Climate change does not equally affect temporal patterns of natural selection on reproductive timing across populations in two songbird species --- There are 8 EXCEL files: **1\. Tbl\_Fitness\_GT\_HVVLOHLBWH\_FirstClutches:** This file provides the breeding data of blue and great tits of four study areas. For each brood, it contains information about mother's identity, laying dates, brood size and whether manipulations were made. Area Four study areas Species Two species YearOfBreeding Year of breeding Mother Ring ID of female parent of the brood LayDateApril Date of first egg of first brood of the year for that mother (in April days, 1 April = day 1) ClutchSize Number of eggs laid within one clutch NumberFledged Number of chicks that fledged NumberFlededDeviation The number gives the number of chicks that might have fledged in addition to the number given in column "NumberFledged". The best estimate of the actual number of fledged chicks is: NumberFledged + 0.5 \* NumberFledgedDeviation NumberRecruitsAllBroodsSummed Number of recruiting offspring produced, summed over all broods of that year Include Is 1 if there has been no manipulation of the brood, otherwise is 0 **2\. Qry\_survival\_04\_Survial\_output:** This file contains information about the survival of each breeding female for all four blue and great tit populations. RingNumberFemale Ring numbers of the breeding females BroodYear Year Area Four study sites Species Great or blue tit LayDate Date of first egg of first brood of the year for that mother (in April days, 1 April = day 1) Survival 0-survival means bird has not been seen again, 1-survival means bird survived/was seen again **3\. tbl\_PeakDate\_Biomass\_AllAreas\_AllSpecies:** This file contains data on the caterpillar biomass and the dates, where biomass reached its maximum, i.e. peak date. AreaName Four study areas Year Year MidDate Date of maximum of the caterpillar biomass (in April days, 1 April = day 1) MaxBiom Maximum biomass on peak date in [g/(day \* m²)] **4\. Tbl\_budburst\_HV which:** This file gives the annual average date of bud burst of oak trees at the Hoge Veluwe. AreaShortName Only data on Hoge Veluwe (= HV) Year Year AprilAVG Average April day of oak bud burst (1 April= day 1) SumOfTrees Total number of trees measured in that year **5\. Tbl\_BeechCropIndex:** This file gives the beech crop index at the Hoge Veluwe. The index is a 3-point scale categorizing the amount of beech nuts into low, intermediate and high crop. Year Year NoTreesSampled Total number of trees sampled in that year BeechCropNet Net beech crop in [g/m²] BCINet Scale of 1 to 3, grouping net beech crop into low (=1), intermediate (=2) and high (=3) **6\. Qry\_mark\_05\_input\_file:** This file was created as the input data for the survival analysis with RMark. It is a more condensed version of the first file (Tbl\_Fitness\_GT\_HVVLOHLBWH\_FirstClutches) and contains information on the identity of each breeding female and the timing of her broods. RingNumberFemale Ring numbers of breeding females BroodYear Year Area Four study sites Species Great or blue tit LayDate Date of first egg of first brood of the year for that mother (in April days, 1 April= day 1) **7\. deBilt\_1955\_2022:** This file contains the daily temperature data of the weather station "de Bilt" for years 1955 to 2022 as derived from the KNMI. Temperatures are given in 0.1 °C. STN = 260 Meteo Station = DeBilt YYYYMMDD Year - Month - Day TN Minimum daily temperature in [0.1 °C] TX Maximum daily temperature in [0.1 °C] **8\. temp\_deKooy\_1955\_2022:** This file contains the daily temperature data of the weather station "de Kooy" for years 1955 to 2022 as derived from the KNMI. Temperatures are given in 0.1 °C. STN = 235 Meteo Station = DeKooy YYYYMMDD Year - Month - Day TN Minimum daily temperature in [0.1 °C] TX Maximum daily temperature in [0.1 °C] Data was derived from the following sources: Temperature data of both stations was derived from the KNMI (https://www.knmi.nl/nederland-nu/klimatologie/daggegevens). There are 3 separate, reproducible R-scripts using the data files listed above: 1. R\_script\_Mainanalysis Code to run all selection and phenology analyses and to create all figures (except Figure S3) from the main manuscript and the electronic supplementary material 2. R\_script\_climwin\_analysis Code to run the climate window analysis with package climwin to find the respective windows in the year in which temperatures are best correlated with either laying date or food peak date for all populations 3. R\_script\_Survival\_analysis Code to run the survival analyses with RMARK (note that program MARK is additionally needed to execute the R package RMARK) and to produce Figure S3 in the supplementary material

Climate change has led to changes in the strength of directional selection on seasonal timing. Understanding the causes and consequences of these changes is crucial to predicting the impact of climate change. But are observed patterns in one population generalisable to others, and can spatial variation in selection be explained by environmental variation among populations? We used long-term data (1955–2022) on blue and great tits co-occurring in four locations across the Netherlands to assess inter-population variation in temporal patterns of selection on laying date. To analyse selection, we combine reproduction and adult survival into a joined fitness measure. We found distinct spatial variation in temporal patterns of selection which overall acted towards earlier laying, and which was due to selection through reproduction rather than through survival. The underlying relationships between temperature, bird and caterpillar phenology were however the same across populations, and the spatial variation in selection patterns is thus caused by spatial variation in the temperatures and other habitat characteristics to which birds and caterpillars respond. This underlines that climate change is not necessarily equally affecting populations, but that we can understand this spatial variation, which enables us to predict climate change effects on selection for other populations.

Long-term data on breeding birds were collected by regular nest checks and by capturing and ringing birds. Data on caterpillar biomass was collected using frass nets. All data was stored in an relational SQL database and analysed using R.

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