A long-term study reveals the impact of weather conditions on avian fitness

A long-term study reveals the impact of weather conditions on avian fitness | 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 Research Article A long-term study reveals the impact of weather conditions on avian fitness Aneta Arct, Rafał Martyka, Krzysztof Miler, Karolina Skorb, Lars Gustafsson, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5608754/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Aug, 2025 Read the published version in Frontiers in Zoology → Version 1 posted 5 You are reading this latest preprint version Abstract Here, we present an analysis based on a 40-year dataset from a nest-box population of the collared flycatcher ( Ficedula albicollis ). Our objective is to examine the impact of weather conditions during the incubation and nestling period on key indicators of individual fitness, including offspring production and local annual recruitment rate. Our findings provide compelling evidence that climatic conditions experienced during both incubation and nestling periods significantly impact the number of fledglings and recruits. Specifically, we observed that higher precipitation during the nestling period negatively affects the number of fledglings and increases brood failure. Interestingly, higher precipitation during the incubation period is linked to increased recruitment numbers. Moreover, we found that warmer weather during both the incubation and nestling periods decreases brood failure, and more importantly, higher temperatures during the nestling period are positively associated with the number of recruits. These results underscore the complex interplay between weather patterns and avian reproductive strategies, highlighting the importance of long-term ecological studies in understanding the impacts of climate change on bird populations. By addressing the variability of climatic influences across different life stages, future research can help develop more comprehensive models for predicting the resilience of avian species in the face of ongoing climate challenges. Figures Figure 1 Figure 2 Figure 3 Introduction The climate has a profound impact on dynamics and long-term trends in wildlife populations (Jenouvrier 2013 , Bailey et al. 2022 ). Dramatic population declines are observed in many avian groups (Rosenberg et al. 2019 ) and troubles in coping with climate changes may largely contribute to the observed declines. Populations may persist or perish depending on how individual organisms respond to the changing environment. Predicting the responses of species to anthropogenic climate change is the greatest scientific challenge of our time, given the need to forecast the responses of organisms to implement appropriate protection actions (Williams et al. 2008 , Huey et al. 2012 ). In general, birds are considered good indicators of anthropogenic disturbance and are valuable in evaluating climate change impacts (Dunn and Møller 2019 ). For the temperate species whose reproductive success depends on microclimate-driven resource availability, changing environmental conditions can present a particular challenge. Unexpected weather might generate mismatches between offspring demands and food occurrence (Visser and Gienapp 2019 ), as observed in many species (e.g., Garrett et al. 2022 ). Although numerous species try to respond to climate change by a shift in phenology (e.g., Murphy et al. 2022 , see Radchuk et al. 2019 for a meta-analysis), this is not always an option (as in the pied flycatcher, Both and Visser 2001). A recent study suggests that the ability to shift phenology differs for resident and migratory birds as the latter have their breeding constrained by the arrival time (Søraker et al. 2022 ). Migrants who can shift their arrival are known to be less vulnerable to population declines (Møller et al. 2008 ) although these effects are highly variable. For example, the vulnerability of migratory bird species to a population decline depends strongly on their ecology and feeding habitat in particular (Both et al. 2009 ). Nevertheless, after initial reproductive decisions have been made, correcting for changed weather conditions becomes difficult or impossible. Weather conditions can have varying impacts at different life stages, however, early life stages appear to be the most sensitive window to any environmental cues. Growing evidence suggests that early life stages, along with their crucial role in developmental plasticity, are particularly important in shaping the lifetime fitness of individuals (Monaghan and Haussmann 2015 , Hoffman et al. 2023 ). In particular, the temperature during pre- and post-hatching development has the potential to affect the physiology and condition of developing precursor tissues of a growing embryo and nestling, and such alterations may, in turn, affect offspring condition and survival (Arct et al. 2022 , Hoffman et al. 2023 ). Generally, it is surprising how little we know about the effect of weather conditions during incubation and early nestling development on an individual's fitness (De Zwaan et al. 2020 ). This gap in our knowledge is staggering because most avian nestlings are particularly vulnerable and depend on their parents to provide warmth, food, and protection, all of which interplay with external environmental conditions. Long-term studies focusing on reproduction and climate change are essential for comprehending how environmental shifts impact the reproductive patterns of birds (Riggio et al. 2023 ). Here, we used a 40-year dataset to examine how ambient temperature and precipitation during both the incubation and nestling period affect important proxies of fitness, i.e., hatching success, the number of fledglings and recruits, in a wild population of the collared flycatcher ( Ficedula albicollis ) inhabiting Swedish island of Gotland. Our research question is of particular importance as the studied population experienced a significant increase in ambient temperature during the breeding season over the study period (Table SM1, Fig. SM1). Although we observed no change in rainfall over the years (Table SM1, Fig. SM2), precipitation can affect food availability and other conditions relevant for fitness (Török and Tóth 1988 ). Therefore we hypothesized that, in the collared flycatcher, offspring production and recruitment are dependent on prevailing abiotic conditions experienced during breeding attempts. Specifically, we expected higher temperatures and higher rainfall to reduce hatching success and the number of produced fledglings and recruits. This is because migratory birds are limited in their response to climate change and local weather conditions when commencing breeding (Søraker et al. 2022 , Halupka et al. 2023 ). Moreover, as a small passerine bird, the collared flycatcher has a high metabolic rate and a limited capacity to deposit long-term body reserves (McNab 2002 ). This likely increases its susceptibility to environmental stress imposed by food shortages or severe weather conditions. Methods Study species The collared flycatcher is a small (~ 13 g) migratory passerine bird. The birds arrive from their wintering areas in southern Africa to the breeding areas in late April to mid-May. The collared flycatchers on Gotland (Sweden) lay usually one clutch per year, consisting of six eggs on average (range 4–8). At the beginning of May, the first eggs are laid and incubation begins, a task which is undertaken solely by females. Nestlings hatch after approximately 14 days of incubation, remain in the nests for an additional 14–16 days, and are fed by both parents. They reach the maximum body mass at the age of 10–11 days and lose some mass before fledging. Then, fledglings stay close to the nest for another two weeks and are still fed by their parents. Monitoring of the population biology of the collared flycatcher has been carried out at Burgsvik (Gotland, Sweden 57°03′ N, 18°17′ E) since 1980 and continues until today (presented analyses use the 40-years subset of data, spanning 1980–2019). The collared flycatcher population on Gotland is isolated from the main species range and for this reason recapture and return rates of individuals are quite high (Gustafsson 1986 ). Moreover, the birds prefer nest boxes over natural tree holes (Gustafsson 1986 ), which makes them easy to handle. General procedures In the studied nest box population of the collared flycatcher, breeding birds were monitored over the whole season to gather data including laying date, clutch size, brood size, and the number of fledglings and recruits (Martyka et al. 2023 ). Females were trapped at the nest during incubation and males were caught while feeding nestlings (May-June). After catching, each bird was banded with a metal band, aged as 1 year old or older (Svensson 1992 ). All breeding attempts were carefully monitored until the chicks fledged. On day 12 after hatching, nestlings were measured and banded with a metal band. In the study, we used the longitudinal dataset, containing all the records on annual female reproductive performance (i.e., the number of hatched nestlings and fledglings reared to independence and the number of recruits from each breeding attempt). However, for the recruitment rate, we utilized data only from the years 1980 to 2016. We excluded from the dataset all breeding events that were involved in any experimental manipulations. Sample sizes across analyses are different due to missing data. Climatic factors We chose average daily temperature and daily sums of precipitation as they are commonly used metrics in ecological studies, providing a balanced representation of overall weather conditions. Daily temperature records and daily sums of precipitation were obtained from the meteorological station at Hoburgen (56.92 °N, 18.15 °E; approximately 10 km from the main study areas). The data were accessed via the website of the Swedish Meteorological and Hydrological Institute ( http://opendata-download-metobs.smhi.se/explore/?parameter=3 ). We calculated the average daily temperature and daily sums of precipitation during the incubation and nestling period. For the incubation period, we calculated the average daily temperature and the daily sums of precipitation over 13 days, starting from the day the last egg was laid. We chose the 13 days because previous work by Husby et al. (2012) showed that the mean incubation duration in this collared flycatcher population was 12.5 days. We assessed these parameters over 15 days for the nestling period, beginning on the final day of the determined incubation period. This duration was chosen based on our observations of collared flycatchers on Gotland island, where fledglings typically leave the nest after an average of 16.2 days, with a range of 14 to 19 days (J. Sudyka, unpublished data). Statistical analysis We used a generalized linear mixed model (GLMM) with a Gaussian error distribution and the identity-link function to test relationships between hatching success (defined as the proportion of hatched nestlings within a clutch) and predictors of interest, which were ambient temperature and sum of precipitation during incubation period, as well as laying date and clutch size (to control for breeding timing and brood size). All predictors were modeled as covariates. To meet the normality and variance homogeneity criteria, the dependent variable was transformed with the arcsin transformation. Further, we used GLMMs with Poisson error distribution and the log-link function to look at how the variation in offspring production (the number of fledglings) and local recruitment rate (the number of recruits) is affected by the weather conditions during the incubation and nestling periods. However, due to the overrepresentation of zeros in data on offspring production, we applied a zero-inflated GLMM with Poisson error variance and log-link function for a conditional component and binomial error variance and the logit-link function for a zero-inflated component. This type of statistical model is appropriate for count data with an excess of zeros (Zuur et al. 2012 ). Ambient temperature and sum of precipitation at the stage of incubation and nestling rearing separately, as well as laying date and clutch size, were entered as covariates in those models. Explanatory variables in all GLMMs were standardized across years using a z-transformation, which adjusted them to have a mean of zero and a standard deviation of one. Because of a strong right-skewed distribution of precipitation data, we coded this variable by adding one and then root-square transformed it (before standardization) to normalize its distribution. In all GLMMs, female identity, study plot identity, and year of the study were treated as random factors. Models were fitted using the ‘glmmTMB’ package ver. 1.1.9 implemented in the R environment ver. 4.3.3 (Brooks et al. 2017 , R Core Team 2024 ). The statistical significance of fixed effects was tested using z-statistics. For each model, marginal and conditional R 2 or pseudo-R 2 (depending on a model) were calculated using the ‘MuMIn’ package ver. 1.48.4 (Bartoń 2024 ). We also assessed multicollinearity by a variance inflation factor (VIF) for fitted models using ‘performance’ package ver. 0.12.2 (Lüdecke et al. 2021 ) and alternatively by the correlation matrix of independent variables (for subsets of data analysing hatching success, offspring production, and local recruitment rate separately). Both approaches indicate existing moderate correlations among explanatory variables; the highest VIF reached 3.25 and the highest coefficient correlation was 0.49 (for details, see Tables SM2 and SM3). Detected levels of VIFs and correlations still allow for keeping all explanatory terms in models without severe consequences for model performance (Dormann et al. 2013 ). In addition, we used the 'DHARMa' package ver. 0.4.6 (Hartig 2022) to test for overdispersion and zero inflation in GLMMs that analyze offspring production and recruitment. Finally, we found no evidence that parameter estimates of fitted models are overdispersed or zero-inflanted. Results Hatching success We revealed that during the incubation period, hatching success was affected by ambient temperature but not the sum of precipitation, implying that higher temperatures are associated with improved egg hatchability (Table 1 ). Furthermore, we found that egg hatchability is negatively correlated with laying date and clutch size (Table 1 ). Table 1 Output of generalized linear mixed model with Gaussian error distribution and the identity-link function testing how ambient temperature and sum of precipitation experienced during the incubation period, as well as laying date and clutch size, affect the hatching success (defined as the proportion of hatched nestlings within a clutch). All explanatory terms were standardized. The female identity, study plot identity, and year of study were included as random factors. Significant terms P < 0.05 are in bold. Sources of variation Estimate SE z P (N = 10890 broods of 7722 females) Intercept 1.333 0.014 93.32 < 0.001 Temperature: incubation period 0.010 0.005 2.12 0.034 Precipitation: incubation period 0.002 0.003 0.88 0.38 Laying date -0.016 0.004 -3.87 < 0.001 Clutch size -0.080 0.004 -22.66 < 0.001 Female ID random 0.339 Plot ID random 0.014 Year of study random 0.058 R 2 marginal/conditional 0.04/0.84 Fledgling production We found that the impact of ambient temperature during the incubation and nestling period on the number of fledglings were not statistically significant (Table 2 ). However, higher precipitation during the nestling period resulted in a decreased number of fledglings (a conditional component of GLMM; Table 2 , Fig. 1 ). A zero-inflated component of GLMM revealed that higher ambient temperatures during both the incubation and nestling periods were related to decreased brood failure (brood failure means a nest producing no fledged offspring; Table 2 ). In contrast, higher precipitation during the nestling phase increased brood failure (Table 2 ). Additionally, earlier laying dates were associated with a higher number of fledglings and decreased reproductive failure, and larger clutch sizes also contributed positively to the number of fledglings (Table 2 ). Table 2 Output of the zero-inflated generalized linear mixed model with Poisson error distribution and the log-link function for a conditional component and with binomial error distribution and the logit-link function for a zero-inflated component testing how ambient temperature and sum of precipitation experienced during the incubation and nestling period, as well as laying date and clutch size, affect the number of fledglings. All explanatory terms were standardized. The female identity, study plot identity, and year of study were included as random factors. Significant terms P < 0.05 are in bold. Sources of variation Estimate SE z P Conditional model (N = 13286 broods of 9337 females) Intercept 1.520 0.021 72.48 < 0.001 Temperature: incubation period 0.002 0.010 0.22 0.82 Temperature: nestling period 0.012 0.012 0.98 0.32 Precipitation: incubation period 0.002 0.009 0.25 0.80 Precipitation: nestling period -0.023 0.008 -2.92 0.004 Laying date -0.075 0.011 -6.77 < 0.001 Clutch size 0.101 0.006 18.15 < 0.001 Female ID random 0.000 Plot ID random 0.056 Year of study random 0.112 Pseudo-R 2 marginal/conditional 0.09/0.15 Zero-inflated model (N = 13286 broods of 9337 females) Intercept -1.583 0.161 -9.84 < 0.001 Temperature: incubation period -0.171 0.051 -3.36 < 0.001 Temperature: nestling period -0.251 0.059 -4.28 < 0.001 Precipitation: incubation period -0.021 0.042 -0.51 0.61 Precipitation: nestling period 0.117 0.041 2.90 0.004 Laying date 0.716 0.053 13.40 < 0.001 Clutch size 0.053 0.027 1.95 0.051 Female ID random 0.236 Plot ID random 0.480 Year of study random 0.877 Offspring recruitment We showed that higher temperatures during the nestling period but not during the incubation period are associated with an increased number of recruits (Table 3 , Fig. 2 ). Moreover, higher precipitation during the incubation period but not during the nestling period also resulted in an increased number of recruits (Table 3 , Fig. 3 ). We also found that earlier laying dates are associated with higher numbers of recruits and clutch size have a significant positive effect on the number of recruits. Table 3 Output of the generalized linear mixed model with Poisson error distribution and the log-link function testing how ambient temperature and sum of precipitation experienced during the incubation and nestling period, as well as laying date and clutch size, affect the number of recruits. All explanatory terms were standardized. The female identity, study plot identity, and year of study were included as random factors. Significant terms P < 0.05 are in bold. Sources of variation Estimate SE z P (N = 15164 broods of 10226 females) Intercept -1.803 0.142 -12.71 < 0.001 Temperature: incubation period 0.020 0.032 0.62 0.54 Temperature: nestling period 0.119 0.041 2.93 0.003 Precipitation: incubation period 0.055 0.027 2.07 0.038 Precipitation: nestling period 0.007 0.024 0.31 0.75 Laying date -0.401 0.037 -10.90 < 0.001 Clutch size 0.040 0.017 2.38 0.017 Female ID random 0.431 Plot ID random 0.776 Year of study random 0.542 Pseudo-R 2 marginal/conditional 0.03/0.24 Discussion Our study demonstrated that climatic conditions influence offspring production and recruitment of the migratory collared flycatcher. These findings align with existing research in the field of population ecology and climate change that shows the strong influence of climatic factors on crucial life history parameters (e.g. Dunn and Møller 2019 , Kämpfer et al. 2022 , Laczi et al. 2024 ). Only a few studies investigated the production of offspring (e.g., the number of fledglings) in avian populations in relation to local weather conditions. Some studies have found a decline in the production of young (Husby et al. 2009 , Shipley et al. 2020 ), and some found increased fledgling production in warmer breeding seasons (Wegge and Rolstad 2017 , Halupka 2021). However, many other studies failed to show any impacts of weather conditions on offspring production (e.g. Dyrcz and Czyż 2018 , Shipley et al. 2020 ). This inconsistency of results can be accounted for by the fact that relatively large sample sizes may be needed to detect relationships between abiotic conditions and reproductive output. In any case, a recent global meta-analysis based on 201 populations of 104 bird species indicates that climate change does not correlate with avian offspring production directly, but through complex interactions with their life history and ecological traits (Halupka et al. 2023 ). In other words, local climatic conditions that vary greatly in time (e.g., seasonally) and space (e.g., geographically) may confound global trends across species that may experience different effects of climate change depending on where and when they live, breed, or migrate (Sparks and Tryjanowski 2007 ). Thus, rather than focusing on general patterns explaining variation in offspring production across all species, a future focus should be on how various aspects of ecology or life history might have driven variation in offspring production among groups of species or populations of the same species. More importantly, our study and a recent study of Riggio et al. ( 2023 ) highlight the importance of long-term monitoring when unravelling the impacts of climate change on fitness. Our findings indicate that ambient temperature during the incubation period has a significant effect on hatching success. This result suggests that higher temperatures may create more favourable conditions for embryo development, possibly by enhancing metabolic rates or by reducing the time needed for incubation, both of which are known to positively impact hatchability. This aligns with other studies showing that variation in incubation temperature significantly influences hatching success (DuRant et al. 2013 , Coe et al. 2015 ). Morover, our study indicates that conditions during incubation can still significantly affect important proxies of fitness, such as the number of fledglings and recruits. In contrast to a recent meta-analysis by Halupka et al. ( 2023 ), which found that variation in offspring production is best explained by temperature trends during the nestling period, we found that higher ambient temperatures during both the incubation and nestling periods were related to decreased reproductive failure (broods that produced no fledged offspring). Moreover, we showed that the higher sum of precipitation during the nestling phase is associated with lower offspring production and increased brood failure. The collared flycatcher is an insectivore species and may be particularly affected by changing rainfall patterns because their main prey is less active during adverse weather, resulting in reduced food availability (Avery and Krebs 1984). Our results confirm previous studies showing the negative effects of rainfall on the number of fledglings (Dawson and Bortolotti 2000 ; Arlettaz et al. 2010 , Öberg et al. 2015 ). While higher temperatures can be beneficial by reducing reproductive failure, increased rainfall during critical developmental periods can negatively impact offspring survival. These findings underscore the need to consider both temperature and precipitation in understanding how climate change influences avian reproductive success. The number of recruits is perhaps the best approximation of the individual contribution to the population of breeding animals in wild populations (Visser et al. 2015 ). However, we should keep in mind that this fitness estimate does not include all offspring that survive and disperse out of the study population. Nevertheless, the decrease in the number of recruits can have more profound implications for the size and sustainability of a bird population than the number of fledglings. This suggests that recent observed population declines across the globe may reflect changes in juvenile survival (Rosenberg et al. 2019 ). Here, we used the yearly number of recruits produced by each breeding female as a proxy of fitness and we found a strong positive effect of higher ambient temperature during the nestling phase on the number of recruits in the wild population of collared flycatcher. In a recent study on the same population of collared flycatchers, we manipulated the developmental conditions of embryos through a modification of nest box thermal microclimate, and we found that offspring from the experimentally heated nests had larger body mass at fledging in comparison to the control ones (Arct et al. 2022 ). This result suggests that higher temperatures in the nest may be beneficial for offspring fitness since body mass just before fledging predicts future offspring survival in this species (Arct et al. 2022 ). Collectively, these findings indicate that the increase in ambient temperature during the breeding season may have a positive effect on the reproductive success of collared flycatcher inhabitants on Gotland Island. Interestingly, we found a significant positive effect of rainfall during incubation on the number of recruits. Similarly, de Zwaan (2020) found that precipitation before and during incubation positively affected nestling mass in savannah sparrows ( Passerculus sandwichensis ), indicating that rainfall can directly improve nestling condition. Furthermore, Pipoly et al. ( 2013 ) demonstrated that increased precipitation before hatching can promote nestling growth in the house sparrow ( Passer domesticus ). Therefore, the positive effects observed in our study could reflect a broader trend where rainfall enhances reproductive success by improving the conditions critical for nestling development and survival. However, precipitation can influence also incubation behaviour, which can directly affect the fitness of offspring. During rainy weather females of many bird species reduce off-bouts, resulting in spending more time in the nest on incubation (Marasco and Spencer, 2015 ). This behaviour can provide more constant thermal conditions for developing embryos, reducing fluctuations in egg temperature, this way supporting higher fitness of offspring after hatching (Hepp et al. 2015 ). While higher rainfall during the incubation phase may support recruitment, our study showed that increased precipitation during the nestling phase can lead to lower fledgling production. The apparent paradox in our findings illustrates the need to consider the specific life stages of birds when assessing the impact of weather conditions on reproductive success. This leads to important considerations regarding how changing weather patterns, especially in the context of climate change, could differentially affect various life stages of birds and ultimately their population dynamics. To conclude in our research, we focused on weather conditions experienced by the birds during both the incubation and nestling phases and showed that environmental variation can have important and long-lasting fitness consequences. The different impacts of temperature and rainfall on studied traits reveal the limitations of generalizing the results when only a few fitness traits are investigated and when different climatic factors are evaluated across distinct developmental stages. Declarations Ethics The study uses the data that has been previously collected during the long-term study in a wild population of collared flycatchers. The data upon which this study is based has been obtained following the Swedish guidelines for work on natural populations and under licenses and permits from the Swedish Ringing Centre (licence no. M716 to SMD) and Swedish National Board for Laboratory Animals, Stockholm (ID 9164-2021; ID 872-2017; 37-15-2015). Consent for publication Not applicable. Availability of data and material Our data are made publicly available via Dryad at doi:10.5061/dryad.w9ghx3g06 ReviewerURL: http://datadryad.org/stash/share/gE_Uxx0BZYC7U9uv2tVyhaKR6lug7cJkossLgWfI9qk Competing interests We declare we have no competing interests. Funding Aneta Arct was supported by the Ministry of Science and Higher Education of Poland within the ‘Mobilność Plus’ programme (1659/MOB/V/2017/0). Rafał Martyka was financially supported by the Polish National Agency for Academic Exchange within a mobility scholarship of the Bekker programme (PPN/BEK/2019/1/00253). A long-term study on the Gotland population of collared flycatchers (led by Lars Gustafsson) got support from the Swedish Research Council (VR) and the Swedish Research Council for Environment, Agricultural Sciences, and Spatial Planning (FORMAS) as well as many smaller grants from several supporters. This work was supported by National Science Centre grant no. UMO-2020/39/B/NZ8/01157 (OPUS20) to AA. Authors' contributions Aneta Arct: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing; Rafał Martyka: Methodology, Conceptualization, Formal analysis, Methodology, Validation, Writing – review & editing; Krzysztof Miler: Conceptualization, Writing – original draft, Writing – review & editing; Lars Gustafsson: Funding acquisition, Project administration, Resources. Szymon M. Drobniak: Methodology, Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing: Review & editing. Acknowledgements Many thanks to (in random order) Blandine Doligez with her team for help with the fieldwork, Tomas Pärt, Anna Qvarnström, Ben C Sheldon, Juho Könönen, Juha Merilä, Mats Linden, Simon Evans, Mårten Hjernquist, Joanna Sendecka, Kevin Fletcher, and countless others who have collected data that has contributed towards this study. 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Decline in the frequency and benefits of multiple brooding in great tits as a consequence of a changing environment. Proceedings of the Royal Society B: Biological Sciences, 276(1663), 1845–1854. https://doi.org/10.1098/rspb.2008.1937 Jenouvrier S. Impacts of climate change on avian populations. Glob Change Biol. 2013;19(7):2036–57. https://doi.org/10.1111/gcb.12195 . Kämpfer S, Engel E, Fartmann T. Weather conditions determine reproductive success of a ground-nesting bird of prey in natural dune grasslands. J Ornithol. 2022;163(4):855–65. https://doi.org/10.1007/s10336-022-01999-w . Laczi M, Sarkadi F, Herényi M, Nagy G, Hegyi G, Jablonszky M, Török J. Responses in the breeding parameters of the collared flycatcher to the changing climate. Sci Total Environ. 2024;926:171945. https://doi.org/10.1016/j.scitotenv.2024.171945 . Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P, Makowski D. performance: an R package for assessment, comparison and testing of statistical models. J Open Source Softw. 2021;6(60):3139. https://doi.org/10.21105/joss.03139 . Marasco V, Spencer KA. Improvements in our understanding of behaviour during incubation. In: Deeming DC, Reynolds SJ, editors. Nests, Eggs, and Incubation. Oxford University Press; 2015. https://doi.org/10.1093/acprof:oso/9780198718666.003.00012 . Martyka R, Arct A, Kotowska D, Gustafsson L. Age- and trait-dependent breeding responses to environmental variation in a short-lived songbird. Sci Rep. 2023;13:14967. https://doi.org/10.1038/s41598-023-42166-2 . Monaghan P, Haussmann MF. The positive and negative consequences of stressors during early life. Early Hum Dev. 2015;91(11):643–7. https://doi.org/10.1016/j.earlhumdev.2015.08.010 . Murphy MT, Redmond LJ, Dolan AC, Cooper NW, Shepherdson K, Chutter CM, Cancellieri S. Weather and climate change drive annual variation of reproduction by an aerial insectivore. Avian Conserv Ecol. 2022. https://doi.org/10.5751/ACE-02009-170142 . Møller AP, Rubolini D, Lehikoinen E. (2008). Populations of migratory bird species that did not show a phenological response to climate change are declining. Proceedings of the National Academy of Sciences, 105(42), 16195–16200. https://doi.org/10.1073/pnas.0803825105 McNab BK. The physiological ecology of vertebrates: a view from energetics. Cornell University Press; 2002. Öberg M, Arlt D, Pärt T, Laugen AT, Eggers S, Low M. Rainfall during parental care reduces reproductive and survival components of fitness in a passerine bird. Ecol Evol. 2015;5(2):345–56. https://doi.org/10.1002/ece3.1345 . Pipoly I, Bókony V, Seress G, Szabó K, Liker A. Effects of extreme weather on reproductive success in a temperate-breeding songbird. PLoS ONE. 2013;8(11):e80033. https://doi.org/10.1371/journal.pone.0080033 . R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Radchuk V, Reed T, Teplitsky C, van de Pol M, Charmantier A, Hassall C, Kramer-Schadt S. Adaptive responses of animals to climate change are most likely insufficient. Nat Commun. 2019;10(1):3109. https://doi.org/10.1038/s41467-019-10924-4 . Riggio J, Engilis Jr A, Cook H, De Greef E, Karp DS, Truan ML. Long-term monitoring reveals the impact of changing climate and habitat on the fitness of cavity-nesting songbirds. Biol Conserv. 2023;278:109885. Rosenberg KV, Dokter AM, Blancher PJ, Sauer JR, Smith AC, Smith PA, Marra PP. Decline of the North American avifauna. Science. 2019;366(6461):120–4. https://doi.org/10.1126/science.aaw1313 . Shipley JR, et al. Birds advancing lay dates with warming springs face greater risk of chick mortality. Proc Natl Acad Sci USA. 2020;117:25590–4. https://doi.org/10.1073/pnas.2005722117 . Søraker JS, Stokke BG, Kleven O, Moksnes A, Rudolfsen G, Skjærvø GR, Ranke PS. Resident bird species track inter-annual variation in spring phenology better than long-distance migrants in a subalpine habitat. Clim Change Ecol. 2022;3:100050. https://doi.org/10.1016/j.ecoenv.2021.100050 . Sparks T, Tryjanowski P. Patterns of spring arrival dates differ in two hirundines. Climate Res. 2007;35(1–2):159–64. https://doi.org/10.3354/cr00722 . Svensson L. Identification Guide to European Passerines. L. Svensson; 1992. Török J, Tóth L. Density dependence in reproduction of the collared flycatcher ( Ficedula albicollis ) at high population levels. J Anim Ecol. 1988;57:251–8. Visser ME, Gienapp P, Husby A, Morrisey M, de la Hera I, Pulido F, Both C. Effects of spring temperatures on the strength of selection on timing of reproduction in a long-distance migratory bird. PLoS Biol. 2015;13(4):e1002120. https://doi.org/10.1371/journal.pbio.1002120 . Visser ME, Gienapp P. Evolutionary and demographic consequences of phenological mismatches. Nat Ecol Evol. 2019;3:879–85. https://doi.org/10.1038/s41559-019-0908-9 . Wegge P, Rolstad J. (2017). Climate change and bird reproduction: warmer springs benefit breeding success in boreal forest grouse. Proceedings of the Royal Society B: Biological Sciences, 284(1866), 20171528. https://doi.org/10.1098/rspb.2017.1528 Williams SE, Shoo LP, Isaac JL, Hoffmann AA, Langham G. (2008). Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biol, 6(12), e325. Zuur AF, Saveliev AA, Ieno EN. Zero Inflated Models and Generalized Linear Mixed Models with R. Newburgh, UK: Highland Statistics Ltd; 2012. 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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-5608754","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398237611,"identity":"97e92582-d86f-49cb-b7c5-b8b003e4797a","order_by":0,"name":"Aneta Arct","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYHAD5gNAQkKGGKWMDWCKjS0BpIWHFC08BiCKsBb+2c3HH/xgsMvnn9/z+dWNGgseBvbDRzfg0yJx51hiYw9DsuWMY7zbrHOOAR3Gk5Z2A681N3IMG3gYmA0M2Hi3GeewAbVI8Jjh1SJ/I/9j4x+GeqAWnmfGOf+I0GJwI4exmYfhMEgL8+PcNiK0GN5IM5wtY3DcQOJYmhlzbp8EDxshv8jdSH7w8U1FtQF/8+HHn3O+1cnxsx8+ht/7EOeBSTYJMElYOQIwfyBF9SgYBaNgFIwcAACV8EGDOmKqOQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6324-6938","institution":"Jagiellonian University: Uniwersytet Jagiellonski w Krakowie","correspondingAuthor":true,"prefix":"","firstName":"Aneta","middleName":"","lastName":"Arct","suffix":""},{"id":398237612,"identity":"1f4ba69e-92ad-4404-a44d-3d625488d287","order_by":1,"name":"Rafał Martyka","email":"","orcid":"","institution":"Institute of Nature Conservation PAS: Instytut Ochrony Przyrody Polskiej Akademii Nauk","correspondingAuthor":false,"prefix":"","firstName":"Rafał","middleName":"","lastName":"Martyka","suffix":""},{"id":398237613,"identity":"9843016c-d23e-4e0c-8063-ca4ab86229a3","order_by":2,"name":"Krzysztof Miler","email":"","orcid":"","institution":"Institute of Systematics and Evolution of Animals PAS: Instytut Systematyki i Ewolucji Zwierzat Polskiej Akademii Nauk","correspondingAuthor":false,"prefix":"","firstName":"Krzysztof","middleName":"","lastName":"Miler","suffix":""},{"id":398237614,"identity":"4132bdae-139c-4bec-9767-b7bb834a646d","order_by":3,"name":"Karolina Skorb","email":"","orcid":"","institution":"Institute of Systematics and Evolution of Animals PAS: Instytut Systematyki i Ewolucji Zwierzat Polskiej Akademii Nauk","correspondingAuthor":false,"prefix":"","firstName":"Karolina","middleName":"","lastName":"Skorb","suffix":""},{"id":398237615,"identity":"7d0b91b5-e6e9-4c5e-b451-130f83717199","order_by":4,"name":"Lars Gustafsson","email":"","orcid":"","institution":"Uppsala University: Uppsala Universitet","correspondingAuthor":false,"prefix":"","firstName":"Lars","middleName":"","lastName":"Gustafsson","suffix":""},{"id":398237616,"identity":"0431bf0d-9403-445c-b7b7-d4b90afa77f4","order_by":5,"name":"Szymon Drobniak","email":"","orcid":"","institution":"Jagiellonian University: Uniwersytet Jagiellonski w Krakowie","correspondingAuthor":false,"prefix":"","firstName":"Szymon","middleName":"","lastName":"Drobniak","suffix":""}],"badges":[],"createdAt":"2024-12-09 11:54:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5608754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5608754/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12983-025-00569-z","type":"published","date":"2025-08-25T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73305757,"identity":"ecaf6301-f76d-4f29-bf47-89e6ca2a01f7","added_by":"auto","created_at":"2025-01-08 16:54:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1194708,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the average sum of precipitation experienced during the nestling period and the number of fledglings. A predicted trend accompanied by 95% confident intervals from the model (in red) as well as raw data points (in grey) are shown.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5608754/v1/a573c906b7d975182be24ee0.jpg"},{"id":73304401,"identity":"27f13767-6e86-41e5-b11c-b94fe4c776e9","added_by":"auto","created_at":"2025-01-08 16:38:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":926376,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the average ambient temperature experienced during the nestling period and the number of recruits. A predicted trend accompanied by 95% confident intervals from the model (in red) as well as raw data points (in grey) are shown.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5608754/v1/966e4e8d64218ecdf7b9fbd8.jpg"},{"id":73304409,"identity":"19c9eb20-61b7-430c-b31d-c103f18c2936","added_by":"auto","created_at":"2025-01-08 16:38:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":932618,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the average sum of precipitation experienced during the incubation period and the number of recruits. A predicted trend accompanied by 95% confident intervals from the model (in red) as well as raw data points (grey) are shown.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5608754/v1/c7551a299d13d13726d28a73.jpg"},{"id":90344886,"identity":"2f77b1e5-b90f-4cce-9fca-e2af4994b8b3","added_by":"auto","created_at":"2025-09-01 16:07:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4046732,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5608754/v1/0361cb21-82c9-4a49-911e-32c3a0319113.pdf"},{"id":73304405,"identity":"b3e029ef-172b-4365-b28b-e4e073b875ff","added_by":"auto","created_at":"2025-01-08 16:38:58","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":307644,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterialfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5608754/v1/00856af26b87ce9ee2493cf6.docx"}],"financialInterests":"","formattedTitle":"A long-term study reveals the impact of weather conditions on avian fitness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe climate has a profound impact on dynamics and long-term trends in wildlife populations (Jenouvrier \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Bailey et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Dramatic population declines are observed in many avian groups (Rosenberg et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and troubles in coping with climate changes may largely contribute to the observed declines. Populations may persist or perish depending on how individual organisms respond to the changing environment. Predicting the responses of species to anthropogenic climate change is the greatest scientific challenge of our time, given the need to forecast the responses of organisms to implement appropriate protection actions (Williams et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Huey et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, birds are considered good indicators of anthropogenic disturbance and are valuable in evaluating climate change impacts (Dunn and M\u0026oslash;ller \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For the temperate species whose reproductive success depends on microclimate-driven resource availability, changing environmental conditions can present a particular challenge. Unexpected weather might generate mismatches between offspring demands and food occurrence (Visser and Gienapp \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as observed in many species (e.g., Garrett et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although numerous species try to respond to climate change by a shift in phenology (e.g., Murphy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, see Radchuk et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e for a meta-analysis), this is not always an option (as in the pied flycatcher, Both and Visser 2001). A recent study suggests that the ability to shift phenology differs for resident and migratory birds as the latter have their breeding constrained by the arrival time (S\u0026oslash;raker et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Migrants who can shift their arrival are known to be less vulnerable to population declines (M\u0026oslash;ller et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) although these effects are highly variable. For example, the vulnerability of migratory bird species to a population decline depends strongly on their ecology and feeding habitat in particular (Both et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Nevertheless, after initial reproductive decisions have been made, correcting for changed weather conditions becomes difficult or impossible. Weather conditions can have varying impacts at different life stages, however, early life stages appear to be the most sensitive window to any environmental cues. Growing evidence suggests that early life stages, along with their crucial role in developmental plasticity, are particularly important in shaping the lifetime fitness of individuals (Monaghan and Haussmann \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Hoffman et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In particular, the temperature during pre- and post-hatching development has the potential to affect the physiology and condition of developing precursor tissues of a growing embryo and nestling, and such alterations may, in turn, affect offspring condition and survival (Arct et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Hoffman et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, it is surprising how little we know about the effect of weather conditions during incubation and early nestling development on an individual's fitness (De Zwaan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This gap in our knowledge is staggering because most avian nestlings are particularly vulnerable and depend on their parents to provide warmth, food, and protection, all of which interplay with external environmental conditions.\u003c/p\u003e \u003cp\u003eLong-term studies focusing on reproduction and climate change are essential for comprehending how environmental shifts impact the reproductive patterns of birds (Riggio et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Here, we used a 40-year dataset to examine how ambient temperature and precipitation during both the incubation and nestling period affect important proxies of fitness, i.e., hatching success, the number of fledglings and recruits, in a wild population of the collared flycatcher (\u003cem\u003eFicedula albicollis\u003c/em\u003e) inhabiting Swedish island of Gotland. Our research question is of particular importance as the studied population experienced a significant increase in ambient temperature during the breeding season over the study period (Table SM1, Fig. SM1). Although we observed no change in rainfall over the years (Table SM1, Fig. SM2), precipitation can affect food availability and other conditions relevant for fitness (T\u0026ouml;r\u0026ouml;k and T\u0026oacute;th \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Therefore we hypothesized that, in the collared flycatcher, offspring production and recruitment are dependent on prevailing abiotic conditions experienced during breeding attempts. Specifically, we expected higher temperatures and higher rainfall to reduce hatching success and the number of produced fledglings and recruits. This is because migratory birds are limited in their response to climate change and local weather conditions when commencing breeding (S\u0026oslash;raker et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Halupka et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, as a small passerine bird, the collared flycatcher has a high metabolic rate and a limited capacity to deposit long-term body reserves (McNab \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This likely increases its susceptibility to environmental stress imposed by food shortages or severe weather conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy species\u003c/h2\u003e \u003cp\u003eThe collared flycatcher is a small (~\u0026thinsp;13 g) migratory passerine bird. The birds arrive from their wintering areas in southern Africa to the breeding areas in late April to mid-May. The collared flycatchers on Gotland (Sweden) lay usually one clutch per year, consisting of six eggs on average (range 4\u0026ndash;8). At the beginning of May, the first eggs are laid and incubation begins, a task which is undertaken solely by females. Nestlings hatch after approximately 14 days of incubation, remain in the nests for an additional 14\u0026ndash;16 days, and are fed by both parents. They reach the maximum body mass at the age of 10\u0026ndash;11 days and lose some mass before fledging. Then, fledglings stay close to the nest for another two weeks and are still fed by their parents. Monitoring of the population biology of the collared flycatcher has been carried out at Burgsvik (Gotland, Sweden 57\u0026deg;03\u0026prime; N, 18\u0026deg;17\u0026prime; E) since 1980 and continues until today (presented analyses use the 40-years subset of data, spanning 1980\u0026ndash;2019). The collared flycatcher population on Gotland is isolated from the main species range and for this reason recapture and return rates of individuals are quite high (Gustafsson \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Moreover, the birds prefer nest boxes over natural tree holes (Gustafsson \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), which makes them easy to handle.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeneral procedures\u003c/h3\u003e\n\u003cp\u003eIn the studied nest box population of the collared flycatcher, breeding birds were monitored over the whole season to gather data including laying date, clutch size, brood size, and the number of fledglings and recruits (Martyka et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Females were trapped at the nest during incubation and males were caught while feeding nestlings (May-June). After catching, each bird was banded with a metal band, aged as 1 year old or older (Svensson \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). All breeding attempts were carefully monitored until the chicks fledged. On day 12 after hatching, nestlings were measured and banded with a metal band. In the study, we used the longitudinal dataset, containing all the records on annual female reproductive performance (i.e., the number of hatched nestlings and fledglings reared to independence and the number of recruits from each breeding attempt). However, for the recruitment rate, we utilized data only from the years 1980 to 2016. We excluded from the dataset all breeding events that were involved in any experimental manipulations. Sample sizes across analyses are different due to missing data.\u003c/p\u003e\n\u003ch3\u003eClimatic factors\u003c/h3\u003e\n\u003cp\u003eWe chose average daily temperature and daily sums of precipitation as they are commonly used metrics in ecological studies, providing a balanced representation of overall weather conditions. Daily temperature records and daily sums of precipitation were obtained from the meteorological station at Hoburgen (56.92 \u0026deg;N, 18.15 \u0026deg;E; approximately 10 km from the main study areas). The data were accessed via the website of the Swedish Meteorological and Hydrological Institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://opendata-download-metobs.smhi.se/explore/?parameter=3\u003c/span\u003e\u003cspan address=\"http://opendata-download-metobs.smhi.se/explore/?parameter=3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We calculated the average daily temperature and daily sums of precipitation during the incubation and nestling period. For the incubation period, we calculated the average daily temperature and the daily sums of precipitation over 13 days, starting from the day the last egg was laid. We chose the 13 days because previous work by Husby et al. (2012) showed that the mean incubation duration in this collared flycatcher population was 12.5 days. We assessed these parameters over 15 days for the nestling period, beginning on the final day of the determined incubation period. This duration was chosen based on our observations of collared flycatchers on Gotland island, where fledglings typically leave the nest after an average of 16.2 days, with a range of 14 to 19 days (J. Sudyka, unpublished data).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used a generalized linear mixed model (GLMM) with a Gaussian error distribution and the identity-link function to test relationships between hatching success (defined as the proportion of hatched nestlings within a clutch) and predictors of interest, which were ambient temperature and sum of precipitation during incubation period, as well as laying date and clutch size (to control for breeding timing and brood size). All predictors were modeled as covariates. To meet the normality and variance homogeneity criteria, the dependent variable was transformed with the arcsin transformation. Further, we used GLMMs with Poisson error distribution and the log-link function to look at how the variation in offspring production (the number of fledglings) and local recruitment rate (the number of recruits) is affected by the weather conditions during the incubation and nestling periods. However, due to the overrepresentation of zeros in data on offspring production, we applied a zero-inflated GLMM with Poisson error variance and log-link function for a conditional component and binomial error variance and the logit-link function for a zero-inflated component. This type of statistical model is appropriate for count data with an excess of zeros (Zuur et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Ambient temperature and sum of precipitation at the stage of incubation and nestling rearing separately, as well as laying date and clutch size, were entered as covariates in those models. Explanatory variables in all GLMMs were standardized across years using a z-transformation, which adjusted them to have a mean of zero and a standard deviation of one. Because of a strong right-skewed distribution of precipitation data, we coded this variable by adding one and then root-square transformed it (before standardization) to normalize its distribution. In all GLMMs, female identity, study plot identity, and year of the study were treated as random factors. Models were fitted using the \u0026lsquo;glmmTMB\u0026rsquo; package ver. 1.1.9 implemented in the R environment ver. 4.3.3 (Brooks et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, R Core Team \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The statistical significance of fixed effects was tested using z-statistics. For each model, marginal and conditional R\u003csup\u003e2\u003c/sup\u003e or pseudo-R\u003csup\u003e2\u003c/sup\u003e (depending on a model) were calculated using the \u0026lsquo;MuMIn\u0026rsquo; package ver. 1.48.4 (Bartoń \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We also assessed multicollinearity by a variance inflation factor (VIF) for fitted models using \u0026lsquo;performance\u0026rsquo; package ver. 0.12.2 (L\u0026uuml;decke et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and alternatively by the correlation matrix of independent variables (for subsets of data analysing hatching success, offspring production, and local recruitment rate separately). Both approaches indicate existing moderate correlations among explanatory variables; the highest VIF reached 3.25 and the highest coefficient correlation was 0.49 (for details, see Tables SM2 and SM3). Detected levels of VIFs and correlations still allow for keeping all explanatory terms in models without severe consequences for model performance (Dormann et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, we used the 'DHARMa' package ver. 0.4.6 (Hartig 2022) to test for overdispersion and zero inflation in GLMMs that analyze offspring production and recruitment. Finally, we found no evidence that parameter estimates of fitted models are overdispersed or zero-inflanted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHatching success\u003c/h2\u003e \u003cp\u003eWe revealed that during the incubation period, hatching success was affected by ambient temperature but not the sum of precipitation, implying that higher temperatures are associated with improved egg hatchability (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, we found that egg hatchability is negatively correlated with laying date and clutch size (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eOutput of generalized linear mixed model with Gaussian error distribution and the identity-link function testing how ambient temperature and sum of precipitation experienced during the incubation period, as well as laying date and clutch size, affect the hatching success (defined as the proportion of hatched nestlings within a clutch). All explanatory terms were standardized. The female identity, study plot identity, and year of study were included as random factors. Significant terms P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are in bold.\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=\"char\" char=\".\" 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\u003e\u003cem\u003eSources of variation\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimate\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;10890 broods of 7722 females)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.333\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e93.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemperature: incubation period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation: incubation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaying date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClutch size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-22.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlot ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of study \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e \u003csub\u003emarginal/conditional\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04/0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFledgling production\u003c/h3\u003e\n\u003cp\u003eWe found that the impact of ambient temperature during the incubation and nestling period on the number of fledglings were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, higher precipitation during the nestling period resulted in a decreased number of fledglings (a conditional component of GLMM; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A zero-inflated component of GLMM revealed that higher ambient temperatures during both the incubation and nestling periods were related to decreased brood failure (brood failure means a nest producing no fledged offspring; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, higher precipitation during the nestling phase increased brood failure (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, earlier laying dates were associated with a higher number of fledglings and decreased reproductive failure, and larger clutch sizes also contributed positively to the number of fledglings (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutput of the zero-inflated generalized linear mixed model with Poisson error distribution and the log-link function for a conditional component and with binomial error distribution and the logit-link function for a zero-inflated component testing how ambient temperature and sum of precipitation experienced during the incubation and nestling period, as well as laying date and clutch size, affect the number of fledglings. All explanatory terms were standardized. The female identity, study plot identity, and year of study were included as random factors. Significant terms P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are in bold.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSources of variation\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimate\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConditional model (N\u0026thinsp;=\u0026thinsp;13286 broods of 9337 females)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.520\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e72.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature: incubation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature: nestling period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation: incubation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrecipitation: nestling period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaying date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.075\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-6.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClutch size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.101\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlot ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of study \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e \u003csub\u003emarginal/conditional\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09/0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZero-inflated model (N\u0026thinsp;=\u0026thinsp;13286 broods of 9337 females)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.583\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.161\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-9.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemperature: incubation period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.171\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.051\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemperature: nestling period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.251\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-4.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation: incubation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrecipitation: nestling period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.117\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaying date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.716\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClutch size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlot ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of study \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOffspring recruitment\u003c/h3\u003e\n\u003cp\u003eWe showed that higher temperatures during the nestling period but not during the incubation period are associated with an increased number of recruits (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, higher precipitation during the incubation period but not during the nestling period also resulted in an increased number of recruits (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We also found that earlier laying dates are associated with higher numbers of recruits and clutch size have a significant positive effect on the number of recruits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutput of the generalized linear mixed model with Poisson error distribution and the log-link function testing how ambient temperature and sum of precipitation experienced during the incubation and nestling period, as well as laying date and clutch size, affect the number of recruits. All explanatory terms were standardized. The female identity, study plot identity, and year of study were included as random factors. Significant terms P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are in bold.\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=\"char\" char=\".\" 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\u003e\u003cem\u003eSources of variation\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimate\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;15164 broods of 10226 females)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.803\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.142\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-12.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature: incubation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemperature: nestling period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.119\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrecipitation: incubation period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.055\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation: nestling period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaying date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.401\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-10.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClutch size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlot ID \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of study \u003csub\u003erandom\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e \u003csub\u003emarginal/conditional\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03/0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study demonstrated that climatic conditions influence offspring production and recruitment of the migratory collared flycatcher. These findings align with existing research in the field of population ecology and climate change that shows the strong influence of climatic factors on crucial life history parameters (e.g. Dunn and M\u0026oslash;ller \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, K\u0026auml;mpfer et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Laczi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Only a few studies investigated the production of offspring (e.g., the number of fledglings) in avian populations in relation to local weather conditions. Some studies have found a decline in the production of young (Husby et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Shipley et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and some found increased fledgling production in warmer breeding seasons (Wegge and Rolstad \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Halupka 2021). However, many other studies failed to show any impacts of weather conditions on offspring production (e.g. Dyrcz and Czyż \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Shipley et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This inconsistency of results can be accounted for by the fact that relatively large sample sizes may be needed to detect relationships between abiotic conditions and reproductive output. In any case, a recent global meta-analysis based on 201 populations of 104 bird species indicates that climate change does not correlate with avian offspring production directly, but through complex interactions with their life history and ecological traits (Halupka et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In other words, local climatic conditions that vary greatly in time (e.g., seasonally) and space (e.g., geographically) may confound global trends across species that may experience different effects of climate change depending on where and when they live, breed, or migrate (Sparks and Tryjanowski \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Thus, rather than focusing on general patterns explaining variation in offspring production across all species, a future focus should be on how various aspects of ecology or life history might have driven variation in offspring production among groups of species or populations of the same species. More importantly, our study and a recent study of Riggio et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlight the importance of long-term monitoring when unravelling the impacts of climate change on fitness.\u003c/p\u003e \u003cp\u003eOur findings indicate that ambient temperature during the incubation period has a significant effect on hatching success. This result suggests that higher temperatures may create more favourable conditions for embryo development, possibly by enhancing metabolic rates or by reducing the time needed for incubation, both of which are known to positively impact hatchability. This aligns with other studies showing that variation in incubation temperature significantly influences hatching success (DuRant et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Coe et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Morover, our study indicates that conditions during incubation can still significantly affect important proxies of fitness, such as the number of fledglings and recruits. In contrast to a recent meta-analysis by Halupka et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which found that variation in offspring production is best explained by temperature trends during the nestling period, we found that higher ambient temperatures during both the incubation and nestling periods were related to decreased reproductive failure (broods that produced no fledged offspring). Moreover, we showed that the higher sum of precipitation during the nestling phase is associated with lower offspring production and increased brood failure. The collared flycatcher is an insectivore species and may be particularly affected by changing rainfall patterns because their main prey is less active during adverse weather, resulting in reduced food availability (Avery and Krebs 1984). Our results confirm previous studies showing the negative effects of rainfall on the number of fledglings (Dawson and Bortolotti \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Arlettaz et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u0026Ouml;berg et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While higher temperatures can be beneficial by reducing reproductive failure, increased rainfall during critical developmental periods can negatively impact offspring survival. These findings underscore the need to consider both temperature and precipitation in understanding how climate change influences avian reproductive success.\u003c/p\u003e \u003cp\u003eThe number of recruits is perhaps the best approximation of the individual contribution to the population of breeding animals in wild populations (Visser et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, we should keep in mind that this fitness estimate does not include all offspring that survive and disperse out of the study population. Nevertheless, the decrease in the number of recruits can have more profound implications for the size and sustainability of a bird population than the number of fledglings. This suggests that recent observed population declines across the globe may reflect changes in juvenile survival (Rosenberg et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Here, we used the yearly number of recruits produced by each breeding female as a proxy of fitness and we found a strong positive effect of higher ambient temperature during the nestling phase on the number of recruits in the wild population of collared flycatcher.\u003c/p\u003e \u003cp\u003eIn a recent study on the same population of collared flycatchers, we manipulated the developmental conditions of embryos through a modification of nest box thermal microclimate, and we found that offspring from the experimentally heated nests had larger body mass at fledging in comparison to the control ones (Arct et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This result suggests that higher temperatures in the nest may be beneficial for offspring fitness since body mass just before fledging predicts future offspring survival in this species (Arct et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Collectively, these findings indicate that the increase in ambient temperature during the breeding season may have a positive effect on the reproductive success of collared flycatcher inhabitants on Gotland Island.\u003c/p\u003e \u003cp\u003eInterestingly, we found a significant positive effect of rainfall during incubation on the number of recruits. Similarly, de Zwaan (2020) found that precipitation before and during incubation positively affected nestling mass in savannah sparrows (\u003cem\u003ePasserculus sandwichensis\u003c/em\u003e), indicating that rainfall can directly improve nestling condition. Furthermore, Pipoly et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) demonstrated that increased precipitation before hatching can promote nestling growth in the house sparrow (\u003cem\u003ePasser domesticus\u003c/em\u003e). Therefore, the positive effects observed in our study could reflect a broader trend where rainfall enhances reproductive success by improving the conditions critical for nestling development and survival. However, precipitation can influence also incubation behaviour, which can directly affect the fitness of offspring. During rainy weather females of many bird species reduce off-bouts, resulting in spending more time in the nest on incubation (Marasco and Spencer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This behaviour can provide more constant thermal conditions for developing embryos, reducing fluctuations in egg temperature, this way supporting higher fitness of offspring after hatching (Hepp et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While higher rainfall during the incubation phase may support recruitment, our study showed that increased precipitation during the nestling phase can lead to lower fledgling production. The apparent paradox in our findings illustrates the need to consider the specific life stages of birds when assessing the impact of weather conditions on reproductive success. This leads to important considerations regarding how changing weather patterns, especially in the context of climate change, could differentially affect various life stages of birds and ultimately their population dynamics.\u003c/p\u003e \u003cp\u003eTo conclude in our research, we focused on weather conditions experienced by the birds during both the incubation and nestling phases and showed that environmental variation can have important and long-lasting fitness consequences. The different impacts of temperature and rainfall on studied traits reveal the limitations of generalizing the results when only a few fitness traits are investigated and when different climatic factors are evaluated across distinct developmental stages.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study uses the data that has been previously collected during the long-term study in a wild population of collared flycatchers. The data upon which this study is based has been obtained following the Swedish guidelines for work on natural populations and under licenses and permits from the Swedish Ringing Centre (licence no. M716 to SMD) and Swedish National Board for Laboratory Animals, Stockholm (ID 9164-2021; ID 872-2017; 37-15-2015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur data are made publicly available via Dryad at doi:10.5061/dryad.w9ghx3g06\u003c/p\u003e\n\u003cp\u003eReviewerURL: http://datadryad.org/stash/share/gE_Uxx0BZYC7U9uv2tVyhaKR6lug7cJkossLgWfI9qk\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare we have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAneta Arct was supported by the Ministry of Science and Higher Education of Poland within the \u0026lsquo;Mobilność Plus\u0026rsquo; programme (1659/MOB/V/2017/0). Rafał Martyka was financially supported by the Polish National Agency for Academic Exchange within a mobility scholarship of the Bekker programme (PPN/BEK/2019/1/00253). A long-term study on the Gotland population of collared flycatchers (led by Lars Gustafsson) got support from the Swedish Research Council (VR) and the Swedish Research Council for Environment, Agricultural Sciences, and Spatial Planning (FORMAS) as well as many smaller grants from several supporters. This work was supported by National Science Centre grant no. UMO-2020/39/B/NZ8/01157 (OPUS20) to AA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAneta Arct: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; Rafał Martyka: Methodology, Conceptualization, Formal analysis, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing; Krzysztof Miler: Conceptualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; Lars Gustafsson: Funding acquisition, Project administration, Resources. Szymon M. Drobniak: Methodology, Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing: Review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMany thanks to (in random order) Blandine Doligez with her team for help with the fieldwork, Tomas P\u0026auml;rt, Anna Qvarnstr\u0026ouml;m, Ben C Sheldon, Juho K\u0026ouml;n\u0026ouml;nen, Juha Meril\u0026auml;, Mats Linden, Simon Evans, M\u0026aring;rten Hjernquist, Joanna Sendecka, Kevin Fletcher, and countless others who have collected data that has contributed towards this study. We also thank the landlords for letting us use their private land to host the study plots. We thank anonymous reviewers and Mariusz Cichoń for their constructive comments and suggestions for earlier versions of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArlettaz R, Schaad M, Reichlin TS, Schaub M. 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Newburgh, UK: Highland Statistics Ltd; 2012.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"frontiers-in-zoology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"finz","sideBox":"Learn more about [Frontiers in Zoology](http://frontiersinzoology.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/finz/default.aspx","title":"Frontiers in Zoology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5608754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5608754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHere, we present an analysis based on a 40-year dataset from a nest-box population of the collared flycatcher (\u003cem\u003eFicedula albicollis\u003c/em\u003e). Our objective is to examine the impact of weather conditions during the incubation and nestling period on key indicators of individual fitness, including offspring production and local annual recruitment rate. Our findings provide compelling evidence that climatic conditions experienced during both incubation and nestling periods significantly impact the number of fledglings and recruits. Specifically, we observed that higher precipitation during the nestling period negatively affects the number of fledglings and increases brood failure. Interestingly, higher precipitation during the incubation period is linked to increased recruitment numbers. Moreover, we found that warmer weather during both the incubation and nestling periods decreases brood failure, and more importantly, higher temperatures during the nestling period are positively associated with the number of recruits. These results underscore the complex interplay between weather patterns and avian reproductive strategies, highlighting the importance of long-term ecological studies in understanding the impacts of climate change on bird populations. By addressing the variability of climatic influences across different life stages, future research can help develop more comprehensive models for predicting the resilience of avian species in the face of ongoing climate challenges.\u003c/p\u003e","manuscriptTitle":"A long-term study reveals the impact of weather conditions on avian fitness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 16:38:53","doi":"10.21203/rs.3.rs-5608754/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-04-04T05:10:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-02-13T11:45:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-06T11:17:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-12T14:46:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Frontiers in Zoology","date":"2024-12-11T13:01:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"frontiers-in-zoology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"finz","sideBox":"Learn more about [Frontiers in Zoology](http://frontiersinzoology.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/finz/default.aspx","title":"Frontiers in Zoology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b5b7871-3795-40a7-8966-109f9dffa342","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:01:19+00:00","versionOfRecord":{"articleIdentity":"rs-5608754","link":"https://doi.org/10.1186/s12983-025-00569-z","journal":{"identity":"frontiers-in-zoology","isVorOnly":false,"title":"Frontiers in Zoology"},"publishedOn":"2025-08-25 15:57:37","publishedOnDateReadable":"August 25th, 2025"},"versionCreatedAt":"2025-01-08 16:38:53","video":"","vorDoi":"10.1186/s12983-025-00569-z","vorDoiUrl":"https://doi.org/10.1186/s12983-025-00569-z","workflowStages":[]},"version":"v1","identity":"rs-5608754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5608754","identity":"rs-5608754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}