Changes in phenology mediate vertebrate population responses to temperature globally

Domain: biology
Published: 2026-01-12
Editorial outcome: major_revision

Abstract
Phenotypic responses to climate affect individual fitness, but the extent to which this translates into effects on population dynamics remains poorly understood. We assemble 213 time series on phenotypes and population sizes of wild vertebrates globally and match them with local climate data. Our meta-analysis shows that morphological traits are mostly climate insensitive. However, phenology is earlier in warmer-than-average years, which contributes positively to population growth in most species. At lower latitudes, temperature has weaker effects on phenology but stronger direct negative effects on population growth, likely because these populations are less capable of tracking climate via plasticity. Variation in the phenology-mediated effect of temperature on population growth cannot be explained by latitude, generation time, migratory mode, or diet. This suggests that simple relationships between species characteristics and population responses to warming may not occur in nature. Instead, we may need to embrace ecological complexity by considering local-scale predictors that capture intra-specific variation.

Introduction
Phenotypic traits are one aspect of biodiversity affected by ongoing climate change1–5. For example, the timing of recurring biological events such as reproduction and migration (phenology) is typically advancing with warming temperatures across taxa1,3. Similarly, morphology (e.g., body mass, size or shape) responds to climate change, although the directions of responses are less uniform than those of phenological responses2,4–8. Climate change has also led to population declines and increased risk of extinction9,10. Importantly, population responses are not independent of trait responses to climate variation, because individuals may adjust to changing climates by altering phenotypic traits (phenotypic plasticity), which, in turn, would enable population persistence (Fig. 1a)11. Studies that consider contributions of traits when assessing population responses to climate are rare and focus mainly on single populations or species6,12,13. Such studies highlight that phenotypic responses are key to understanding the mechanisms behind climate effects on populations14–17. However, little is known on how commonly traits mediate population responses to climate variation and allow for population persistence across species. Yet, such knowledge is crucial for the field of population ecology, as well as that of functional ecology, which relies heavily on the assumption that traits have direct population consequences and thus can serve as proxies to reflect community composition18.

Earlier timing of biological events in warmer years is associated with fitness benefits on average19 but it may also have costs by exposing individuals to extreme events, such as cold spells20,21. Along with understanding how responses of traits to climate relate to individual fitness, it is essential to assess their consequences for population growth, as changing population numbers are what ultimately determines the impact of climate on biodiversity. Although population growth is of primary importance to conservation, population sizes remain little studied in research focusing on phenotypic traits, such as phenology22. Indeed, whilst many studies have examined the consequences of trait change on single demographic rates (e.g., reproduction, survival) at the individual16,23 or population level4,6,21,24, few have examined the consequences of climate-driven changes in traits on population growth rate8,11,12,14,25. Studying the consequences for population growth rate is important because changes in fitness components (e.g. survival, reproduction) may not translate into changes in population growth rate12,26 if demographic compensation is occurring27,28. Demographic compensation is a common phenomenon29 whereby population-level declines in a given demographic rate are offset by increases in another demographic rate23,26,30. Understanding the influence of climatic variation on populations therefore requires that we quantify the effects of climate-driven changes in phenotypic traits on population growth rates (Fig. 1). To assess how general such effects are across species, comparative analyses such as a meta-analysis are especially valuable31.

In addition to quantifying the typical strength of trait-mediated effects of climate on population growth, another unanswered question is: in what type of species and geographic regions they will be strongest? Addressing these questions for vertebrates requires collating many decades-long time series of both phenotypic traits and population abundances that cover a range of climatic conditions for each study. Until recently, a sufficient number of such studies to fuel a rigorous comparative analysis was simply lacking. In situations of limited data, climate change ecologists often generalise the inferences from a group of well-studied species to predict the responses to climate for data-deficient species (i.e. most other species). These generalisations usually rely on species characteristics such as life-history traits, migratory mode, diet or latitude of occurrence.

Such species characteristics are likely to also explain trait-mediated effects of climate on population growth, but this has not been well investigated. We expect the effects of temperature on population growth rates, as mediated by changes in phenology, to be weaker in long-lived vertebrates compared to short-lived ones. This is because, in long-lived species, population growth rates are generally less sensitive to changes in reproduction and more sensitive to changes in survival32,33. Phenological shifts, such as changes in the timing of breeding or migration, are more likely to impact reproductive success than survival, and therefore should have a relatively weak effect on the overall population growth rate of long-lived species. Whether generation time is indeed a good indicator of how phenology mediates responses of population growth rate to climate remains to be tested. Similarly, long-distance migrants advance their phenology to a lesser degree than residents because they have less reliable information about shifting environmental conditions at the breeding grounds34–36. However, we do not know whether migratory mode predicts how such phenological trait responses propagate further to affect population growth rate. Finally, phenological responses to climate may also vary with latitude, as the drivers of seasonality change from precipitation in the tropics to temperature at higher latitudes1. Consequently, tropical species are expected to respond more strongly to precipitation, whereas those at higher latitudes should respond more strongly to temperature changes. We expect the overall phenological responses to temperature to be stronger at higher latitudes1 because of both faster warming37–39 and higher sensitivity of species to warming at higher latitudes39. Whether latitude also explains variation in phenology-mediated effects of temperature on population growth remains unknown.

Responses of morphological traits to climate tend to be highly heterogeneous and no universal response has emerged so far7,40,41. A potential reason is that morphological traits are more heterogeneous than phenological traits, as they comprise both structural skeletal body size metrics (such as wing length) and non-structural traits (such as body mass). While body mass is a highly plastic trait that changes rapidly due to fat gains and losses, structural traits may be slower to change. Nonetheless, recent studies highlight that mean population values of body size metrics can vary considerably from year to year due to either reversible plasticity within a lifetime or developmental plasticity causing variation among generations4,5,42. Overall, the heterogeneity in morphological responses across species remains poorly understood, largely due to a scarcity of studies investigating how species characteristics mediate these40. Scarce research limits our ability to formulate expectations as clear as those for phenological traits and highlights the need for exploratory comparative studies to investigate how morphological responses to climate and their consequences for population growth rate depend on species characteristics.

Here, we collate long-term data from 213 studies on 73 wild vertebrates (birds, mammals, reptiles and fish) from around the globe (Fig. 2). Each study consists of time series of annual mean population phenotypic traits values and population sizes recorded for a unique combination of the species, trait and location. We focus on phenology and morphology as two categories of phenotypic traits often measured and frequently reported to play a role in climate responses1–8,40,43–47. We investigate the effects of two climate variables, temperature and precipitation, because these represent the main components of climate change48 and are widely reported to affect phenology1,3 and morphology2,4–8,26,40,43–47. In all analyses we consider year-detrended temperature and precipitation (i.e. residuals) to avoid potential spurious effects caused by other environmental temporal trends49,50. We use path analysis to (1) quantify the trait-mediated effects of climate on population growth rate (Fig. 1a), and meta-analyses to (2) assess how general those trait-mediated effects are across the studies and to (3) identify which type of species and regions exhibit the strongest trait-mediated effects of population growth rate (using migratory mode, diet and generation time as explanatory species characteristics, and latitude to explain geographic variation among locations). Our study represents the first comprehensive assessment of the importance of phenotype-mediated population responses to climate variation across vertebrates, enabling us to test for general patterns that are hard to detect in more geographically- or taxonomically-focused studies.

Results
Our systematic literature review resulted in 116 relevant studies focusing on morphology and 97 studies focusing on phenological traits. Median duration of morphological studies was 14.5 and of phenological studies 25 years (Supplementary Fig. S1). Roughly half of the studies focusing on morphology measured body mass (56%), with the other half recording different body size metrics (e.g. snout-vent length: 25%, body length: 5%, tarsus length: 4%). Most of the studies on phenology recorded onset of breeding (74%), followed by first egg laying date (8%), arrival date (6%), parturition date (4%), traits related to rutting (4%) and a few other phenological traits represented by very few studies. Most of the phenological studies (75%) focused on spring events such as return of migrants to breeding grounds, onset of breeding, and parturition dates. The dataset was dominated by studies on birds (65%), followed by reptiles (23%) and mammals (10%), with only a few studies on fish (2%, Fig. 2). The majority of data stem from the northern hemisphere, particularly Europe and North America (Fig. 2), as is the case with many recent global meta-analyses of ecological time-series1,19,51.

Conceptual framework
For each study, we assessed the effects of trait changes across years, associated with each climate variable, on interannual variation in population growth rate (henceforth ‘G’) using path analysis. We followed the framework of McLean et al.11 that reflects the general expectation that the effects of climate variation on traits have consequences for species demography, such that the effect of climate C on G is expected to be mediated (at least in part) by the considered trait Z (Fig. 1a). Additionally, the effect of climate variation on G via all other non-considered traits is included as ‘CG’ (Fig. 1a, black dotted line) and hereafter referred to as the ‘direct’ (i.e. not mediated by the focal trait) effect of climate on G (though note that other traits may be involved in this pathway). We used the standardised path coefficients extracted from the path analysis to calculate the trait-mediated effect of climate on G, henceforth ‘CZG’, i.e. the change in G per unit change in climate C due solely to a change in the trait Z, where both changes are measured in standard deviation units (Fig. 1a, solid black line from C to G via Z). This was achieved by multiplying the path coefficient reflecting the effect of climate on trait (CZ) with the path coefficient reflecting the effect of trait on G52 (ZG; Fig. 1a, Methods).

We were specifically interested in the sign of the trait-mediated effect of climate on G: for each single study, we expect CZG to be zero or positive if the trait response to climate variation is adaptive. For example, in insectivorous birds, earlier egg laying in warmer years corresponds to a negative CZ (Fig. 1b). In this case, earlier breeding is expected to lead to an increase in G (negative ZG, Fig. 1c) if the optimal breeding time also advances, as trophic mismatches are then reduced53. The product of CZ and ZG (CZG) is thus positive. CZG will be close to zero if the trait responds to climate such that no effects on population growth rate are detectable. In other words, trait changes owing to adaptive phenotypic plasticity would allow accurate tracking of changing climate while avoiding possible detrimental effects of climate on population growth, such that ZG is close to zero (and hence so is CZG). On the contrary, we expect CZG to be negative for traits that show maladaptive responses, because in that case climate-driven trait changes would increase the gap between mean and optimum phenotype, rather than decrease it. For example, if breeding is delayed in warmer years, when in fact earlier breeding would improve fitness (e.g., facilitate better matching with an earlier food peak), then CZ (effect of temperature on phenology) is positive and ZG (effect of phenology on population growth) is negative, such that their product CZG is negative. In summary, if phenological or morphological responses to climate variation are adaptive, on average, across all studies in our dataset, we would expect the overall CZG to be non-negative, while negative CZG would suggest a maladaptive response.

The path coefficients obtained with path analyses from each study were used to fit meta-analytical models to assess the generality of responses across the studies. Meta-analytical models were fitted separately per combination of climate variable (temperature or precipitation) and trait category (phenology or morphology). Since there is evidence of phylogenetic structuring for phenological events51 and evolutionary history may potentially shape trait-mediated effects of climate on populations, we accounted for phylogenetic relatedness in our meta-analytical models. Note that non-negative CZG on average across studies can be obtained not only when a majority of studies show positive or close-to-zero CZG, but also when effects of different sign are found across studies, e.g. some studies showing positive CZG (adaptive responses) and others showing slightly negative CZG (maladaptive responses).

Climate effects are mediated by phenology
Our sliding window climate signal analysis provided strong evidence that phenological traits were sensitive to temperature, while most correlations between phenological traits and precipitation were likely spurious (see Methods and Supplementary Fig. S2). We thus henceforth focus on phenology-mediated temperature effects on G and use temperature to refer to year-detrended temperature. Warmer years typically led to advanced phenology across studies (β = −0.37, Wald test: χ2 = 12.5, df = 1, p ≤ 0.001), corroborating previous research1,3. However, we found considerable among-study heterogeneity in phenological responses to temperature (CZ, Higgins I2 = 0.96, p < 0.001), with both advances and delays with warmer temperatures found for individual studies (Fig. 3a). The fact that none of the associations between temperature and phenology (CZ) were close to 0 (Fig. 3a) was a result of using sliding window analyses to detect the best climatic windows over which temperature affects phenology (see “Methods”). This analysis identified the climate signals (models) best supported by the data; consequently, it was unlikely to select weak relationships between temperature and phenology.

The across-study effect of phenology on G (ZG), after accounting for both the direct effects of temperature (CG) and population size (PG), did not significantly differ from zero (β = 0.05, χ2 = 1.64, df = 1, p = 0.2), meaning that phenology was not associated with population growth rates across studies. The among-study heterogeneity in ZG was moderate (I2 = 0.48, p < 0.001, Fig. 3b). Phenological responses to temperature (CZ) were positively correlated with phenological effects on population growth rate (ZG, Pearson r = 0.33, df = 91, p = 0.0013), meaning that in studies that recorded positive effects of temperature on phenology, the phenology was also positively associated with population growth rate (after accounting for the effects of CG and PG). Similarly, in studies that recorded negative effects of temperature on phenology, the phenology was negatively associated with population growth rate.

Phenological responses to temperature were often consistent with adaptive responses, as indicated by a significantly higher proportion of studies with non-negative CZG than expected by chance (binomial test, proportion of studies with either positive or zero CZG = 0.59, p = 0.048, Fig. 3d). After accounting for the phenology-mediated effects of temperature on G, we found no ‘direct’ effect of temperature on G across studies (Fig. 3c, β = −0.07, χ2 = 2.34, df = 1, p = 0.13) and moderate among-study heterogeneity (I2 = 0.40, p = 0.01). This suggests that the net combined effects of temperature on population growth via traits other than phenology are weak across studies, with a tendency towards negative direct temperature effects on population growth.

Climate effects are not mediated by morphology
We found little support for morphological traits being climate-sensitive in vertebrates as correlations between both climate variables and morphological traits were mainly spurious, according to the sliding window analyses (see Methods, Supplementary Fig. S2 and Supplementary Note 2: Other combinations of climate variables and traits). This finding echoes previous research8,43 on passerines, where little consistency was found across species in the timing and duration of windows over which precipitation was associated with body condition. In turn, this limited evidence for climate sensitivity in morphological traits in our dataset (CZ ≈ 0 in most studies) suggests that climate effects on G are unlikely to be mediated by morphology.

Explaining among-study heterogeneity in climate effects
We investigated drivers of among-study heterogeneity in path coefficients by considering phylogenetic relatedness, differences among specific trait types and by adding species characteristics and latitude as predictors to the meta-analytical models. Phylogenetic signal λ (Pagel’s λ is a scaling parameter related to phylogenetic signal54) was not distinguishable from 0 in the models focusing on phenological responses to temperature, nor in those focussing on phenological effects on population growth rate (Supplementary Fig. S3). Phylogenetic signal λ was effectively 0 in all models fitted for 100 randomly drawn posterior vertebrate phylogenies, suggesting that evolutionary history does not explain CZ and ZG. However, phylogenetic structuring was evident for the phenology-mediated effect of temperature on population growth rate (CZG: λ = 0.65, min = 0.61, max = 0.68) and the direct effect of temperature on G (CG: λ = 0.62, min = 0.56, max = 0.66).

The across-study patterns in phenological responses to temperature (CZ), effects of phenology on G while accounting for climate and population size (ZG), phenology-mediated effect of temperature on G (CZG) and direct effect of temperature on G (CG) seemed to be predominantly driven by birds, which constituted most of our dataset (Supplementary Figs. S4 and S5). Since different types of phenological traits (e.g., onset of breeding, parturition date, oestrus date, and arrival date) may differ in their sensitivity to temperature, we tested whether the specific type of phenological trait could explain the heterogeneity in CZ. As expected, the among-study variation in phenological sensitivity to temperature was explained by the type of phenological trait considered (Wald test: χ2 = 19.6, df = 8, p = 0.012, Supplementary Table S1). For example, the response to temperature was strongest for spring phenological events, such as parturition date and onset of breeding, whereas the association of autumn phenological events (such as rutting date) with temperature was less clear.

We expected (see Introduction) that the considerable heterogeneity in phenological responses to temperature (CZ; Fig. 3a) across studies would be partly explained by species characteristics such as generation time, diet (herbivore, omnivore and carnivore), and migratory mode (resident vs. migratory), and by latitude. The model including these effects was significantly better than the null model (Wald test: χ2 = 17.2, df = 6, p = 0.009; Supplementary Fig. S6). Absolute latitude tended to affect phenological sensitivity to temperature (β = −0.015, χ2 = 5.6, p = 0.018; with the p-value threshold of 0.01, adjusted for multiple comparisons, Fig. 4a and Supplementary Table S2). Phenological responses to temperature were weak at lower and became stronger toward higher latitudes. Neither the model that explained among-study variation in ZG (χ2 = 9.13, df = 6, p = 0.104) nor the model that explained heterogeneity in CZG (χ2 = 6.14, df = 6, p = 0.407) was significantly better than a null model (Supplementary Tables S3 and S4). However, we found that the model explaining CG fitted the data better than the intercept-only model (χ2 = 21.3, df = 6, p = 0.002). The ‘direct’ effect of temperature on G that remained after accounting for the phenology-mediated effect of temperature on G was positively associated with absolute latitude, such that it changed from being negative at lower latitudes to around zero toward higher latitudes (β = 0.008; χ2 = 14.3, p = 0.0002, with the p-value threshold of 0.01, adjusted for multiple comparisons, Fig. 4d and Supplementary Table S5).

Sensitivity analyses
Our analyses revealed a strong negative effect of population size on G (Supplementary Fig. S7)12,55. In line with the large magnitude of estimated effect sizes for population size, we also found that population size explained the largest proportion of variation in G compared to other predictors (see Supplementary Note 3: Variance partitioning in SEM). Furthermore, the probability that detected windows were spurious was not negligible for phenological responses to precipitation, and for morphological responses to both temperature and precipitation (Supplementary Fig. S2). However, the results of the meta-analyses were not qualitatively affected by inclusion/exclusion of (1) the less supported climate signals in the models and (2) population size as a covariate (Supplementary Figs. S8–S11). The probability of the temperature signals being spurious (PΔAICc) was higher for morphological studies and declined with the study duration (Supplementary Fig. S12). The magnitude of the absolute CZ effects declined slightly with increase in PΔAICc, so that the effects of climate on trait (irrespective of the trait category) tended towards 0 as PΔAICc approached 1 (Supplementary Fig. S13).

Methods
Biological data
Our dataset was compiled by combining an existing global multi-species dataset19 (Phenotypic Responses to Climate and data on Selection; PRCS) with a new systematic literature review. Studies were included if they reported both a time series of annual mean population trait (phenological or morphological) values (+SE), and a time series of population size estimates (based on a capture-mark-recapture study or a count of individuals or breeding pairs) of at least 9 years. Only quantitative traits were considered, and only studies investigating the impact of a climatic variable on traits were included.

We conducted a systematic literature review using Web of Knowledge (now called Web of Science; search conducted in April 2019) including the keywords for climate, phenotypic traits, demographic rates and population size. We used the following keywords for climate (climat* OR temperature OR precipitation OR weather), phenotypic trait (phenotyp* OR morphol* OR “body mass” OR “body size” OR phenol* OR “emerg* date” OR “arriv* date” OR “breed* date” OR “laying date”), demographic rate (demograph* OR “demographic rate” OR surviv* OR reproduc* OR fecundity OR “breeding success”), and population size (“population growth” OR “population dynamics” OR “population size” OR abundance). For taxa, we first used broad taxon names (“bird*” OR “mammal*” OR “arachnid*” OR “insect*” OR “reptil*” OR “amphibia*” OR “spider*” OR “fish”), then proceeded by using more specific taxon names to find as many relevant studies as possible (e.g. for mammals we searched for (rodent* OR mammal* OR primate* OR carnivore* OR bat*)) (see SI Methods). The review was conducted within a larger research project (https://www.idiv.de/research/sdiv/working-groups/straitchange/), where we look at how the effects of climate on population growth rates are mediated by phenotypic traits and demographic rates (e.g., survival and reproduction). Therefore, we searched for studies reporting the time series of annual population-level trait values, demographic rates and population sizes, but in this study we used only the data on traits and population sizes.

The search returned 1124 abstracts, which were divided among eight researchers (VR, CVJ, GC, EM, TER, JC, SKS, and NMcL) who screened the abstracts to identify those that satisfied the above-mentioned criteria. This resulted in 197 scientific papers that were read in detail (by VR and CVJ), of which 60 papers were retained (Supplementary Fig. S14 shows PRISMA flow diagram). Wherever possible, we extracted the data from the papers directly, either from tables or by digitising the plots with WebPlotDigitiser74. Otherwise, we contacted the authors and asked them to share their data. The 82 studies (stemming from 13 papers) from the PRCS dataset originally did not contain data on population size. We therefore compiled the time series on population size by either extracting it from the papers ourselves or by contacting the authors of the original papers. We define ‘study’ as a unique combination of a species, location and trait (phenology or morphology). Our final dataset consisted of 213 studies extracted from 73 papers. Our studies cover four vertebrate classes: birds (53 species), mammals (10 species), reptiles (7 species) and fish (3 species; see Supplementary Data 1 and Fig. 2 for study sample sizes per trait category). Some papers contributed multiple studies to our dataset when multiple traits and/or species and/or locations were recorded. Morphological traits were represented by 116 and phenological by 97 studies.

Identifying suitable climatic windows
The spatial resolution of the daily climate data ranged from 0.05 deg in Australia, to 0.1 deg in Europe and 0.25 in North America and 0.5 deg elsewhere (Supplementary Table S7). For each study, we extracted the daily climate data from the respective gridded datasets as the mean of the climate value in the grid cell that overlapped with the study location and the four neighbouring grid cells (von Neumann neighbours). Such spatial averaging was done to account for the possibility that home ranges of study species may be larger than a single grid cell. The climate conditions reflect conditions at breeding sites, and do not consider potential effects during migration or at wintering sites. In cases where study locations did not overlap with the available gridded data (some of the island populations: 83 studies, 39% of all studies), we used the closest grid located on the mainland. These climate data were potentially less precise, which we accounted for by adding a categorical variable ‘Climate data quality’ with values ‘exact’ and ‘approximate’ to the meta-analysis (see section ‘Across-study inferences’). Importantly, most islands were <50 km offshore or focused on seabirds for which we used sea surface instead of air temperature.

We identified a period of the year (climatic window) during which the climatic variable best explained the studied trait by applying a systematic sliding window analysis using the R package climwin75. The analyses were conducted separately for precipitation and temperature, by fitting a linear model with Gaussian error distribution for each study. We used the studied phenotypic trait as the response variable. As temperature predictor we used the mean temperature over the climatic window, and as precipitation predictor we used the total sum of precipitation over that window. We included year as a quantitative variable to avoid potential spurious effects caused by other environmental temporal trends49,50. We weighed model residuals by the inverse of the squared SE of the annual trait values, to account for varying sample size across years. For our sliding window analyses we assumed that all individuals in the population had the same climatic window (i.e., we treated windows as ‘absolute’). We tested for all possible windows (see Supplementary Note 5: Climatic window durations) over a period of 2 years before a so-called reference day, the value of which was study-specific. The reference day was the latest date when the phenological event was observed or morphological trait was measured in the year over the study period. By using the latest day in the year when the phenological trait was observed over the study period we assured that no relevant windows was missed (Supplementary Fig. S15). The use of a 2-year period prior to the biological event ensured that the analyses captured carry-over effects of climatic conditions experienced by individuals previously. Our dataset includes diverse species, ranging from birds to mammals, reptiles and fish. For many of these species biological knowledge on what constitutes a relevant climatic window is unavailable. Therefore, the use of sliding window analyses allowed to assess the climatic window in a standardised way, systematically, across all studies and species. Our identified climatic windows for passerines correspond to the windows that are known to be important in driving egg-laying of these birds based on long-term studies, suggesting that our approach is valid. Our identified climatic windows may reflect proxies of the true underlying biological mechanisms. Our consideration of sliding windows up to 2 years before an observation may include potential climatic windows that are not very meaningful for short-lived species (those whose life span is <2 years). However, only five of the species (7%) in our dataset have generation time ≤2 years, justifying the use of the 2-year period to capture potential carry-over effects for most of the much longer-lived species in our dataset. To reduce computation time, the daily climate data were aggregated to weekly resolution (option interval = ‘week’ in climwin), as preliminary analyses showed similar outcomes for weekly and daily resolution (Supplementary Fig. S16). The median window duration across the studies was 3 and 2 weeks for precipitation and temperature, respectively (Supplementary Fig. S17).

To ensure that the climatic window selected as the best (as determined by the model’s ΔAICc) was not a spurious result due to overfitting, we randomised the original data to remove any potential relationship between the climate and trait variables and refitted the model as explained above (for details see ref. 76). The randomisation procedure was repeated 200 times. The results from the randomised data were compared to those from the observed data by calculating the metric PΔAICc, which we henceforth call “the probability of the climate signal being spurious” and which reflects the probability of obtaining the same results as those that would be produced by chance only. To account for the less accurate identification of the climate signal in some studies, we included the probability of the climate signal being spurious (PΔAICc) as a covariate in the meta-analyses (see section ‘Across-study inferences’).

Trait-mediated effects of climate on G
To assess the consequences of the effects that climate variables on traits have for the population growth rate (G, Fig. 1), we used the path analysis52,77 framework proposed by McLean et al.11, and further extended it to account for potential effects of density dependence (Fig. 1a), a common phenomenon in natural populations12,55. Path coefficients were estimated with Structural Equation Models (SEM77), an extension of path analysis that allows for non-independence in the data (e.g., autocorrelation). We fitted a single SEM to each study in our dataset, and separately for each climatic variable C. We calculated the annual population growth rate as

G t = ln(P t+1 / P t) (1)

where P t and P t+1 are the annual population sizes at year t and t+1, respectively. Each SEM consisted of two regression models: one model (“the trait model”) with annual trait values (Z) used as response and climate variable (C) as a quantitative predictor (providing CZ, see Fig. 1); and the second model (“the population growth model”) with population growth rate G t as response and climate variable (C t), trait values (Z t) and population size P t used as quantitative predictors (providing ZG, CG and PG, Fig. 1). Both linear models were fitted by generalised least squares, with Gaussian error distributions and identity links and included temporal autocorrelation by using a first-order autoregressive residual model structure. Additionally, for the “trait models” we weighted the residual variation by the inverse of the squared SE of the traits to account for differences in sample sizes among the years. Population size was used as a predictor in “population growth models” to account for the possible changes of population size over time that could have obscured the effects of climate C on G, which are the focus of our study. We fitted linear relationships between predictor and response variables, as linearity was supported by (1) visual inspection of the scatterplots per study and (2) mixed-effects models testing for non-linearity in the relations between (a) climate variable and trait values, and (b) trait values and population growth (see Supplementary Note 6: Testing for non-linearity in relations). SEMs were fitted with the R package piecewiseSEM78. For all analyses in this study we used R Statistical Software (v4.4.179).

Yearly climate values from the best climate window could correlate with other unmeasured environmental variables that changed concurrently over time49,80. Therefore, to ensure that we focus on the changes in traits that are caused by the considered climate variable only, we first detrended our climate time series49. For this, we fitted a linear model using the climate variable extracted from the best-identified climate window as a response variable and year as a quantitative predictor variable. The residuals of this model were then used as the explanatory climate variable in our SEMs (Supplementary Fig. S18 for methodological workflow of this study).

Prior to fitting SEMs, we z-transformed (subtracted the mean and divided by the SD of the time series) all variables to ensure that the extracted path coefficients were standardised. We also scaled the standard errors of traits by dividing them by the temporal SD in traits over the study period. The advantage of the standardised path coefficients is that they are unitless and can be compared among different variables (i.e., different traits: phenology vs. morphology; and different climate variables: temperature vs. precipitation).

SEMs for most of the studies satisfied the goodness of fit test according to Fisher’s C statistics (Supplementary Fig. S19): the goodness of fit test was satisfied (i.e. p-value > 0.05) for 72% of phenological studies and 84% of morphological studies. Although goodness of fit was not satisfactory for a small fraction of the models, we still retained all the studies for subsequent meta-analyses because we wanted to assess the trait-mediated effects across the studies in a comparable way. So, the addition of some missing paths (that could potentially increase the goodness of fit for that fraction of the models), would have made those models not comparable with the others.

SEMs failed to converge for several studies resulting in sample sizes for meta-analyses being somewhat lower than the number of originally retrieved studies: 93 studies for phenological responses to temperature, 95 studies for phenological responses to precipitation, 109 studies for morphological responses to temperature and 115 studies for morphological responses to precipitation (Supplementary Fig. S7). The models constituting the SEMs explained a non-negligible amount of data variation (Supplementary Figs. S20 and 21), with the largest R2 obtained for the “population growth models”, i.e. models explaining G (median across all studies 0.47), followed by the “trait models” (median of 0.37).

We used the rules of path coefficients52 to compute the trait-mediated effects of climate on G, and the total effects of climate on G. Specifically, the trait-mediated effect of climate on G (CZG, shown with solid black arrows from C to G via Z in Fig. 1a) is calculated as:

CZG = CZ * ZG (2)

where CZ denotes the path coefficient corresponding to the effect of climate C on trait Z and ZG denotes the partial path coefficient corresponding to the effect of trait Z on G after accounting for the effect of climate C and population size P. The total effect of climate on G (Total CG) is then calculated as the sum of the trait-mediated effect (solid black line in Fig. 1a) and the climate effect via other unconsidered traits (dotted black line in Fig. 1a):

Total CG = CZG + CG (3)

where CG corresponds to the effect of climate on G that is not mediated by the focal trait but acts via other traits not considered in the model. We obtained estimates of the trait-mediated and total effects of climate on G and their standard errors by non-parametric bootstrapping. In this bootstrap the trait-mediated and total effects of climate on G were calculated using Eqs. (2) and (3), respectively, but instead of using single estimates of each path coefficient as obtained with the SEM, we calculated these coefficients 10,000 times by drawing the estimates each time randomly from the normal distribution with the mean being the beta estimate for each single path coefficient and the variance being the standard error (SE) of this estimate. This bootstrap allows us to consider the uncertainty in the estimates of each path coefficient on the path of interest, so that the path coefficients that are more precisely estimated (with low SE), would have very similar estimates each time the bootstrap is run, whereas the path coefficients with large uncertainty (with high SE), will have their value vary widely between the bootstrap runs. We then summarised the results of 10,000 runs by taking median as our estimate of the trait-mediated (total) effect of climate on G, and by using the 0.025% and 0.975% quantiles as its 95% confidence intervals.

Across-study inferences
To assess whether there are common patterns across the studies in how climate affects traits (CZ), traits affect G (ZG), and in trait-mediated effects of climate on G (CZG), we next analysed the standardised path coefficients from SEMs (Fig. 1a) with mixed-effects meta-analytical models with Gaussian error distributions. Separate meta-analyses were fitted for each climate variable and trait category, using each path coefficient that reflects the causal relation on the path diagram (e.g., CZ in Fig. 1a) as a response variable. To assess how general responses are across the species, we fitted intercept-only models that accounted for variation between studies, locations and species by including these terms as random intercepts. Additionally, these models also accounted for the phylogenetic relatedness by allowing the values of the random species effect to be correlated according to the phylogenetic correlation matrix that was derived from the phylogenetic tree81.

We constructed the phylogeny across all vertebrate classes present in our dataset by using the rtrees R package82 to randomly draw one phylogeny from the megatrees available for each phylogenetic class. We used the following megatrees: for birds: Jetz et al.83, for mammals: Upham et al.84, for fish: Rabosky et al.85 and for reptiles: Tonini et al.86. Since for reptiles rtrees only has the phylogeny for squamates, we added the turtle phylogeny from Thomson et al.87. Once we obtained a randomly drawn mega-tree per class, we then grafted them on the order-based backbone for which the node times were taken from the Timetree of life (https://timetree.org/).

To avoid overfitting, we refitted meta-analytical models without accounting for phylogeny. We then compared both models and retained the one that fitted the data best based on the lower marginal AIC. To account for uncertainty in available megatrees (e.g., birds, mammals) that were used to build the synthetic phylogeny, we randomly selected 100 trees from available posterior phylogenies82 and thus fitted both types of models 100 times. For the models that accounted for phylogenetic relatedness we calculated Pagel’s λ following Cinar et al.81, as λ = (σ2p / (σ2p + σ2n)); where σ2p is between-species variance due to the phylogeny and σ2n is between-species variance due to other effects, e.g. the environments the species inhabit.

We accounted for uncertainty in the path coefficients estimated with SEMs by weighting the residual variation by the inverse of the squared SEs of the path coefficients. Additionally, meta-analytical models with path coefficients that included climate as a dependent variable (i.e., those with the response variable CZ, CZG, CG and Total CG) included a quantitative variable reflecting the probability of the climate signal being likely spurious (PΔAICc) and a qualitative variable for climate data quality (two levels: ‘exact’ and ‘approximate’). These meta-analytical models assess whether the intercept differs from 0, that is, whether each relationship in our path diagram differs from 0 across the studies. To test our expectation that phenological responses to temperature allow avoiding population declines, we assessed whether the proportion of studies with non-negative CZG was higher than by chance only. For this, we used a one-sided binomial test and tested whether the proportion of studies with non-negative CZG was greater than 0.5 for a given combination of climate variable and trait category.

All meta-analyses were fitted with the R package metafor88, using the “uobyqa” optimiser. We tested significance with the Wald test. For each fitted model, we visually assessed the normality of residuals. We assessed the amount of heterogeneity among studies in our meta-analyses with commonly used metrics31: Higgins I2 and the total amount of heterogeneity (Q), significance of which was estimated. Higgins I2 reflects the proportion of total heterogeneity due to between-study variation. It ranges from 0 (heterogeneity is due to within-study variation only) to 1 (heterogeneity is due to between-study variation only) and is comparable among different meta-analyses. We re-run meta-analyses also separately for birds (n=84) and mammals (n=9). We could not run them for reptiles because of an insufficient sample size (n=4).

Explaining heterogeneity in CZ
We tested whether the high heterogeneity in the phenological sensitivity to temperature (CZ) might be due to species responses differing with latitude or species-specific characteristics (such as migratory mode, diet, and generation time). Based on previous studies we formulated several a priori expectations. The expectations about migratory mode, generation time and latitude are detailed in the Introduction. Additionally, diet may also moderate phenological responses to temperature, with phenological shifts in herbivores previously shown to track the climate better than those of organisms at higher trophic levels1,3.

To test our hypotheses about possible drivers of the heterogeneity in CZ, we fitted extended versions of mixed-effects meta-analytical models that were used to infer across-study effect sizes (see “Across-study inferences”). Specifically, we used the same random effects as in the mixed-effects models used to infer across-study effects (see “Across-study inferences”) and propagated uncertainty in the same way. We additionally included as predictors the variables hypothesised to affect the phenological responses to temperature, i.e. absolute latitude (quantitative, in degrees), generation time (quantitative, in years), diet (qualitative, three levels: herbivore, carnivore, omnivore), and migratory mode (qualitative, two levels: resident and migrant; for data sources see Supplementary Note 7: Sources of species-specific characteristics). As the response variable we used study-specific CZ. The model also included as a covariate the quantitative variable reflecting the probability of the climate signal being spurious (PΔAICc). We grand-mean centred (i.e. subtracted the overall mean from each value) the quantitative predictors prior to fitting the model, so that the intercept estimate reflects the mean value of the CZ path and slopes reflect the change in traits per change in one unit of the predictor.

We expected that species characteristics and latitude explain not only sensitivities of phenology to temperature (CZ) but also other paths in the path diagram, and, particularly, trait-mediated effects of climate on population growth rate (CZG). Therefore, analogous models were also fitted with ZG, CZG and CG used as a response variable. The statistical inference and model diagnostics were performed in the same way as for the meta-analytical models with intercept only. The overview of all meta-analytical models fitted in this study is given in Supplementary Table S8.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors do a great job addressing a timely question, by pulling together an impressively broad dataset and using a sophisticated multi-step analysis to arrive at a meta-analysis that teases apart direct and indirect effects of climate on vertebrate population growth rates. I am impressed that the authors were able to find so many species with concurrent population size and trait time series; I was only aware of a handful of these previously. The work should be of interest to a broad audience.\nI think the simple SEM (conceptually simple, not so simple to implement) is the correct way to approach these data, and I am impressed by the care the authors took to propagate estimate uncertainties into the meta-analysis and the range of covariates that were used in the meta-analysis. I have no concerns about the analyses, and the script files are well annotated. I’m not at all surprised that the phylogeny didn’t improve model fit, but it was worth testing anyway, and I applaud the extra work that went into its inclusion. I also appreciate the inclusion of the null models and intercept-only models as reference points.\nBetween lines 439 and 440, or somewhere else appropriate, please add the summary information from Fig. S16, that the median window duration was 2 or 3 weeks, for temp and precip respectively. I think that result belongs in the main text.\nAt L407, please add the range of grid sizes to the main text (0.05 to 0.5 degrees), so that readers don’t have to check Table S6.\nThe manuscript is written well. Here are a couple of minor suggestions for awkward places I spotted, but I had no major concerns about the writing.\nL519: I’d use ‘among studies’ instead of ‘between studies’ (as you did on L600).\nL551: should be ‘probability … was’ or ‘probabilities … were’\nThe order of panels in Fig. 3 is odd. Wouldn’t it be just as clear to have panel a) in the upper left, and swap it with panel b)? I don’t think the arrows showing relationship to axes are necessary. In panel a) its suspicious that none of the phenologies are insensitive to climate, i.e. the blank gap around a slope of 0; the same gap is present in panel c of course. Is that gap a sign of publication bias? Other meta-analyses of phenological change don’t show that gap. I’m also curious whether there is any phylogenetic signal to the variation in the slopes of panel a. Could those individual lines be color coded instead of all grey, to represent the groups of vertebrates (birds, mammals, fish)? A recent paper suggested there may be some phylogenetic signal there (Loughnan, D., S. Joly, G. Legault, H. M. Kharouba, M. Betancourt and E. M. Wolkovich. 2024. Phenology varies with phylogeny but not by trophic level with climate change. Nature Ecology & Evolution 8(10): 1889-1896.).\nIn Fig. S8 some of the models have extremely high R2 values. Is this potentially because in Fig. S9 many of the species have very strong density dependence (PG effects are much larger than any others)? While density dependence is biologically important, it would be nice to know how much of the high R2s is due to the PG arrow rather than CZG or CG.\nIn Fig. S9 it would be nice to see which effect sizes for the different arrows belong to which species. I’m wondering if lines that connected the points within a column would reveal patterns of covariances among those, for example species with higher CZ would also have higher ZG, or vice versa? Of course, its also possible that that many lines would be a horrible visual mess and not lend any insights, but it may be worth checking. If there are any strong covariances among those (which would be easy to check), that would be worth mentioning in the main text too.\nIn S16, can the length of the window be related to generation time, or something else about the pace of life? I’m surprised by the length of the longer windows.", "reviewer_2": "This paper uses path analyses and meta-analytical approaches to explore phenotypic trait mediated climate impacts on population growth in vertebrates. They used 213 time series of phenological and morphological traits to test the path of influence of temperature and precipitation effects on population growth. They found highly variable results across the species and locations. In general, phenological events were more climate influenced than morphological traits, which showed no clear trait-mediated climate effects. The most noteworthy results are that phenology was predominantly temperature driven with then positive impacts on population growth, suggesting adaptative phenological responses to temperature change in many species. This effect of phenology was however shown to be weaker at lower latitudes. Additionally, morphology was insensitive to climate changes.\nHere, I begin with some overarching summaries covering topics requested in the reviewer guidelines, before giving a detailed review.\nThis study provides some original insights into the topic of climate change impacts on animal populations growth, thus giving an important contribution into the field. It is a novel study researching trait-mediated effects of temperature at a global scale and for multiple populations. The findings from this work will be of significant value in the field and related fields in demonstrating why researchers need to consider different pathways of influence of climate effects. It is also a pertinent reminder to consider population level impacts. The methodology is sound, and I would say exceeds expected standards in the field.\nThe paper is well written, easy to follow, and has a good flow. The methodology in particular explains some very complex and detailed analyses in a clear and concise way, and I would like to complement the authors on this. The data analyses used in this paper are really clearly written and explained. These are complex analyses but well thought through and supported with a well-documented R package, which is to be commended! I would especially like to highlight the treatment and propagation of uncertainties as a really nice part of the method. However, the discussion of the previous literature to support the context and claims (primarily in the introduction and discussion) could do with improvement. Specifically, the taxonomic scope of the references is quite limited, particularly to bird species, and given the broad scope of the paper, this should be reflected in the cited literature. Examples of particular areas where this could happen are detailed below but I would recommend reviewing the whole introduction and discussion with this in mind as well. Following from this, the work conducted does support the broad conclusions, however, there is some over-generalisation of some of the conclusions, e.g. not take into full account the limitations of the (granted very good) dataset e.g. geographically and taxonomically biased. Please also see other comments about using averages to draw conclusions from highly variable inputs and results.\nThere are a few areas where I believe this manuscript would benefit from addressing, which I detail below. Any consideration of the points mentioned would require minor revision and should not prohibit publication.\nMain areas for improvement:\n1. Expanding literature support, particularly increasing taxonomic scope of cited literature. There are also some cases where claims made are not currently sufficiently supported.\nThe authors could, for example, pitch their study importance more effectively in the introduction, by highlighting how the importance of looking at population growth effects has been previously underscored in the literature, yet there is a need for more studies to test this (e.g., Samplonius et al., 2021). Samplonius, J.M., Atkinson, A., Hassall, C., Keogan, K., Thackeray, S.J., Assmann, J.J., Burgess, M.D., Johansson, J., Macphie, K.H., Pearce-Higgins, J.W. and Simmonds, E.G., 2021. Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts. Nature Ecology & Evolution, 5(2), pp.155-164.\nL295 – This is a good example of lack of support, reference 34 is one example of one species and one phenological trait that is being used to support a statement that phenological traits in general are plastic. Either change the claim or add greater support from a wider range of taxa and phenological events. (There are many other examples like this and I will not have found them all).\nL312 – again a single reference of support to a very general claim.\nThe support for the use of morphological traits and particularly those chosen, several of which seem that they would not change within life beyond developmental changes e.g. snout-vent length, is not sufficient.\ne.g. L184 – not many references chosen and not linked to morphological traits included. L186 – could not see any supporting references for morphological traits beyond body mass and body condition (which is actually calibrated using body length, further suggesting this trait should not be considered to be labile or plastic). Support for snout-vent length, body length, and bill length (another which I’m not sure should change interannually in response to weather conditions) was not given.\n2. Consider whether the averaging of results in the meta-analysis is a meaningful metric to assess the results.\nAveraging the path coefficients to give (what I assume is a mean) result, I feel, is not the most meaningful metric. This is particularly the case given the different phenological or morphological traits being considered and due to the variability of results where a mean of 0 could result from all results being close to 0 or from as many above 0 as below. By saying that the average of 0 indicated adaptive responses does not give more insight than looking at the number of non-negative results but it does perhaps have some biological issues. The traits being considered are quite different, it is perhaps not to be expected that things like lay date would have the same response and effect as emergence date or parturition, in the same way body mass would not be expected to respond in the same way as body length or bill size. Therefore, comparisons among these responses or analysis of modal or majority trends are ok but direct averaging ignores these differences.\nL233-236 – this is an example where I do not find the averaging so helpful as it you may not expect all traits to behave the same, and a lack of an average response in a particular direction does not mean that for individual species and traits that they are not adaptive.\nL295-297 – again, not sure about the averaging particularly given the highly variable results. I would find it more meaningful to look at majorities of directions or between group differences etc. The average does not really say anything biologically.\n3. Reduce over generalisations. E.g. saying phenology when mean spring phenology.\nThis study only looks at spring phenological events (as far as I can tell) but this is not mentioned anywhere. This needs to be mentioned from the very beginning as it strongly impacts the interpretation of the results. It is not a drawback, just a clarification that is important.\nL141 – this is certainly one example of the impact of phenology; however, it is very specific and it does not hold for all biological events e.g. those in Autumn or if caterpillars hatch prior to their food source. Please be more specific about the scope of events you cover – just spring phenology in a few species. The caterpillar example is still spring but would not always fit this pattern.\nL289 – this is a bit over generalised due to the limitations mentioned below, in particular geographic and taxonomic biases. In addition, it is only a subset of spring phenology that has been looked at. I suggest moderating the language here to focus on spring phenology, clarify that vertebrates are mostly birds, and across most of the globe (not all). Given that you have results for individual studies – can you say for which groups these temperature mediated effects are most clear?\nL596-603 – I find this a more specific result and interesting, I think this should be elevated to the main text not hidden in the methods.\n4. Include more comment or a specific section on the study limitations. e.g. geographic spread, taxon spread, type of morphological traits chosen – possible not plastic or labile (some of the references they have use body length to standardise body mass to look at body condition). Also include the different types of phenology and morphology and whether they can truly be compared.\nThe geographic limitations of the dataset need to be acknowledged early, especially the lack of studies in the tropics and from low latitudes and the bias to Europe and North America. This is particularly important given the latitude effect that was found, some comment on whether this effect would hold into the tropics or with more data would be good. E.g. L283 it mentions close to the equator, but it seems there is limited data in this study from the equator.\nAnother potential limitation of the study is the substantial variation in climate data resolution across the countries considered. Specifically, the resolutions for North America, South America, and Africa are relatively coarse. Such large-scale temperature data may not accurately capture the environmental conditions experienced by animal populations, particularly the microclimates to which they respond. Please include a small mention of this.\nL318 – this comes in very late and is not fully explained. What does this mean for all other results?\nL399 – 402 – I feel that these limitations need to be mentioned further up somewhere.\nMinor comments:\nGeneral\nL130 – I would replace ‘global warming’ with ‘climate change’, I appreciate it is said a few times in these sentences but it is a more accurate characterisation of the actual changes that will be experienced.\nL311 – the population is not plastic or not, the trait is.\nL312-315 – I do not agree with the reasoning here. Surely physiology mediates or plays a role in all traits, particularly phenological traits and therefore, it will be playing a role at all latitudes. I do not see physiology and phenology as separate, physiological processes will be the basis of the phenological events we observe (e.g., gonadal development for laying date). Perhaps you could be more specific about which physiological processes you think are influencing things at lower latitudes. Or perhaps the authors are referring to physiological effects as direct temperature effects and phenological effects as indirect temperature effects (e.g., mediated through food supply). If so, then perhaps this distinction could be clearer.\nL344 – again, not sure about this distinction, could you be specific about the physiological traits. Also again, phenological traits will be built up from physiological traits and indeed in some species they could be the same e.g. trees and invertebrates. Perhaps add a qualifier about vertebrates here.\nL252 – what about empirical models? I do not think this is a clear distinction as models can be applied to empirical datasets, did you mean theoretical or simulation studies?\nL380 – this link does not work – takes me to a 404 page\nData and methodology\nMore detail on how the biological realism of the identified climate windows would be a nice addition. While searches like sliding window can identify climate drivers that make excellent explanatory and predictive models, they are not always biological realistic. In this paper some choices around the window searching could lead to biological realism being reduced, for example, 2-year search could actually exceed the lifespan of some short-lived species in the dataset, or at least be before they are born when the reach maturity at age 1. In addition, the use of a reference day that is the latest date a phenological event was observed will in many years be occurring after the event takes place. Some discussion of the biological realism and implications for interpretation would be a good addition.\nWhy was the detrending of the climate variables done after the sliding window analysis not before? I am curious as the given rationale would seem to hold for detrending prior to the sliding window.\nL257 – p-values are not typically indicative of trend directions alone (could be in a one tailed test of some kind). Is this what is meant here, or did you mean beta value? If you did mean p-value, maybe explain how it indicates the trend direction.\nL406 – please mention that the resolutions vary here.\nL438 – please provide these preliminary analyses somewhere like SI.\nL537 – is the difference to 0 something you would expect?\nL540 – could you give a bit more detail on the binomial test please e.g. I assume it has an assumed probability of some kind?\nL579 - the same way as what? The reference here is not clear.\nConclusions\nSee above on generalisations.\nFigures\nFigure 1 – Conceptual diagrams need to be really clear. The overlapping of arrow heads with boxes makes this less so, could you add just a tiny bit of breathing room around each arrow head please. Esp in panel a under the top box.\nFigure 3 – The order of the panels here is not very clear. Perhaps the letters could be changed to match the order – that makes it easier for the reader to assimilate the information. This figure also needs tidying, the arrows are different sizes and the panels are not full aligned.\nSupporting information\nSupplementary figure S9: and any similar figures. I find the colour and shape differentiation very hard to see. Please choose some that are more distinct. At the size they are, even on a big monitor, I cannot easily see the difference between the circle and the square, nor light-dark blue/red.\nL313 – I found this conceptual framework hard to follow and it did not seem to fit with the results in the main text. I hope I did not miss something, if I did, please correct me. But especially given that the climate data were detrended in the main text, I do not see how these two scenarios were tested. Scenario is also not mentioned in the main text. I suggest removing this section.\nL340 – GR is not defined.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "In this manuscript, the authors Radchuk et al. conduct a meta-analysis using time series of vertebrate phenology, morphology, and population growth. Using this diverse data, the authors test the relationships between temporal and morphological traits, climate, and population dynamics. Morphological traits showed no relationship to temperature or precipitation and population trends, but a relationship was found between temperature and phenology---but not phenology and population responses. These results suggest that other traits are shaping population responses to climate change than those represented in the current literature.\nUnderstanding the relationships between traits and population trends is important, as it may provide insights needed to forecast and manage species under climate change. Synthesizing trends globally and across diverse species assemblages also allow us to identify general trends and large-scale spatial patterns. The dataset presented in this study is impressive, particularly given the efforts to collect raw data from the original authors. However, I was surprised that the phylogenetic signal was weak. There is strong evidence of phylogenetic conservatism in phenology, with the variation due to latent traits potentially reflecting traits favourable under ancestral climates. While I appreciate you did include a phylogeny in your analysis, there was no justification or discussion of the importance of evolutionary history in the introduction. I would have like more detail on exactly how phylogeny was included in the model, whether specific packages were used, and what the estimated phylogenetic structure was. As an additional supporting analysis, I am curious whether relationships would be stronger within the individual taxonomic groups, especially given the diversity in the types of phenological events included in the study.\nThe framing of the project and writing could also be improved in my opinion. I felt the state of the literature could be better represented and some of the broad statements tempered. I disagree that no work has been conducted on this topic before. But perhaps more elaboration and nuance would address this, as I do believe that there are few robust and global studies on this topic and appreciate the novelty of these large-scale studies. The discussion of the results would also benefit from more nuance, as the lack of statistical significance was at times not clear from how trends were discussed. Finally, the discussion would benefit from more discussion of future directions and applications of these findings to conservation or species management.\nSpecific comments:\nThe title should more accurately reflect the study results. The current title makes it seem like the relationships between temperature and precipitation were strong, but in reading the text, it was only temperature.\nLine 110: More context for your study is needed here, it sounds like it was exploratory, but I am sure a lot of thought when into why you collected all these time series\nLine 114-119: I think these sentences could be edited to better guide your reader and make it clearer that you are testing several relationships and why.\nLine 127: This introductory paragraph introduces some big topics that are not fully developed. Could you break this into multiple paragraphs and further develop some of these topics?\nLine 132-135: This should be its own paragraph to convince the reader this is important and true.\nLine 141: Earlier phenologies can have huge costs as well, your argument would be stronger if you include some of these examples.\nLine 145: What is meant by “net demographic consequences of interannual variation in phenotypes”? Is there not an existing literature of traits being included in integral projection models to do exactly this?\nLine 150-152: Could you elaborate and give an example of this? How common is this actually?\nLine 165: I don’t agree that it has “not been investigated” and would consider even some of your own coauthor’s work (e.g. Jenouvrier et al. 2018. Journal of Animal Ecology) to have done so. I would temper sentences like this and elaborate on them to more accurately reflect the state of the field or provide more nuance to your position.\nLine 178: Can you elaborate on why?\nResults:\nLine 196: The use of the term “study” is misleading, would it not be more appropriate to refer to 116 time-series for example?\nLine 204-236: This text would be better integrated into the introduction. The text also does not reference any of the other panels in Fig 1 and readers would benefit from more detail. Or for the results to be discussed in the context of this figure.\nLine 244: Do you mean “found for individual studies” or interannually for a given study?\nMethods:\nLine 394: What are the four classes? More detail, especially sample sizes, would be useful.\nLine 399-402: Could you add the percentage of each taxonomic group and types of phenological events?\nDiscussion:\nLine 290-291: This is a key point that is missing from the introduction.\nLine 303: What are the units?\nLine 520: How was phylogenetic relatedness estimated? Was a specific package used? What was the estimated value of lambda?\nLine 564-575: This provides important context and justification that might be better suited in the introduction.\nLine256, line 440: Minor typos\nFigures & Tables:\nFig 1: Does the slope and the direction of the trends in b-d reflect your predictions?\nDoes the order of the subplots, which I initially read as c, a, d, b, have any significance. I think figure a would be clearer if it was separated from b-d into different panels.\nFigure 2:\nThe inset should be wider to better separate the two categories. As is, the three words read more like the x-axis label.\nThis figure is very visually appealing, but the font color makes some of the diet letters hard to read. I also did not feel the black bars were necessary as the numbers themselves are present and easily interpretable.\nWhat does the diet “LC” stand for?\nFigure 3:\nTo better illustrate the relationship between a and c, I would put panel a under panel c and have a upward pointing arrow.\nFigure 4:\nLine 828-831: As written the caption makes it sound like there is a trend in panel a, but it is also not significant. This could be more clearly written to clearly reflect the statistics.\nHaving the full terms on the axes, not the letter acronym, would make these plots easier to understand with a quick glance."}, "author_response": "The authors do a great job addressing a timely question, by pulling together an\nimpressively broad dataset and using a sophisticated multi-step analysis to arrive at a\nmeta-analysis that teases apart direct and indirect effects of climate on vertebrate\npopulation growth rates. I am impressed that the authors were able to find so many\nspecies with concurrent population size and trait time series; I was only aware of a\nhandful of these previously. The work should be of interest to a broad audience.\nI think the simple SEM (conceptually simple, not so simple to implement) is the correct\nway to approach these data, and I am impressed by the care the authors took to\npropagate estimate uncertainties into the meta-analysis and the range of covariates that\nwere used in the meta-analysis. I have no concerns about the analyses, and the script\nfiles are well annotated. I’m not at all surprised that the phylogeny didn’t improve model\nfit, but it was worth testing anyway, and I applaud the extra work that went into its\ninclusion. I also appreciate the inclusion of the null models and intercept-only models as\nreference points.\nOur answer: We thank the reviewer for the positive feedback!\nComment 1. Between lines 439 and 440, or somewhere else appropriate, please add the\nsummary information from Fig. S16, that the median window duration was 2 or 3 weeks,\nfor temp and precip respectively. I think that result belongs in the main text.\nOur answer: As suggested by the reviewer, we have now added “The median window\nduration across the studies was 2 weeks when using temperature as predictor and 3\nweeks when using precipitation as predictor (Fig. S9).” in the Methods of the main text\n(LL 577-578).\nComment 2. At L407, please add the range of grid sizes to the main text (0.05 to 0.5\ndegrees), so that readers don’t have to check Table S6.\nOur answer: Agreed, we have now included the sentence “The grid sizes ranged from\n0.05 deg in Australia, to 0.1 deg in Europe and 0.25 in North America and 0.5 deg\nelsewhere.” on LL 532-533.\nComment 3. The manuscript is written well. Here are a couple of minor suggestions for\nawkward places I spotted, but I had no major concerns about the writing.\nL519: I’d use ‘among studies’ instead of ‘between studies’ (as you did on L600).\nOur answer: Done.\nComment 4. L551: should be ‘probability … was’ or ‘probabilities … were’\nComment 5. The order of panels in Fig. 3 is odd. Wouldn’t it be just as clear to have\npanel a) in the upper left, and swap it with panel b)? I don’t think the arrows showing\nrelationship to axes are necessary. In panel a) its suspicious that none of the phenologies\nare insensitive to climate, i.e. the blank gap around a slope of 0; the same gap is present\nin panel c of course. Is that gap a sign of publication bias? Other meta-analyses of\nphenological change don’t show that gap.\nOur answer: Thank you. As several reviewers pointed out that it is rather confusing\nthan helpful to have the arrows between the panels pointing out the relationships, we\nhave now revised this plot to organize panels in the conventional order, i.e. from a)\nto c).\nThe gap that you describe arises due to the use of sliding window analyses to identify\nthe best climatic window for each trait. Since this analysis identifies the strongest\nsignals, it dismisses weak relationships between climate and trait. We have now\nincluded a mention of this in LL 291-295 in the manuscript.\nComment 6. I’m also curious whether there is any phylogenetic signal to the variation in\nthe slopes of panel a. Could those individual lines be color coded instead of all grey,\nto represent the groups of vertebrates (birds, mammals, fish)? A recent paper suggested\nthere may be some phylogenetic signal there (Loughnan, D., S. Joly, G. Legault, H. M.\nKharouba, M. Betancourt and E. M. Wolkovich. 2024. Phenology varies with phylogeny\nbut not by trophic level with climate change. Nature Ecology & Evolution 8(10): 1889-\n1896.).\nOur answer: Thank you. We did colour-code this figure but as majority of the studies\nare on birds find it not very informative. Therefore, we opted for including this figure\nin the Supplementary Materials (new Supplementary Fig. S4). We have also re-run the\nmeta-analyses per taxon now, whenever the sample size allowed doing so: for birds (n\n= 82) and for mammals (n = 7). We briefly mention (LL 336-340) now that “The across-\nstudy patterns in phenological responses to temperature (CZ), effects of phenology\nd\non G while accounting for climate and population size (ZG), phenology-mediated\neffect of temperature on G (CZG) and direct effect of temperature on G (CG) seemed\nd d\nto be predominantly driven by birds, which constituted most of our dataset.” and\ninclude the results of these taxon-specific analyses in Supplementary Fig. S5.\nWe also now include a paragraph in the discussion (LL 427-444) in which we compare\nour results on the degree of phylogenetic structuring estimated when using the full\ndataset and the subsets focusing on birds and mammals and discuss our findings in\nthe context of the previous literature (e.g. Loughnan et al. 2024).\nComment 7. In Fig. S8 some of the models have extremely high R2 values. Is this\npotentially because in Fig. S9 many of the species have very strong density dependence\n(PG effects are much larger than any others)? While density dependence is biologically\nimportant, it would be nice to know how much of the high R2s is due to the PG arrow\nrather than CZG or CG.\ndynamics. As suggested by the reviewer, we have now partitioned the variance in G\nexplained by C (temperature), Z (phenology) and P (population size) in “the population\ngrowth model” that is included in our SEMs (see Methods). As expected by the\nreviewer, the largest proportion of variation in G is typically due to population size\n(median = 0.26, 5th percentile = 0.01, 95th percentile =0.65 across studies). Phenology\nexplains a lower proportion of the variation in G, although in some models it can be\nquite high (median across the models = 0.05, 5th percentile = 0.001, 95th percentile =\n0.43 across studies). Finally, the direct effect of temperature explained a median 0.03\nof the variation in G across the models (5th percentile = 0.001, 95th percentile =\n0.42). \nWe included these results briefly in LL 697-699 and, in more detail, in the\nSupplementary Results and as Fig S21. In the Supplementary Results we also clarify\nthat “The contribution of temperature to population growth rate is only partial here, as\nit only captures the direct effect of temperature on G (i.e. we applied variance\nd\npartitioning to G). The effect of Phenology on G includes an indirect effect of\ntemperature (CZG pathway), and we note that a large proportion of variation in Z was\nd\nexplained by C (see R2 of the “trait model” within our SEM, Supplementary Fig. S12).”\nComment 8. In Fig. S9 it would be nice to see which effect sizes for the different arrows\nbelong to which species. I’m wondering if lines that connected the points within a\ncolumn would reveal patterns of covariances among those, for example species with\nhigher CZ would also have higher ZG, or vice versa? Of course, its also possible that that\nmany lines would be a horrible visual mess and not lend any insights, but it may be\nworth checking. If there are any strong covariances among those (which would be easy\nto check), that would be worth mentioning in the main text too.\nOur answer: We had tried several different ways to show the relations between CZ\nand ZG on former Fig. S9 but that unfortunately led to very complex figures that were\ndifficult to read and looked rather ugly. We also note that we already show the\nrelationship between CZ and ZG for each study in Fig. 3d and panels c in\nSupplementary Figures S26-S28 (focusing on other combinations of phenotypic traits\nand climate variables). We think these existing figures are the best and most direct\nway of visualizing this information. As suggested by the reviewer, we have now\ncalculated Pearson correlation (i.e. standardized covariance) between CZ and ZG. For\nthe dataset looking at phenological responses to temperature this correlation is\npositive and significant, and we mentioned this in the main text (LL 300-306), as\nsuggested by the reviewer. We report the correlations for other combinations of\nphenotypic traits and climate variables in the legends to respective figures in\nSupplementary Information (Figs. S26-S28).\nComment 9. In S16, can the length of the window be related to generation time, or\nsomething else about the pace of life? I’m surprised by the length of the longer\nwindows.\nOur answer: We have checked whether there is a relation between window duration\nand generation time, but found none: for temperature windows the correlation\nbetween their duration and generation time was Pearson r (df = 155) = -0.124, p =\n0.12; and for precipitation windows the correlation between their duration and\ngeneration time was Pearson r (df = 159) = -0.02, p = 0.82. See also the Fig. 1 below.\nReviewer #1 (Remarks on code availability):\nThe data are accessible, and the scripts look to be well annotated. There are MANY script\nfiles, and I did not open all of them. The ones I checked looked good.\nOur answer: Thank you for your positive feedback.\nReviewer #2 (Remarks to the Author):\nThis paper uses path analyses and meta-analytical approaches to explore phenotypic\ntrait mediated climate impacts on population growth in vertebrates. They used 213 time\nseries of phenological and morphological traits to test the path of influence of\ntemperature and precipitation effects on population growth. They found highly variable\nresults across the species and locations. In general, phenological events were more\nclimate influenced than morphological traits, which showed no clear trait-mediated\nclimate effects. The most noteworthy results are that phenology was predominantly\ntemperature driven with then positive impacts on population growth, suggesting\nadaptative phenological responses to temperature change in many species. This effect of\nphenology was however shown to be weaker at lower latitudes. Additionally,\n4\nmorphology was insensitive to climate changes.\nHere, I begin with some overarching summaries covering topics requested in the\nreviewer guidelines, before giving a detailed review.\nThis study provides some original insights into the topic of climate change impacts on\nanimal populations growth, thus giving an important contribution into the field. It is a\nnovel study researching trait-mediated effects of temperature at a global scale and for\nmultiple populations. The findings from this work will be of significant value in the field\nand related fields in demonstrating why researchers need to consider different pathways\nof influence of climate effects. It is also a pertinent reminder to consider population level\nimpacts. The methodology is sound, and I would say exceeds expected standards in\nthe field.\nOur answer: Thank you for your compliments and positive feedback!\nThe paper is well written, easy to follow, and has a good flow. The methodology in\nparticular explains some very complex and detailed analyses in a clear and concise way,\nand I would like to complement the authors on this. The data analyses used in this paper\nare really clearly written and explained. These are complex analyses but well thought\nthrough and supported with a well-documented R package, which is to be commended!\nI would especially like to highlight the treatment and propagation of uncertainties as a\nreally nice part of the method.\nOur answer: Thank you for the positive feedback. We are happy to hear the methods\nare easy to follow and hope they will be easy to replicate in the future.\nComment 1. However, the discussion of the previous literature to support the context\nand claims (primarily in the introduction and discussion) could do with improvement.\nSpecifically, the taxonomic scope of the references is quite limited, particularly to bird\nspecies, and given the broad scope of the paper, this should be reflected in the cited\nliterature. Examples of particular areas where this could happen are detailed below but I\nwould recommend reviewing the whole introduction and discussion with this in mind as\nwell.\nOur answer: Thank you for highlighting this skew in the cited literature. We have now\nrevised the Introduction and Discussion carefully and paid attention to integrate more\ndiverse references, especially regarding their taxonomic scope. As a result of expanding\nthe cited literature, our reference list has now 27 new added resources. We answer your\ndetailed comments below.\nComment 2. Following from this, the work conducted does support the broad\nconclusions, however, there is some over-generalisation of some of the conclusions, e.g.\nnot take into full account the limitations of the (granted very good) dataset e.g.\ngeographically and taxonomically biased. Please also see other comments about using\naverages to draw conclusions from highly variable inputs and results.\nwhich I detail below. Any consideration of the points mentioned would require minor\nrevision and should not prohibit publication.\nOur answer: Thank you very much for your constructive comments. We have revised\nthe manuscript to address the issues raised by the reviewer, e.g. to extend the\nreferenced literature beyond birds and to underline the limitations of our dataset. For\nmore details see below the response to each of your specific points.\nMain areas for improvement:\n1. (Comment 3). Expanding literature support, particularly increasing taxonomic scope\nof cited literature. There are also some cases where claims made are not currently\nsufficiently supported.\nThe authors could, for example, pitch their study importance more effectively in the\nintroduction, by highlighting how the importance of looking at population growth\neffects has been previously underscored in the literature, yet there is a need for more\nstudies to test this (e.g., Samplonius et al., 2021). Samplonius, J.M., Atkinson, A., Hassall,\nC., Keogan, K., Thackeray, S.J., Assmann, J.J., Burgess, M.D., Johansson, J., Macphie, K.H.,\nPearce-Higgins, J.W. and Simmonds, E.G., 2021. Strengthening the evidence base for\ntemperature-mediated phenological asynchrony and its impacts. Nature Ecology &\nEvolution, 5(2), pp.155-164.\nOur answer: Agreed. We have extended the cited literature, trying to cover as broad\nrange of species as possible. We also follow the suggestion of the reviewer and\nhighlight the previous research (Samplonius et al. 2021) showing that population sizes\nmust be recorded in parallel with phenotypic traits to allow for a better, mechanistic,\nunderstanding of climate-driven changes (LL 154-156).\nComment 4. L295 – This is a good example of lack of support, reference 34 is one\nexample of one species and one phenological trait that is being used to support a\nstatement that phenological traits in general are plastic. Either change the claim or add\ngreater support from a wider range of taxa and phenological events. (There are many\nother examples like this and I will not have found them all).\nOur answer: Agreed. We have now added additional references that range broadly in\nthe species they studied and regarding the studied phenological event (LL 377).\nComment 5. L312 – again a single reference of support to a very general claim.\nOur answer: We have added additional references to support this claim (LL 396).\nComment 6. The support for the use of morphological traits and particularly those\nchosen, several of which seem that they would not change within life beyond\ndevelopmental changes e.g. snout-vent length, is not sufficient.\nOur answer: We have now extended the list of references, also citing studies that\nshowed responses of other, more labile, morphological traits, such as body mass.\nFurther, we tried to cover as diverse taxa as possible: e.g. phytoplankton,\nzooplankton,\nplants, birds and mammals.\ntraits included.\nOur answer: We extended the list of references, see our responses to similar\ncomments (comments 1, 3-6, 8) above and below, see LL 208-210 in the revised\nmanuscript.\nComment 8. L186 – could not see any supporting references for morphological traits\nbeyond body mass and body condition (which is actually calibrated using body length,\nfurther suggesting this trait should not be considered to be labile or plastic). Support for\nsnout-vent length, body length, and bill length (another which I’m not sure should\nchange interannually in response to weather conditions) was not given.\nOur answer: We have now extended the list of the studies we refer to, including the\npapers that looked at body mass as well as at more structural morphological\ncharacteristics (such as snout-vent length, body size, bill and wing length) and trying\nto cover as diverse taxa as possible (LL 208, 210). We appreciate the reviewer's point\nthat often body size metrics are considered to be less plastic than for example body\nmass within a lifetime of a species with determinate growth. However, recent studies,\nnow cited in our revision, have shown that mean population values in body size\nmetrics can vary considerably from year to year. Such short-term interannual variability\nin body size can be due to labile plasticity within a lifetime (e.g wingspan depends on\nannual wear and investment in feather growth after moult). It can also be due to\ndevelopmental plasticity causing variation among generations, which in turn will affect\nmean population values in body size metrics from year to year due to the new cohorts\nentering annually. We thus think there are sufficient reasons to investigate how body\nsize metrics may vary from year to year and how this is affected by climate.\n2. (Comment 9). Consider whether the averaging of results in the meta-analysis is a\nmeaningful metric to assess the results.\nAveraging the path coefficients to give (what I assume is a mean) result, I feel, is not the\nmost meaningful metric. This is particularly the case given the different phenological or\nmorphological traits being considered and due to the variability of results where a mean\nof 0 could result from all results being close to 0 or from as many above 0 as below. By\nsaying that the average of 0 indicated adaptive responses does not give more insight\nthan looking at the number of non-negative results but it does perhaps have some\nbiological issues. The traits being considered are quite different, it is perhaps not to be\nexpected that things like lay date would have the same response and effect as\nemergence date or parturition, in the same way body mass would not be expected to\nrespond in the same way as body length or bill size. Therefore, comparisons among\nthese responses or analysis of modal or majority trends are ok but direct averaging\nignores these differences.\nOur answer: Meta-analysis leverages the effects reported in multiple studies (often\nstudies that lack power) to assess a general, “global effect size”, across the studies. This\nwas exactly our aim in this study: to reveal the general response of traits to climate in\nvertebrates and to assess how these climate effects on traits propagate to population\ngrowth rate. Even though it is tempting to think of a meta-analysis as averaging across\n7\nthe studies, we note that the studies with larger sample sizes will contribute more\nstrongly to the global effect size than those with smaller sample sizes, all other things\nbeing equal.\nWith regards to reporting the majority or modality trends: such counting of\npositive or negative effects is against the whole philosophy of meta-analysis of\nborrowing the information from each study to be able to say how general the\nresponse is (see e.g. (Koricheva et al., 2013)). Therefore, we decided against reporting\nsuch counts.\nWe do agree with the reviewer that the variability across studies is at least as\ninteresting as the mean across studies. Therefore, in the original version of the\nmanuscript, we already analysed how the variation among studies in effect sizes (a)\ndepended on species characteristics (in the section on ‘Explaining among-study\nheterogeneity in climate effects’), and (b) varied between different types of traits.\nThis comment (and one of your comments below) made us realise that some of\nthese analyses and their findings were perhaps too much hidden in the Methods\nsection of the manuscript (formerly on LL 596-603). Therefore, we have now included\nthese findings in the main text (LL 340-347), to highlight the comparison in responses\nof different trait types, as suggested by the reviewer.\nComment 10. L233-236 – this is an example where I do not find the averaging so helpful\nas it you may not expect all traits to behave the same, and a lack of an average response\nin a particular direction does not mean that for individual species and traits that they are\nnot adaptive.\nOur answer: Thank you. This sentence states the general expectation of our study. We\nthink formulating such expectations is very useful and helpful and the hypothesis-\ndriven research was based on them. Moreover, this specific expectation was tested\nwith the binomial test (see LL 679-684) which is not based on averaging.\nComment 11. L295-297 – again, not sure about the averaging particularly given the\nhighly variable results. I would find it more meaningful to look at majorities of directions\nor between group differences etc. The average does not really say anything biologically.\nOur answer: Please see the response to your comment 9 above. We hope it clarifies\nthe difference between meta-analysis and averaging and why simple “vote counting” is\nagainst the underpinnings of meta-analysis.\nRegarding the specific lines to which the reviewer refers, this text emphasizes\nthat our findings suggest that phenotypic plasticity seems to be the mechanism\nunderlying the observed effect of temperature on population growth rate as mediated\nby phenology. Indeed, these lines refer to our prediction that CZG close to zero is\nexpected to be observed if “trait changes owing to adaptive phenotypic plasticity\nwould allow accurate tracking of changing climate while avoiding possible detrimental\neffects of climate on population growth, such that ZG is close to zero (and hence so is\nCZG)”. (LL 269-271)\n8\n3. (Comment 12) Reduce over generalisations. E.g. saying phenology when mean spring\nphenology.\nThis study only looks at spring phenological events (as far as I can tell) but this is not\nmentioned anywhere. This needs to be mentioned from the very beginning as it strongly\nimpacts the interpretation of the results. It is not a drawback, just a clarification that is\nimportant.\nOur answer: Thank you! The reviewer is correct, most of the studies (75%) focus on\nspring phenological events. We now clearly stated at the beginning of the Results\nsection (LL 231-233) that most of our data are on spring phenology. We also extended\nour Supplementary data with a column that specifies the season in which events are\nobserved for each study.\nComment 13. L141 – this is certainly one example of the impact of phenology; however,\nit is very specific and it does not hold for all biological events e.g. those in Autumn or if\ncaterpillars hatch prior to their food source. Please be more specific about the scope of\nevents you cover – just spring phenology in a few species. The caterpillar example is still\nspring but would not always fit this pattern.\nOur answer: We are not sure what the reviewer means by “caterpillars” in the\ncontext of the text on former L141 (and this whole paragraph). This paragraph\ntransmits the message that looking at how changes in phenotypic traits affect fitness\ncomponents tells us just part of the story and we argue it may also be useful to look at\nhow trait changes relate to population growth rate. In this whole paragraph we do not\nmention caterpillars and the paper we refer to in the first sentence (Radchuk et al.\nmisunderstood this comment, we are happy to consider it if we receive more specific\nexplanation.\nRegardless, we have now carefully revised the text throughout the manuscript\nto clearly state that the majority of the studied phenological events are spring ones, as\nsuggested by the reviewer and detailed in our answer to their comment above.\nComment 14. L289 – this is a bit over generalised due to the limitations mentioned\nbelow, in particular geographic and taxonomic biases. In addition, it is only a subset of\nspring phenology that has been looked at. I suggest moderating the language here to\nfocus on spring phenology, clarify that vertebrates are mostly birds, and across most of\nthe globe (not all). Given that you have results for individual studies – can you say for\nwhich groups these temperature mediated effects are most clear?\nOur answer: Done. We have now revised this sentence to account for the reviewer’s\nsuggestion. It now reads: “Here, by using a large set of species and studies, we\ndemonstrate that spring phenology mediates effects of temperature on population\ngrowth in vertebrates found across most of the globe. Our results are predominantly\ndriven by birds, because the relevant data were mainly found for this taxon despite our\ninitial intention to address this question for vertebrates broadly” (LL 367-371). Further,\nwe have added the results of the analyses run separately for birds and mammals to\nSupplementary Figs. S4 and S5. We could not run a meta-analysis for reptiles only\nbecause of a too small sample size (n = 4). We also mention now in the Results that\n9\nthe across-study results were mainly driven by birds, which constitute most of our\ndataset (LL 336-340).\nComment 15. L596-603 – I find this a more specific result and interesting, I think this\nshould be elevated to the main text not hidden in the methods.\nOur answer: Agreed, we have now shifted these findings to the main text (LL 340-\n347), see the answer to your other comment above.\n4. (Comment 16) Include more comment or a specific section on the study limitations.\ne.g. geographic spread, taxon spread, type of morphological traits chosen – possible not\nplastic or labile (some of the references they have use body length to standardise body\nmass to look at body condition). Also include the different types of phenology and\nmorphology and whether they can truly be compared.\nOur answer: Agreed, we have now included a new paragraph in the discussion (LL\n457-468) that is devoted to the limitations of our dataset, as suggested by the\nreviewer.\nComment 17. The geographic limitations of the dataset need to be acknowledged early,\nespecially the lack of studies in the tropics and from low latitudes and the bias to Europe\nand North America. This is particularly important given the latitude effect that was\nfound, some comment on whether this effect would hold into the tropics or with more data\nwould be good. E.g. L283 it mentions close to the equator, but it seems there is limited\ndata in this study from the equator.\nOur answer: Agreed, we now point out the geographic bias in the assembled dataset\nat the very beginning of the Results section (LL 235-237) focusing on the dataset\ndescription. As suggested by the reviewer, we now also highlighted this geographic\nbias in the Discussion (LL 460-464). In this passage we warn that our findings remain\nto be tested in the future, when the studies covering latitudinal gradient more\nsystematically are available.\nFor the specific point regarding the former L283, we have now revised the text to\n“The ‘direct’ effect of temperature on G that remained after accounting for the\nd\nphenology-mediated effect of temperature on G was positively associated with\nd\nabsolute latitude, such that it changed from being negative at lower latitudes to\naround zero towards higher latitudes” (current LL 360-364).\nComment 18. Another potential limitation of the study is the substantial variation in\nclimate data resolution across the countries considered. Specifically, the resolutions for\nNorth America, South America, and Africa are relatively coarse. Such large-scale\ntemperature data may not accurately capture the environmental conditions experienced\nby animal populations, particularly the microclimates to which they respond. Please\ninclude a small mention of this.\nOur answer: We mention this in a new paragraph (LL 457-468) that we added to the\ndiscussion to focus on the limitations of our dataset (see also a response to your\ncomment above).\nmean for all other results?\nOur answer: We have added more details now to better explain what is meant and\nthe possible implications for other results: “Our dataset comprised 97 time series,\nwhich is not a small sample per se, but because we relied on previously published\ndata, we had no control over the distribution of the species characteristics within this\ndataset. Therefore, future extensions of the dataset aimed at covering a wider range of\nspecies characteristics would allow for better addressing of this particular question.”\n(LL 405-409).\nComment 20. L399 – 402 – I feel that these limitations need to be mentioned further up\nsomewhere.\nOur answer: Thank you. As already mentioned above, we shifted all of this\ninformation to the very beginning of the Results section that describes the dataset (LL\n226-237).\nMinor comments:\nGeneral\nComment 21. L130 – I would replace ‘global warming’ with ‘climate change’, I appreciate\nit is said a few times in these sentences but it is a more accurate characterisation of the\nactual changes that will be experienced.\nOur answer: Changed accordingly.\nComment 22. L311 – the population is not plastic or not, the trait is.\nOur answer: We have now revised this sentence to read “Another is that the traits of\nlower-latitude populations are in general less plastic and hence these populations\nhave narrower thermal tolerance than higher-latitude populations61–64, who typically\nexperience higher intra-annual temperature variation.” (LL 394-397)\nComment 23. L312-315 – I do not agree with the reasoning here. Surely physiology\nmediates or plays a role in all traits, particularly phenological traits and therefore, it will\nbe playing a role at all latitudes. I do not see physiology and phenology as separate,\nphysiological processes will be the basis of the phenological events we observe (e.g.,\ngonadal development for laying date). Perhaps you could be more specific about which\nphysiological processes you think are influencing things at lower latitudes. Or perhaps\nthe authors are referring to physiological effects as direct temperature effects and\nphenological effects as indirect temperature effects (e.g., mediated through food supply).\nIf so, then perhaps this distinction could be clearer.\nOur answer: We have now deleted this sentence as this paragraph focuses on\nlatitudinal effect and the mention of physiology was out of place.\nComment 24. L344 – again, not sure about this distinction, could you be specific about\nthe physiological traits. Also again, phenological traits will be built up from physiological\n11\ntraits and indeed in some species they could be the same e.g. trees and invertebrates.\nPerhaps add a qualifier about vertebrates here.\nOur answer: Thank you. We have now revised as suggested, the sentence reads: “As\nchanges in both morphology and phenology arise from changes in underlying\nphysiological traits (e.g., gonadal development for laying date)67, we expect that\nphysiological traits would respond much faster than phenological or morphological\nones and likely mediate many climate effects on populations.” (LL 451-455)\nComment 25. L352 – what about empirical models? I do not think this is a clear\ndistinction as models can be applied to empirical datasets, did you mean theoretical or\nsimulation studies?\nOur answer: Thank you for spotting. We have now revised to “whether based on\nobservational, experimental or modelling studies” (LL 474-475).\nComment 26. L380 – this link does not work – takes me to a 404 page\nOur answer: The link is updated.\nData and methodology\nComment 27. More detail on how the biological realism of the identified climate\nwindows would be a nice addition. While searches like sliding window can identify\nclimate drivers that make excellent explanatory and predictive models, they are not\nalways biological realistic. In this paper some choices around the window searching\ncould lead to biological realism being reduced, for example, 2-year search could actually\nexceed the lifespan of some short-lived species in the dataset, or at least be before they\nare born when the reach maturity at age 1. In addition, the use of a reference day that is\nthe latest date a phenological event was observed will in many years be occurring after\nthe event takes place. Some discussion of the biological realism and implications for\ninterpretation would be a good addition.\nOur answer: In our dataset only 5 species (7%) have generation time <= 2 years (see\nthe histogram of generation times in the Fig. 2 below). A fair share of the species in\nour dataset are long-lived species and for them being able to account for potential\ncarry-over effects of the previous years is important compared to the very low\nproportion of species whole generation time is shorter than 2 yrs.\nWe have now included text in the Methods section explaining in more details\nour choice of the 2 years period prior to the biological event for the sliding window\nanalysis: “Our dataset includes diverse species, ranging from birds to mammals,\nreptiles and fish. For many of these species biological knowledge on what constitutes a\nrelevant climatic window is unavailable. Therefore, the use of sliding window analyses\nallowed to assess the climatic window in a standardized way, systematically, across all\nstudies and species. Our identified climatic windows for passerines correspond to the\nwindows that are known to be important in driving egg-laying of these birds based on\ntheir long-term studies, suggesting that our approach is valid. Our consideration of\nsliding windows up to two years before an observation may include potential climatic\nwindows that are not very meaningful for short-lived species (those whose life span is\n< 2 years). However, only five of the species (7%) in our dataset have generation time\n12\n<= 2 years, justifying the use of the two-year period to capture potential carry-over\neffects for most of the much longer-lived species in our dataset.” (LL 563-574).\nRegarding the use of the latest date that phenological event was observed over\nthe whole study period: it is needed to not miss the event if it, as the reviewer correctly\npoints out, occurs earlier in the year. Had we used the earliest day as a reference day,\nwe would not be able to correctly identify the windows in those years when the event\ntook place after that day.\nFigure 2. Histogram of the generation times of species in our dataset. The red vertical\nline shows the median across the species (7.9 years).\nComment 28. Why was the detrending of the climate variables done after the sliding\nwindow analysis not before? I am curious as the given rationale would seem to hold for\ndetrending prior to the sliding window.\nOur answer: That is because prior to performing sliding window analyses it would be\nunclear what data to detrend as the climatic window is not identified yet. In other\nwords, we first need to have a climatic variable value for each year in order to be able\nto then detrend it over time. This comment made us realise that the fact the climatic\nvariables were year-detrended in our analyses was perhaps not very clearly stated in\nthe original submission. Therefore, we have now revised the text to state this early on,\nalready in the end of the Introduction: “In all analyses we considered year-detrended\ntemperature and precipitation (i.e. residuals) to avoid potential spurious effects caused\nby other environmental temporal trends46,47.” (LL 210-212) Further, we now used\n‘temperature ’ in the methods and results sections as well as on the axes labels (Fig. 3)\nd\nto refer to the year-detrended temperature.\n(could be in a one tailed test of some kind). Is this what is meant here, or did you mean\nbeta value? If you did mean p-value, maybe explain how it indicates the trend direction.\nOur answer: Agreed. We have now revised to read “…., with a tendency towards\nnegative direct temperature effects on population growth.” Indeed, you are right, we\nreferred to the negative beta value that was in the original manuscript reported on LL\n254 and is now found on LL 314-316.\nComment 30. L406 – please mention that the resolutions vary here.\nOur answer: We have now added “The grid sizes ranged from 0.05 deg in Australia, to\n0.1 deg in Europe and 0.25 in North America and 0.5 deg elsewhere.” (LL 526-527),\nalso to address a similar concern raised by the Rev 1.\nComment 31. L438 – please provide these preliminary analyses somewhere like SI.\nOur answer: We have done so now, by including the new Supplementary Fig. S8 that\nshows the relation between window durations computed using daily and weekly\nresolutions, for a set of 35 studies.\nComment 32. L537 – is the difference to 0 something you would expect?\nOur answer: This is an excellent point. Yes, as outlined in our expectations (LL 261-\n281), CZ would differ from 0 if phenology has advanced with temperature (negative CZ\nvalues) or if it was delayed with increasing temperatures (positive CZ values). Testing\nwhether ZG differs from 0 allows discerning whether phenotypic responses to\ntemperature are likely due to plasticity. The ZG that does not differ from 0 implies a\nplastic response and ability of individuals to respond to changing temperatures by\nadjusting their phenology (with no association between phenology and population\ngrowth rate for each given temperature). Further, we would not expect 0 necessarily\nfor CZG, as explained on LL 267-274. For CZG we actually rely on a binomial test to\nassess whether the proportion of studies with non-negative CZG was higher than by\nchance only. To clarify this, we revised this sentence as follows: “To test our\nexpectation that phenological responses to temperature allow avoiding temperature-\ninduced population declines, we assessed whether the proportion of studies with non-\nnegative CZG was higher than by chance only. For this we used a binomial test and\ntested whether the proportion of studies with non-negative CZG differed from 0.5 for\na given combination of climate variable and trait category.” (LL 679-685).\nRegarding the tests of whether CZ and ZG differ from 0: these allow to\ndecompose the overall CZG path into phenological sensitivity and the association\nbetween phenology and population growth rate (given the temperature and\npopulation size, see our conceptual diagram Fig. 1). We think that such a\ndecomposition is useful as it breaks down the overall path of phenology-mediated\nimpact of temperature on population growth rate (CZG) into its underlying\ncomponents.\nassume it has an assumed probability of some kind?\nOur answer: Indeed, as the text says, “To test our expectation that phenological\nresponses to temperature allow avoiding temperature-induced population declines,\nwe assessed whether the proportion of studies with non-negative CZG was higher\nthan by chance only. For this we used a binomial test and tested whether the\nproportion of studies with non-negative CZG differed from 0.5 for a given combination\nof climate variable and trait category.” (LL 679-685). We hope it is clear, that we\ncompared the proportion of the studies with 0.5 and that 0.5 reflects the expectation\nwhen the pattern would be due to chance.\nComment 34. L579 - the same way as what? The reference here is not clear.\nOur answer: We referred to the previous sentence. To clarify now we have extended\nthe sentence to say “Specifically, we used the same random effects as in the mixed-\neffects models used to infer across-study effects (see “Across-study inferences”) and\npropagated uncertainty in the same way. We additionally included as predictors the\nvariables hypothesised to affect the phenological responses to temperature…” (LL 729\n-731).\nConclusions\nComment 35. See above on generalisations.\nOur answer: Done. See answers to your comments 12, 14, 16, 17 above.\nFigures\nComment 36. Figure 1 – Conceptual diagrams need to be really clear. The overlapping\nof arrow heads with boxes makes this less so, could you add just a tiny bit of breathing\nroom around each arrow head please. Esp in panel a under the top box.\nOur answer: Changed as suggested.\nComment 37. Figure 3 – The order of the panels here is not very clear. Perhaps the\nletters could be changed to match the order – that makes it easier for the reader to\nassimilate the information. This figure also needs tidying, the arrows are different sizes\nand the panels are not full aligned.\nOur answer: We have removed the arrows from panels a) and b) to the panel c) and\nnow use the conventional order of the panels and letters. We have also adjusted the\nfigures as suggested.\nSupporting information\nComment 38. Supplementary figure S9: and any similar figures. I find the colour and\nshape differentiation very hard to see. Please choose some that are more distinct. At the\nsize they are, even on a big monitor, I cannot easily see the difference between the circle\nand the square, nor light-dark blue/red.\nOur answer: We modified the colour-scheme, and the symbols used in this figure as\nwell as in former Supplementary Figs. S12 and S13 (current Supplementary Figs. S14,\nS17, S18).\nseem to fit with the results in the main text. I hope I did not miss something, if I did,\nplease correct me. But especially given that the climate data were detrended in the main\ntext, I do not see how these two scenarios were tested. Scenario is also not mentioned in\nthe main text. I suggest removing this section.\nOur answer: Thank you, we have now removed this section from the Supplementary\nmaterial. Indeed, there is not much mention of this in the main text. This is a\nsimulation study that we have done prior to performing the meta-analysis, to\nformulate our expectations about CZG and CG. But we realise now that it may be more\nconfusing than helpful and therefore have deleted this section.\nComment 40. L340 – GR is not defined.\nOur answer: We completely deleted this section following reviewer’s advice, this is\nobsolete.\nReviewer #2 (Remarks on code availability):\nComment 41. I have looked through the repository but have not tried to run all of the\ncode. The repository is well organised with a nice ReadME to begin and all code looks\ngreat. The functions I have looked at are really comprehensively commented and clear.\nOur answer: Thank you for your positive feedback. We do hope that the repository is\nstructured and documented well enough to allow those interested to replicate the\nresults or to apply this framework to the new datasets. The repository does contain\nmany scripts as our framework required a multi-step approach with several different\nanalyses.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #4 (Remarks to the Author):\nIn this manuscript, the authors Radchuk et al. conduct a meta-analysis using time series\nof vertebrate phenology, morphology, and population growth. Using this diverse data,\nthe authors test the relationships between temporal and morphological traits, climate,\nand population dynamics. Morphological traits showed no relationship to temperature\nor precipitation and population trends, but a relationship was found between\ntemperature and phenology---but not phenology and population responses. These\nresults suggest that other traits are shaping population responses to climate change\nthan those represented in the current literature.\nimportant, as it may provide insights needed to forecast and manage species under\nclimate change. Synthesizing trends globally and across diverse species assemblages\nalso allow us to identify general trends and large-scale spatial patterns. The dataset\npresented in this study is impressive, particularly given the efforts to collect raw data\nfrom the original authors. However, I was surprised that the phylogenetic signal was\nweak. There is strong evidence of phylogenetic conservatism in phenology, with the\nvariation due to latent traits potentially reflecting traits favourable under ancestral\nclimates. While I appreciate you did include a phylogeny in your analysis, there was no\njustification or discussion of the importance of evolutionary history in the introduction. I\nwould have like more detail on exactly how phylogeny was included in the model,\nwhether specific packages were used, and what the estimated phylogenetic structure\nwas. As an additional supporting analysis, I am curious whether relationships would be\nstronger within the individual taxonomic groups, especially given the diversity in the\ntypes of phenological events included in the study.\nOur answer: Thank you for this comment. As suggested, we now point out on LL 241-\n244 that “Since there is evidence of phylogenetic structuring for phenological events46\nand evolutionary history may potentially shape trait-mediated effects of climate on\npopulations, we accounted for phylogenetic relatedness in our meta-analytical\nmodels.” We explain how phenology was included on LL 659-662: “…these models also\naccounted for the phylogenetic relatedness by allowing the values of the random\nspecies effect to be correlated according to the phylogenetic correlation matrix that\nwas derived from the phylogenetic tree77”. We have thus used phylogenetic distances\nbetween the species in the dataset to account for potential correlation in the random\nstructure of the respective effect sizes. To our knowledge that is how phylogeny is\ncommonly accounted for in such kind of analyses, see e.g. Cinar et al. (2022). We fitted\nthese analyses with the R package metafor, as mentioned on LL 686. We also now\nreport the estimated Pagel’s  values on LL 328-335 and in Supplementary Fig. S3.\nAs requested by the reviewer, we have now re-run the meta-analyses separately\nper taxonomic group, specifically for birds (n = 82) and mammals (n = 7). The sample\nsize for reptiles was too low (n = 4) to fit phylogenetically-corrected analyses. The\nfindings for these additional analyses are reported on LL 336-340 as well as in the\nSupplementary Results and Supplementary Figs. 5 and S22-25. We have also now\ndevoted a separate paragraph in the discussion to interpretation of these findings and\ndiscussing them in light of previous research (LL 427-444).\nComment 2. The framing of the project and writing could also be improved in my\nopinion. I felt the state of the literature could be better represented and some of the\nbroad statements tempered. I disagree that no work has been conducted on this topic\nbefore. But perhaps more elaboration and nuance would address this, as I do believe\nthat there are few robust and global studies on this topic and appreciate the novelty of\nthese large-scale studies.\nOur answer: Thank you. A very similar point was raised by another reviewer, and we\nhave now revised the Introduction carefully to tone down the statements as well as to\nbroaden the cited literature.\nlack of statistical significance was at times not clear from how trends were discussed.\nFinally, the discussion would benefit from more discussion of future directions and\napplications of these findings to conservation or species management.\nOur answer: We have now revised the discussion carefully, paying particular attention\nto stating clearly what findings were significant or not. We also added a new\nparagraph that discusses the limitations of the current dataset and specifies the\ndirections for future research efforts in order to more systematically cover different\ntaxonomic groups and geographic regions (LL 457-468). We also explicitly stated in\nthe discussion that “we may need to embrace ecological complexity by studying\nmultiple phenotypic traits and considering predictors that capture intra-specific\nvariation.” (LL 487-488). We refrained from discussing the applications of our findings\nto conservation and management because we find it is not straightforward yet to give\nconservation recommendations to mitigate the impacts of climate change, this will\nbecome more feasible once the field expands and matures. Further, as our dataset\ncovers species that differ widely in their biology and habitat requirements, we think\nthat such general statements are rather difficult to make, and recommendations\nshould be species-specific.\nSpecific comments:\nComment 4. The title should more accurately reflect the study results. The current title\nmakes it seem like the relationships between temperature and precipitation were strong,\nbut in reading the text, it was only temperature.\nOur answer: Agreed. We have modified the title to “Changes in phenology mediate\nvertebrate population responses to temperature globally”.\nComment 5. Line 110: More context for your study is needed here, it sounds like it was\nexploratory, but I am sure a lot of thought when into why you collected all these time\nseries.\nOur answer: We are not quite sure what exactly the reviewer means. Our research is not\nexploratory, as is explained in the Introduction, we aimed to assess to what extent the\neffects of climate on populations are mediated by phenotypic traits, across species. The\nrequirements of Nature Communications to the word count of the abstract are rather\nstrict (150 words). Therefore, we tried to succinctly highlight the research gap that our\nstudy addresses already in the first sentence. That is why, the second sentence (the one\nto which the reviewer refers to) directly explains what kind of data were gathered to\naddress that research gap. We do not see how we can give more details while sticking to\nthe word limit, but we are open to suggestions.\nComment 6. Line 114-119: I think these sentences could be edited to better guide your\nreader and make it clearer that you are testing several relationships and why.\nOur answer: Please see the answer to the above comment 5. The abstract is limited to\n150 words, and we thus cannot afford to explain in detail all the relationships that we\nhave tested and why we have done so. Instead, we opted to directly present the findings.\nnot fully developed. Could you break this into multiple paragraphs and further develop\nsome of these topics?\nLine 132-135: This should be its own paragraph to convince the reader this is important\nand true.\nOur answer: Thank you. Surely this text can be “decompressed” and presented in two\nor three separate paragraphs. But we here intended to “come to the point of our\nmanuscript” as quick as possible, so that the reader knows rather quickly what is the\nmain topic that we are dealing with. We think it is rather a matter of taste and of\ncourse there are different approaches to writing. We did revise the Introduction and\nDiscussion to explain more in detail some specific points and to pitch our study better,\nas suggested by this and other reviewers, but we decided against expanding the first\nparagraph into three paragraphs.\nComment 8. Line 141: Earlier phenologies can have huge costs as well, your argument\nwould be stronger if you include some of these examples.\nOur answer: Thank you. We have now revised this sentence accordingly: “Earlier\ntiming of biological events in warmer years is associated with fitness benefits on\naverage19 but it may also have huge costs by exposing individuals to extreme events,\nsuch as cold spells20,21 .” (LL 149-151).\nComment 9. Line 145: What is meant by “net demographic consequences of interannual\nvariation in phenotypes”? Is there not an existing literature of traits being included in\nintegral projection models to do exactly this?\nOur answer: We have reworded it now to “Indeed, whilst many studies have\nexamined the consequences of trait change on single demographic rates (e.g.,\nreproduction, survival) at the individual16,23 or population level4,6,21,24, few have\nexamined the consequences of climate-driven changes in traits on the net\ndemographic consequences of interannual variation in phenotypes that is driven by\nclimate on interannual variation in population growth rate8,11,12,14,25.” and hope it reads\nbetter. Also, among the examples of studies that did look at how climate-driven\nchanges in traits affect population growth rate we have now included these IPM\nstudies: Simmonds (2020 ELE) and Coulson et al. (2011).\nComment 10. Line 150-152: Could you elaborate and give an example of this? How\ncommon is this actually?\nOur answer: We have now given more details, as following: “Studying the\nconsequences for population growth rate is important because changes in fitness\ncomponents (e.g. survival, reproduction) may not translate into changes in population\ngrowth rate21,23 if demographic compensation is ocurring26,27. Demographic\ncompensation is a common phenomenon28 whereby population-level declines in a\ngiven demographic rate are offset by increases in another demographic rate23,29,30. “\n(LL 160-165)\nconsider even some of your own coauthor’s work (e.g. Jenouvrier et al. 2018. Journal of\nAnimal Ecology) to have done so. I would temper sentences like this and elaborate on\nthem to more accurately reflect the state of the field or provide more nuance to your\nposition.\nOur answer: To tone down we have changed to “not been investigated much” (LL\n179). We have revised the manuscript text carefully to tone down the sentences that\nwere over-stating the results (also in line with suggestions from another reviewer).\nWith regard to the study by Jenouvrier et al (2018): even though we admire this work,\nwe do not think it does what we refer to in this sentence. This sentence states “Species\ncharacteristics are likely to also explain trait-mediated effects of climate on population\ngrowth, but this has not been investigated yet.” (former LL 165) and we mean here that\nsuch species characteristics as generation time and diet can explain the trait-mediated\neffect of climate on population dynamics (the CZG path on our conceptual Fig. 1a).\nJenouvrier et al. (2018) performs a detailed investigation of how several different traits\nmediate effects of climate on population dynamics, i.e. estimates several CZG paths.\nHowever, because Jenouvrier et al (2018) focuses on a single species it cannot explain\nthe variation in CZG paths by species characteristics, and that is what we meant in this\nsentence. To improve the clarity of the text we have now revised it, as follows: “Such\nspecies characteristics are likely to also explain trait-mediated effects of climate on\npopulation growth, but this has not been investigated much” (LL 178-179).\nComment 12. Line 178: Can you elaborate on why?\nOur answer: We have now expanded this sentence to “We expect the overall\nphenological responses to temperature to be stronger at higher latitudes1, because of\nboth the faster warming there36–38 and higher sensitivity of species to warming at\nhigher latitudes38.” (LL 197-199).\nResults:\nComment 13. Line 196: The use of the term “study” is misleading, would it not be more\nappropriate to refer to 116 time-series for example?\nOur answer: We find this is rather a matter of taste and as long as the term is defined\nclearly (which we do now in a more extensive manner on LL 204-206: “Each study\nconsists of time series of annual mean population phenotypic trait values and\npopulation sizes recorded for a unique combination of the species, location and\ntrait.”), it should not affect the clarity of the manuscript. Personally, we prefer to use\n“study” because in some instances it is easier to talk about studies than time series.\nFor example, when describing the data, we find “Most of the studies on phenology\nrecorded onset breeding…” reads better than “Most of the time series on phenology\nrecorded onset breeding…”. Similarly, when describing the random structure of the\nmixed-effects meta-analytical models, we find it is easier to talk about “using the study\nID as the random effect” compared to “using the time series ID as random effect”.\nThe text also does not reference any of the other panels in Fig 1 and readers would\nbenefit from more detail. Or for the results to be discussed in the context of this figure.\nOur answer: Thank you. This part focusing on the description of our conceptual\nframework is rather technical and it would be usually found under “Methods” in the\npapers with classical structure (where Methods precede the Results). The Intro usually\nends with the research questions/hypotheses but should not include such more\ntechnical and detailed explanations of the performed work. As the format of the\nNature Communications does not allow Methods to come before Results and this\nrather technical description of our framework is essential to follow the Results, we\nprefer to place it just before the Results.\nAs suggested by the reviewer, we now incorporate the reference to other\npanels in Fig. 1 (LL 252, 257, 264, 265) in this section of the MS and believe it strongly\nimproves the clarity of the text.\nComment 15. Line 244: Do you mean “found for individual studies” or interannually for\na given study?\nOur answer: Thank you for spotting this. We meant “found for..”, now changed\naccordingly.\nMethods:\nComment 16. Line 394: What are the four classes? More detail, especially sample sizes,\nwould be useful.\nOur answer: Thank you, we have now extended: “Our final dataset consisted of 213\nstudies extracted from 73 papers. Our studies cover four vertebrate classes: birds (54\nspecies), mammals (10 species), reptiles (7species) and fish (3 species; see\nSupplementary Data and Fig. 2 for study sample sizes per trait category).” (LL 517-520)\nComment 17. Line 399-402: Could you add the percentage of each taxonomic group\nand types of phenological events?\nOur answer: We have done so now. It is on LL 226-235, as we shifted this information\nto earlier in the text.\nDiscussion:\nComment 18. Line 290-291: This is a key point that is missing from the introduction.\nOur answer: We have now added the following sentence in the introduction, on LL\n146-148: “Yet, such knowledge is crucial for the field of population ecology, as well as\nthat of functional ecology, which relies heavily on the assumption that traits have\ndirect population consequences and thus can be used as proxies to reflect community\ncomposition18.”\nComment 19. Line 303: What are the units?\nOur answer: We have now added the units (standard deviations in trait per °C per\ndegree latitude) in the text.\npackage used? What was the estimated value of lambda?\nOur answer: See our response to your comment 1.\nComment 21. Line 564-575: This provides important context and justification that might\nbe better suited in the introduction.\nOur answer: Thank you. We have now integrated most of this text in the Introduction,\nLL 187-202 (and removed it here).\nComment 22. Line256, line 440: Minor typos\nOur answer: Thank you, we have now changed “nett” to “net” on former L256.\nFigures & Tables:\nComment 23. Fig 1: Does the slope and the direction of the trends in b-d reflect your\npredictions?\nOur answer: yes, for phenological responses. We have now stated that in the legend.\nComment 24. Does the order of the subplots, which I initially read as c, a, d, b, have any\nsignificance. I think figure a would be clearer if it was separated from b-d into different\npanels.\nOur answer: The fact the other panels are located around the panel a) is because the\npanel a) represents the main conceptual framework and the insets detail the relations\nreflected by each arrow in a). We preferred to keep the original order of the panel\nlabels. However, to separate the main conceptual framework in a) better from the\ninsets we have now enclosed insets in boxes. Hope this improves the readability.\nFigure 2:\nComment 25. The inset should be wider to better separate the two categories. As is, the\nthree words read more like the x-axis label.\nOur answer: Thank you for pointing this out, we have now replotted this figure to\nmake the inset wider.\nComment 26. This figure is very visually appealing, but the font color makes some of the\ndiet letters hard to read. I also did not feel the black bars were necessary as the numbers\nthemselves are present and easily interpretable.\nOur answer: Thank you. We have now chosen different colours for the diet and\nmigratory mode. We have decided to retain the bars next to the numbers.\nComment 27. What does the diet “LC” stand for?\nOur answer: Thank you for spotting, this was a typo, it should have been “C”. Now\ncorrected.\nFigure 3:\nComment 28. To better illustrate the relationship between a and c, I would put panel a\nunder panel c and have a upward pointing arrow.\nhighlighted the concerns about non-conventional order of the panels (not starting\nwith the a) as is usually done), we have now modified the figure to remove the arrows\nbetween the panels and rearranged them such that they follow the conventional order\n(i.e. a-c).\nFigure 4:\nComment 29. Line 828-831: As written the caption makes it sound like there is a trend in\npanel a, but it is also not significant. This could be more clearly written to clearly reflect\nthe statistics.\nOur answer: Thank you for spotting this! We have mistakenly plotted the line as\ndashed and not a solid one, in fact this relation is significant (see SI Table S2 and main\ntext LL 347-348). We have now corrected the figure.\nComment 30. Having the full terms on the axes, not the letter acronym, would make\nthese plots easier to understand with a quick glance.\nOur answer: Agreed, we have replotted the figure now with the full names of the\neffects. Hopefully this improved the readability.\nReviewer #4 (Remarks on code availability):\nComment 31. I looked through the GitHub repository and several of the R code files.\nThe repo structure is clear and well organized. It also includes a README that identifies\nall the key files.\nLooking through the code, I thought it was well formatted and would be easy to run if I\nhad the climate data.\nOur answer: Thank you for your positive feedback.\nWe are glad that R1, R3, and R4 were happy with how we addressed their previous\ncomments in our previous revised MS. In our new revision, we have addressed the\nremaining comments of R2, as well as accommodated the two remaining minor points of\nR4. All line numbers are for the manuscript version (and SI) with the “Track changes”\nmode activated.\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nResponse to response\nAll line numbers are for track changes doc\nI would like to start by thanking the authors for their efforts in addressing my\ncomments. There has clearly been a lot of work put in particularly to improve the\ndiversity of literature cited, to reduce the over-generalisation, and to have a better\nacknowledgement of the study limitations. However, there are a few conceptual and\ninterpretation concerns that I do not feel were sufficiently addressed. I’m afraid I also\nhave an additional request/suggestion for the supporting information that has arisen\nwith the addition of new components. Overall, I still think this will be an important\npaper, but this means it is even more crucial to make sure it is as robust as possible,\nand no conclusions are misleading.\nThe key areas that I feel were not yet addressed sufficiently were (split into changes\nI suggest and then rationale for changes):\nJustification of morphological traits considered here (Comment 8).\nProposed changes:\nAdd more contextual discussion on the morphological traits considered to the\nintroduction (like L179-201 for phenology). Should include some expectations and\ndiscussion of the different ways morphological traits could show interannual variation\nat the population level – as in response to comment 8.\nRationale: In the response to reviewer comments the justification for including non-\nlabile traits put forward was population level mean changing between cohorts\npossibly due to labile or developmental plasticity. However, these suggested\nmechanisms are quite different processes to the other traits considered (phenology\nand labile morphology e.g. body mass). Therefore, greater discussion of the\nmorphological traits is required in the manuscript itself. At present the introduction\nreads like a phenology introduction – a good one for phenology – but missing\n1\nmorphology. Morphology is only mentioned 3 times in the whole introduction, once to\nsay morphology responds to climate change then to introduce the data and approach\n– I think `this can leave a reader questioning the inclusion of morphology, particularly\nthe variety of traits. As an example, the authors could expand on the call for more\nstudies in the Teplitsky and Millien (2014) paper they cite to help support the\ninclusion of morphology.\nOur answer: Done. We have now included a paragraph in the Introduction\nthat motivates further research on morphological responses to climate\n(inspired by Teplitsky & Millien 2014) and on explaining heterogeneity in such\nresponses by species traits. This paragraph also highlights different ways\nmorphological traits could show interannual variation at the population level\n(LL 196-210).\nInterpretation and conclusions drawn from meta-analysis average effect\n(Comment 9, 10 , 11).\nProposed changes 1: remove or tone-down/carefully caveat the conclusion\nstatements e.g. ‘demonstrate that phenology mediates effects of temperature on\npopulation growth in vertebrates found across most of the globe’, L367-369 (e.g.\nresult of meta-analysis showed non-negative population growth response on\naverage, but highly variable across studies with many showing negative response,\npartly driven by type of phenological event), L378-382 (I really don’t think this part is\nneeded – it just detracts from the other results – suggest remove), L472 (add in\n‘many’ vertebrates).\nRationale: Thank you for your detailed responses here and clarifications on the\nmeta-analysis. I think I can understand better your motivation and expectations for\nthe meta-analysis. Here is my interpretation: you expected that you would be able to\nfind a global effect size for trait-mediated responses to climate for phenology and\nmorphology and that therefore the traits included and species etc were assumed to\nall be telling you about the same process – therefore a global effect made sense. I\nunderstand this motivation; but then we get to the results. At this point it is shown\nthat there is actually a lot of variability in the process (probably even different\nprocesses going on) with some studies showing opposite patterns to others (for\nphenology and morphology). This is all very well assessed in the newly moved text in\n‘Explaining among-study heterogeneity in climate effects’. My issue is then that the\nfirst paragraph of the discussion where you conclude that you ‘demonstrate that\nphenology mediates effects of temperature on population growth in vertebrates\nfound across most of the globe’. I do not feel this a valid conclusion to draw. Yes, it is\nthe result of the meta-analysis, but I think one has to consider whether it is sensible\nto draw such a global or average result when there are influential structuring\nelements missing from the model e.g. phenological trait type, which you know are\nimportant. The conclusion could even be considered misleading making it sound like\nthere is universal mediation, when in fact several studies show quite strong negative\nimpacts on population size and misses all the nice nuance from the results section. I\nwould recommend analysing the heterogeneity not reducing a global effect on\nsomething so variable, given the results found. If you wish to retain a global effect\nsummary, I recommend it be clearly caveated as a statistical result rather than a\nbiological conclusion. This is done well in the results already. I would like to add here\nthat all the rest of the results from the path analysis and analysis of heterogeneity\nare already interesting and important enough, I do not feel collapsing to a global\neffect adds anything scientifically.\nOur answer: Done. We have now toned down the statement at the beginning\nof the discussion, it reads as follows: “Here, by using a large set of species\nand studies, we demonstrate that the phenology-mediated effect of\ntemperature on population growth rate was on average non-negative across\nstudies. Thus, spring phenology facilitates adaptive population growth\nresponses to temperature in many vertebrate species found across much of\nthe globe. However, we found high heterogeneity across studies, with a\nsubstantial number showing a negative maladaptive response, partly driven\nby type of phenological event.” We also deleted the former sentence on\nLL378-382 and added “many” in front of “vertebrates”, as suggested by the\nreviewer.\nThis way we highlight heterogeneity across the studies. But we still report our\nmain finding that phenology-mediated effect of temperature on population\ngrowth rate was non-negative across the studies. We disagree with the\nreviewer that if one finds variability in a process it is no longer possible to\nsummarize the overall effect anymore. That is how scientific process works\nmore generally, and meta-analysis in particular: we describe summary\nstatistics and perform hypothesis testing on means. Besides, we can never\nexpect all species and all studies to show exactly the same response. It is not\nthe aim of the meta-analysis to reveal that all species respond exactly in the\nsame way. Instead, the aim and the advantage of the meta-analysis is to\nborrow strength from studies that may have low power (and non-significant\neffects) if considered alone to infer about a general, across-study effect.\nProposed changes 2: Elevate the prominence of the meta-analysis as a key aim of\nthe study (at end of intro), including discussion of how you think the ‘global effect’\ncan actually be interpreted biologically. Particularly in the case when effects of\ndifferent sign are found between studies. But avoiding overgeneralised or misleading\nconclusions (where important and explainable variability is missed). Change\nterminology around ‘average or on average’ to better reflect the meta-analytical\norigin of the number e.g. global effect or meta-analysis effect or meta-analysis\naverage.\nRationale: In the response the authors mention the importance of the average being\na meta-analysis average. However, in the text, it is referred to it as ‘on average’ or\njust ‘average’ several times (e.g. L279, 314, 379, 1030). This explains my previous\nuse of such language too. If the meta-analysis part is important, this should be\nelevated in the text so much misreadings are less likely. It should also be clearer in\nthe text that a meta-analysis is a main aim of the paper. Currently that can be\nsomewhat missed as the introduction positions the paper as if the main aim is a path\nanalysis. Meta-analysis is not even mentioned there. This also happens in the results\nwhere the cross-study effect ZG is only mentioned in one sentence (and not made\nclear this is from the meta-analysis), but there is a large discussion of the binomial\ntest results (paragraph at L307). This binomial test was what I referred to previously\nas the analysis of majority trends in comments 9 and 11, I was not suggesting vote\ncounting. I had meant the results of the binomial test felt more informative than a\nglobal effect, but I now see the value of the heterogeneity results.\nOur answer: Done. As suggested, we now explicitly state in the Introduction\nthat “To assess how general such effects are across species, comparative\nanalyses such as a meta-analysis are especially valuable.” (LL 162-163). We\nalso mention that meta-analysis is one of the two methods used in this study:\n“We used path analysis to (i) quantify the trait-mediated effects of climate on\npopulation growth rate (Fig. 1a) and meta-analysis to (ii) assess how general\nthose trait-mediated effects are across the studies and to (iii) identify which\ntype of species and regions exhibit the strongest trait-mediated effects of\nclimate on population growth rate (using migratory mode, diet and generation\ntime as explanatory species characteristics, and latitude to explain geographic\nvariation among locations).” (LL 220-226)\nRegarding the terminology, we have used “across-study effect”, which is an\nestablished term in meta-analysis to refer to what the reviewer calls “global\neffect size” (also known as “mean effect size” or “combined effect size”).\nWhen referring to the results of meta-analysis we also often have used\n“across studies”, which we think best (and in a most accessible and non-\ntechnical way) captures the result of meta-analysis. We have carefully read\nover the manuscript and in several places where we used “on average”\nchanged it to “across studies”, to avoid a potential misunderstanding that the\nreviewer mentions.\nWe also introduce the possibility when effects of different sign are found\nacross the studies (LL 291-295), see also a response to your next comment.\nProposed changes 3: following from Comment 10, when introducing the expectation,\nit would also be good to acknowledge that there are several ways to end up with a\nnon-negative meta-analysis average, not just from all studies being adaptive to\ndifferent degrees. It could arise from some highly adaptive results and others\nshowing mild maladaptation, but still with a global effect that is non-negative (this is\nan extreme example to try and illustrate the point).\nRationale: Included in the proposed change.\nOur answer: Done. We addressed this by including the text “Note that non-\nnegative CZG on average across studies can be obtained not only when\nmajority of the studies show positive or close-to-zero CZG but also when\neffects of different sign are found across studies, e.g. some studies showing\npositive CZG (adaptive responses) and others showing slightly negative CZG\n(maladaptive responses).” on LL 291-295.\nAccidental under-generalisation that has arisen from my suggestion L367\n(Comment 14) L367 – my previous suggestion to limit this to spring phenology was\na mistake given the new information on the type of phenological events included\nbeing only 75% spring. But overall would consider removing this whole sentence for\nreasons given above.\nOur answer: This sentence is revised in response to the respective comment\nof the reviewer above (“proposed changes 1” above).\nComment 17. Thank you for this change but the reference to the equator is still in\nthe abstract, could that be changed as well?\nOur answer: Done. We have revised that sentence in the abstract as follows\n“At lower latitudes, temperature had weaker effects on phenology but stronger\ndirect negative effects on population growth, likely because these populations\nare less capable of tracking climate via plasticity.” (LL115-118)\nComment 27, not sure I fully understand the response. I’ve tried here to explain\nmy point and interpretation of the response a bit more with a specific improvement\nsuggestion.\nProposed changes: to acknowledge that any identified drivers will be proxies so do\nnot need full biological realism (impossible really with an absolute window).\nRationale: To my knowledge of how sliding windows work, they start at a reference\nday and search back in time i.e. earlier than the reference. An example would be a\nreference day of 20th May then a 1 year search would be from 20th May Year1 –\n20th May Year0. If the event was for example, breeding timing and recorded\nbreeding was 24th May to 5th June across years, this would all be ok as the\nidentified window was occurring before the event in all years. I find a bit of a problem\nwith a temperature window for phenology occurring after the event has happened,\nwhich could occur if you set the reference day to the latest observation e.g. 5th June\nin the previous example. It could select a window of first week of June when most\nevents have happened in May and therefore biologically could not have had any\ninfluence on those events. A causal relationship is impossible. However, this debate\ndoes not really matter that much, as all absolute windows will be proxies anyway and\nunlikely to be causal. However it is worth clarifying as sliding windows are often\nassumed as representing causal cues in phenology and could be misinterpreted in\nthat field. For morphology, taking the latest date could make more sense, though,\nhaving the driver occurring before morphology is measured does still make sense.\nMy key point is a driver window later than an event or measurement cannot influence\nit, one earlier can. But do let me know if I have misunderstood this.\nOur answer: We try to explain here better why we have chosen to use as a\nreference day the latest day in the year when the phenological event was\nobserved over the study period. For this example, let us use as a phenological\ntrait of interest a median (across sampled nests, within a year) egg laying\ndate. Let us consider, for simplicity, a time series of 10 years. Let us say that\nacross these 10 years the latest the median date was observed was 5th of\nJune. And the earliest recorded median egg laying date (across 10 years) was\n10th of May. In our analyses we use the 5th of June as a reference day and\nlook backwards (up to 2 years) to identify a climatic window that best predicts\nthe egg laying date across all years. So, the climatic window always precedes\nthe latest day (across the time period) when phenotypic trait was measured\nbecause this climatic window is assumed to trigger the trait of interest. We\nrepresent this schematically in the figure 1 below (by using somewhat different\negg laying dates, typical of passerines in Europe). By using the latest date\nover the study period when phenological trait was recorded we avoid missing\nor misestimating the relevant climatic window (see Fig. 1). We hope this\nclarifies the confusion around the reference day. To clarify this in the text, we\nstate that “By using the latest day in the year when the phenological trait was\nobserved over the study period we assure that no relevant windows will be\nmissed (Supplementary Fig. S15)” (LL 588-590). We also say that the\nidentified windows may reflect proxies of the true underlying biological\nmechanisms (LL 598-599). We also include a (somewhat modified) figure\npresented here as Supplementary Fig. S15 in the Supplementary Information.\nFigure 1. Schematic representation of the sliding window analysis and the rationale\nfor using as a reference day the latest date when the phenological event was\nobserved or morphological trait was measured in the year over the study period. In\nthis hypothetic study phenological trait (median egg laying date) of passerine birds\nwas recorded over 10 years, from 2000 to 2009. The example focuses on the year\n2001 and the orange and the green vertical lines depict, respectively, the latest and\nthe earliest recorded median egg laying date over the 10-year study period. The grey\ndouble-headed arrows show (a selection) of tested windows. The blue double-\nheaded horizontal arrow shows the best selected climatic window. Had we used as\nthe reference day the earliest median egg laying date recorded over the 10 years,\nwe would have failed to identify this climatic window (as any climatic window when\nusing this reference day must stop at the latest by the time point depicted with green\nvertical line). By using the latest day when the even was observed as our reference\nday we thus try to strike a balance between false negatives and false positives.\nNew comment: Structure of Methods and Supplementary Material. Having re-\nread the Methods and Supporting information and possibly because of all the\nadditions in the revision, I found some of the styles and structure hard to follow. I will\ntry and be as specific as I can.\nProposed changes: more thought about the structure of the supporting information\nwould be really helpful, I would recommend structuring to mirror the mentions in the\nmain text or a standard paper i.e. intro related, methods related, results related, etc. I\nfound the current structure with so much material and so many different models to be\nquite confusing to follow.\nOur answer: In our supporting information the numbering of the figures and\ntables follows the order in which they are mentioned in the main text. To our\nknowledge that is a common practice and the figures and tables in the main\ntext actually must be cited in that consequent order. To help the reader\nnavigate the supplementary information, we split the materials into\nSupplementary Figures, Supplementary Tables, Supplementary Methods,\nSupplementary Results and provide table of contents at the beginning of the\nSI with respective page numbers.\nSupplementary Information is not meant to be read as a stand-alone\ndocument and therefore we do not think that re-structuring it to “mirror a\nstandard paper into intro-related, methods-related…” sections is useful.\nRestructuring in this way will also result in the numbering of the Tables and\nFigures that will not be sequential when mentioned in the main text.\nTherefore, we have kept the original structure of SI but are willing to\nrestructure it differently if such a change is deemed useful by the editor.\nExplain more clearly how many meta-analytical models are fitted (maybe a table of\nthem all) and justify the model structures e.g. why not include all important drivers\nthroughout? Also, explain which model has led to the ‘global effect’ quoted and how\nany other model structures impact this quantity (global effect).\nOur answer: Answered in detail below, where reviewer 2 goes into this point\nin more detail. Briefly: only two main model types were fitted, addressing two\nmain research questions of this study. As suggested by the reviewer, we\ninclude a table of all models as Supplementary Table S8.\nRe-name the sensitivity section, include how these models were run (as this is the\nmethods not results) and move results to supporting information.\nOur answer: See the detailed response below. We moved this section to the\nResults section, as its contents indeed better fit there.\nBe clearer about the population size impact, or better yet, just include this variable in\nall models. If not the latter, justify why it is not included. Also correct Figure S18 as\nfigure does not match interpretation text below i.e. graph says adding population size\nmakes the effects significant, text says opposite.\nOur answer: Population size was included as an explanatory variable of\npopulation growth rate in the structural equation models fitted to each study\n(see conceptual Fig. 1, LL 632-634 and LL 638-641, and Fig. S18 that shows\nthe methodological workflow of this study). Thus, it is included already in all\nmodels and this was explained already in the previous version of the\nmanuscript.\nSee below for a more detailed answer to the comment on Figure S18.\nMain areas of confusion:\n1. How many models? It is not clear to me how many meta-analytical models were\nactually fitted and which are interpreted in the main text. In the methods in main text,\nit seems like an initial one with effects of study, location, and species as well as\nphylogenetic relatedness and also accounting for probability of being spurious and\nclimate data quality, then one without phylogeny, then a third with the variables\nhypothesised to impact phenological responses (but not done for morphology?), then\na fourth to test the phenological trait variable. I have a question as to why there are\nso many different models. Could the full model with variables hypothesized to be\nimportant not just have been used from the start and phylogeny influence checked\nthen? It is not clear currently why so many model variants are being used in different\nparts and what impact this has on the results. Apologies if I have missed this, if I\nhave, please just elevate mentions. Currently a reader can be left questioning\nreasons for choices.\nOur answer: To clarify this point we have now included the Supplementary\nTable S8 in SI that provides a detailed overview of the fitted meta-analytical\nmodels and refer to this table on LL 773-774.\nIn more detail, all fitted models are described in the Methods section of the\nmanuscript. Also, our former Supplementary Figure S10 (now Supplementary\nFig. S18) schematically represents the methodological workflow and shows\nseparately the analyses that were run for each single study and those that\nwere run across studies. For each single study we fitted structural equation\nmodel, to estimate for each study the trait-mediated effect of climate on\npopulation growth rates (and other paths visualised in our conceptual Fig. 1).\nThese estimates were then used as response variables in a meta-analysis\n(while propagating the uncertainty). We fitted two types of meta-analytical\nmodels, to address two main questions of our study (as highlighted on LL 222-\n226): “We used … meta-analysis to (ii) assess how general those trait-\nmediated effects are across the studies and to (iii) identify which type of\nspecies and regions exhibit the strongest trait-mediated effects of climate on\npopulation growth rate (using migratory mode, diet and generation time as\nexplanatory species characteristics, and latitude to explain geographic\nvariation among locations).”\nFirst, to assess how general the responses are across the studies (aim “ii”\nabove), we fitted a meta-analytical model to assess the across-study effect\nsize. This model controlled for the effects of study, location, and species\n(included as random effects), and for phylogenetic relatedness. We fitted\nthese models separately for each combination of the phenotypic trait type\n(phenology, morphology) and climate variable (temperature and precipitation).\nOne model was fitted per path shown in our conceptual diagram in Fig. 1 (CZ,\nZG, PG, CG, CZG, total CG), using the path as a response variable. For the\nmodels that had as response variables the paths that included climate (e.g.\nCZ, CZG), we also included probability of the climate effect being spurious\nand climate data quality as covariates. We then fitted exactly the same model\nstructure but without phylogenetic relatedness and compared both models\nusing AIC, to avoid overfitting. In the main text we only report the results of the\nmodels for temperature-phenology combinations, while the results for three\nother combinations of traits and climate variable (precipitation-phenology,\ntemperature-morphology and precipitation-morphology) are only reported in SI\nbecause traits were insensitive to climate in these combinations, as reported\nin the main text (LL 298-301 and LL 333-341).\nSecond, to assess how species characteristics and latitude affect trait-\nmediated effects of climate on population growth rate (aim “iii” above), we\nhave fitted a meta-analysis with the hypothesised species characteristics and\nthe latitude as predictors. These models included probability of the climate\neffect being spurious as a covariate and were fitted only for studies focusing\non phenological traits and effects of temperature, because only for this trait-\nclimate combination the likelihood that the identified climate windows were\nspurious was low across the studies. And, these models were fitted for the key\npaths of interest from our conceptual diagram in Fig. 1: CZ, ZG, CZG, and\nCG. Finally, to assess how well heterogeneity in phenological responses to\ntemperature can be explained by a specific phenological trait measured, we\nrun a meta-analysis with the specific phenological trait type as a predictor.\nSo, there are no different model variants used, as the reviewer seems to\nsuggest. There are really two types of models that are needed to address our\ntwo research questions.\nWhile constructing the table we have realised that we have not always\nsystematically reported the effects for one of the considered paths, CG. We\ntherefore updated respective figures to visualise CG along with other paths\n(this concerns several figures in SI).\n2. Sensitivity analysis/Missing variables. This doesn’t feel like the correct title for\nthis section. It seems more like it is justifying not including certain variables in all\nmodels rather. I also typically consider sensitivity to assess impact of elements of the\nfinal model, not additional things that were not included. It was also a bit confused\nincluding both a missed variable and justification of not removing possibly spurious\nrelationships. Furthermore, it reads as results not methods. It does not actually say\nhow these models were run, just saying what their results were. It seemed a bit\nconfusing here and should be in results? Or referenced in results but in supporting\ninformation and just referenced at relevant places such as L676-677 when the\nvariable accounting for the probability of being spurious is mentioned.\nOur answer: Exactly as the reviewer describes, we here assessed sensitivity\nof the meta-analyses results to the predictors that were included in the model.\nSee below for the reviewer’s comment on population size – it was included in\nall structural equation models (SEMs from now on) by default and we\nconducted the sensitivity analysis to test how excluding this variable from our\nSEMs affects the findings. In a similar way, we here test what is the effect of\nincluding potentially spurious relationships (by using P as a covariate) on\nΔAICc\nour findings. We did not exclude this variable from the analyses, but rather\ntested how it affected the estimated across-study effect sizes. Perhaps the\nconfusion stems from the original wording of this section, so we have revised\nit now to read as follows: “However, the results of the meta-analyses were not\nqualitatively affected by inclusion/exclusion of (i) the less supported climate\nsignals in the models and (ii) population size as a covariate (Supplementary\nFigs S8-S11)” (LL 390-392).\nWe agree with the reviewer that this information belongs rather to the Results\nthan Methods section and have now shifted it to the Results part. Shifting it to\nthe Results led to the change in the numbering of some of the figures in SI.\n3. Population size. It is clear that population size is highly important for G, so why\nwas this variable not just included in the models interpreted in the text rather than\nbeing pushed to the supplementary? It seems that inclusion or not impacted\nsignificance of some effects, which is important but shrugged off here. I do not think\nit is currently helped that wording in the Supporting Information does not match the\ngraph i.e. graph says adding population size makes the effects significant, text says\nopposite (Figure S18). But I wonder if there is an effect you know even a priori will be\nimportant, why not include it throughout? If there is a reason, it should be included.\nSo, either switch the model or add a concrete reason why population size is omitted.\nOur answer: Population size was already included in all SEMs described in\nthe main text, as the Reviewer asks for. This was already explained in the\nconceptual Fig. 1, stated on LL 632-634 and LL 638-641 (in the section “Trait-\nmediated effects of climate on G”) and shown in the figure S18 that depicts\nthe methodological workflow. This analysis intended to assess, exactly as the\nreviewer suggests, what would be the impact on the conclusions drawn from\nthe meta-analysis had we not included population size in our SEMs. So\nindeed, we expected the effect of population size to be important and we did\ninclude it in all structural equation models fitted to each study.\nWe disagree with the suggestion of the reviewer that the wording in the SI\ndoes not match the figure: the figure says that if we exclude the population\nsize from the SEMs (depicted with circles on Fig. S11 (former Fig. S18); the\ncrosses show results when population size is included in SEMs, our default\napproach reported in the main text) then the across-study effect of the meta-\nanalysis becomes significant for the path ZG (precipitation effect).\nSo, the finding becomes significant if we exclude population size from the\nanalyses. And that is exactly what the legend to this figure says: “The\nexclusion of population size from path analysis does not affect the meta-\nanalysis’ results qualitatively, except that for studies on phenology (b) omitting\nthe population size (DD on the figure legend) results in one coefficient being\nsignificant: the effect of trait on population growth rate when assessing the\neffects of precipitation (ZG, compare dark green circle vs light green cross).”\n4. Structure of the supporting information. I would like to suggest structuring as\n‘Supplementary Methods’, then Results, then Figures, or interspersing the figures\nwhere relevant. The current structure makes some things hard to understand. Some\nexamples: Figure S7 – should this not be first in figures as it refers to an initial step in\nthe analysis? Also, Figure S10 possibly could go earlier.Table S7 appears between\nseveral with results which feels a bit odd, could be first table as it is contextual or\nabout data not results. Moving methods before figures and tables of results would\nmake them much easier to understand, e.g. Table S8.\nOur answer: See our previous answer above, where the reviewer made the\nsame comment.\nSmall details: Figure S5 mentions different sample size as SEMs for two taxon did\nnot converge, was this also the case for the main analyses in main text.\nI might have missed it but this did not seem to be mentioned in relation to those\nanalyses but should be.\nOur answer: This was shown in the Supplementary Fig. S14 (now\nSupplementary Fig. S7). We have now also added a sentence to the Methods\nsection (LL 662-667) stating this: “SEMs failed to converge for several studies\nresulting in sample sizes for meta-analyses being somewhat lower than the\nnumber of originally retrieved studies: 93 studies for phenological responses\nto temperature, 95 studies for phenological responses to precipitation, 109\nstudies for morphological responses to temperature and 115 studies for\nmorphological responses to precipitation (Supplementary Fig. S7).”\nFigure S8 – don’t jitter if you want to show comparability between two metrics.\nAlso, why only for 35 studies? And how were they chosen?\nOur answer: Thank you! Done. We do not jitter in the vertical direction\nanymore and use a different symbol (an empty one), that enables visualising\nthe symbols even if they overlap. We have added the following rationale of our\nchoice if 35 studies to the legend of the Figure S16 (former Fig. S8): “We\nchose 35 studies as a compromise between running time and having a broad\nenough and representative sample of all studies. Running sliding window\nanalysis for a study with duration of 30 years using daily resolution on a\ncomputing cluster required around one day while the same analyses with\nweekly resolution required two-three hours. We therefore have chosen 35\nstudies that constitute >1/3 of phenological studies and focus on birds only (as\nbirds represent 87% of all studies)”.\nFigure S17 – what are the numbers? Are they sample size? It seems sample sizes\nmight be more variable than has been mentioned, could this please be addressed.\nOur answer: These are indeed sample sizes, we have now added a sentence\nto the legend of this figure (and other similar figures) clarifying this. See the\nresponse to your other comment above: we have also added a text in the\nMethods section clarifying that sample sizes for meta-analyses are somewhat\nlower (as indicated in Supplementary Fig. S7) because SEMs failed to\nconverge for several studies: LL 662-667.\nSection ‘testing for non-linearity in relations’ is very repetitive.\nOur answer: We have revised this section to avoid repetitions.\nReviewer #2 (Remarks on code availability):\nAs before, all good.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers\nwho co-review manuscripts.\nReviewer #4 (Remarks to the Author):\nThank you very much for the extensive revision of the paper. The additional citations,\nelaboration in the introduction and details in the methods greatly improved the\nmanuscript and addressed my comments.\nLine 508-516: These sentences could be reorganized to improve the flow of ideas\nand reduce the repetitive phrasing.\nOur answer: Done, we have revised this section to improve the flow.\nLine 559-560: Can you be clearer as to why you think this is unfortunate? I assume\nyou mean it is unfortunate that we are still biased in where most ecological time-\nseries data are collected, but perhaps you mean something else about these studies\nspecifically.\nOur answer: Indeed, this is what we meant. To state it unambiguously, we\nhave revised this sentence as follows: “The majority of data stem from the\nnorthern hemisphere, particularly Europe and North America (Fig. 2), as is the\ncase with many recent global meta-analyses in ecology that are unfortunately\nbiased in where time-series data are collected 1,19,48.”\nREVIEWERS' COMMENTS\nReviewer #2 (Remarks to the Author):\nI would like to thank the authors for their careful consideration of my comments and\nadditions/changes to the manuscript. In particular, the wording of ‘across-study effect’,\nthe tweaking of the conclusion statements, the clarification of models (Supplementary\nTable S8), and addition of motivation for morphology in the intro.\nI also appreciate the clarification on Figure S11, I can now see that the figure and text\ndo match. Though it seems the Figure has changed from the previous version so now only\none effect is made significant by the removal of population size. I think it was the lack of\ncircle for the PG effect that confused me before, I assume this is because it cannot be\nestimated without population size (which makes sense), but perhaps this could be stated\nin the legend just to make sure it cannot be missed by readers.\nOur answer: We have now revised the legend to this figure to be as explicit as\npossible about the fact that analyses were run either with or without the population\nsize to test how sensitive our conclusions are: “The across-study effect sizes for each\nrelation in the path diagram used in the path analysis (Fig. 1, main text) are shown\nfor analyses with (a cross symbol for ‘Yes’) and without (an empty circle symbol for\n‘No’) the effect of population size on population growth rate (signified as DD on the\nfigure legend)”.\nI also appreciate the time spent to explain the choice of reference day for the sliding\nwindow and the new figure.\nI do think that the figure the authors presented in their response should be the one in the\ntext. Showing the earliest date as well as the latest is very important, which I explain\nbelow. While I would like to emphasize that I think that the author’s response is sufficient\nnow for publication (thank you for this), I would still like to justify the use of the figure in\nthe response. To do this, I have edited the authors’ nice figure (attached as I cannot upload\ninto this text box). What I have done is cut out the window around Oct (the shortest\nwindow in the figure) and move it to closer to the reference day. So, this should be a\npossible window that could be selected as the ‘best’ window. I hope this now shows the\nbiological issue, which is that this window cannot have influenced egg laying for the\nearliest recorded events, as the window occurs after these events. It can correlate with\nthem, but it cannot be causal for all years. Such an outcome may not have happened in any\ncases in this study (I don’t know) but it could by using the latest event as the reference\nday. The response figure already starts to illustrate this as the ‘best’ window continues\nbeyond the earliest event, but the Supplementary Figure does not. Therefore, including the\nschematic with the earliest recorded line will better illustrate this possibility to readers and\nwill help correct interpretation of results, adding a possible window fully after the first date\nwould be even better and more transparent. I would suggest a small wording change here,\nas a window that occurs after earlier events cannot be biologically 'relevant' in those years\n— though it may still correlate statistically. Thank you for the involved discussion on this, I\ndo hope you will consider this point in any final adjustments.\nOur response: We thank the reviewer for the detailed explanation. We have now included\nthe exact figure that was previously included in the response letter as the Supplementary\nFigure S15. We also revised the figure legend to highlight the possibility of the phenomenon\nthat is described by the reviewer, in some years (when egg laying could start earlier than the\nselected climatic window). The legend now reads: “Schematic representation of the sliding\nwindow analysis and the rationale for using the latest date when the phenological event was\nobserved or morphological trait was measured as a reference day. In this hypothetic study\nthe phenological trait (median egg laying date) of passerine birds was recorded over 10\nyears, from 2000 to 2009. The example focuses on the year 2001 and the orange and the\ngreen vertical lines depict, respectively, the latest and the earliest recorded median egg\nlaying date over the 10-year study period. The grey double-headed arrows show (a selection)\nof tested windows. The blue double-headed horizontal arrow shows the best selected\nclimatic window. Had we used the earliest median egg laying date recorded over the 10\nyears as the reference day, we would have failed to identify this climatic window (as any\nclimatic window when using this reference day must stop at the latest time point depicted\nwith green vertical line). Note that if the best selected window occurs after the earliest\nrecorded egg laying date, it may not be relevant for egg laying dates observed in each year\nduring the study (as in some years it may potentially occur after the egg laying started). By\nusing the latest day when the event was observed as our reference day, we thus try to strike\na balance between false negatives and false positives.”\nReviewer #2 (Remarks on code availability):\nDidn't re-check this time but assume still good as before.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and\nto provide appropriate recognition for Early Career Researchers who co-review manuscripts."}]

Decision letter