• Energy Research
• 2016-2025close

In migratory species, the timing of arrival at the breeding grounds is a life-history trait with major fitness consequences. The optimal arrival date varies from year-to-year, and animals use cues to adjust their arrival dates to match this annual variation. However, which cues they use to time their arrival and whether these cues actually predict the annual optimal arrival date is largely unknown. Here, we integrate causal and evolutionary analysis by identifying the environmental variables used by a migratory songbird to time its arrival dates and testing whether these environmental variables also predicted the optimal time to arrive. We used 11 years of male arrival data of a pied flycatcher population. Specifically, we tested whether temperature and normalized difference vegetation index (NDVI) values from their breeding grounds in the Netherlands and from their wintering grounds in Ivory Coast explained the variation in arrival date, and whether these variables correlated with the position of the annual fitness peak at the breeding grounds. We found that temperature and NDVI, both from the wintering and the breeding grounds, explained the annual variation in arrival date, but did not correlate with the optimal arrival date. We explore three alternative explanations for this lack of correlation. Firstly, the date of the fitness peak may have been incorrectly estimated because a potentially important component of fitness (i.e., migration date dependent mortality en route or directly upon arrival) could not be measured. Secondly, we focused on male timing but the fitness landscape is also likely to be shaped by female timing. Finally, the correlation has recently disappeared because climate change disrupted the predictive value of the cues that the birds use to time their migration. In the latter case, birds may adapt by altering their sensitivity to temperature and NDVI.

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 predict the impact of climate change. But are observed patterns in one population generalizable 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.

Climate change has led to phenological shifts in many species, but with large variation in magnitude among species and trophic levels. The poster child example of the resulting phenological mismatches between the phenology of predators and their prey is the great tit (Parus major), where this mismatch led to directional selection for earlier seasonal breeding. Natural climate variability can obscure the impacts of climate change over certain periods, weakening phenological mismatching and selection. Here, we show that selection on seasonal timing indeed weakened significantly over the past two decades as increases in late spring temperatures have slowed down. Consequently, there has been no further advancement in the date of peak caterpillar food abundance, while great tit phenology has continued to advance, thereby weakening the phenological mismatch. We thus show that the relationships between temperature, phenologies of prey and predator, and selection on predator phenology are robust, also in times of a slowdown of warming. Using projected temperatures from a large ensemble of climate simulations that take natural climate variability into account, we show that prey phenology is again projected to advance faster than great tit phenology in the coming decades, and therefore that long-term global warming will intensify phenological mismatches. Data was collected in our long-term population of great tits (Parus major) at the Hoge Veluwe population (Netherlands). It was processed using ACCESS database queries and R-scripts. See the ReadMe file.

Setting up common garden experiment Birds: In the spring of 2021, freshly laid, unincubated eggs were transported from eight populations to the NIOO: La Rouvière (France), Boshoek (Belgium), Wytham Woods (England), Gotland (Sweden) and four Dutch populations (Hoge Veluwe, Vlieland, Oosterhout and Liesbos). The eggs were then taken to a Dutch population (Bennekomse Bos, lat: 52,003; long: 5,708) where they were placed in foster nests for incubating and early parental care. At 10 days post-hatching, these chicks were transported to the Netherlands Institute of Ecology and hand raised, following procedures as outlined in [1]. They were blood sampled and based on their genotypes (using five microsatellite regions Pma-TGAn33, PmaC25, PmaTAGAn71, PmaGAn27 and PmaD10 [2]) and that of the potential parents, they were assigned to a family following a standard protocol [3]. For a number of these populations there were severe problems leading to very low numbers of chicks (see Appendix 1 for more info on all eight populations). Hence, we only include offspring from Gotland (Sweden, lat: 57,063, long: 18,278) and Hoge Veluwe (Netherlands, lat: 52,041, long: 5,856) in our analysis. Note that due to the different timing of eggs laying the two populations the Gotland eggs were placed seven days later at the foster parent nests than the Hoge Veluwe eggs (7 May vs 30 April). The following year (2022) we formed first generation (F1) breeding pairs (within populations) from these birds and kept them from January onwards in pairs in open aviaries at the Netherlands Institute of Ecology with ad libitum food (constant daily amount of food consisting of a mixture of minced beef, proteins and vitamins, sunflower seeds, fat, a mix of dried insects, a mixture of proteins, vitamins, minerals and trace elements (Ce´De´-mix), a surplus of calcium, water for drinking and bathing, nesting material and four nestboxes as nesting opportunities. The eggs produced where collected every morning and put in an egg turner (i.e. a device that gently rocks eggs during storage – a CocinaCo 154 Eggs Quail Turner Tray Container). Eggs were, within five days of laying, taken to the Bennekomse Bos to foster parents. We put Gotland and Hoge Veluwe eggs together in foster broods (with a total clutch between 5 and 11 eggs, 9 on average) to ensure common conditions during incubation and early chick rearing. Note that as there was no difference in laying date between the populations for the F1 pairs, eggs were taken to their foster nests during the same period. At day 10 post-hatching the chicks were taken to the Netherlands Institute of Ecology, hand raised, blood sampled and assigned to a family. In the following year (2023) 20 Gotland and 20 Hoge Veluwe second generation (F2) breeding pairs were set-up. As these F2 birds originate from eggs produced in a common garden setting, and thus any carry over effects of the location the eggs were produced are excluded, any differences between them will be genetic. In total 7 Hoge Veluwe and 8 Gotland F1 pairs and 20 Hoge Veluwe and 20 Gotland F2 pairs were used (number of pairs that produced a clutch was 7, 7, 20 and 17 respectively). Aviaries: Breeding pairs were set up in 40 outdoor aviaries of 4 m x 2 m x 2 m (l x w x h) with on one side a mesh, allowing natural light and ambient temperatures. Despite being exposed to natural light, all aviaries are still darker than natural conditions. This causes the birds in aviaries to consistently lay later than wild birds if left without additional light. Thus, a fluorescent light tube provided additional light in the morning for all breeding pairs. In January and February lights were on from sunrise until midday (i.e. same as normal housing conditions) and from March onwards lights were on 2h and 15 min before sunrise until midday. This additional light was crucial for eggs to be laid while foster nests were still available to produce the F2 birds. The aviary building consisted of two rows of aviaries (20 West and 20 East facing) and to minimize the impact of any systematic variation in conditions between aviaries we kept the Gotland and Hoge Veluwe pairs in alternating aviaries. Phenotyping Laying date: Laying dates were recorded for both the Hoge Veluwe and Gotland F1 and F2 generations (see Appendix 1 for laying dates of F1 birds from other populations). Nest boxes were checked daily for nest building progress and new eggs, and the laying date was the day the first egg was laid. Eggs were replaced by plastic dummy eggs, and upon clutch completion females were allowed to incubate for four complete days after which nests and dummy eggs were removed on the fifth day. Frequently, pairs would initiate replacement clutches. Here, we only analyse the laying date of the first broods, i.e. the first clutch of the season. Moult: Moult was scored once in both years at the end of the breeding season (F1 = 17th of June in 2022 and F2 = 16th of June in 2023). We inspected the right wing of each bird and gave 10 scores per bird, one for each primary feather (P1-P10), from 0 to 5 (0 old feather, 5 fully grown), reflecting how much it had grown [4]. Then, we obtained a moult score per individual by converting each of the 0-5 moult scores into an approximate proportion of feather grown [following 5], multiplied by the respective mass of that particular feather, and finally summed the values of all feathers. This resulted in a single value ranging between 0 and 1. The mass-corrected moult score serves as a proxy for moult timing because all birds were scored on the same day and feather mass increases fairly linearly throughout the season [5–7]. Thus, the larger the score, the furthest the bird is in its moult progress and consequently the earlier it started moulting. Gonadal size: We measured the gonadal sizes of F2 birds on 22, 23, 26 & 27 Feb 2024. Birds, 19 Hoge Veluwe and 19 Gotland pairs, were kept in the same outdoor aviaries as in the F2 breeding season of 2023. The birds, alternating between pairs from Gotland and Hoge Veluwe, were put under isoflurane after which they were decapitated and the length (mm), width (mm) and fresh mass (mg) of their gonads measured. We calculated testis and ovary volume as V = 4/3.π.a2.b, where a is width (mm)/2 and b is length (mm)/2. Statistics Laying date, moult score and gonad volume were analysed using generalized linear models using the lm function of lme4 [8] in R version 4.3.2 [9], with population (Gotland or Hoge Veluwe), generation (F1 or F2) and (for moult and gonads) sex (male or female), and their interactions, as explanatory variables. For testis the right testis was analysed (as one left testis was missing, reducing the sample size and as left and right testis volume were highly correlated (Pearson correlation: 0.96)). Following Schaper et al. [10], we use the 10log gonadal volume as the distribution of the logged values better follow a normal distribution. References: 1. Drent PJ, van Oers K, van Noordwijk AJ. 2003 Realized heritability of personalities in the great tit (Parus major). Proc. R. Soc. Lond. Ser. B-Biol. Sci. 270, 45–51. 2. Saladin V, Bonfils D, Binz T, Richner H. 2003 Isolation and characterization of 16 microsatellite loci in the Great Tit Parus major. Mol. Ecol. Notes 3, 520–522. 3. Greives TJ et al. 2015 Costs of sleeping in: circadian rhythms influence cuckoldry risk in a songbird. Funct. Ecol. 29, 1300–1307. (doi:10.1111/1365-2435.12440) 4. Ginn HB, Melville DS. 1983 Moult in Birds. British Trust for Ornithology. See https://books.google.nl/books?id=Sd9FAAAAYAAJ. 5. Underhill LG, Zucchini W. 1988 A model for avian primary moult. Ibis 130, 358–372. (doi:10.1111/j.1474-919X.1988.tb00993.x) 6. Dawson A, Newton I. 2004 Use and validation of a molt score index corrected for primary feather mass. Auk 121, 372–379. 7. Dawson A. 2003 A detailed analysis of primary feather moult in the Common Starling Sturnus vulgaris– new feather mass increases at a constant rate. Ibis 145, E69–E76. (doi:10.1046/j.1474-919X.2003.00161.x) 8. Bates D, Machler M, Bolker BM, Walker SC. 2015 Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48. 9. R_Core_Team. 2021 R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 10. Schaper SV, Gienapp P, Dawson A, Visser ME. 2013 Heritability of gonad size varies across season in a wild songbird. J. Evol. Biol. 26, 2739–2745. (doi:10.1111/jeb.12249) # Geographic differences in the phenology of gonadal development and moult, but not of egg laying, are genetically based in a small songbird [https://doi.org/10.5061/dryad.zkh1893k8](https://doi.org/10.5061/dryad.zkh1893k8) ## Description of the data and file structure To study to what extent geographic variation in laying date in great tits has a genetic basis, we carried out a two-generation common garden experiment in which we bred great tits that originated from eggs retrieved from two populations that in the Bailey et al. (2002, Nature Communications) analysis showed different temperature sensitivity. Such a common garden approach can show that there are genetic differences underlying this difference in sensitivity, as the confounding effects of different environments that shape the phenotype, as it occurs in the wild, are lifted. Such local adaptation would demonstrate that evolution has occurred over time, providing insights into the potential for genetic adaptation in current populations under selection. We measured three life cycle events: laying date of the breeding pairs (primarily sensitive to temperature) as well as seasonal timing of moult and gonadal development (primarily sensitive to photoperiod). ### Files and variables #### File: Common\_garden\_gonads\_follicle.csv **Description:** We measured the gonadal sizes of F2 birds on 22, 23, 26 & 27 Feb 2024. Birds, 19 Hoge Veluwe and 19 Gotland pairs, were kept in the same outdoor aviaries as in the F2 breeding season of 2023. The birds, alternating between pairs from Gotland and Hoge Veluwe, were put under isoflurane after which they were decapitated and the length (mm), width (mm) and fresh mass (mg) of their gonads measured. We calculated testis and ovary volume as V = 4/3.π.a2.b, where a is width (mm)/2 and b is length (mm)/2. ##### Variables * RingNumber: Unique number per individual * Population: Population (Hoge Veluwe or Gotland) * FollicleVolume: Volume of female follicle (mm3) * LogFollicleVolume: 10log of FollicleVolume * FemaleMother: ring number of female's mother #### File: Common\_garden\_gonads\_testis.csv **Description:** We measured the gonadal sizes of F2 birds on 22, 23, 26 & 27 Feb 2024. Birds, 19 Hoge Veluwe and 19 Gotland pairs, were kept in the same outdoor aviaries as in the F2 breeding season of 2023. The birds, alternating between pairs from Gotland and Hoge Veluwe, were put under isoflurane after which they were decapitated and the length (mm), width (mm) and fresh mass (mg) of their gonads measured. We calculated testis and ovary volume as V = 4/3.π.a2.b, where a is width (mm)/2 and b is length (mm)/2. ##### Variables * RingNumber: Unique number per individual * Population: Population (Hoge Veluwe or Gotland) * RightTestisVolume: Volume of male right testis (mm3) * LogRightTestisVolume: 10log of RightTestisVolume * MaleMother: ringnumber of male's mother #### File: Common\_garden\_gonads.R **Description:** R script to analyse the gonadal data #### File: Common\_garden\_gonads.xlsx **Description:** Excel version of Common_garden_gonads_follicle.csv & Common_garden_gonads_testis.csv #### File: Common\_garden\_laying\_date.csv **Description:** Nest boxes were checked daily for nest building progress and new eggs, and the laying date was the day the first egg was laid. We only analyse the laying date of the first broods, i.e. the first clutch of the season. ##### Variables * Year: Year * Aviary: Aviary number in building 9 of the NIOO (1-40, missing numbers are aviaries in which no first brood was produced * Female: Unique number per individual female * Female: Generation F1=first generation, F2=second generation female * FemaleMother: Ring number of female's mother * FemaleArea: Population from which the female originates (Hoge Veluwe or Gotland) * Male: Unique number per individual male * MaleGeneration: F1=first generation, F2=second generation male * MaleMother: Ring number of male's mother * MaleArea: Population from which the male originates (Hoge Veluwe or Gotland) * BroodType: 0 = first brood (see above) * LayDate: Laying date in DD/MM/YYYY * LayAprilDate: Laying date in April days (i.e. 1 April = day 1) #### File: Common\_garden\_laying\_date.R **Description:** R script to analyse the laying date data #### File: Common\_garden\_laying\_date.xlsx **Description:** Excel version of Common_garden_laying date.csv #### File: Common\_garden\_moult.csv **Description:** Moult was scored once in both years at the end of the breeding season (F1 = 17th of June in 2022 and F2 = 16th of June in 2023). We inspected the right wing of each bird and gave 10 scores per bird, one for each primary feather (P1-P10), from 0 to 5 (0 old feather, 5 fully grown), reflecting how much it had grown [1]. Then, we obtained a moult score per individual by converting each of the 0-5 moult scores into an approximate proportion of feather grown [following 2], multiplied by the respective mass of that particular feather, and finally summed the values of all feathers. This resulted in a single value ranging between 0 and 1. The mass-corrected moult score serves as a proxy for moult timing because all birds were scored on the same day and feather mass increases fairly linearly throughout the season [2–4]. Thus, the larger the score, the furthest the bird is in its moult progress and consequently the earlier it started moulting. 1\. Ginn HB, Melville DS. 1983 Moult in Birds. British Trust for Ornithology. See [https://books.google.nl/books?id=Sd9FAAAAYAAJ](https://books.google.nl/books?id=Sd9FAAAAYAAJ). 2\. Underhill LG, Zucchini W. 1988 A model for avian primary moult. Ibis 130, 358–372. (doi:10.1111/j.1474-919X.1988.tb00993.x) 3\. Dawson A, Newton I. 2004 Use and validation of a molt score index corrected for primary feather mass. Auk 121, 372–379. 4\. Dawson A. 2003 A detailed analysis of primary feather moult in the Common Starling Sturnus vulgaris– new feather mass increases at a constant rate. Ibis 145, E69–E76. (doi:10.1046/j.1474-919X.2003.00161.x) ##### Variables * RingNumber: Unique number per individual * Sexe: 1=female, 2=male * Generation: F1=first generation, F2=second generation * Area: Population (Hoge Veluwe or Gotland) * MoultWeight: Moult score - see above * Mother: Ring number of individual's mother #### File: Common\_garden\_moult.R **Description:** R script to analyse the moult data #### File: Common\_garden\_moult.xlsx **Description:** Excel version of Common_garden_moult.csv To forecast how fast populations can adapt to climate change, it is essential to determine the evolutionary potential of different life-cycle stages under selection. In birds, timing of gonadal development and moult are primarily regulated by photoperiod, while laying date is highly phenotypically plastic to temperature. We tested whether geographic variation in phenology of these life-cycle events between populations of great tits (Parus major) has a genetic basis, indicating that contemporary genetic adaptation is possible. We carried out a common garden experiment in which we bred first- and second-generation pairs in captivity originating from eggs from Gotland (Sweden) and Hoge Veluwe (Netherlands), two populations that showed different temperature sensitivity of laying date in a recent meta-analysis. We recorded the phenology of egg-laying, moult and gonadal size in early spring. We found no significant differences in laying date between the populations, but they did differ in moult timing and testis size. This implies that under climate change the timing of gonadal development and moult, which are mainly regulated by photoperiod, will not respond to increased temperature but can respond by genetic adaptation in response to selection, while the opposite holds for laying date, perhaps indicating that plasticity is constraining genetic adaptation.

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. Excel & R

AbstractThe phenology of many species shows strong sensitivity to climate change; however, with few large scale intra-specific studies it is unclear how such sensitivity varies over a species’ range. We document large intra-specific variation in phenological sensitivity to temperature using laying date information from 67 populations of two co-familial European songbirds, the great tit (Parus major) and blue tit (Cyanistes caeruleus), covering a large part of their breeding range. Populations inhabiting deciduous habitats showed stronger phenological sensitivity than those in evergreen and mixed habitats. However, populations with higher sensitivity tended to have experienced less rapid change in climate over the past decades, such that populations with high phenological sensitivity will not necessarily exhibit the strongest phenological advancement. Our results show that to effectively assess the impact of climate change on phenology across a species’ range it will be necessary to account for intra-specific variation in phenological sensitivity, climate change exposure, and the ecological characteristics of a population.

Global warming has shifted phenological traits in many species, but whether species are able to track further increasing temperatures depends on the fitness consequences of additional shifts in phenological traits. To test this, we measured phenology and fitness of great tits ( Parus major ) with genotypes for extremely early and late egg lay dates, obtained from a genomic selection experiment. Females with early genotypes advanced lay dates relative to females with late genotypes, but not relative to nonselected females. Females with early and late genotypes did not differ in the number of fledglings produced, in line with the weak effect of lay date on the number of fledglings produced by nonselected females in the years of the experiment. Our study is the first application of genomic selection in the wild and led to an asymmetric phenotypic response that indicates the presence of constraints toward early, but not late, lay dates.

AbstractLocal biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15–91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe.