Growth patterns of common kestrel (Falco tinnunculus) nestlings: effects of hatching order, brood size and habitat factors

Growth patterns of common kestrel (Falco tinnunculus) nestlings: effects of hatching order, brood size and habitat factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Growth patterns of common kestrel (Falco tinnunculus) nestlings: effects of hatching order, brood size and habitat factors Zbigniew Kasprzykowski, Urszula Zaremba, Artur Golawski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9245510/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Nestling growth is an important trait in the life-history strategy of avians and influences later survival and reproductive success. In addition to the determination of individual fitness, growth rates might also be sensitive indicators of ecological conditions and environmental changes. This is particularly important in the case of birds, which are the top predators in the food chain. In this study, we examined the combined effects of habitat characteristics and biological factors on the nestling growth of the common kestrel ( Falco tinnunculus ). Daily changes in four biometric traits, expressed as percentages of their baseline values, were used to determine relative growth rates. Brood size, hatching order, and breeding phenology significantly influenced the growth rates of Kestrel nestlings. Nestlings in broods four presented the lowest growth increases, whereas those in larger broods presented greater values. Among the largest broods, the fifth and sixth nestlings grew faster than their younger siblings did. Relative growth rates were also positively correlated with the date of the first egg. In contrast, habitat characteristics had no measurable effect on growth rates. Our findings highlight the relevance of nestling growth parameters as indicators of breeding performance and may support the effectiveness of kestrel conservation programs in rural landscapes. We recommend incorporating chick growth rates as a valuable biological parameter in the evaluation of conservation efforts. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Zoology brood size chick growth hatching order nest boxes raptors rural landscapes Figures Figure 1 Figure 2 Figure 3 Introduction The postnatal growth rates of altricial terrestrial birds are among the fastest of all vertebrates [ 1 ]. Because of its survival advantages, the growth rate is an important trait in the life-history strategy of avian ontogeny and is subject to strong directional selection [ 2 , 3 ]. Nestling growth rates and size during fledging strongly influence later survival and reproductive success [ 4 ]. Studies indicate that climate-driven shifts in breeding phenology may indirectly affect nestling growth patterns by altering the temporal match between peak food availability and nestling demand 5 . Integrative analyses further indicate that the nestling growth rate is embedded within a broader suite of life-history traits shaped by ecological constraints, including food quality, parental effort, and predation risk [ 3 , 6 ]. Experimental evidence also highlights the role of environmental factors such as photoperiod in modulating growth dynamics via changes in provisioning opportunities [ 7 ]. Together, these findings reinforce the view that the nestling growth rate is not only a key determinant of individual fitness but also a sensitive indicator of ecological conditions and environmental change. The main external factors determining growth patterns are the environmental conditions that regulate food availability [ 8 , 9 ]. The presence of suitable habitats near nests enhances hunting efficiency [ 10 , 11 ], whereas a greater proportion of unsuitable habitats around nesting sites forces adults to forage farther away, increasing energetic costs[ 12 ]. This reduces food delivery to chicks and can negatively affect brood condition and productivity [ 13 ]. In addition to environmental influences, reproductive biology plays a crucial role. Factors such as asynchronous hatching, clutch size, and the extent of parental care shape nestling development [ 14 , 15 ]. Asynchronous hatching generates intrabrood size hierarchies, and sibling competition is thought to exert strong selective pressure on nestling growth[ 16 – 18 ]. Food allocation within broods intensifies competition, particularly in raptors, often resulting in reduced growth and increased mortality among later-hatched chicks [ 19 ]. Despite the recognized importance of these mechanisms, relatively few empirical studies have investigated how environmental and biological factors jointly influence the growth trajectories of specific body traits in raptor nestlings [ 20 , 21 ]. The common kestrel ( Falco tinnunculus ), hereafter referred to as the kestrel, is a raptor species that has benefited the most from conservation programs involving nest box installation [ 22 – 24 ]. Although not a strict cavity nest, this falcon readily accepts nest boxes and may even prefer them when available [ 22 ]. Breeding success in nest boxes is often greater than that at natural sites [ 23 , 25 ]. While kestrels prefer open habitats that facilitate hunting [ 10 , 11 ], the environmental factors affecting their breeding performance remain poorly understood [ 26 ]. Previous studies on kestrel growth patterns have focused mainly on metabolic activity [ 27 ] and the effects of elevated corticosterone levels [ 28 ]. In this study, we examined the combined effects of habitat composition and biological factors on individual variation in Kestrel nestling growth. We hypothesized that the availability of preferred habitats, such as grasslands with abundant prey [ 10 , 29 ], would positively influence growth rates. Conversely, a greater proportion of woodlands around nest sites, which are considered suboptimal habitats for kestrels [ 22 , 26 , 30 ], is expected to have a negative effect. In addition to habitat composition, we predicted that breeding timing, brood size, and hatching order would significantly affect nestling growth [ 21 ]. Materials and methods Study area The study was conducted on 750 km² of farmland in east-central Poland (52°14ʹN, 22°27ʹE). The landscape was dominated by extensive agriculture, with arable fields covering 46.9% of the area. Woodlands account for 17.8%, meadows and pastures 14.7%, urbanized areas 5.7%, water bodies and wastelands 5.3%, and orchards, mainly apple trees, 2.6% 26 . The region has a temperate transitional climate [ 31 ], with average annual air temperatures ranging from 7.2°C to 9.1°C. July is the warmest month, with an average temperature of 19.4°C. Data collection The study was carried out during the 2020 and 2021 breeding seasons. During this period, 40 nest boxes were monitored, and 207 kestrel nestlings were investigated. Broods occupy nest boxes measuring 30 cm × 40 cm × 30 cm and are suspended on utility poles at a height of 6–7 m above the ground [ 24 ]. All nests were discovered during the egg incubation phase. To account for potential year effects [ 32 ], the data were standardized against the first egg date (FED) for the population in each year. Hatching dates were determined by direct observation or by estimating nestling age. After hatching, the nests were visited twice at intervals of 4–7 days (mean = 5.4 days). Nestlings were individually marked on their claws via organic nail varnish. Nestling ages ranged from 6–21 days depending on the brood. During each visit, the number of nestlings in each nest was recorded, and biometric measurements, including head length, tarsus length, wing length, and body mass, were taken. Head length (mm) was measured from the occiput to the base of the bill, and tarsus length (mm) was measured on the left leg between the joints via callipers (mm). Wing length (mm) was measured on the folded wing from the carpal joint to the tip via a ruler accurate to the nearest millimeter, and body mass (g) was recorded via a Pesola spring scale. Habitat components The land cover around each nest box was analysed via data from the Corine Land Cover (CLC) system, which was obtained from the Polish Office of the Inspectorate of Environmental Protection ( https://clc.gios.gov.pl ). Vector data from the Database of Topographic Objects (BDOT10k), corresponding to maps at a scale of 1:10,000, were also used. The open-source software QGIS v.3.22.1 was employed to calculate the areas of dirt and asphalt roads (Road), cultivated fields (Field), meadows and pastures (Meadow), woodlands (Forest), habitat mosaics (Mosaic), and dense rural buildings (Build, Table 1 ). These habitat components are commonly used in kestrel studies [ 26 , 30 , 33 ]. All calculations were performed within a 1 km radius around each nest box. Table 1 Characteristics of the variables describing the habitat parameters. Variable description Code Mean SD Range Dirt and asphalt roads (ha) Road 1.16 0.34 0.57–1.82 Cultivated fields areas (ha) Field 149.97 83.18 4.38-287.31 Meadows and pastures (ha) Meadow 81.51 68.06 0.00-226.10 Woodland areas (ha) Forest 32.85 31.37 0.00-146.37 Habitat mosaic (ha) Mosaic 19.93 22.36 0.00-94.38 Dense rural buildings (ha) Build 24.50 15.12 0.00-65.86 Statistical analyses Relative growth rates (RGRs) for each nestling were calculated following Brody [ 34 ] and You et al. [ 35 ] as follows: RGR = [(lnW t – lnW 0 )/t] × 100 (%), where W 0 and Wt are biometric measurements at the beginning and end of the growth period t. Daily increments in biometric traits (tarsus length, wing length, head length and body mass) were expressed as percentages of their initial values. RGRs were summarized via principal component analysis (PCA). The first principal component (PC1), representing overall growth, was used as a composite growth metric. Eight potential predictors of the nestling RGR were considered: brood size (number of live nestlings; 3 categories: 4, 5, and 6); hatch order (labels a–f); first egg laying date (FED); and habitat variables: forest, meadow, mosaic, built, and road. The variance inflation factor (VIF) was calculated to assess multicollinearity. Initially, Field and Meadow exhibited high VIFs (> 7); after removing Field, all remaining predictors had VIFs < 2, indicating acceptable collinearity. To identify important predictors and model structure, we fitted linear mixed models (LMMs) with predictors as fixed effects and nest identity as a random effect. The effects of the predictors on overall growth (PC1) were analysed via an information-theoretic approach [ 36 ]. Multiple competing models were compared via the AIC. Hatching order and brood size were treated as categorical variables, whereas FED and habitat variables were treated as numeric variables. The optimal random structure was identified following Zuur et al. [ 37 ]. The model including random nests (AIC = 470.77) outperformed the models with no random component (AIC = 584.21) or with random years only (AIC = 586.21), and the inclusion of both random years and nests (AIC = 472.31) did not improve the fit. Because each nestling contributed only one observation (RGR value), the chick-level random effect (1|idnest/idpull) could not be estimated. Consequently, only nest identity was included as a random effect to account for the nonindependence of siblings within broods. The global model was as follows: PC1 ∼ Brood Size + Hatch Order + FED + Forest + Mosaic + Meadow + Build + Road + (1∣nest). All possible subsets of the global model were evaluated via AICc ( MuMIn package) [ 38 ], and model averaging within the 95% confidence set was performed. Variable importance was assessed on the basis of cumulative Akaike weights [ 36 ]. The predicted values and 95% confidence intervals for categorical effects were generated via the ggeffects package [ 39 ]. All the candidate models were compared with a null model that included only the random effect of the nest. All models in the 95% confidence set had ΔAICc > 2 relative to the null, indicating that the included predictors improved model fit beyond the intercept and random effect alone. Model assumptions, including normality, homoscedasticity, and dispersion, were checked via the DHARMa package [ 40 ]. The pseudo-R² values were calculated following Nakagawa and Schielzeth [ 41 ]. Analyses were conducted in R Studio [ 42 ]. Results Principal component analysis (PCA) was performed on nestling morphological traits (tarsus length, wing length, head length and body mass) to obtain a composite measure of growth. The first principal component (PC1) explained 70.2% of the total variance, and all the traits loaded positively on this axis (Table 2 ). Therefore, PC1 was interpreted as an index of overall nestling growth and used as the response variable in subsequent mixed-effects models. The second component (PC2) explained 20.5% of the total variance. The vectors for weight and head almost completely overlapped (Fig. 1 ), indicating a strong positive correlation between these traits. The tarsus points in a similar direction, whereas the wing aligns more with PC2, following a different pattern. Table 2 Loadings of morphological traits on the first four principal components derived from a principal component analysis (PCA) of kestrel ( Falco tinnunculus ) nestling growth traits (tarsus length, wing length, head length and body mass). The table also presents the standard deviation, the proportion of variance explained by each component, and the cumulative variance. Trait PC1 PC2 PC3 PC4 Tarsus 0.555 -0.234 -0.111 -0.791 Wing 0.302 0.951 -0.005 -0.069 Body mass 0.551 -0.141 -0.639 0.518 Head 0.545 -0.146 0.761 0.319 Standard deviation 1.676 0.906 0.478 0.375 Variance explained (%) 70.22 20.54 5.72 3.52 Cumulative variance (%) 70.22 90.76 96.48 100.00 To evaluate which factors most strongly influenced nestling growth, we summed the Akaike weights of all the models containing each predictor. Overall, Hatch order had the greatest influence, followed by FED, Brood Size, Mosaic, Road, Meadow, Build, and Forest (Table 3 ). The 95% confidence set included 28 models, with 4 models within ∆AICc < 2 (Table 4 ), highlighting some uncertainty in the contributions of secondary variables. The best-supported model included hatch order, brood size, FED, and mosaic (Table 5), which collectively explained approximately 86% of the variation in nestling daily growth (pseudo-R², LMM). Growth increased with brood size: nestlings from broods of six sizes grew significantly faster than those from smaller broods did, whereas broods of five sizes showed a nonsignificant trend toward faster growth (Table 5). Hatching order also influenced growth: the latest-hatched nestlings (“e” and “f”) grew faster than their older siblings did (Table 5), whereas earlier hatchlings (“b”, “c”, and “d”) had no significant effect (Fig. 2 ). These results suggest that later-hatched chicks may partially compensate for their delayed start in development. The first egg date (FED) also had a strong positive effect on growth (Table 5, Fig. 3 ), indicating that chicks from nests initiated later in the season tended to grow faster than those from earlier clutches. In contrast, mosaic habitat had only a small and statistically nonsignificant effect on growth (Table 5), suggesting that landscape composition played a relatively minor role in determining growth rates in this population. Model diagnostics indicated a good fit of the model to the data (DHARMa test: dispersion = 1.028, P = 0.8). Considering all the measured morphological traits, the mean daily increments for nestlings were 0.49% for wing length, 0.20% for tarsus length, 0.10% for head length, and 0.42% for body mass. These values represent average daily relative growth rates calculated across all hatch-order categories, although growth rates varied among individuals. Greater relative growth was generally observed in later-hatched chicks and in broods with more nestlings, suggesting that both seasonal timing and within-brood dynamics contribute to shaping nestling growth rates, which may have important consequences for fledging success and survival. Table 3 Relative importance of predictor variables for models of (PC1) relative growth rate increments of kestrel ( Falco tinnunculus ) nestlings. For each response variable, the importance of the summed Akaike weights of all models containing the focal predictor was calculated across the entire set of 28 competing models. Variable PC1 Hatch order 1.00 FED 1.00 Brood size 0.97 Mosaic 0.52 Road 0.35 Meadow 0.28 Build 0.27 Forest 0.27 Table 4 The table summarizes the top four models forming the 95% confidence set for explaining the variation in the relative growth rate (PC1) of kestrel ( Falco tinnunculus ) nestlings. Models are ranked by the corrected Akaike information criterion (AICc) The degrees of freedom (df), model log-likelihood (LL), corrected AIC (AICc), difference between the AICc of the focal model and the best model in the dataset (∆AICc), and weight of the model (AICcwt) are shown Model (fixed effects) df LL AICc ∆AICc AICcwt RGR PC1 Intercept+HatchOrder+BroodSize + FED+Mosaic 12 -220.103 466.3 0.00 0.406 Intercept+HatchOrder+BroodSize + FED 11 -221.797 467.3 1.06 0.239 Intercept+HatchOrder+BroodSize + FED+Road 12 − 220.856 467.8 1.51 0.191 Intercept+HatchOrder+BroodSize + FED+Mosaic+Build 13 − 219.834 468.1 1.81 0.163 Table 5. Estimated model coefficients for the best LMM model of PC1 relative growth rate increments in kestrel ( Falco tinnunculus ) nestlings. For fixed effects, standard errors (SEs) and p values are shown. For random effects, the values are variance estimates and their standard deviations (SDs). The reference value for brood size was “4”, and that for hatch order was “a”. Fixed effects Estimate SE p value df (Intercept) -5.890 1.573 0.001 28.029 Brood size 5 1.051 0.547 0.065 26.800 Brood size 6 2.341 0.637 0.001 27.975 Hatch order b 0.150 0.177 0.398 122.813 Hatch order c 0.139 0.180 0.439 131.106 Hatch order d 0.178 0.184 0.334 142.267 Hatch order e 0.664 0.214 0.002 155.667 Hatch order f 1.760 0.317 < 0.001 159.703 FED 0.130 0.027 < 0.001 50.151 Mosaic 0.012 0.006 0.071 25.873 Random effects Variance SD Nest 1.487 1.220 Residual 0.501 0.708 Discussion Our study revealed that the growth rates of kestrel nestlings were influenced most significantly by biological factors, such as brood size, hatching order, and breeding time. This finding aligns with our predictions and generally corresponds with patterns reported for other bird species [ 15 , 21 ]. Surprisingly, growth rates were lowest in broods of four broods and highest in larger broods, which contrasts with the typical expectation that larger broods result in slower growth due to increased competition [ 43 ]. The high energy demands of chicks are widely recognized to influence development, and numerous studies have shown that the relative growth rate often declines in experimentally enlarged broods [ 44 – 46 ] and in some natural broods [ 15 , 47 ]. However, evidence for birds of prey is limited. For example, in sparrowhawks ( Accipiter nisus ), chick growth rates are unaffected by brood size but vary with hatching order [ 48 ]. A similar trend was observed in marsh harriers, where brood size did not significantly influence growth rates [ 21 ]. In american kestrels, experimental studies have indicated that brood size does not strongly affect nestling growth or skeletal development, although feather growth and fledging weights may be reduced in larger broods [ 49 ]. Kestrel growth patterns may differ from those of other species due to the interplay of hatching asynchrony and food availability. The unpredictable abundance of rodents may make asynchronous hatching an adaptive reproductive strategy [ 50 ]. Asynchronous hatching creates a size hierarchy and sibling rivalry, which exerts selective pressure on growth (16,18]. In Kestrels, synchronous broods tend to occur in years of stable prey abundance, whereas asynchronous broods are more common during periods of food scarcity [ 51 – 53 ]. Food supplementation experiments have shown that raising synchronous broods can require more energy than asynchronous broods of the same size [ 54 ]. The nutritional richness of the breeding habitat and parental investment strategies likely explain the observed growth patterns [ 55 ]. Daily food delivery and parental flight activity are also influenced by laying date [ 56 , 57 ]. In our study, nestling growth was positively correlated with the first egg date, which is consistent with previous findings in other bird species [ 58 , 59 ]. While larger broods may increase chick competition, parents can compensate by increasing provisioning efforts or delivering larger prey, maintaining growth rates comparable to those of smaller broods [ 60 ]. High-quality parents may produce larger broods, and clutch size could reflect parental condition and experience, whereas collective thermal advantages in larger broods may reduce heat loss and allow more energy to be allocated to growth [ 61 , 62 ]. Interestingly, among the broods of six broods, the youngest nestlings grew the fastest, and the fifth chick among the broods of six also presented higher growth rates. This may allow later-hatched chicks to “catch up” with older siblings, reducing size hierarchy before fledging [ 21 ]. Such a strategy may mitigate the negative effects of hatching asynchrony and increase the chances of survival of younger chicks by maximizing size-related fitness benefits [ 63 ]. Fast growth of the youngest kestrel nestlings is rare among birds, as delayed growth of the youngest offspring is typical in species with asynchronous hatching [ 15 , 64 , 65 ]. In falcons, hatching asynchrony can strongly affect rank in the size hierarchy [ 66 ], with poor growth and sibling rivalry contributing to high mortality among the youngest chicks [ 19 ]. In the studied kestrel population, abundant prey may have mitigated these negative effects, indirectly reflecting the success of the conservation measures. Among the habitat variables, none significantly influenced growth. Mosaic habitats, mainly orchards with scattered buildings, were included in the final models but had no significant effect. Other important hunting grounds, such as meadows and pastures, which are known for supporting larger broods and higher breeding success [ 67 ], also do not affect growth. Kestrels prefer grasslands for prey availability but avoid areas with high numbers of brood predators, such as corvids [ 68 ]. Similarly, forests, which are considered low-quality habitats [ 30 , 31 ], had no negative impact, possibly because of the avoidance of nesting boxes in heavily forested areas [ 26 ]. Overall, the high diversity of habitats in the studied foraging area likely buffered the environmental effects on growth. Conclusion Nestling growth patterns in bird populations managed under conservation programs are important indicators of the effectiveness of management strategies. Our results indicate that the scale and distribution of nesting boxes allow kestrels to select suitable nesting habitats with adequate food resources. This is reflected in higher growth rates of younger chicks and elevated average growth rates in larger broods, suggesting effective parental compensation and overall favourable conditions for offspring development. Declarations Funding The study was supported by the University of Siedlce (229/26/B). Author Contribution ZK conceived and designed the experiments, collected and analysed the data, wrote the manuscript and reviewed drafts of the paper. UZ collected and analysed the data, reviewed drafts of the paper and approved the final draft. AG collected the data, reviewed drafts of the paper and approved the final draft. Acknowledgements We thank Dorota Banaszewska and Waldemar Seredziński for their help in carrying out the fieldwork. We are also grateful to Mirosław Rzępała (Nature Society “Stork”) for the organizational support. 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Brood size and its importance for nestling growth in the Biscutate Swift ( Streptoprocne biscutata , Aves: Apodidae). Braz J. Biol. 68 , 851–857 (2008). Nicolaus, M. et al. Sex-specific effects of altered competition on nestling growth and survival: an experimental manipulation of brood size and sex ratio. J. Anim. Ecol. 78 , 414–426 (2009). Reichert, S. et al. Immediate and delayed effects of growth conditions on ageing parameters in nestling zebra finches. J. Exp. Biol. 218 , 491–499 (2015). Zhu, Z. Q. et al. A diagnosis model of parental care: how parents optimize their provisioning strategy in brood reduction? Curr. Zool. 69 , 385–392 (2022). Moss, D. Growth of nestling Sparrowhawks ( Accipiter nisus ). J. Zool. 187 , 297–314 (1979). Gard, N. W. & Bird, D. M. Nestling growth and fledging success in manipulated American Kestrel broods. Can. J. Zool. 70 , 2421–2425 (1992). Lack, D. The natural regulation of animal numbers (Clarendon, 1954). Wiebe, K. L. & Bortolotti, G. R. Food supply and hatching spans of birds: energy constraint or facultative manipulation? Ecology 75, 813–823 (1994a). Wiebe, K. L., Korpimäki, E. & Wiehn, J. Hatching asynchrony in Eurasian Kestrels in relation to the abundance and predictability of cyclic prey. J. Anim. Ecol. 67 , 908–917 (1998). Wiehn, J., Ilmonen, P., Korpimäki, E., Pahkala, M. & Wiebe, K. L. Hatching asynchrony in the Eurasian kestrel Falco tinnunculus : an experimental test of the brood reduction hypothesis. J. Anim. Ecol. 69 , 85–95 (2000). Wiebe, K. L. & Bortolotti, G. R. Energetic efficiency of reproduction: the benefits of asynchronous hatching for American Kestrels. J. Anim. Ecol. 63 , 551–560 (1994b). Deerenberg, C. et al. Parental energy expenditure in relation to manipulated brood size in the European Kestrel Falco tinnunculus . Zoology 99 , 39–48 (1995). Meijer, T., Masman, D. & Daan, S. Energetics of reproduction in female kestrels. Auk 106 , 549–559 (1989). Masman, D., Dijkstra, C., Daan, S. & Bult, A. Energetic limitation of avian parental effort: field experiments in the Kestrel ( Falco tinnunculus ). J. Evol. Biol. 2 , 435–455 (1989). Dawson, R. D. & Clark, R. G. Effects of hatching date and egg size on growth, recruitment, and adult size of Lesser Scaup. Condor 102 , 93–100 (2000). DiMatteo, J. J. & Clark, M. E. Growth and development of American White Pelican ( Pelecanus erythrorhynchos ) chicks at Marsh Lake, Minnesota, USA. Waterbirds 40, 207–220 (2017). McKinnon, R., Hawkshaw, K., Hedlin, E., Nakagawa, S. & Mathot, K. Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand. Behav. Ecol. 35 , arad103 (2023). Nilsson, J. Å. & Nord, A. Parental roosting behavior and brood size influence nest microclimate and nestling development in Marsh Tits ( Poecile palustris ). Behav. Ecol. Sociobiol. 72 , 164 (2018). Horton, K. G. & Holyoak, M. Nest microclimate and its effects on nestling growth and survival in Tree Swallows ( Tachycineta bicolor ). Oecologia 145 , 618–627 (2005). Jones, T., Ward, M., Benson, T. & Brawn, J. Variation in nestling body condition and wing development predict cause-specific mortality in fledgling Dickcissels. J. Avian Biol. 48 , 439–447 (2017). Mock, D. W. & Parker, G. A. The evolution of sibling rivalry (Oxford University Press, 1997). Moreno-Rueda, G., Soler, M., Soler, J. J. & Martínez, J. G. Pérez-Contreras, T. Rules of food allocation between nestlings of the Black-billed Magpie Pica pica , a species showing brood reduction. Ardeola 54 , 15–25 (2007). Leonardi, G. Behavioural ecology of Western Palearctic Falcons (Springer Nature, 2020). Avilés, J. M., Sánchez, J. M. & Parejo, D. Breeding rates of Eurasian kestrels ( Falco tinnunculus ) in relation to surrounding habitat in southwest Spain. J. Raptor Res. 35 , 31–34 (2001). Kuznetsov, A. V. Biocenological bond patterns in Common Kestrel and Hooded Crow. Sovremennaya Ornitologiya . 28 , 193–203 (1998). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 May, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor invited by journal 02 Apr, 2026 Editor assigned by journal 28 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9245510","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":623912599,"identity":"e8d0651d-7d54-49f1-ba76-cfa1fd3792d2","order_by":0,"name":"Zbigniew Kasprzykowski","email":"data:image/png;base64,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","orcid":"","institution":"University of Siedlce","correspondingAuthor":true,"prefix":"","firstName":"Zbigniew","middleName":"","lastName":"Kasprzykowski","suffix":""},{"id":623912600,"identity":"606265db-98e1-4edd-8dd1-034911bb8e58","order_by":1,"name":"Urszula Zaremba","email":"","orcid":"","institution":"University of Siedlce","correspondingAuthor":false,"prefix":"","firstName":"Urszula","middleName":"","lastName":"Zaremba","suffix":""},{"id":623912605,"identity":"2901ab55-6e3b-4340-86db-b8bab3005ef8","order_by":2,"name":"Artur Golawski","email":"","orcid":"","institution":"University of Siedlce","correspondingAuthor":false,"prefix":"","firstName":"Artur","middleName":"","lastName":"Golawski","suffix":""}],"badges":[],"createdAt":"2026-03-27 13:54:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9245510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9245510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107482423,"identity":"2d990128-ccc4-4677-b1d0-57fdf127d8d1","added_by":"auto","created_at":"2026-04-22 02:23:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16886,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of growth traits in kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) nestlings. The biplot illustrates the relationships among four growth traits: tarsus, wing, weight, and head. Dim1 (70.2%) reflects overall body size, whereas Dim2 (20.5%) captures additional variation.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9245510/v1/b0dd378985206ac6b053d761.jpg"},{"id":107245209,"identity":"96f4987b-6e1c-4f39-8d81-f04d49311b26","added_by":"auto","created_at":"2026-04-19 08:03:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32607,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in the wing relative growth rate (PC1) of kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) nestlings in relation to hatching order and brood size. Modelled means +/- 95% confidence limits for the best model selected are shown. Values were extracted via the \u003cem\u003eggeffects\u003c/em\u003e R package.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9245510/v1/89661b2d856a9dbd48eb988b.jpg"},{"id":107482424,"identity":"5c2e4984-a667-46ad-94a8-eb1840fb78d8","added_by":"auto","created_at":"2026-04-22 02:23:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13201,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the standardized first egg laying date (FED) and nestling growth (PC1). The solid black line represents the predicted values from the linear mixed-effects model, whereas the shaded gray area indicates the 95% confidence interval.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9245510/v1/420f507d852f30808b1c3afd.jpg"},{"id":107485421,"identity":"2bff9cf3-21a9-48b5-ae62-a693875b85ce","added_by":"auto","created_at":"2026-04-22 02:34:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":558519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9245510/v1/bfe58aa9-74c5-4219-9d24-1c9d0dad0be5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Growth patterns of common kestrel (Falco tinnunculus) nestlings: effects of hatching order, brood size and habitat factors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe postnatal growth rates of altricial terrestrial birds are among the fastest of all vertebrates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Because of its survival advantages, the growth rate is an important trait in the life-history strategy of avian ontogeny and is subject to strong directional selection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Nestling growth rates and size during fledging strongly influence later survival and reproductive success [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies indicate that climate-driven shifts in breeding phenology may indirectly affect nestling growth patterns by altering the temporal match between peak food availability and nestling demand\u003csup\u003e5\u003c/sup\u003e. Integrative analyses further indicate that the nestling growth rate is embedded within a broader suite of life-history traits shaped by ecological constraints, including food quality, parental effort, and predation risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Experimental evidence also highlights the role of environmental factors such as photoperiod in modulating growth dynamics via changes in provisioning opportunities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Together, these findings reinforce the view that the nestling growth rate is not only a key determinant of individual fitness but also a sensitive indicator of ecological conditions and environmental change.\u003c/p\u003e \u003cp\u003eThe main external factors determining growth patterns are the environmental conditions that regulate food availability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The presence of suitable habitats near nests enhances hunting efficiency [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], whereas a greater proportion of unsuitable habitats around nesting sites forces adults to forage farther away, increasing energetic costs[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This reduces food delivery to chicks and can negatively affect brood condition and productivity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition to environmental influences, reproductive biology plays a crucial role. Factors such as asynchronous hatching, clutch size, and the extent of parental care shape nestling development [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Asynchronous hatching generates intrabrood size hierarchies, and sibling competition is thought to exert strong selective pressure on nestling growth[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Food allocation within broods intensifies competition, particularly in raptors, often resulting in reduced growth and increased mortality among later-hatched chicks [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite the recognized importance of these mechanisms, relatively few empirical studies have investigated how environmental and biological factors jointly influence the growth trajectories of specific body traits in raptor nestlings [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe common kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e), hereafter referred to as the kestrel, is a raptor species that has benefited the most from conservation programs involving nest box installation [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Although not a strict cavity nest, this falcon readily accepts nest boxes and may even prefer them when available [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Breeding success in nest boxes is often greater than that at natural sites [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. While kestrels prefer open habitats that facilitate hunting [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the environmental factors affecting their breeding performance remain poorly understood [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Previous studies on kestrel growth patterns have focused mainly on metabolic activity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and the effects of elevated corticosterone levels [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we examined the combined effects of habitat composition and biological factors on individual variation in Kestrel nestling growth. We hypothesized that the availability of preferred habitats, such as grasslands with abundant prey [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], would positively influence growth rates. Conversely, a greater proportion of woodlands around nest sites, which are considered suboptimal habitats for kestrels [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], is expected to have a negative effect. In addition to habitat composition, we predicted that breeding timing, brood size, and hatching order would significantly affect nestling growth [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eThe study was conducted on 750 km\u0026sup2; of farmland in east-central Poland (52\u0026deg;14ʹN, 22\u0026deg;27ʹE). The landscape was dominated by extensive agriculture, with arable fields covering 46.9% of the area. Woodlands account for 17.8%, meadows and pastures 14.7%, urbanized areas 5.7%, water bodies and wastelands 5.3%, and orchards, mainly apple trees, 2.6%\u003csup\u003e26\u003c/sup\u003e. The region has a temperate transitional climate [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], with average annual air temperatures ranging from 7.2\u0026deg;C to 9.1\u0026deg;C. July is the warmest month, with an average temperature of 19.4\u0026deg;C.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eThe study was carried out during the 2020 and 2021 breeding seasons. During this period, 40 nest boxes were monitored, and 207 kestrel nestlings were investigated. Broods occupy nest boxes measuring 30 cm \u0026times; 40 cm \u0026times; 30 cm and are suspended on utility poles at a height of 6\u0026ndash;7 m above the ground [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All nests were discovered during the egg incubation phase. To account for potential year effects [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], the data were standardized against the first egg date (FED) for the population in each year. Hatching dates were determined by direct observation or by estimating nestling age. After hatching, the nests were visited twice at intervals of 4\u0026ndash;7 days (mean\u0026thinsp;=\u0026thinsp;5.4 days). Nestlings were individually marked on their claws via organic nail varnish. Nestling ages ranged from 6\u0026ndash;21 days depending on the brood. During each visit, the number of nestlings in each nest was recorded, and biometric measurements, including head length, tarsus length, wing length, and body mass, were taken. Head length (mm) was measured from the occiput to the base of the bill, and tarsus length (mm) was measured on the left leg between the joints via callipers (mm). Wing length (mm) was measured on the folded wing from the carpal joint to the tip via a ruler accurate to the nearest millimeter, and body mass (g) was recorded via a Pesola spring scale.\u003c/p\u003e \u003cp\u003eHabitat components\u003c/p\u003e \u003cp\u003eThe land cover around each nest box was analysed via data from the Corine Land Cover (CLC) system, which was obtained from the Polish Office of the Inspectorate of Environmental Protection (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clc.gios.gov.pl\u003c/span\u003e\u003cspan address=\"https://clc.gios.gov.pl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Vector data from the Database of Topographic Objects (BDOT10k), corresponding to maps at a scale of 1:10,000, were also used. The open-source software QGIS v.3.22.1 was employed to calculate the areas of dirt and asphalt roads (Road), cultivated fields (Field), meadows and pastures (Meadow), woodlands (Forest), habitat mosaics (Mosaic), and dense rural buildings (Build, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These habitat components are commonly used in kestrel studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. All calculations were performed within a 1 km radius around each nest box.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the variables describing the habitat parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirt and asphalt roads (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u0026ndash;1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivated fields areas (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.38-287.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeadows and pastures (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeadow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00-226.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoodland areas (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00-146.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat mosaic (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMosaic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00-94.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense rural buildings (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00-65.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eRelative growth rates (RGRs) for each nestling were calculated following Brody [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and You et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] as follows: RGR = [(lnW\u003csub\u003et\u003c/sub\u003e \u0026ndash; lnW\u003csub\u003e0\u003c/sub\u003e)/t] \u0026times; 100 (%), where W\u003csub\u003e0\u003c/sub\u003e and Wt are biometric measurements at the beginning and end of the growth period t. Daily increments in biometric traits (tarsus length, wing length, head length and body mass) were expressed as percentages of their initial values. RGRs were summarized via principal component analysis (PCA). The first principal component (PC1), representing overall growth, was used as a composite growth metric. Eight potential predictors of the nestling RGR were considered: brood size (number of live nestlings; 3 categories: 4, 5, and 6); hatch order (labels a\u0026ndash;f); first egg laying date (FED); and habitat variables: forest, meadow, mosaic, built, and road. The variance inflation factor (VIF) was calculated to assess multicollinearity. Initially, Field and Meadow exhibited high VIFs (\u0026gt;\u0026thinsp;7); after removing Field, all remaining predictors had VIFs\u0026thinsp;\u0026lt;\u0026thinsp;2, indicating acceptable collinearity.\u003c/p\u003e \u003cp\u003eTo identify important predictors and model structure, we fitted linear mixed models (LMMs) with predictors as fixed effects and nest identity as a random effect. The effects of the predictors on overall growth (PC1) were analysed via an information-theoretic approach [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Multiple competing models were compared via the AIC. Hatching order and brood size were treated as categorical variables, whereas FED and habitat variables were treated as numeric variables. The optimal random structure was identified following Zuur et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The model including random nests (AIC\u0026thinsp;=\u0026thinsp;470.77) outperformed the models with no random component (AIC\u0026thinsp;=\u0026thinsp;584.21) or with random years only (AIC\u0026thinsp;=\u0026thinsp;586.21), and the inclusion of both random years and nests (AIC\u0026thinsp;=\u0026thinsp;472.31) did not improve the fit. Because each nestling contributed only one observation (RGR value), the chick-level random effect (1|idnest/idpull) could not be estimated. Consequently, only nest identity was included as a random effect to account for the nonindependence of siblings within broods.\u003c/p\u003e \u003cp\u003eThe global model was as follows: PC1 \u0026sim; Brood Size\u0026thinsp;+\u0026thinsp;Hatch Order\u0026thinsp;+\u0026thinsp;FED\u0026thinsp;+\u0026thinsp;Forest\u0026thinsp;+\u0026thinsp;Mosaic\u0026thinsp;+\u0026thinsp;Meadow\u0026thinsp;+\u0026thinsp;Build\u0026thinsp;+\u0026thinsp;Road + (1∣nest). All possible subsets of the global model were evaluated via AICc (\u003cem\u003eMuMIn\u003c/em\u003e package) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and model averaging within the 95% confidence set was performed. Variable importance was assessed on the basis of cumulative Akaike weights [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The predicted values and 95% confidence intervals for categorical effects were generated via the \u003cem\u003eggeffects\u003c/em\u003e package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. All the candidate models were compared with a null model that included only the random effect of the nest. All models in the 95% confidence set had ΔAICc\u0026thinsp;\u0026gt;\u0026thinsp;2 relative to the null, indicating that the included predictors improved model fit beyond the intercept and random effect alone. Model assumptions, including normality, homoscedasticity, and dispersion, were checked via the \u003cem\u003eDHARMa\u003c/em\u003e package [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The pseudo-R\u0026sup2; values were calculated following Nakagawa and Schielzeth [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Analyses were conducted in R Studio [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePrincipal component analysis (PCA) was performed on nestling morphological traits (tarsus length, wing length, head length and body mass) to obtain a composite measure of growth. The first principal component (PC1) explained 70.2% of the total variance, and all the traits loaded positively on this axis (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, PC1 was interpreted as an index of overall nestling growth and used as the response variable in subsequent mixed-effects models. The second component (PC2) explained 20.5% of the total variance. The vectors for weight and head almost completely overlapped (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating a strong positive correlation between these traits. The tarsus points in a similar direction, whereas the wing aligns more with PC2, following a different pattern.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLoadings of morphological traits on the first four principal components derived from a principal component analysis (PCA) of kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) nestling growth traits (tarsus length, wing length, head length and body mass). The table also presents the standard deviation, the proportion of variance explained by each component, and the cumulative variance.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ePC4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTarsus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e-0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBody mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariance explained (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e70.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e20.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCumulative variance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e70.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e90.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e96.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo evaluate which factors most strongly influenced nestling growth, we summed the Akaike weights of all the models containing each predictor. Overall, Hatch order had the greatest influence, followed by FED, Brood Size, Mosaic, Road, Meadow, Build, and Forest (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The 95% confidence set included 28 models, with 4 models within ∆AICc\u0026thinsp;\u0026lt;\u0026thinsp;2 (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), highlighting some uncertainty in the contributions of secondary variables. The best-supported model included hatch order, brood size, FED, and mosaic (Table 5), which collectively explained approximately 86% of the variation in nestling daily growth (pseudo-R\u0026sup2;, LMM). Growth increased with brood size: nestlings from broods of six sizes grew significantly faster than those from smaller broods did, whereas broods of five sizes showed a nonsignificant trend toward faster growth (Table 5). Hatching order also influenced growth: the latest-hatched nestlings (\u0026ldquo;e\u0026rdquo; and \u0026ldquo;f\u0026rdquo;) grew faster than their older siblings did (Table 5), whereas earlier hatchlings (\u0026ldquo;b\u0026rdquo;, \u0026ldquo;c\u0026rdquo;, and \u0026ldquo;d\u0026rdquo;) had no significant effect (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results suggest that later-hatched chicks may partially compensate for their delayed start in development. The first egg date (FED) also had a strong positive effect on growth (Table 5, Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating that chicks from nests initiated later in the season tended to grow faster than those from earlier clutches. In contrast, mosaic habitat had only a small and statistically nonsignificant effect on growth (Table\u0026nbsp;5), suggesting that landscape composition played a relatively minor role in determining growth rates in this population.\u003c/p\u003e\n\u003cp\u003eModel diagnostics indicated a good fit of the model to the data (DHARMa test: dispersion\u0026thinsp;=\u0026thinsp;1.028, P\u0026thinsp;=\u0026thinsp;0.8). Considering all the measured morphological traits, the mean daily increments for nestlings were 0.49% for wing length, 0.20% for tarsus length, 0.10% for head length, and 0.42% for body mass. These values represent average daily relative growth rates calculated across all hatch-order categories, although growth rates varied among individuals. Greater relative growth was generally observed in later-hatched chicks and in broods with more nestlings, suggesting that both seasonal timing and within-brood dynamics contribute to shaping nestling growth rates, which may have important consequences for fledging success and survival.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelative importance of predictor variables for models of (PC1) relative growth rate increments of kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) nestlings. For each response variable, the importance of the summed Akaike weights of all models containing the focal predictor was calculated across the entire set of 28 competing models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eHatch order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eFED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eBrood size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eMosaic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eRoad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eMeadow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eBuild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 71.1712%;\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\" style=\"width: 28.8288%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe table summarizes the top four models forming the 95% confidence set for explaining the variation in the relative growth rate (PC1) of kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) nestlings. Models are ranked by the corrected Akaike information criterion (AICc) The degrees of freedom (df), model log-likelihood (LL), corrected AIC (AICc), difference between the AICc of the focal model and the best model in the dataset (∆AICc), and weight of the model (AICcwt) are shown\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModel (fixed effects)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAICc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e∆AICc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eAICcwt\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRGR PC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntercept+HatchOrder+BroodSize\u0026thinsp;+\u0026thinsp;FED+Mosaic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-220.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e466.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntercept+HatchOrder+BroodSize\u0026thinsp;+\u0026thinsp;FED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-221.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e467.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntercept+HatchOrder+BroodSize\u0026thinsp;+\u0026thinsp;FED+Road\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;220.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e467.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntercept+HatchOrder+BroodSize\u0026thinsp;+\u0026thinsp;FED+Mosaic+Build\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;219.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e468.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Estimated model coefficients for the best LMM model of PC1 relative growth rate increments in kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) nestlings. For fixed effects, standard errors (SEs) and p values are shown. For random effects, the values are variance estimates and their standard deviations (SDs). The reference value for brood size was \u0026ldquo;4\u0026rdquo;, and that for hatch order was \u0026ldquo;a\u0026rdquo;.\u003c/p\u003e\n\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep \u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-5.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e28.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBrood size 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e26.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBrood size 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e27.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHatch order b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e122.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHatch order c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e131.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHatch order d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e142.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHatch order e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e155.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHatch order f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e159.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e50.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMosaic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e25.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study revealed that the growth rates of kestrel nestlings were influenced most significantly by biological factors, such as brood size, hatching order, and breeding time. This finding aligns with our predictions and generally corresponds with patterns reported for other bird species [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Surprisingly, growth rates were lowest in broods of four broods and highest in larger broods, which contrasts with the typical expectation that larger broods result in slower growth due to increased competition [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The high energy demands of chicks are widely recognized to influence development, and numerous studies have shown that the relative growth rate often declines in experimentally enlarged broods [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and in some natural broods [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, evidence for birds of prey is limited. For example, in sparrowhawks (\u003cem\u003eAccipiter nisus\u003c/em\u003e), chick growth rates are unaffected by brood size but vary with hatching order [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A similar trend was observed in marsh harriers, where brood size did not significantly influence growth rates [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In american kestrels, experimental studies have indicated that brood size does not strongly affect nestling growth or skeletal development, although feather growth and fledging weights may be reduced in larger broods [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKestrel growth patterns may differ from those of other species due to the interplay of hatching asynchrony and food availability. The unpredictable abundance of rodents may make asynchronous hatching an adaptive reproductive strategy [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Asynchronous hatching creates a size hierarchy and sibling rivalry, which exerts selective pressure on growth (16,18]. In Kestrels, synchronous broods tend to occur in years of stable prey abundance, whereas asynchronous broods are more common during periods of food scarcity [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Food supplementation experiments have shown that raising synchronous broods can require more energy than asynchronous broods of the same size [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The nutritional richness of the breeding habitat and parental investment strategies likely explain the observed growth patterns [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Daily food delivery and parental flight activity are also influenced by laying date [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In our study, nestling growth was positively correlated with the first egg date, which is consistent with previous findings in other bird species [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. While larger broods may increase chick competition, parents can compensate by increasing provisioning efforts or delivering larger prey, maintaining growth rates comparable to those of smaller broods [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. High-quality parents may produce larger broods, and clutch size could reflect parental condition and experience, whereas collective thermal advantages in larger broods may reduce heat loss and allow more energy to be allocated to growth [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, among the broods of six broods, the youngest nestlings grew the fastest, and the fifth chick among the broods of six also presented higher growth rates. This may allow later-hatched chicks to \u0026ldquo;catch up\u0026rdquo; with older siblings, reducing size hierarchy before fledging [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Such a strategy may mitigate the negative effects of hatching asynchrony and increase the chances of survival of younger chicks by maximizing size-related fitness benefits [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Fast growth of the youngest kestrel nestlings is rare among birds, as delayed growth of the youngest offspring is typical in species with asynchronous hatching [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In falcons, hatching asynchrony can strongly affect rank in the size hierarchy [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], with poor growth and sibling rivalry contributing to high mortality among the youngest chicks [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the studied kestrel population, abundant prey may have mitigated these negative effects, indirectly reflecting the success of the conservation measures.\u003c/p\u003e \u003cp\u003eAmong the habitat variables, none significantly influenced growth. Mosaic habitats, mainly orchards with scattered buildings, were included in the final models but had no significant effect. Other important hunting grounds, such as meadows and pastures, which are known for supporting larger broods and higher breeding success [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], also do not affect growth. Kestrels prefer grasslands for prey availability but avoid areas with high numbers of brood predators, such as corvids [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Similarly, forests, which are considered low-quality habitats [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], had no negative impact, possibly because of the avoidance of nesting boxes in heavily forested areas [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Overall, the high diversity of habitats in the studied foraging area likely buffered the environmental effects on growth.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNestling growth patterns in bird populations managed under conservation programs are important indicators of the effectiveness of management strategies. Our results indicate that the scale and distribution of nesting boxes allow kestrels to select suitable nesting habitats with adequate food resources. This is reflected in higher growth rates of younger chicks and elevated average growth rates in larger broods, suggesting effective parental compensation and overall favourable conditions for offspring development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by the University of Siedlce (229/26/B).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZK conceived and designed the experiments, collected and analysed the data, wrote the manuscript and reviewed drafts of the paper. UZ collected and analysed the data, reviewed drafts of the paper and approved the final draft. AG collected the data, reviewed drafts of the paper and approved the final draft.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank Dorota Banaszewska and Waldemar Seredziński for their help in carrying out the fieldwork. We are also grateful to Mirosław Rzępała (Nature Society \u0026ldquo;Stork\u0026rdquo;) for the organizational support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset supporting the reported results can be found at the Mendeley Data Repository: https://data.mendeley.com/datasets/ny29jpfz54/1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCase, T. J. On the evolution and adaptive significance of postnatal growth rates in terrestrial vertebrates. \u003cem\u003eQ. Rev. Biol.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 243\u0026ndash;282 (1978).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDmitriew, C. M. The evolution of growth trajectories: what limits growth rate? \u003cem\u003eBiol. Rev.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, 97\u0026ndash;116 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin, T. E. et al. Growth rate variation among passerine species in tropical and temperate sites: an antagonistic interaction between parental food provisioning and nest predation risk. \u003cem\u003eEvolution\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 1607\u0026ndash;1622 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraziotis, S. et al. Patterns of postnatal growth in a small falcon, the lesser kestrel \u003cem\u003eFalco naumanni\u003c/em\u003e (Fleischer, 1818) (Aves: Falconidae). \u003cem\u003eEur. Zool. J.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e, 277\u0026ndash;285 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSauve, D., Friesen, V. L. \u0026amp; Charmantier, A. The effects of weather on avian growth and implications for adaptation to climate change. \u003cem\u003eFront. Ecol. Evol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 569741 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSen\u0026eacute;cal, S. et al. 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Biocenological bond patterns in Common Kestrel and Hooded Crow. \u003cem\u003eSovremennaya Ornitologiya\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e, 193\u0026ndash;203 (1998).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"brood size, chick growth, hatching order, nest boxes, raptors, rural landscapes","lastPublishedDoi":"10.21203/rs.3.rs-9245510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9245510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNestling growth is an important trait in the life-history strategy of avians and influences later survival and reproductive success. In addition to the determination of individual fitness, growth rates might also be sensitive indicators of ecological conditions and environmental changes. This is particularly important in the case of birds, which are the top predators in the food chain. In this study, we examined the combined effects of habitat characteristics and biological factors on the nestling growth of the common kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e). Daily changes in four biometric traits, expressed as percentages of their baseline values, were used to determine relative growth rates. Brood size, hatching order, and breeding phenology significantly influenced the growth rates of Kestrel nestlings. Nestlings in broods four presented the lowest growth increases, whereas those in larger broods presented greater values. Among the largest broods, the fifth and sixth nestlings grew faster than their younger siblings did. Relative growth rates were also positively correlated with the date of the first egg. In contrast, habitat characteristics had no measurable effect on growth rates. Our findings highlight the relevance of nestling growth parameters as indicators of breeding performance and may support the effectiveness of kestrel conservation programs in rural landscapes. We recommend incorporating chick growth rates as a valuable biological parameter in the evaluation of conservation efforts.\u003c/p\u003e","manuscriptTitle":"Growth patterns of common kestrel (Falco tinnunculus) nestlings: effects of hatching order, brood size and habitat factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 08:03:54","doi":"10.21203/rs.3.rs-9245510/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-11T22:26:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17279009757323216019302009044959761862","date":"2026-04-13T08:34:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T15:39:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T10:37:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T10:58:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T10:58:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-27T13:47:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bbe3911c-431d-4f96-9142-53f1ad0d874a","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T22:26:45+00:00","index":51,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66400895,"name":"Biological sciences/Ecology"},{"id":66400896,"name":"Earth and environmental sciences/Ecology"},{"id":66400897,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-04-19T08:03:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 08:03:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9245510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9245510","identity":"rs-9245510","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}