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ISSN: 0006-3657 (Print) 1944-6705 (Online) Journal homepage: http://www.tandfonline.com/loi/tbis20

Adaptive habitat preferences in the Tawny OwlStrix aluco

Saulius Rumbutis, Daiva Vaitkuvienė, Gintarė Grašytė, Mindaugas Dagys,Deivis Dementavičius & Rimgaudas Treinys

To cite this article: Saulius Rumbutis, Daiva Vaitkuvienė, Gintarė Grašytė, Mindaugas Dagys,Deivis Dementavičius & Rimgaudas Treinys (2017): Adaptive habitat preferences in the Tawny OwlStrix aluco, Bird Study

To link to this article: http://dx.doi.org/10.1080/00063657.2017.1369001

Published online: 11 Sep 2017.

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Adaptive habitat preferences in the Tawny Owl Strix alucoSaulius Rumbutisa, Daiva Vaitkuvienėb, Gintarė Grašytėb, Mindaugas Dagysb, Deivis Dementavičiusa andRimgaudas Treinysb

aT. Ivanauskas Zoological Museum, Kaunas, Lithuania; bNature Research Centre, Vilnius, Lithuania

ABSTRACTCapsule: Tawny Owls Strix aluco occupying nest boxes preferred habitats which were positivelyassociated with the probability of nesting success.Aims: We aimed to determine whether or not: (1) Tawny Owls showed habitat preferences whenoccupying nest boxes; (2) nesting performance was related to the habitats around occupied nestboxes and (3) habitat availability had changed around available and occupied nest boxesbetween 1995–2004 and 2005–14.Methods: Tawny Owls were studied using nest boxes erected in a commercial forest. During nestboxes checks (724 cases), data on occupancy and nesting performance (88 cases) were recorded,and habitat within a 0.4 km radius around nest boxes was analysed.Results: Tawny Owls had preferences for clearings within forests, mature forests and grasslands butavoided young forests. We found a positive relationship between nesting success and abundance ofclearings within the forest, and a negative relationship between nesting success and abundance ofyoung forests. A change in habitat preferences over the two decades was evident, but habitatavailabilities remained similar.Conclusions: Findings indicate adaptive habitat selection in Tawny Owls because preferredhabitats were associated with higher fitness and this type of habitat became more frequentlyselected over time.

ARTICLE HISTORYReceived 4 March 2017Accepted 15 August 2017

The need to find a suitable place to live is a fundamentalfeature of an organism (Morris 2003); thus, individualsrecognizing and occupying high-quality sites in aheterogenous environment will enhance their fitness(Sergio & Newton 2003). This positive habitat–fitnessrelationship is presumed to drive the evolution ofhabitat preferences (Clark & Shutler 1999). Becauselong-living territorial raptors occupy sites for morethan one year (Krüger 2002), the habitat compositionof their sites is of utmost importance for survival andlifetime reproductive success (Laaksonen et al. 2004,Hakkarainen et al. 2008). As expected, raptors areusually distributed in space non-randomly, with well-expressed habitat preferences at multiple spatial scales(Martínez et al. 2003, Sergio et al. 2003, 2006, Treinys& Mozgeris 2010, Treinys et al. 2016, but see Gamaufet al. 2013). Although previous studies have indicatedthe positive effect of settling in relation to habitatquality on the fitness of birds (Forero et al. 1999,Lõhmus 2001, Sergio et al. 2007), adaptive habitatselection is not a universal rule. The lack of a positiveor negative relationship between habitat preferencesand fitness returns may be a consequence of limitedindividual perception for recognizing patch quality, a

weak link between cues used for patch selection andquality, or even a mismatch between the attractivenessof a patch and its true quality arising from humanhabitat alterations (Zimmerman et al. 2003, Arlt &Pärt 2007, Hollander et al. 2011). Hence, identifyingassociations between habitat preferences anddemographic rates is important for understandingpopulation persistence in a human-driven landscape.

Habitat preferences may change over time due to avariety of stand-alone or spatiotemporally relatedfactors. Some of the most important drivers shapingthe habitat–bird relationship are fluctuations inpopulation abundance. Density-dependent birdsettlement at a coarse spatial scale (i.e. across habitatpatches) is usually explained by the Ideal FreeDistribution (Fretwell & Lucas 1970), whereassettlement at the fine spatial scale (i.e. within a habitat)tends to be explained by the Habitat HeterogeneityHypothesis, also known as the site-dependentregulation (Krüger et al. 2012 and references therein).These hypotheses commonly rely on a heterogeneousenvironment and sequential habitat/site occupation: atlow population density, only good habitat/sites is/areoccupied, whereas with increasing density, more

© 2017 British Trust for Ornithology

CONTACT Rimgaudas Treinys rimga.t@gmail.com Nature Research Centre, Akademijos str. 2, 08412 Vilnius, Lithuania

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individuals are relegated to poor quality habitat or sites.Indeed, density-dependent sequential habitat/siteoccupation is frequently demonstrated in raptor studies(Lõhmus 2001, Sergio & Newton 2003, Carrete et al.2006, Sergio et al. 2007, Treinys et al. 2016). Moreover,population increases may also stimulate speciesexpansion into a novel habitat (Bai et al. 2009) oradaptation to exploit a new resource within the typicalhabitat (Vaitkuvienė & Dagys 2014). The bird–habitatrelationship, however, may also change as aconsequence of population responses to anthropogeniclandscape alterations (Hollander et al. 2011). Timberharvesting, the major force acting on boreal andtemperate forest ecosystems, negatively affects thehabitat of forest-dwelling birds by changing thestructure of occupied sites, destroying structuralelements necessary for breeding (Widén 1997, Lõhmus2003a, Angelstam et al. 2004). Conversely, some ofthose species may simultaneously benefit from certainforestry practices, which may, for example, createbetter hunting opportunities (Sulkava & Huhtala 1997,Petty 1999). In summary, habitat preferences in a localpopulation in the long-term may be shaped bymultiple demographic and/or anthropogenic factors.

Territorial site-tenacious raptors, however, may beultimately dependent on habitat choice because all oftheir physiological demands, costs and fitness gains aredirectly related to their occupied sites (e.g. Tengmalm’sOwl Aegolius funereus – Laaksonen et al. 2004,Northern Spotted Owl Strix occidentalis – Franklinet al. 2000). Conversely, in certain avian predatorspecies, ecological plasticity, as reflected in a widespectrum of habitat and prey use, may relax thehabitat–fitness relationship. In this study, we analysehabitat preferences and their changes, as well as theconsequences of these preferences and changes forfitness in a local population of Tawny Owls Strix aluconesting in nest boxes in a commercial temperate forestin Central Lithuania. This widespread owl species isdistributed across a very wide habitat gradient withstrongly expressed intraspecific territoriality (Mikkola1983, Sunde & Bølstad 2004, Köning et al. 2008).Moreover, the local distribution pattern of occupiedterritories holds for many years, despite the turnover ofindividual birds (Sunde & Bølstad 2004). In a previousstudy, we found that the number of nesting pairsfluctuated over several decades, with a marked decreasebetween 2003 and 2008 (Grašytė et al. 2016). In thesame study, we also found some support for a long-term shift in diet during the breeding season,preference for nesting in the forest interior andimproved breeding performance over time. Thesementioned changes in this local population have

appeared since the 2000s, whereas timber harvestinghas increased and agricultural land use has intensifiedsince the mid-1990s.

In the present study, we aim to determine whether ornot: (1) Tawny Owls prefer certain habitat features whenoccupying nest boxes; (2) habitats differ around nestboxes occupied in poor and good years; (3) breedingperformance is related to the habitats around occupiednest boxes and (4) habitats have changed aroundavailable and occupied nest boxes between 1995–2004and 2005–14. First, we expect neither preferences forhabitat nor a relationship between habitat and breedingsuccess, due to high ecological plasticity (Mikkola1983) and strong territoriality (Sunde & Bølstad 2004).Second, we expect that, in comparison to good years,during poor years, Tawny Owls will initiate nesting onsites with a larger proportion of forest habitat becausethe forest supports alternative prey for generalistpredators when the main prey decreases (Laaksonenet al. 2004, Hakkarainen et al. 2008), providing betterresources necessary for initiation of breeding (Sunde &Bølstad 2004). Third, we expect changes to occur inthe habitats around available and occupied nest boxesbecause of increased intensity of forest (State ForestService 2014) and agricultural land use since the mid-1990s (Bukantis et al. 2008, Bykovienė et al. 2014).

Methods

Data collection

All available nest boxes in Dotnuva forest (CentralLithuania, 55° 23′ 27′′ N, 23° 46′ 25′′ E; area: 39 km2;Figure 1) were checked for occupation from Februaryto March, depending on the winter weather conditions.For the study, data collected between 1995 and 2014were included. A nest box was considered occupied if aclutch was found. Occupied nest boxes were checkedlater between April and May to record breedingperformance. More detailed descriptions of the studyarea and field procedures are provided elsewhere(Grašytė et al. 2016). From 1995 to 2014, 30–49(median 36) nest boxes were available for occupation,and a median of five pairs of Tawny Owls attempted tobreed annually.

Habitat characteristics

Spatial data on occupied nest boxes throughout the studyperiod were analysed in the geographical informationsystems (GIS) environment (ArcGIS 10.0 software). Ahabitat layer was created following the standardCORINE Land Cover methodology (Heymann et al.

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1994, Bossard et al. 2000, European EnvironmentAgency 2007), based on photo-interpretation ofsatellite images (Landsat, IRS, SPOT) andorthophotographs at a scale of 1:10 000 for non-forested land, and State Forest Cadastre data, derivedfrom stand-wise inventories, for forested land. Datasources for the years 2000 and 2010 were used torepresent the mid-periods of the two decades of thestudy, (i.e. 1995–2004 and 2005–14). Buffers with aradius of 0.4 km were created around each occupiednest box to describe habitats within the Tawny Owlhome-range (approximately 50 ha; Sunde & Bølstad2004). The proportion of intensively cultivated fields(mainly for crop growing), extensive fields (pastures,grasslands), clearings within forests (recently, i.e. <10years, felled areas, small forest meadows, etc.), youngforest and mature forest were calculated within eachbuffer. Young and mature forests were classifiedaccording to the age of stands, considering thedominant tree species by volume proportion. Forest

was considered young if (a) dominant oak Quercus,maple Acer, lime Tilia and ash Fraxinus stands wereno more than 60 years of age; (b) dominant birchBetula, Black Alder Alnus glutinosa and Grey AlderAlnus incana, and Aspen Populus tremula stands wereno more than 40 years of age and (c) dominantconiferous (mainly spruce Picea) stands were no morethan 50 years of age. Older stands of the correspondingdominant tree species were considered a mature forest.Thereafter, we checked for pairwise correlations amongall variables around unique nest boxes that wereavailable in at least one year between 1995 and 2014 toreduce collinearity. Correlation coefficients betweenproportions of different habitats ranged from −0.37 to0.02, with the exception of intensive fields versusmature forests (r =−0.5). These two variables wereconsidered redundant and for further analyses weretained only the proportion of mature forest, as it wasmore relevant for the ecology of the study species (seeTreinys et al. 2011 and references therein).

Figure 1. Study site location within Lithuania (inset) and plan of forest (black fill) area land cover within the study site.

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Data analysis

To evaluate whether habitat composition around nestboxes was important for occupancy, we used data from724 nest box checks and assessed the proportions ofthe above-mentioned habitat variables around occupiedand unoccupied nest boxes through generalized linearmixed models (GLMMs). Nest box occupancy during agiven year (0 – unoccupied, 1 – occupied) was aresponse variable, all possible combinations of habitatvariables (i.e. extensive field, clearing within forest,young and mature forest) were included as fixedvariables, and the decade and a nest box identificationvariable (ID) were included as independent randomfactors. Binomial error structure and logit link functionwere used for these models.

To estimate habitat composition effects underdifferent resource availability conditions, we comparedoccupied nest boxes during good years (68 cases ofoccupation) and poor years (20 cases of occupation).Balčiauskienė & Naruševičius (2006) found inter-annual fluctuation in the abundance of small mammalpopulations in the same region, and a recent study inthe Baltic region demonstrated breeding performancefluctuation in the vole-eating Lesser Spotted EagleClanga pomarina (Treinys et al. 2017). Unfortunately,no data concerning food abundance were available forour study site. Considering the widely demonstratednumerical response of raptors to abundancefluctuations in their main prey (Newton 2003 andreferences therein, Sundell et al. 2004, Solonen 2005),however, we used the number of nesting pairs at ourstudy site (Grašytė et al. 2016) as a proxy index forprey availability. We used the median number ofnesting pairs during the period 1995–2014 as athreshold value for classifying years as good (≥5nesting pairs; 11 years; mean annual number of nestingpairs – 6.2) or poor (<5 nesting pairs; 9 years; meanannual number of nesting pairs – 2.2). In our GLMMs,the binary response variable was year type (0 – nestbox occupied during a poor year, 1 – nest boxoccupied during a good year), all possiblecombinations of habitat variables were included asfixed variables, and the decade and the nest box IDwere included as independent random factors.Binomial error structure and logit link function wereused for these models.

To assess the effect of habitat composition on nestingsuccess (88 cases), we used GLMMs with binomial errorstructure and logit link function. A breeding attempt wasconsidered successful if at least one nestling older than

two weeks of age was recorded in a nest box. Mortalityof juveniles during the rest of the fledging and post-fledging period may take place, but our data collectionmethod did not follow the fate of nestlings. Hence,nesting success in our case is only an index and doesnot cover the entire period to fledgling independence.Nesting success was used as a binary response variable(0 – unsuccessful, 1 – successful), all possiblecombinations of habitat variables were included asfixed variables, and the decade, the nest box ID andthe year type (poor or good, see above) were includedas independent random factors.

Generalized linear models were used to analysetemporal changes in a habitat composition around thenest boxes in 1995–2004 and 2005–14. In the first setof models, available nest box was a binary responsevariable (0 – unique nest box available at least one yearbetween 1995 and 2004, 1 – unique nest box availableat least one year between 2005 and 2014) and samplesizes were 65 and 66 unique nest boxes for eachperiod, respectively. In the second set of models,occupied nest box was a binary response variable (0 –unique nest box occupied at least once between 1995and 2004, 1 – unique nest box occupied at least oneonce between 2005 and 2014) and sample sizes were 31and 24 occupied nest boxes for each period,respectively. Fixed habitat effects (extensive field,clearing within forest, young and mature forest and allpossible combinations of these variables) were includedin both sets of models with binomial error structureand logit link function.

The information-theoretical approach for modelselection and multi-model inference procedure wereapplied (Burnham & Anderson 2002). Akaike’sinformation criterion with a correction for smallsample size (AICc) was used to select the best modelsfrom model sets. The relative importance of eachmodel was estimated through ranking the models byΔAICc = AICci−AICcmin (where AICcmin is the bestmodel in a model subset). Model weight was estimatedthrough the normalized Akaike weights,exp (–0.5 × DAICc)/

∑Rr=1 exp (–0.5 × DAICcr). A

threshold of ΔAICc≤ 2 was used to separate modelssupported by the data from poorly supported models.Due to model selection uncertainty, we applied anaveraging procedure for the models in a subset (i.e.ΔAICc≤ 2) to obtain estimates of the relativeimportance of fixed effects variables (RIV). Thepackages lme4 (Bates et al. 2013) and MuMIn (Bartoń2013) in the statistical environment R were used forthese calculations (R v.2.15.2; R Core Team 2012).

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Results

Habitat preferences

The subset of models that best explained the effects ofhabitat variables in a 0.4 km radius on the occupancyprobability of nest boxes included three models (Table1, section a). The model with the most support (weight= 0.5) included all four habitat variables. Based on theevidence ratio, this model was approximately two timesmore likely than the other two models, which eachincluded fewer habitat variables. Similar model weights,however, indicated model selection uncertainty.Occupancy probability was positively related to theproportions of extensive fields, clearings within a forestand mature forest stands, but was negatively related tothe proportion of young forest stands in the areasurrounding the nest boxes. Moreover, proportions oftwo hunting habitats, extensive fields and clearingswithin forests, were more influential (both RIV = 1.00)than were proportions of mature (RIV = 0.77) andyoung (RIV = 0.73) forest stands.

Habitat composition differences between poorand good years

The analysis of habitat and nest box use by owls duringgood but not during poor years resulted in foursupported models (Table 1, section b). Models weights(0.4–0.15) indicated model selection uncertainty.Moreover, the intercept-only model received the mostsupport (ΔAICc = 0.00, weight = 0.4), whereas the threeremaining models had 1.5–2.6 times lower supportcompared to the best model. This would indicate thatoccupation of a nest box in good years is more likelythan in poor years when extensive agricultural areasare more abundant, but clearings within a forest andmature forest stands are less available in a 0.4 kmradius around the nest boxes. Small RIV values forthese three variables (0.26–0.15), however, indicate lowsupport for the importance of habitat composition inrelation to the occupation probability of nest boxesduring poor and good years.

Relationship between habitat composition andnesting success

The probability of successful nesting was associated withhabitat composition around the occupied nest boxes.Two models were supported by the data (Table 1,section c). The first model received strong support(weight = 0.73) and included two fixed effects: theproportions of clearings within a forest and young

forest stands. The next model had only one fixed effect:the proportion of clearings within a forest. Theprobability of successful nesting for Tawny Owlsincreased with a higher abundance of clearings withinthe forest and fewer young forest stands.

Habitat composition around available andoccupied nest boxes between 1995–2004 and2005–14

Five models were supported by the data when analysingthe differences in habitat composition around availablenest boxes between two decades (Table 1, section d).Again, similarity in model weights indicated highmodel selection uncertainty. The best (intercept-only)model had 2–2.7 times more support compared to theremaining models in the subset. Together with lowRIV values (Table 1, section d), this indicates onlymarginal changes in the availability of analysedhabitats between 1995–2004 and 2005–14 (Figure 2(a)).

Analysis of changes in habitat composition aroundnest boxes occupied by Tawny Owls between 1995–2004 and 2005–14 resulted in two models that weresupported by the data (Table 1, section e). The bestmodel (weight = 0.69) included only the proportion ofclearings within a forest as a fixed effect. It was 2.2times more supported than the next best model in theset (ΔAICc = 1.6, weight = 0.31), which includedproportions of both clearings within a forest andmature forest stands as fixed effects. According to RIVvalues, the proportion of clearings within a forest isapproximately three times more important than is theproportion of mature forest stands in differentiatingoccupied nest boxes during the two decades. Clearingswithin a forest in the 0.4 km radius around occupiednest boxes increased during the last decade (Figure 2(b)).

Discussion

The main findings of our study are (a) clear preferencesby Tawny Owls for certain habitat structures around nestboxes; (b) low importance of habitat structure for nestinginitiation under varying (food) conditions; (c) a positiverelationship between nesting success and the abundanceof clearings within a forest, and a negative relationshipbetween nesting success and the abundance of youngforest stands and (d) clear changes in habitatpreferences over the two decades. Our findingssupport, at least partially, adaptive habitat selection inthe Tawny Owl, because preferred habitats wereassociated with higher fitness and this type of habitat,despite a small increase in availability, was selectedmore frequently with time.

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Contrary to our prediction, we found preferences forclearings in forests, mature forests and extensive fields,but avoidance of young forests. Why, despite wideenvironmental (Mikkola 1983, Redpath 1995, Sunde &Redpath 2006) and dietary (Galeotti et al. 1991,Jędrzejewski et al. 1996, Köning et al. 2008, Solonen2011) flexibility, do Tawny Owls not distributethemselves randomly? Taking into consideration thefacts that the availability of nest boxes remainedsimilar over the study period and that the spatial scaleused for habitat analysis (∼50 ha) corresponds to thecore area used by individual owls (Sunde & Bølstad2004), we believe that the explanation for thesefindings is the need for a constant food supply whenestablishing territories. Preference for high quality and/or typical feeding habitats has been frequentlydemonstrated in raptor studies (Väli et al. 2004, Sergio

et al. 2006, Ortego 2007, Bai et al. 2009, Treinys et al.2016, Heuck et al. 2017, but see Gamauf et al. 2013).Despite the wide dietary niche, prey species of thissedentary opportunistic raptor vary within and amongbreeding seasons (Zalewski 1994, Petty 1999, van Veen& Kirk 2000). During the autumn and winter months,Tawny Owls rely mainly on rodents (Petty 1999,Capizzi 2000), which fluctuate greatly in density attemperate and higher latitudes (Pucek et al. 1993, Petty1999, Hörndfelt 2004). Watson & Langslow (1989)found that the density of the sedentary Golden EagleAquila chrysaetos was regulated by food availability notin summer, but in winter. Taking this intoconsideration, the demand for food availability duringthe autumn and winter seasons may be the main drivercausing Tawny Owls to maximize foraging areas whenselecting sites, in order to avoid or mitigate fitness

Table 1. Summary results of analyses of (a) the occupancy of Tawny Owl nest boxes, (b) year effect, (c) nesting success and habitatassociations, (d) changes of habitat around available and (e) occupied nest boxes between 1995–2004 and 2005–14. Responsevariables, fixed and random (if applicable) terms of the best model subsets, ΔAICc, model weights, estimates and the relativeimportance of variables (RIV). Estimates and se are given on the logit scale. ‘+’ and ‘−’ indicate positive and negative effects,respectively, of explanatory variables.

Fixed effects

Model Extensive fields Clearings within forest Young forests Mature forests ΔAICc Weight

(a)OccupancyNo.1 + + − + 0.00 0.506No.2 + + + 1.28 0.267No.3 + + − 1.60 0.227Estimates ± se 2.45 ± 1.1 5.9 ± 1.8 −2.8 ± 1.4 1.9 ± 1.0RIV 1.00 1.00 0.73 0.77Random effects:Nest box ID, Decade(b)Year typeNo.1 (null) 0.00 0.399No.2 + 0.82 0.265No.3 − 1.54 0.185No.4 − 1.95 0.151Estimates ± se 2.0 ± 1.8 −2.8 ± 3.4 −0.7 ± 1.4RIV 0.26 0.19 0.15Random effects: Nest box ID, Decade(c)Nesting successNo.1 + − 0 0.73No.2 + 1.99 0.27Estimates ± se 11.1 ± 4.0 −6.5 ± 3.1RIV 1.00 0.73Random effects: Nest box ID, Decade, Year type(d)Available nest boxes in decadeNo.1 (null) 0.00 0.379No.2 + 1.47 0.182No.3 − 1.74 0.159No.4 + 1.98 0.141No.5 − 1.99 0.140Estimates ± se −0.6 ± 1.0 1.4 ± 1.9 0.4 ± 1.4 −0.3 ± 0.9RIV 0.16 0.18 0.14 0.14(e)Occupied nest boxes in decadeNo.1 + 0 0.692No.2 + + 1.62 0.308Estimates ± se 7.8 ± 3.4 1.2 ± 1.5RIV 1.00 0.31

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costs such as starvation, subsequent skipping of breedingor even mortality.

Our results suggest a lack of differences in habitatcomposition around nest boxes occupied during poorand good years. Lõhmus (2003b), however, found thathabitat effects were more important during poor thangood years for an opportunistic predator, the CommonBuzzard Buteo buteo. We propose several mutuallynon-exclusive explanations of our findings.

The first reason may be the lack of spatialheterogeneity in prey abundance, due to a small areaand/or uniform environmental conditions. Sasvári &Hegyi (2011) demonstrated an altitude-dependentquality of individual Tawny Owl territories, but ourstudy site was located in lowland with no directionalchanges in environmental conditions. Spatialsynchrony in fluctuations of rodent populations acrossdistances of up to several hundred kilometres is a well-known phenomenon (Sundell et al. 2004, Huitu et al.2008). Korpimäki et al. (2008) found, however, that ina temperate zone, spatial synchrony acts at much

smaller spatial scale (within 8 km). Although we donot know if or at what spatial scale rodent populationsare fluctuating in our region, we suppose that ourstudy area may be too small (maximum distancebetween the outermost points is approximately 15 km)to produce marked patchiness of food resources acrossthe sites occupied by Tawny Owl pairs during pooryears. Second, heterogeneity in the individual quality ofbreeding birds, brought about by variation in age orother features, may outweigh habitat importance.Studies have demonstrated that foraging and huntingskills improve with age in male raptors (Sasvári et al.2000, Rutz et al. 2006). Similarly, age-dependentbreeding performance of females has beendemonstrated in Tengmalm’s Owl (Laaksonen et al.2002), Tawny Owl (Sasvári & Hegyi 2005) andGoshawk Accipiter gentilis (Nielsen & Drachmann2003, Krüger 2005). Some studies stress that age-related breeding performance within populationsemerge when food resources are scarce (Laaksonenet al. 2002 and references therein, Bunce et al. 2005).

Figure 2.Mean (±95% CI) proportion of habitats (clearings within forest, extensive fields, young forest, mature forest) in a 0.4 km radiusaround (a) available and (b) occupied nest boxes during 1995–2004 and 2005–14. Sample sizes: (a) 65 nest boxes were available duringthe first decade and 66 during the second and (b) 31 unique nest boxes were occupied at least once during the first decade and 24during the second.

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Furthermore, Lõhmus & Väli (2004) demonstrated apositive effect of body size on reproduction forbreeding Lesser Spotted Eagle females, namely in yearswhen the main prey decreased.

Our results indicate that the same type of habitat thatwas over-selected by Tawny Owls when occupying nestboxes was positively related with nesting success, andalso increased in the area surrounding occupied nestboxes during the study period. Similarly, the avoidedhabitat (young forest) was negatively related with thenesting success. Based on these consistent findings, wesuggest that Tawny Owls have the ability to estimatepatch quality, and this behaviour corresponds to the‘ideal’ assumption of the theoretical habitat selectionmodel (Sergio et al. 2007). Correct assessment of sitequality has been demonstrated in previous raptorstudies (Forero et al. 1999. Lõhmus 2001, Sergio et al.2007, Sasvári & Hegyi 2011, but see Zimmerman et al.2003), and evolution of habitat preferences is aconsequence of a positive relationship between habitatchoice and fitness (Clark & Shutler 1999). This also fitswith our findings, because Tawny Owl preference forclearings within a forest, and subsequent shift towardsnest boxes with a greater abundance of this habitat intheir surroundings, had a positive effect on breedingperformance. Ortego (2007) also found that Eagle OwlsBubo bubo gained fitness returns from preferredhabitats; interestingly, this gain was reflected in thequality of offspring but not in their number. Althoughwe do not have direct evidence of hunting benefits forTawny Owls in clearings within forests, this could verywell be the case. First, in our previous study, we found asignificant increase in detection of avian prey in nestboxes during the breeding season, especially since the2000s (Grašytė et al. 2016). Second, passerine birds areconsidered an important alternative prey for owlsduring periods when voles are scarce (Jędrzejewski et al.1996, Sasvári et al. 2000, Solonen & Karhunen 2002,Hakkarainen et al. 2008) or in otherwise adverseenvironmental conditions (Sasvári & Hegyi 2011).Third, in the present study, we detected an increase inthe proportion of clearings within forest aroundoccupied nest boxes since the mid-2000s. Finally, otherTawny Owl dietary studies have supported arelationship between habitat composition and dietduring the breeding season (Capizzi 2000, Balčiauskienėet al. 2008).

We conclude that despite our expectations, increasedforest resource use did not markedly change the habitatstructure within the study site through increasing clearcut areas (included in clearings within forests) orreducing the proportion of mature forests, nor did itcontribute negatively to the breeding performance of this

generalist predator. Raptor populations are regulatedmainly by food and nest site supply (Newton 2003). Thenest site supply remained constant and food availabilitydid not likely decrease, as the nesting success of TawnyOwls has improved despite the dietary changes since the2000s (Grašytė et al. 2016). Unfortunately, our data donot allow us to identify the main driving force thatmakes Tawny Owls rely more on forest habitats inrecent years. We can only speculate, based on the well-established alternative prey hypothesis (Korpimäki et al.1990, Reif et al. 2001) and the decrease in detection ofMicrotus voles in nest boxes since the 1990s (Grašytėet al. 2016), that the increase in demand for avian preycompensated for the decrease in availability/accessibilityof Microtus voles in an open landscape. Vole declineshave also been observed in Fennoscandia in recentdecades (Solonen & Karhunen 2002, Hörnfeldt 2004 andreferences therein), suggesting large-scale responses ofvoles to climatic changes.

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