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Sampling site and regimes
Field work was carried out in the agricultural catchment of Grytelandsbekken (a creek of approx. 2.5 km in length) also known as Skuterud catchment, located in the municipality of Ås (20,000 people), 30 km southeast of Oslo (Fig. 1). The catchment area is of approx. 4.5 km2 and largely consists of farmlands (60%) and forest/marshlands (31%). Grytelandsbekken has previously been studied for spatial variations in the microbial communities of various lotic freshwater ecosystems in different regions of Norway21. Based on that study, it was characterised as a rural creek with the highest diversity and abundance of microbial communities. Thus, in this follow-up study, Grytelandsbekken, specifically, was selected for assessing the seasonal dynamics of lotic freshwater bacterial communities.
All field measurements and samplings were carried out over a 2-year period at the same site of the creek, i.e. at about 0.25 km. In total, there were 16 sampling events, split equally between cold and warm seasonal regimes. The cold season refers to December–March, while the warm season includes June–September. In detail, there were four events during the cold season in the first year, Cold_1 (Cold_1a/Dec.2014, Cold_1b/Jan.2015, Cold_1c/Feb.2015, and Cold_1d/Mar.2015) and four events during the cold season in the second year, Cold_2 (Cold_2a/Dec.2016, Cold_2b/Jan.2017, Cold_2c/Feb.2017, and Cold_2d/Mar.2017). A similar sample assembly was applied to the warm seasonal regimes, i.e. four in the first year, Warm_1 (Warm_1a/Jun.2015, Warm_1b/Jul.2015, Warm_1c/Aug.2015, and Warm_1d/Sep.2015) and four in the second year, Warm_2 (Warm_2a/Jun.2016, Warm_2b/Jul.2016, Warm_2c/Aug.2016, and Warm_2d/Sep.2016). The entire study duration and sampling sets were conceived based on similar settings reported in aquatic research worldwide, profiling microbial communities through seasonal and spatial variations20,35,36.
Environmental measures
Primary physico-chemical parameters that are routinely measured in standard catchment water quality control37,38 were selected for abiotic characteristics of the lotic environment. These included organic matter content (expressed as CODCr), TSS, nutrients (Ptot and Ntot), EC, pH, and Temp. The latter was measured in situ at one of the weather stations, administrated by the Norwegian Institute of Bioeconomy Research (NIBIO) and located at the field measuring/sampling site of Grytelandsbekken. The station is equipped with a number of automatic sensors, providing hourly measurements and registering various climatic data online, which are all open and available at the AgroMetBase hosted by NIBIO (https://lmt.nibio.no/agrometbase/getweatherdata.php). The former parameters were analysed post-sampling in an accredited laboratory of the ALS Laboratory Group Norway AS. These analyses were performed in accordance with ISO and national standards for the respective parameters: CODCr (ISO 15705), TSS (CSN EN 872, NS 4733), Ptot (ISO 6878, ISO 15681-1), Ntot (EN 12260), EC (EN 27 888, SM 2520B, EN 16192), and pH (ISO 10523, EPA 150.1, EN 16192).
Genomic DNA purification: 16S rRNA amplicon library preparation and MiSeq sequencing
The seasonal water samples underwent DNA extraction using the QIAGEN DNeasy PowerWater Kit (QIAGEN GmbH, Hilden, Germany). In practice, 0.5 L of water was subjected to ultrafiltration to obtain a solid mass on a membrane filter (0.45 µm). DNA was then extracted from the collected filters, following the manufacturer’s instruction. The concentration and quality of the purified DNA was analysed on the NanoDrop Spectrophotometer (Thermo Fisher Scientific, Wilmington, USA). Five nanograms of purified DNA was used in PCR to prepare an amplicon library using the NEXTflex 16S V4 Amplicon-Seq Kit 2.0 (Bioo Scientific Corporation, Austin, TX, USA), following the provided protocol, which has previously been described in detail21. The applied specific 16S V4 forward primer (5′-GACGCTCTTCCGATCTTATGGTAATTGTGTGCCAGCMGCCGCGGTAA-3′) and reverse primer (5′-TGTGCTCTTCCGATCTAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT-3′) were provided by the manufacturer and included in the sequencing kit. The library was prepared in triplicate for each sample. The final concentration of each library was measured on a Qubit Fluorometer (Life Technologies, Eugene, OR, USA) using the Quant-IT dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA). Pooling of all prepared libraries was achieved after concentration normalization using the SequalPrep Normalization Plate Kit (Thermo Fisher Scientific, Wilmington, USA). The pooled library was analysed on the Agilent 2100 Bioanalyzer system using the Agilent High Sensitivity DNA Kit (Agilent, CA, USA). It indicated a single band/unique product at 451 bp. The multiplexed library was sequenced on the Illumina MiSeq system using the MiSeq Reagent Kit V3, 600 Cycles (Illumina Inc., San Diego, CA, USA), following the default standard procedures.
Sequence data processing
The output sequence datasets were analysed using Microbial Genomics Module 2.0, added onto the CLC Genomic Workbench 10.1.1 (CLC Bio, QIAGEN Company, Aarhus, Denmark, https://www.qiagenbioinformatics.com/products/clc-genomics-workbench). The processing workflow consisted of four key components: quality filtration, OTU clustering, and alpha and beta diversity measures. Adapter and primer sequences were trimmed. Unqualified reads were trashed when the quality score was less than 20 or a higher number of ambiguous nucleotides (more than two) were detected. The average length after trimming was between 220–230 bp. Chimeric sequences and singletons were detected and discarded. The remaining qualified reads were used to characterize OTUs based on a reference database (Greengenes v_13_5)39 at a 97% identity level. The bacterial alpha diversity of each sample was estimated in rarefaction analysis with a depth cutoff at 50,000 reads. The bacterial beta diversity applied the Euclidean distance criterion (EDC) to estimate the community similarities between the examined samples. All sequence data are available at NCBI Sequence Read Archive, under accession number SRR10835654-669, as part of BioProject PRJNA599104.
Statistical analyses
Alpha diversity differences were tested using a two-tailed Student’s t-test at 0.05 significance. This was performed in the XLSTAT-ECOLOGY statistical software package version 2019.1.1 (Addinsoft 2020, Boston, USA, https://www.xlstat.com). A hierarchical clustering heat map was created to elucidate the bacterial community similarities/relatedness (pairwise) among all examined samples. It was conducted on a subset of top 700 OTUs, based on the EDC using the trimmed mean of M-values and Z-score (standard deviation numbers from the population mean) normalizations. This was further supported by the PERMANOVA analysis, performed to ascertain statistical significance of the clusters (p-value = 0.00001). These tests were executed using a package of functional features included in the Microbial Genomics Module (CLC Bio, QIAGEN Company, Aarhus, Denmark, https://www.qiagenbioinformatics.com/products/clc-genomics-workbench). Furthermore, the LEfSe tool40 was applied to identify the responsible bacterial members, accounting for community discrepancy between cold and warm seasons. The formatted abundance table of bacterial classes was uploaded to the Galaxy/Hutlab application web-based platform (Biostatistics Department, Harvard School of Public Health, Boston, MA, USA, https://huttenhower.sph.harvard.edu/galaxy) for pairwise comparison. Statistical significance of the comparison was determined by the Wilcoxon rank-sum test at the alpha value of 0.05. The identified features characterising the microbial differences among samples were processed using LDA, with a threshold score set at 2.0. Beyond that, RDA was carried out to determine the key abiotic environmental variables driving seasonal changes of the bacterial community based on the Pearson correlation test, with a statistical significance level higher than 95% (p  More

Phenological and climatic data
We used data from the Pan European Phenology Project (PEP725)29, an open-access database with long-term plant phenological observations across 25 European countries (http://www.pep725.eu/). The regional/national network partners of PEP725 are following a consistent guideline for phenological observations30 and prepare the data for submission to the PEP725 database curators29. We selected 30 species for which sufficient observational data were available: 21 deciduous broadleaved trees or shrubs, 6 herbaceous perennials, 2 evergreen coniferous trees, and 1 deciduous coniferous tree (Supplementary Table 3). Particularly, our data set included one fruit tree (Prunus avium) and one nut tree (Corylus avellana) since some of the chilling models are specifically developed for fruit and nut trees. A total of 2,493,644 individual records from 15,533 phenological stations were used. The stations were mainly distributed in moderate climates in Central Europe (Supplementary Fig. 3). Four spring events based on the BBCH code were investigated: BBCH 10, 11, 60, and 69, representing first leaves separated, first leaves unfolded, first flowers open, and end of flowering, respectively31.
We used the E-OBS v19.0eHOM data set32 with a spatial resolution of 0.1 × 0.1° for 1950–2018 for calculating CA and HR of the in situ phenological records. This data set is provided by the European Climate Assessment & Data set project and includes homogenized series of daily mean, minimum, and maximum temperatures. We also use the daily maximum and minimum temperature data from the GHCN data set33 to assess the scale effect. The GHCN data set contains station-based measurements from over 90,000 land-based stations worldwide, but only parts of PEP725 stations match with the GHCN stations.
For future climatic data (2019–2099), we used daily minimum and maximum temperatures simulated by the HADGEM2-ES model (with a spatial resolution of 0.5 × 0.5°) under two climatic scenarios (RCP 4.5 and RCP 8.5). These data have been bias-corrected by applying the method used in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)34, which were available on the ISIMIP server (https://esg.pik-potsdam.de/projects/isimip2b/).
Chilling models
We used 12 chilling models to measure the amount of chilling. One type of chilling model is based on several specific temperature thresholds. The most commonly used model, developed in the 1930s and 1940s for peach35, calculates the number of hours or days with temperatures 0 °C for calculating CA11,38. Models C1–C6 were developed based on various combinations of the upper and lower temperature limits. The rate of chilling was 1 for daily temperatures 5,{mathrm{or}},{T} < - 10} hfill end{array}} right.,$$ (2) $${mathop{rm{CU}}nolimits} _3 = left{ {begin{array}{*{20}{l}} 1 hfill & {0 le {mathop{T}nolimits} le 5} hfill \ 0 hfill & {{T} > 5,{mathrm{or}},{T} < 0} hfill end{array}} right.,$$ (3) $${mathrm{CU}}_{mathrm{4}} = left{ {begin{array}{*{20}{c}} 1 & {{mathop{T}nolimits} le 7} \ 0 & {{T} > 7} end{array}} right.,$$
(4)

$${mathop{rm{CU}}nolimits} _5 = left{ {begin{array}{*{20}{l}} 1 hfill & { – 10 le {mathop{T}nolimits} le 7} hfill \ 0 hfill & {{T} > 7,{mathrm{or}},{T} < - 10} hfill end{array}} right.,$$ (5) $${mathop{rm{CU}}nolimits} _6 = left{ {begin{array}{*{20}{l}} 1 hfill & {0 le {mathop{T}nolimits} le 7} hfill \ 0 hfill & {{T} > 7,{mathrm{or}},{T} < 0} hfill end{array}} right.,$$ (6) where CUi is the rate of chilling for Model Ci, and T is the daily mean temperature (°C). Model C7 is also known as the Utah Model39, which assigned different weights to different ranges of temperatures and was first used to measure the chilling requirements of peach (Eq. (7)). The Utah Model was modified to produce Model C8 (Eq. (8)), which removed the negative contributions of warm temperatures to accumulated chilling20. $${mathop{rm{CU}}nolimits} _7 = left{ {begin{array}{*{20}{l}} 0 hfill & {{mathop{T}nolimits} le 1.4} hfill \ {0.5} hfill & {1.4 < {mathop{T}nolimits} le 2.4} hfill \ 1 hfill & {2.4 < {mathop{T}nolimits} le 9.1} hfill \ {0.5} hfill & {9.1 < {mathop{T}nolimits} le 12.4} hfill \ 0 hfill & {12.4 < {mathop{T}nolimits} le 15.9} hfill \ { - 0.5} hfill & {15.9 < {mathop{T}nolimits} le 18} hfill \ { - 1} hfill & {{mathop{T}nolimits} > 18} hfill end{array}} right.,$$
(7)

$${mathop{rm{CU}}nolimits} _8 = left{ {begin{array}{*{20}{l}} 0 hfill & {{mathop{T}nolimits} le 1.4} hfill \ {0.5} hfill & {1.4 < {mathop{T}nolimits} le 2.4} hfill \ 1 hfill & {2.4 < {mathop{T}nolimits} le 9.1} hfill \ {0.5} hfill & {9.1 < {mathop{T}nolimits} le 12.4} hfill \ 0 hfill & {{mathop{T}nolimits} > 12.4} hfill end{array}} right.,$$
(8)

where CUi is the rate of chilling for Model Ci, and T is the daily mean temperature (°C).
Model C9 is a dynamic model developed for peach in Israel and South Africa40,41 and now adopted for apricot cultivars42. The most important characteristic of Model C9 was that a previous intermediate product affected the rate of chilling in the current hour or day. We did not provide equations for Model C9 for simplicity (see the equation in Luedeling et al.20).
Harrington et al.43 summarized published results for chilling units and constructed a chilling function based on a three-parameter Weibull distribution, coded as Model C10 (Eq. (9)). Model C11 has a triangular form, which was fitted by Hänninen44 using previous experimental results for Finnish birch seedlings (Eq. (10)). Zhang et al.45 recently fitted observational data to the triangular model for 24 plant species and found that a mean optimal chilling temperature of 0.2 °C and an upper limit of the chilling temperature of 6.9 °C were most effective. Model C12, therefore, uses the triangular form with parameters of 0.2 and 6.9 °C (Eq. (11)).

$${mathop{rm{CU}}nolimits} _{10} = left{ {begin{array}{*{20}{l}} 1 hfill & {2.5 < {mathop{T}nolimits} < 7.4} hfill \ 0 hfill & {{mathop{T}nolimits} < - 4.7,{mathrm{or}},{mathop{T}nolimits} > 16} hfill \ {3.13left( {frac{{{mathop{T}nolimits} + 4.66}}{{10.93}}} right)^{2.10}{mathop{rm{e}}nolimits} ^{ – left( {frac{{{mathop{T}nolimits} + 4.66}}{{10.93}}} right)^{3.10}}} hfill & {{mathrm{else}}} hfill end{array}} right.,$$
(9)

$${mathrm{CU}}_{11} = left{ {begin{array}{*{20}{l}} 0 hfill & {{mathop{T}nolimits} le – 3.4,{mathop{rm{or}}nolimits} ,{mathop{T}nolimits} ge 10.4} hfill \ {frac{{{mathop{T}nolimits} + 3.4}}{{5 + 3.4}}} hfill & { – 3.4 < {mathop{rm{T}}nolimits} le 5} hfill \ {frac{{{mathop{T}nolimits} - 10.4}}{{5 - 10.4}}} hfill & {5 < {mathop{T}nolimits} < 10.4} hfill end{array}} right.,$$ (10) $${mathop{rm{CU}}nolimits} _{12} = left{ {begin{array}{*{20}{l}} 0 hfill & {{mathop{T}nolimits} le - 6.5,{mathop{rm{or}}nolimits} ,{mathop{T}nolimits} ge 6.9} hfill \ {frac{{{T + }6.5}}{{6.9 - 0.2}}} hfill & { - 6.5 < {mathop{T}nolimits} le 0.2} hfill \ {frac{{6.9 - {T}}}{{6.9 - 0.2}}} hfill & {0.2 < {T} < 6.9} hfill end{array}} right.,$$ (11) where CUi is the rate of chilling for Model Ci, and T is the daily mean temperature in °C. Forcing models Forcing models were used to measure HR for the spring events of plants. The GDD model is the most commonly used forcing model, which assumes that the rate of forcing is linearly correlated with temperature if the temperature is above a particular threshold. We mainly used Model F1 (Eq. (12)), which adopts a temperature threshold of 0 °C46,47,48, for examining the relationship between CA and HR: $${mathrm{FU}}_{mathrm{1}} = {mathrm{max}}({T},0),$$ (12) where FU1 is the rate of forcing for Model F1, and T is the daily mean temperature (°C). We also validated the chilling models by correlating them with HR based on seven other forcing models to assess the impact of the choice of forcing model on the results. Model F2 (Eq. (13)) is also a GDD model but has a temperature threshold of 5 °C12,18: $${mathrm{FU}}_{mathrm{2}} = {mathrm{max}}(T - 5,0),$$ (13) where FU2 is the rate of forcing for Model F2, and T is the daily mean temperature (°C). Piao et al.48 found that leaf onset in the Northern Hemisphere was triggered more by daytime than nighttime temperature. They thus proposed a GDD model using maximum instead of mean temperature. Models F3 (Eq. (14)) and F4 (Eq. (15)) are based on maximum temperature with thresholds of 0 and 5 °C, respectively. $${mathop{rm{FU}}nolimits} _3 = max ({mathop{T}nolimits} _{rm{max}},0),$$ (14) $${mathop{rm{FU}}nolimits} _4 = max ({mathop{T}nolimits} _{rm{max}} - 5,0),$$ (15) where FUi is the rate of forcing for Model Fi, and Tmax is the daily maximum temperature (°C). A recent experiment demonstrated that the impact of daytime temperature on leaf unfolding for temperate trees was approximately threefold higher than the impact of nighttime temperature49. Model F5 (Eq. 16) thus uses two parameters (0.75 and 0.25) to weigh the impact of daytime and nighttime temperatures on HR. $${mathop{rm{FU}}nolimits} _5 = 0.75 times max ({mathop{T}nolimits} _{rm{max}} - 5,0){mathrm{ + }}0.25 times max ({mathop{T}nolimits} _{rm{min}} - 5,0),$$ (16) where FU5 is the rate of forcing for Model F5. Tmax and Tmin are the daily maximum and minimum temperatures (°C), respectively. Many studies have suggested that the rate of forcing followed a logistic function of temperature44,50. Model F6 (Eq. (17)) uses a logistic function proposed by Hänninen44, and Model F7 (Eq. (18)) uses another logistic function proposed by Harrington et al.43. $${mathop{rm{FU}}nolimits} _6 = left{ {begin{array}{*{20}{l}} {frac{{28.4}}{{1 + {mathop{rm{e}}nolimits} ^{ - 0.185({mathop{T}nolimits} - 18.5)}}}} hfill & {{mathop{T}nolimits} > 0} hfill \ 0 hfill & {{mathop{rm{else}}nolimits} } hfill end{array}} right.,$$
(17)

$${mathop{rm{FU}}nolimits} _7 = frac{1}{{1 + {mathop{rm{e}}nolimits} ^{ – 0.47{mathop{T}nolimits} {mathrm{ + 6}}{mathrm{.49}}}}},$$
(18)

where FUi is the rate of forcing for Model Fi, and T is the daily mean temperature (°C).
Model F8 is a growing degree hour (GDH) model, where species have an optimum temperature for growth and where temperatures above or below that optimum have a smaller impact51. Model F8 (Eq. (19)) was first designed for calculating HR at hourly intervals, but we applied it at a daily interval. The stress factor in the original GDH model was ignored, because we assumed that the plants were not under other stresses.

$${mathrm{FU}}_{mathrm{8}}{mathrm{ = }}left{ {begin{array}{*{20}{l}} {mathrm{0}} hfill & {{T < }T_{mathrm{L}},{mathrm{or}},{T > }T_{mathrm{c}}} hfill \ {frac{{T_{mathrm{u}} – T_{mathrm{L}}}}{{mathrm{2}}}left( {{mathrm{1 + cos}}left( {pi {mathrm{ + }}pi frac{{{T} – T_{mathrm{L}}}}{{T_{mathrm{u}} – T_{mathrm{L}}}}} right)} right)} hfill & {T_{mathrm{L}} ge {T} ge T_{mathrm{u}}} hfill \ {left( {T_{mathrm{u}} – T_{mathrm{L}}} right)left( {{mathrm{1 + cos}}left( {frac{{uppi }}{{mathrm{2}}}{mathrm{ + }}frac{{uppi }}{{mathrm{2}}}frac{{{T} – T_{mathrm{u}}}}{{T_{mathrm{c}} – T_{mathrm{u}}}}} right)} right)} hfill & {T_{mathrm{u}}{ < T} le T_{mathrm{c}}} hfill end{array}} right.,$$ (19) where FU8 is the rate of forcing for Model F8, T is the daily mean temperature (°C), TL = 4, Tu = 25, and Tc = 36. Analysis We assessed the ability of each chilling model to represent long-term trends in the chilling conditions by calculating CA using each chilling model for each station for 1951–2018. CA was calculated as the sum of CUi from 1 November in the previous year to 30 April. The trend of CA at each station was visualized as the slope of the linear regression of CA against year. We also calculated Pearson’s r between each pair of chilling models for each station to determine if the chilling models were interrelated. We calculated HR and CA of spring events for each species, station, and year to determine if the chilling models are consistent with the physiological assumption that the reduction in chilling would increase HR (Fig. 1). HR was calculated as the sum of FUi from 1 January to the date of onset of spring events using Model F1, and the performances of the other forcing models (F2–F8) were also tested. We also compared 1 January with the other two starting dates of temperature accumulation (15 January and 1 February) to test any potential difference causing by the date when temperature accumulation begins. CA was calculated as the sum of CUi from 1 November in the previous year to the date of onset of spring events. We chose 1 November as the start date for CA because the endodormancy of temperate trees began around 1 November52. We only tested the linear relationship because the data were better fitted by the linear regression than the exponential model (Fig. 3), even though CA was linearly or nonlinearly negatively correlated with HR17. Pearson’s r between CA and HR for all records was calculated for each species, with a significantly negative Pearson’s r (p  More

Acoustic data and analysis
Audio data were collected from online sound archives (Xeno-Canto—https://www.xeno-canto.org—and Macaulay libraries—https://www.macaulaylibrary.org), creating a pool of over 2000 audio tracks. We assessed the sound quality of these audio tracks by listening and through visual inspection of sound spectrograms. To capture intra-specific variation, we limited audio extraction to one drum per audio track (which also avoided pseudoreplication) and only included species for which at least 3 high-quality drums could be extracted. We retained 736 high-quality drums suitable for further analyses. These drums were distributed among 92 species (out of the 217 recognized species of woodpeckers50 and 22 genera, providing a representative sampling of the phylogenetic diversity found in this family (Fig.1b)). Background noise and other artifacts were reduced by wavelet continuous reconstruction (R ‘WaveletComp’ package72), following the methods and description outlined in previous work73. The full script is available on demand. Finally, 22 acoustic variables were extracted from these filtered sound samples using the R ‘Seewave’ package74. Given the pulsed-like nature of drumming, the chosen variables emphasized the temporal and amplitude-related (all normalized to the maximal amplitude within a given drumming signal) features of the sounds (Supplementary Table 1). These 22 variables were z-scored and then used in all subsequent analyses. Since these variables were partly correlated to varying degrees, we performed a principal component analysis (PCA) to reduce the number of descriptive variables quantifying drumming acoustic structure. This dimensionality reduction was useful for visualization and necessary for regularization (i.e. to prevent overfitting) in many of our analyses. This resulted in six principal components (PCs) with eigenvalues >1 which together explained 75% of the variance (Supplementary Table 11).
We used these variables to evaluate the similarity between species-specific drums, by performing a hierarchical cluster analysis (HCA)75,76 based on Euclidean distances and the ‘Ward.D2’ method (‘NbClust’ R package77). NbClust provides a clustering output resulting from the use of multiple indices (in the case of our analysis, 26 indices were used). The best number of clusters is chosen according to the majority rule, i.e. it is the one supported by the highest number of indices used. This entails creating a vector of acoustic features (22 raw acoustic measures or 22 PCs) for each of the 92 species in our dataset and calculating the Euclidean distance between these vectors to evaluate how close acoustically species were. Note that one can still use Euclidian distances in a non-orthonormal space to calculate the ‘distance’ between signals. The result is a distance metric that might give more weights to measures that co-vary. This could theoretically affect the clustering results. However, when we performed the same analysis using the 22 PCs, we obtained the same grouping (6 clusters) with very minor differences in species grouping and distances between clusters as shown in the relative length of branches (Supplementary Fig. 13). The output of this HCA established an optimal classification of woodpeckers’ drums into 6 main drumming types (Fig. 2a), described as follows:
– Acceleration (AC): Beak strikes decrease in amplitude as they are produced within successively shorter time intervals.
– Regular sequence (RS): Beak strikes are produced in bouts, each comprising a relatively fixed (stereotyped) number of strikes.
– Irregular sequence (IS): Beak strikes are produced in bouts, each comprising a variable number of strikes (as opposed to RS).
– Steady fast (SF): Beak strikes are produced with constant time intervals and at a similar amplitude, with a high pulse rate (on average >20 strikes/s).
– Steady slow (SS): Beak strikes are produced with constant time intervals and at a similar amplitude, with a low pulse rate (on average 2; see models Supplementary Tables 3–6), a likelihood ratio test (LRT) was conducted to test for the specific effect of predictor variables (Supplementary Table 4). Because both models (null and fitted) differ in their fixed effects, model comparison was performed on models fit by maximum likelihood (ML) with the phylogenetic correlation structure (Pagel’s λ) fixed to the estimates obtained from initial fit by REML. The statistics reported for model comparison are likelihood ratios.
PGLS models testing for a relationship between life-history variables and drumming structure included either of PC1 to PC6 as the dependent variable to investigate whether differences exist between these proxies for acoustic structure. Similarly, LDs were used to verify our results with these different loading combinations of drumming acoustic variables. No significant correlations were found between life-history traits and acoustic structure using LDs instead of PCs (Supplementary Table 6), indicating that the combination of structural variation captured by the LDs differed from that of the PCs, while not leading to fundamentally different conclusions. Similarly, no significant correlations were found between life-history traits and information content (no decrease in AICc >2; Supplementary Table 3), overall emphasizing that none of the variables investigated here (and which could have potentially affected drumming structure) seemed to have influenced species-specific information, or at least not directly.
Ancestral states reconstructions
We carried out two types of ancestral state reconstructions: discrete reconstruction of drumming status in Fig. 1b and of drumming types in Fig. 3a (using ‘ace’ from the R ‘ape’ package81), or continuous character reconstruction of drumming acoustic structure based on Brownian motion models (using ‘fastanc’ from the R ‘phytools’ package82 in Supplementary Figs. 4 and 5) and using relaxed Brownian motion model (using ‘rjmcmc.bm’ from the R ‘geiger’83 package in Fig. 4a and Supplementary Figs. 8 and 9).
While evaluating the likelihood that drumming was already present at an early stage of woodpecker’s phylogeny, we tried to represent the most complete tree of the family, based on very recent molecular data50. Note that strictly speaking, we evaluate the state at the root but at the next internal node, i.e. at the node including Picumninae and Picinae (the largest pie-chart in our Fig. 1b), as Wrynecks do no drum, and neither do honeyguides or barbets). To include species with an unknown drumming status in this discrete reconstruction, we attributed equal probability distribution between the 3 states (i.e. when the ‘drummer state’ of a species is unknown, the species is given, prior to ancestral state reconstruction, a 1/3 probability of belonging to each of the three categories ‘drummer’, ‘occasional drummer’ and ‘non-drummer’). Stochastic mapping was performed under an MCMC model, sampling the rate matrix from its posterior distribution for Q (‘Q = mcmc’ in make.simmap function from the R ‘phytools’ package), with an equiprobable default prior at the root, and 200 simulations. Under a symmetrical model for the probability to change among the three states, scaled likelihood on woodpeckers’ ancestral node indicated 56.4%, 38.3% and 5.3% probabilities of being a drummer, an occasional drummer and a non-drummer, respectively. This is in line with the fact that morphological adaptations for drilling (including reinforced rhamphotheca, frontal overhang and processus dorsalis pterygoidei) evolved in the ancestral lineage of Picumninae and Picinae64.
To prevent overfitting, the discrete reconstructions for drumming types were estimated for six different rate models: equal rate model (ER), symmetric rate model (SYM), all rates difference model (ARD) and three sequential transition models based on the normalized MIL as measures of complexity as shown in Supplementary Fig. 7 ((SF leftrightarrow SS leftrightarrow DK leftrightarrow AC leftrightarrow RS leftrightarrow IS)). These three models assumed (1) sequential and equal, (2) sequential and incremental and (3) sequential and reversed transition rates, respectively. The number of parameters for these 6 rate models were 25, 1, 15, 1, 2 and 10. The final regularized likelihoods of each ancestral states were then obtained by model averaging using Akaike weights.
Calculation of information at different evolutionary steps was carried out as an extension of the drumming types reconstruction described above. From the discrete ancestral reconstruction procedure, probability distributions of drumming types were obtained for each node of the phylogenetic tree. We then obtained probability distributions at 20 fixed time intervals (dt = 1 myr) by linear interpolation. Using these probability distributions, we sampled drumming types proportionally from extant species descending the node closest to the time interval to estimate ancestral information values. This bootstrap procedure was repeated 30 times in order to obtain reliable estimates of mean and standard error. In this manner, we obtained information-through-time plots. These plots quantify a putative diversity of drumming signals in the clade at a particular point in time. They are similar in spirit to the disparity-through-time plots that have been used to measure specific morphological diversity in a clade through time using phylogenetic trees based on molecular data in combination with morphological measures in extant species84.
Continuous ancestral character trait reconstruction of drumming acoustic structure was carried out using either the six PCs that explain variation among the 22 drumming acoustic variables, or the six LDs that explain the variation in discriminating potential among the same variables (see above, ‘Acoustic data and analysis’ and ‘Calculation of information’ sections; Supplementary Figs. 4 and 5). The results and conclusions were similar for all PC’s and since the PC1 component has strong loading of multiple acoustic variables and the highest acoustic structure variance explained (Supplementary Table 11) it serves well as an illustrative example. The measure of phylogenetic signal on continuous traits (i.e. the historical contingency between species-specific drums that renders a trait non-randomly distributed along the phylogenetic tree) was made using Pagel’s lamba (Supplementary Table 2).
Reconstructing information content from raw MIL values would not have been biologically relevant since information calculation is based on the number of species involved, a factor that changes as branches merge going backward along the phylogenetic tree. We thus reconstructed MIL based on the normalized MIL values to avoid this pitfall. We used a Bayesian model implemented in the R package ‘Geiger’83 (model ‘rbm’ in the function ‘rjmcmc.bm’) to estimate branch-specific rates of trait evolution (i.e. changes in rates through time and across lineages). In this method, a reversible jump Markov Chain Monte Carlo (MCMC) sampling algorithm is used to detect shifts in rates of continuous traits evolution under a relaxed Brownian motion model85. The results of the model fit were summarized by the branch-specific average rate, estimated from the posterior samples. To obtain relative variations in posterior average rates, drumming structure (PC1-PC6) and MIL were standardized, i.e. these traits were divided by their standard deviation prior to running the ‘rbm’ models.
Analytical simulations of selection for information
In Fig. 3c, we compared that reconstructed evolution of information to what might be expected in different scenarios to further support those conclusions. More specifically, we estimated the ancestral MI for two simulated scenarios using an analytical model that describes species-specific information based on the probability of correct detection and the number of species (see ‘Calculation of information’ section). In the ‘No Diversifying Selection’ scenario (dark brown), the probability of correct detection for the initial pair of species, p2, is first estimated from the data using the approach described in the main text. It is then assumed that that additional species are randomly just as different/similar than these original species pair, yielding a probability of correct detection through time given by (p_c(t) = p_2^{n_s(t) – 1}), where ns(t) is the number of species at a given time. In the ‘Strong Diversifying Selection’ scenario (light brown), the probability of correct detection estimated at the first time point in our reconstruction (−20 M years ago, 3 species) is kept constant, (p_c(t) = p_2). In other words, the only species that survive would be species that can discriminate themselves from all other species equally well than the currently existing species. The reconstructed (actual) scenario is found between these two extreme values, showing that the drumming types are clearly not random but were also not under high evolutionary pressure to increase species-specific information. New drumming types evolved and species within types used signals that were distinct enough to result in the maintenance of normalized MI.
In Supplementary Fig. 6, we showed that the non-normalized reconstructed MI increased more rapidly when new drumming types appeared but that the normalized MI was relatively constant, reflecting the fact that the appearance of novel drumming types could co-occur with rapid radiation and increase in species numbers.
Playback experiments
Initial preparation involved identifying and mapping the areas prone to high densities of great-spotted woodpeckers Dendrocopos major, the study species of this experimental phase, using GIS maps provided by the LPO (French Bird Protection Organization). D. major is commonly found in European forests, ranging from open coniferous to mature deciduous forests. Playback experiments were carried out on wild individuals around Saint-Etienne, France, during this species’ breeding season (February–April 2017). All experiments were performed in accordance with relevant guidelines and regulations including French national guidelines, permits and regulations regarding animal care and experimental use (approval no. D42-218-0901, ENES lab agreement, Direction Départementale de la Protection des Populations, Préfecture du Rhône).
Two sets of experiments were conducted over the course of the breeding season, although we implemented the same general design which consisted in simulating a territorial intrusion. Playback stimuli tracks consisted of eight drums spread unevenly over about 60 s, aiming at representing the variation encountered in natural sequences (ref. 44 and personal observations). The first experiment (Exp. 1) aimed at investigating D. major’s response to conspecific vs. heterospecific drums. The other experiment (Exp. 2) aimed at investigating D. major’s response to drums from conspecifics vs. drums modified through acoustic manipulation (i.e. signal re-synthesis). D. major typically drums with an ‘acceleration’ pattern, which is mainly characterized by a shortening of the inter-strike time interval, a progressive decrease in strikes’ amplitude, and a gradual change in spectral properties as strikes get faster and weaker.
In Exp. 1, we used a paired and randomized order design, presenting each focal individual with one D. major drum and one drum from one out of 4 different species: 2 of which have very different drumming patterns (Picus canus and Dryobates minor, both producing ‘steady fast’ drums), and 2 others which have similar (accelerating) drumming patterns (Dendrocopos syriacus and Dendrocopos hyperythrus). A potentially confounding factor (which is nevertheless in line with our phylogenetic analyses) lies in that the allopatric species producing a drum similar to that of D. major also happened to be closely related to our model species. We carried out 48 playback experiments (testing 24 individuals with one of 4 categories of paired signals).
In Exp. 2, we altered one of the 3 acoustic features described above or all of them together (thus having 4 categories of modified signals), using Praat sound analysis software86. The design was paired so that each focal individual was exposed to one conspecific drum and one modified drum, following a randomized presentation order. This led to 48 playback experiments (24 individuals, each tested with one of 4 categories of paired signals).
Within each of Exp. 1 and Exp. 2, tested individuals were all separated by at least 500 m, ensuring different identities since their territory sizes vary between 200 and 400 m87,88. Upon visual or aural detection of (an) active individual(s), the experimenter set up an Anchor Megavox loudspeaker at about 1–1.5 m from ground level. The speaker was connected to an Edirol R-09 recorder (stimuli tracks were created and stored as WAV files, 44.1 kHz sampling frequency). Playbacks started at about 50 m from where the experimenter last saw or heard the focal individual. Following the work from Schuppe and colleagues89, playback intensity was calibrated and kept at about 80 dB measured 1 m away from the speaker. Behavioural data collection started when the first drum of the stimuli track was broadcasted and lasted 10 min from that moment on. To document focal individuals’ responses, notes were taken manually and continuously, while audio was recorded with a Sennheiser ME67 microphone mounted on a tripod and connected to a digital recorder (Zoom H4N, 44.1 kHz, 16 bit). If a response was elicited from multiple individuals in the area, only the one from a particular individual (ideally the one seen or heard before setting up the experiment) was monitored and used in further analyses. Six behavioural variables were reported, namely the number of screams, the number of drums, the approach (which was divided into three categories: ‘within 25 m’, ‘25–50 m’ and ‘further than 50 m’) as well as the latencies to first scream and drum and the latency to closest approach. When no occurrence was observed for the first three behaviours, latencies were set by default to the maximum value, i.e. the duration of the full experiment (10 min = 600 s). To characterize D. major’s behaviour, a PCA was then performed on scaled/centred data, where we retained the first principal component (‘Playback-PC1’) as an indicator of the behavioural response’s strength. A higher Playback-PC1 score indicates a stronger territorial response, i.e. more screams, a closer approach to the speaker and shorter latencies to these 2 behaviours. A second significant component resulted from this PCA, which represented the drumming’s response (inversely related: a higher Playback-PC2 score indicates fewer drums and a longer latency to drum; see Supplementary Table 13). None of the pairwise comparisons were statistically significant for PC2, besides a stronger drumming response to drums resynthesized without temporal variation than to D. major drums (Supplementary Fig. 15). This can be explained by the fact that birds were tested during their breeding season. At this time, drumming behaviour is likely to occur more consistently and commonly across experiments, independently from the stimulus played back, while screams and approach do not occur unless threat of intrusion is clear. Therefore, we used Playback-PC1 to represent birds’ behavioural response in our analysis (as it is in addition explaining much more variance in the behavioural data than Playback-PC2). Note that, as two playback sets were involved in this study, while we considered them independently in our statistical analysis, for standardization of the behavioural scale, we used the same polynomial equation. More specifically, the linear equation obtained from the loading scores of Exp. 1 was applied to the behavioural data of Exp. 2 for computation of Playback-PC2 scores.
Finally, distances were approximated during continuous note-taking and confirmed post-experimentally using a National Geographic 4*21 rangefinder (measurement accuracy: ±1 m up to 200 m). Sex was not documented as sometimes birds were not seen (but just heard drumming or calling back at our playback), which we nevertheless believe to be negligible since both sexes drum and are territorial in this monogamous species44,90.
Statistical analyses tested for differential responses of focal birds to drums of their own species versus either another species or a modified resynthesized condition. A paired comparison design was used by means of LMMs and contrasts using R software (‘lme4’ and ‘lsmeans’ packages)91,92. LMMs included study day and time, order of presentation and focal bird identity as random factors, and tested for a fixed effect of the interaction between treatment and group of paired condition. Contrasts were then computed between treatments (i.e. conspecific versus non-conspecific drums) for each group (i.e. each paired testing condition, such as D. major versus D. minor for which n = 6 birds were exposed to paired playback presentations—see Fig. 5a, b). Before contrasts and using the ‘lsmeans’ function, a Tukey adjustment for multiple testing was used; two-sided statistics are reported.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More