  0.04 
Emberiza hortulana 0.07 0.03  0.00 
Phylloscopus trochilus 0.07 0.03 −0.08 
Sylvia curruca 0.07 0.04  0.13 
Lanius collurio 0.06 0.03  0.04 
Muscicapa striata 0.04 0.03 −0.08 
Turdus iliacus 0.03 0.03  0.00 
Carduelis cannabina 0.03 0.03  0.05 
Sylvia borin 0.02 0.02  0.03 
Sylvia communis 0.01 0.01 −0.03 

Species are organized in the descending order of density.

Figure 1

Species richness of the migratory species in the simulated nest predator and the control treatment in the settlement phase (in spring) and final breeding phase (summer) surveys after the treatments. The error bar indicates SEM.

Figure 1

Species richness of the migratory species in the simulated nest predator and the control treatment in the settlement phase (in spring) and final breeding phase (summer) surveys after the treatments. The error bar indicates SEM.

Figure 2

The average population densities of the resident bird and migratory species in the simulated nest predator and the control treatment in the (a) settlement phase and (b) final breeding phase surveys after the treatments. The error bar indicates SEM.

Figure 2

The average population densities of the resident bird and migratory species in the simulated nest predator and the control treatment in the (a) settlement phase and (b) final breeding phase surveys after the treatments. The error bar indicates SEM.

In the breeding phase surveys, the average density of migratory passerines (calculated across all plots) was 2.5 times higher than in the settlement phase surveys. The average density of the yellowhammer increased by about a factor of 1.5 in the breeding phase surveys relative to settlement phase surveys implying that some yellowhammers selected breeding habitats after the initiation of the experiment. The density of yellowhammers did not differ between treatments ( t10 = 1.719, P = 0.116, Figure 2b ). However, the species richness of migratory passerines ( t10 = 2.267, P = 0.047, Figure 1 ) and their pooled population density ( t10 = 2.982, P = 0.014, Figure 2b ) were lower in the simulated nest predator treatment than in the control treatment. These differences correspond to 48.4% and 54.1% reductions in species richness and density of passerines, respectively, compared with the control treatment ( Figures 1 and 2b ).

Discussion

As predicted, our experimental manipulation of predation risk corresponded with a reduction in avian diversity and abundance. Although some of the rare species (1–3 observations altogether) appeared to respond positively to the treatment, this is likely due to sampling error. The observed effect cannot result from systematic variation in habitat structure because we matched study plots relative to their vegetation structure prior to the randomization of the treatments. The fact that the density of a resident bird, the yellowhammer, which selected breeding sites before launching the experiment, did not differ between treatments further strengthens this conclusion.

The model of the ideal free distribution predicts that breeding sites should be selected in the order of their quality. We did not observe such an effect because the density of migrant passerines did not differ between treatments in the settlement phase surveys but a final density effect emerged gradually during the settlement period. This result matches with that of Fontaine and Martin (2006b) who did not find difference in the settlement order between high and low nest predator risk habitat patches. Several factors may explain this result. First, age-dependent habitat choice strategy is a plausible explanation because old individuals usually arrive first and are more site-faithful than young individuals ( Pärt and Gustafsson 1989 ). Philopatry may convey a fitness advantage if the value of private information and site familiarity exceed the costs of predation risk. Second, it is also possible that the effect of the mink treatment accumulated during the course of the experiment and had a stronger effect on birds later in the settlement period. This effect, however, most likely is not strong because weekly rains cleaned the vegetation and ground and, in particular, the sprouting vegetation along ditches probably made it more difficult to perceive the treatments near the end of experiment than in the beginning.

The most likely explanation for our results is that passerine birds can detect the presence of mammalian nest predators among habitat patches by perceiving their excrements on the ground. Higher perceived nest predation risk of detecting predator excrements leads to adaptive habitat choices. The observed effect may have been intensified by indirect treatment effects. It is possible that cumulating density differences among plots and effects of manipulation on behavior of birds may also have had an impact on the observed density responses. Slowly cumulating density differences between control and predator urine plots may affect the final breeding densities via con- and heterospecific attraction ( Stamps 1988 , Mönkkönen et al. 1999 ; Forsman et al. 2009 ), and how individuals weigh the costs of intra- and interspecific competition and nest predation risk in habitat choices. Our treatment may also have affected the behavior of settled birds, such as decreasing singing rate ( Fontaine and Martin 2006b ). Newly arriving individuals may use already settled individuals as cues in habitat choices, and decreased singing may alter the cue. An alternative explanation is that the manipulation has affected the behavior of real mustelids by attracting them to the treatment sites. This may have intensified our mink urine treatment effect, but is unlikely for many reasons. First, mink home range size can be tens of hectares ( Niemimaa 1995 ; Salo et al. 2010 ) and mustelid densities in the study area were likely low in early 2011 because of extremely low vole densities in 2010 (Korpimäki E, unpublished data). Second, matched treatment and control sites were so closely situated (ca. 300–500 m apart) that attracted mustelids would have covered control sites too. Third, mustelids are asocial territorial animals and are aggressive toward intruders outside of the mating season ( Erlinge 1977 ). Hence, it is highly unlikely that manipulation would have affected the natural mustelid density in the study plots and affected the observed response in birds.

Our preliminary results suggest that manipulation also reduced the abundance of small mammals (voles [ Microtus ] and shrews [Soricidae]) in the mink treatment ditches compared with the control ditches (unpublished data). These small mammals, especially the most abundant field vole ( M. agrestis ) and sibling vole ( M. levis ), are herbivorous and not known as nest predators in Finnish farmlands. Nevertheless, higher mammal densities on control ditches cannot explain increased avian densities on these ditches. If birds did perceive small mammals, potentially the shrews, as potential nest predators, we would expect avoidance of their presence and not apparent attraction.

The present study cannot distinguish between the plausible perception mechanisms, that is, whether birds use visual sensing of the UV reflection (or other wavelengths of light) of the excrements or the sense of smell. The perception of UV light reflection is a strong candidate because mammalian excrements reflect UV light and many birds have the ability to perceive UV light reflectance ( Honkavaara et al. 2002 ). It has been shown that birds use reflected UV light in locating the prey ( Viitala et al. 1995 ; Church et al. 1998 ) and in selecting a mate ( Bennett et al. 1996 ; Siitari et al. 2002 ). The information content of the reflected UV light is evidently high. For example, Koivula et al. (1999) showed that the UV reflection of excrements differs among vole species and between sexes, and that this information was used by the birds of prey. Birds may also use olfactory cues in selecting predation free habitats. In contrast to general belief, avian genomic composition affecting olfactory receptors is well developed implying that birds may have a better sense of smell than assumed ( Steiger et al. 2008 ). Recent studies have indeed shown that birds respond to the smell of predators ( Amo et al. 2008 ; Roth et al. 2008 ). Further experiments are needed to disentangle the sensory mechanisms birds may use in detecting the presence of mammalian nest predators.

To conclude, we have demonstrated that birds avoided settling on plots where we experimentally increased perceived nest predation risk, which most likely resulted from birds’ ability to detect predator excrements on ground. The mere simulated increase of mammalian nest predator density impoverished the species richness and population density of a passerine community. This result emphasizes that incorporating the sources and acquisition mechanisms of information about the major selective factors, such as the survival of offspring and adults, into the theories of habitat selection (e.g., Fretwell 1972 ) would allow us to make better predictions about animals’ habitat choices and consequent community structure. Accurate predictions about factors affecting habitat choices would be valuable also in applied conservation issues. For example, using a treatment that averts animals in settling areas of poor quality can be relatively cheap, yet an effective management action.

Funding

Academy of Finland (projects # 122665 and 125720 to J.T.F., and 138049 to R.L.T.).

We also thank Sini Tuoriniemi and Delphine Mathy for help in the field. We are also grateful to 4 Peers in Peerage of Science and 2 other referees that provided a thorough peer review and many valuable improving suggestions to our manuscript.

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