Climate change is one of the greatest challenges we face in the twenty-first century (Diaz, 2019). Although habitat loss and direct exploitation are currently the most important drivers of extinction in the natural world (Daskalova et al., 2020a; Diaz, 2019), changes to the climate altering both the mean and variance of weather conditions and the frequency of extreme events, are predicted to cause widespread declines of global biodiversity in the coming decades (Almond et al., 2020; Newbold, 2018; Soroye et al., 2020; Thomas et al., 2004). For mammals and birds, temperature increases are already associated with declining population trends (Spooner et al., 2018) and many endangered species have already been negatively impacted by climate change in at least part of their range (Pacifici et al., 2017; Pacifici et al., 2020). Perhaps more worryingly, abrupt ecological disruption due to climate change has been predicted to have large negative future impacts on biodiversity, with tropical ecosystems being affected as early as 2030 (Newbold, 2018; Trisos et al., 2020). Furthermore, these future impacts will likely be exacerbated by interactions between the climate and other drivers of extinction such as habitat loss or disease prevalence (Brook et al., 2008; Cohen et al., 2020; Williams et al., 2019). Research highlighting the species and ecosystems that are most sensitive to climate-change impacts will therefore provide crucial knowledge to prevent future losses to global biodiversity.

Not all species are equally sensitive to changes in the climate. Species vary in their climatic niches and in their behavioural, physiological, and demographic responses to environmental change and we therefore expect there to be both climate ‘winners’ and ‘losers’ (Antão et al., 2020; Bellard et al., 2012; Moritz and Agudo, 2013). At the macro scale, species-occupancy data highlight that geographic range shifts are the key response associated with climate change across taxa, resulting in changes to community composition, but not necessarily population decline (Antão et al., 2020; Chen et al., 2011; Dornelas et al., 2019). Furthermore, this pattern also occurs in broad species-assemblage and abundance-change data (Dornelas et al., 2019; Leung et al., 2020). While there is a lack of consistent directional temperature-related biodiversity change effects today (Antão et al., 2020), many species are predicted to become at risk of climate-induced population declines before 2,100 (Trisos et al., 2020). Therefore, investigating the mechanisms of how climate change induces population change may improve our predictions of the species most-affected by warming temperatures.

Understanding the mechanisms underpinning population declines in response to climate change and the temporal scale at which these mechanisms occur, at short- or long-term timescales, remains one of the main challenges when investing the influence of climate change on population change. Population abundance time-series and demographic data have begun to reveal how climate change leads to population decline. For birds and mammals average abundance trends were negatively associated with long-term rates of climate warming (Spooner et al., 2018), but sparse data in short time-series are at risk of overfitting weather effects (Knape and de Valpine, 2011). Therefore, targeted demographic studies unpicking how changes in weather patterns cause population change (Cordes et al., 2020; Coulson et al., 2001; Layton-Matthews et al., 2021; Paniw et al., 2019; Paniw et al., 2021; Woodroffe et al., 2017) and whether species traits can help predict these changes (Pacifici et al., 2017) are vital to highlight vulnerable species. While emphasis is often on long-term temperature trends, the immediate impact of the weather on populations, particularly in the context of extreme or anomalous events (e.g. heat waves and droughts), is also important (Maxwell et al., 2018). Furthermore, there may also be impacts of increased variance in weather conditions as opposed to changes in central tendency (Lawson et al., 2015; Le Coeur et al., 2021; Stenseth et al., 2003). Applying these concepts at a comparative scale and assessing finer-scale population changes with respect to changes in the weather (and particularly extreme weather events or weather variance), and their relationship to species traits, will aid in illuminating climate-change responses across the tree of life (Compagnoni et al., 2021; Paniw et al., 2021).

Life-history variation is a promising factor that could explain observed variation in responses to climate change (Pacifici et al., 2017). The timing of key demographic events of survival and recruitment across the life cycle, or life-history traits, are evolved responses to the environment, and characteristics relating to both ‘slow’ and ‘fast’ life histories are therefore adaptive in different environmental contexts (Stearns, 1992). For example, life-history differences between three amphibian species in Western Europe drove predicted survival and reproduction responses to the North Atlantic Oscillation (Cayuela et al., 2017). Generally, organisms with slower life histories are better adapted to cope with environmental fluctuations. Longer-lived organisms have a reduced relative effect of variability in vital rates, variability which is expected during environmental change, on population growth rates (Morris et al., 2008) and long-lived plants have weaker absolute demographic responses to weather (Compagnoni et al., 2021). However, while generally buffered, long-lived, slow-reproducing animals are often more at risk of extinction (Cardillo et al., 2005), and slower to recover when perturbed (Gamelon et al., 2014; Jackson et al., 2019; Turkalo et al., 2017). Comparative approaches linking life-history traits to climate-change responses may therefore provide a useful predictive link to improve our understanding of climate vulnerability.

In this study, we investigated annual population responses to temperature and precipitation anomalies (i.e. weather deviation from average conditions) in populations of terrestrial mammals across the world’s ecosystems. We tested whether life history predicts population responses to the weather, and therefore its utility in assessing vulnerability to climate change. We addressed these questions using 486 long-term (≥10 consecutive years) abundance records from 157 species of terrestrial mammal obtained from the Living Planet Database (Almond et al., 2020), by implementing a two-step meta-regression framework. First, for each abundance record, we assessed how observed annual population growth rates were influenced by standardised weather anomalies (annual deviation from long-term average weather patterns) and intra-annual weather variance using autoregressive additive models that accounted for temporal autocorrelation in abundance records and overall abundance trends.

Then, we used a phylogenetically controlled Bayesian meta-regression with weather-effect coefficients as the response variable to address three key questions: (1) Are there directional (i.e. different from 0) temperature and precipitation effects on abundance changes across the terrestrial mammals? (2) How are these patterns influenced by covariance both within and between species, and are there vulnerable biomes or spatial patterns in responses? (3) Can species-level life-history traits predict the absolute magnitude of population responses to the weather? We characterised population responses to weather anomalies/variance accounting for both within- and among- species variance, incorporating a categorical predictor of ecological biome (Olson et al., 2001), and three broad continuous life-history traits that are widely characteristic of the key axes of life-history variation (Stearns, 1992). Due to a lack of overall biodiversity change globally in response to climate (Antão et al., 2020), we predicted that there will be no clear directional patterns in population responses to weather anomalies or weather variance overall. Instead, because life-history traits are an evolved response to the environment (Stearns, 1992), we predicted that mammals with ‘slow’ life-history traits will be buffered against weather anomalies and have responses with a lower absolute magnitude (Morris et al., 2008). We focused on absolute responses to weather anomalies in the context of life history because we had no a priori expectation for directional patterns in response to weather, but instead expected greater variance in (or more extreme) responses from ‘fast’ species (Compagnoni et al., 2021; Le Coeur et al., 2021). We expected that the link between population responses and life history would result in strong phylogenetic autocorrelation in weather responses (James et al., 2021; Melero et al., 2022). Finally, we predicted that population responses to weather anomalies would be more pronounced in biomes that experience more stable average climatic conditions.

The terrestrial mammals are an ideal study system to explore the predictors of population responses to climate change because they are a well-studied group with a combination of intensive abundance monitoring across the globe (Almond et al., 2020), detailed life-history information for hundreds of species (Conde et al., 2019; Myhrvold et al., 2015) and a highly-resolved phylogeny to facilitate phylogenetic comparative analyses (Upham et al., 2019). Furthermore, there is growing evidence from the mammals of the mechanistic links between the climate, demography, and population dynamics (Coulson et al., 2001; Paniw et al., 2019; Paniw et al., 2021; Woodroffe et al., 2017).

We assessed population responses to weather anomalies in 486 long-term abundance time-series records from 157 species of terrestrial mammals globally (Figure 1). The time-series records ranged in duration from 10 years to 35 years, with mean and median record lengths across records of 15.7 and 14 years, respectively (Figure 1). The records were distributed across 13 terrestrial biomes (Olson et al., 2001), including both tropical and temperate regions, but were generally biased towards north western Europe and North America. We had records from 12 of 27 mammalian orders recognised by the IUCN Red List for threatened species (IUCN, 2016), but most densely in the Artiodactyla (n=172), Carnivora (n=127) and Rodentia (n=82) (Figure 1). The number of records for each species ranged from 1 to 17, with a mean of 3.1 and median of 2 records per species (Figure 2).

Figure 1

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Figure 2

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Life history predicts absolute population responses to weather anomalies

Across terrestrial mammals, we found that longer-living species with smaller litter sizes had lower absolute population responses to weather anomalies. We tested a set of Gamma models incorporating univariate, multivariate and two-way interaction effects of maximum longevity, litter size, and adult body mass and their influence on the absolute magnitude of temperature/precipitation effects using model selection and leave-one-out cross-validation (Supplementary file 1). As with our Gaussian models of overall weather effects, we found that record length had a strong negative impact on the absolute magnitude of temperature and precipitation responses, with posterior estimates on the linear predictor scale of ${\displaystyle {\beta }_N}$ = –0.30 [-0.38––0.21] and ${\displaystyle {\beta }_N}$ = –0.37 [–0.47- –0.26], respectively (Appendix 1—figure 18). Namely, shorter records were associated with larger absolute temperature and precipitation responses. We found no association between adult body mass and either temperature (${\displaystyle {\beta }_{BODYMASS}}$ = –0.02 [-0.15,0.10]) or precipitation responses (${\displaystyle {\beta }_{BODYMASS}}$ = –0.00 [-0.17,0.17]). Furthermore, we found no strong evidence for any two-way interactions between life-history variables (Supplementary file 1). For both temperature and precipitation effects, the most competitive model was the univariate model including maximum recorded longevity (Δelpd = 5.44 and Δelpd = 1.03, compared to the base model for temperature and precipitation, respectively; Supplementary file 1 - Table S1). However, univariate models including litter size also had a higher predictive performance than the base model (Δelpd = 3.98 and Δelpd = 0.8 for temperature and precipitation, respectively). For temperature, the second-best predictive model was the one that included univariate effects for longevity, body mass and litter size (Δelpd = 4.54; Supplementary file 1 - Table S1), and this model was also competitive for precipitation (Δelpd = 0.69; Supplementary file 1 - Table S2). Therefore, in both cases we selected the models including all univariate life-history effects.

For both temperature and precipitation, our results highlight that shorter living mammals with greater litter sizes had greater absolute responses to weather anomalies than longer-living, slower-reproducing mammals (Figure 3). Absolute weather responses were negatively associated with longevity, with posterior means on the linear predictor scale of ${\displaystyle {\beta }_{LONGEVITY}}$ = –0.20 [-0.41,0.02] and ${\displaystyle {\beta }_{LONGEVITY}}$ = –0.17 [-0.42,0.09] for temperature and precipitation, respectively (Figure 3a and c). Thus, a maximum longevity change from 10 months (Akodon azarae) to 80 years (Loxodonta africana) was associated with a 2.36-fold and 2.05-fold decrease in the predicted absolute magnitude of responses to temperature and precipitation. So, for every additional 5 years of life, there was a 16.8% decrease in absolute responses to temperature and 14.6% decrease in the absolute responses to precipitation. An organism’s longevity is strongly correlated to their body mass, but the effect of longevity held irrespective of whether adult body mass was also included in the model.

Figure 3

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Absolute weather responses were also positively associated with litter size, with posterior means of ${\displaystyle {\beta }_{LITTER}}$ = 0.16 [0.02,0.32] and ${\displaystyle {\beta }_{LITTER}}$ = 0.11 [-0.08,0.30] for temperature and precipitation, respectively (Figure 3b and d). In other words, mammals bearing more offspring in a single litter had greater absolute responses to temperature and precipitation anomalies. A change in litter size from 1 (monotocous species, various) to 17 (Thylamys elegans) was associated with a 1.99-fold and 1.60-fold increase in the predicted temperature and precipitation responses. For every additional offspring invested into at the litter stage, there was a 12.4% increase in the absolute magnitude of temperature responses and 10% increase in the absolute magnitude of precipitation responses.

Our results provide an important empirical link between a species’ life history and its population responses to environmental change. While we found no directional patterns of responses to temperature and precipitation anomalies or intra-annual variance across the mammals, life-history traits relating to the pace of life were associated with absolute responses to weather. Namely, shorter living species with increased litters sizes, or species characterised with ‘fast’ life-history traits, responded with a greater absolute magnitude compared to those with ‘slow’ life-history traits. Practically, our results suggest that increased extreme/anomalous weather events will have a greater impact (both positive or negative) on the abundance of short-living mammals with higher reproductive output. Therefore, increased monitoring of vulnerable species with ‘fast’ life-history characteristics may benefit mammal conservation as global weather anomalies are increasingly common.

We find support for the hypothesis that longevity, and ‘slow’ life-history traits more generally, buffer organisms against short-term variability in the environment (Morris et al., 2008). Life history evolves in response to, and as an adaptation to, environmental conditions (Stearns, 1992), but rarely has the link between life history and responses to the environment been demonstrated at a global scale. We add to a small number of studies linking population demography and the climate (Compagnoni et al., 2021; Paniw et al., 2021). We do not argue that long-lived species are less vulnerable to climate change. Over longer time-scales, species with slow life-history traits are also slower to recover from perturbations (Gamelon et al., 2014), which would include sustained environmental change. Furthermore, many long-lived mammal species are affected by other threats such as poaching and habitat loss (Cardillo et al., 2005). Critically, however, our results highlight the potential utility of life-history traits for predicting species vulnerability to climate change.

More generally, demography has a role to play in predicting population declines in the Anthropocene and in highlighting targets for conservation management (Conde et al., 2019; Richards et al., 2021). Our study emphasises this role, demonstrating the predictive power of life-history traits when investigating responses to environmental change. However, there are limitations and barriers to the utility of demography in conservation. Only 1.3% of tetrapods globally have sufficient demographic information with which to estimate population dynamics (Conde et al., 2019). Here, we used summary traits that are available for many species (maximum longevity and mean litter size). Although these summary traits were well correlated with more robust demographic traits for a smaller subset of species (Appendix 1—figure 26), maximum recorded longevity, while sufficient as a broad indicator, is strongly influenced by sampling variance and a flawed measure of longevity differences between taxa (Moorad et al., 2012). Ideally, lifetables with mortality and reproduction trajectories across the life cycle can be combined with data on external drivers to investigate detailed patterns in population dynamics, rather than relying on abundance trends (Desforges et al., 2018; Jackson et al., 2019). The recent development of the demographic resilience framework, which uses demographic data across the life cycle to simulate how a population may respond to perturbations (Capdevila et al., 2020), has excellent potential in extending these findings to explore demographic relationships with climate responses in detail. Unfortunately, however, detailed (st)age-specific demographic information is currently available only for a minority of species, but growing in availability rapidly (Salguero-Gómez et al., 2016). Therefore, there is a need to continue to increase the collection of demographic data (and other traits) for many more species than are currently available (Conde et al., 2019), so that we may predict population changes with respect to environmental change. Achieving this target may revolutionise the way we quantify species vulnerability to climate change (Antão et al., 2020; Dornelas et al., 2019; Leung et al., 2020; Paniw et al., 2021), helping to prevent extinctions before they occur.

In line with recent global assessments of biodiversity in the face of climatic change (Paniw et al., 2021), we did not find an overall directional effect of weather anomalies on population growth rates. This may in part reflect the fact that abundance changes are a higher order process determined by complex interactions between demographic components that counteract each other (Leung et al., 2020; Paniw et al., 2021). However, our results contrast with findings of linear associations between mammal abundance and temperature change (Spooner et al., 2018). These differences may reflect our approach to investigate annual changes, rather than long-term trends. Significant population trends from long time-series are detectable from smaller component time-series even when sampling is incomplete (Wauchope et al., 2019), and thus responses detected in trends may reflect broader changes in response to the climate that are not detected in models of annual change. Furthermore, we estimated linear, annual effects of weather on population growth rates, where population responses may actually be more complex non-linear patterns or lagged effects. However, the detection of climate effects on average trends may also be confounded by effects of other (sometimes more dominant) drivers (e.g. habitat loss) (Daskalova et al., 2020a). While out of the scope of the current study, population dynamics in endothermic mammals have been linked to other drivers such as diet specialisation, body size and human influence (Pacifici et al., 2020). Nevertheless, our findings can be explained in light of recent studies from the Living Planet Database that have found that the large majority of records do not exhibit population declines (Leung et al., 2020).

Interestingly, we did not find evidence for phylogenetic covariance in weather responses between species. Closely related species can have disparate responses to weather anomalies. Recent evidence from birds indicated strong phylogenetic covariance in vital rates, particularly in adult survival, and the incorporation of phylogenetic information greatly improved predictive performance when imputing vital rates (James et al., 2021). In line with the overall patterns, our findings may reflect the trade-offs between vital rates, which cancel one another out when scaling up to population-level processes such as population growth rates in response to the weather (Paniw et al., 2021). However, this result is in contrast to findings from butterflies, which suggested that local responses to weather anomalies had a strong phylogenetic signal (Melero et al., 2022). One potential explanation for our finding is that for long-term time-series, there may also be temporal trade-offs in vital rates, where for example investing heavily into survival in one year (in response to weather) may impact subsequent reproduction for several years, decreasing the magnitude of population growth rates. The extent of phylogenetic covariance in vital rate responses and trade-offs remains unknown, but understanding how the climate impacts demographic rates across species may provide a useful tool for imputing population responses to the climate across the tree of life (James et al., 2021).

Instead, we highlight the importance of variation in population responses to weather anomalies within a species. Different populations of the same species can have different responses to weather anomalies. Sampling heterogeneity has recently been shown to have broad implications for metrics of population dynamics, where demographic rates are poorly correlated among sampling sites for the same species (Engbo et al., 2020; Römer et al., 2021). Demographic differences within a species range may reflect broader environmental gradients or wider climatic niche (Römer et al., 2021). Therefore, inferences obtained from monitoring single populations or studies may not accurately portray species-level variability (Gaillard et al., 2013). This has broad implications for macroecology, particularly in population viability assessments (PVA) and species-distribution modelling. As well as suffering from data quality issues in their parameterisation (Chaudhary and Oli, 2020), our findings suggest that PVAs based on data from a single population may not accurately reflect population viability across a species’ geographic range. Therefore, incorporating detailed demographic data, and investigating differences in population responses across a range, could greatly improve our perspective on population viability (Desforges et al., 2018). Furthermore, presence-only models of species distributions that do not account for responses to the environment within a species range do not accurately represent species distributions (Benito Garzón et al., 2019). Moving towards trait-based monitoring and explicitly including demographic processes with mechanistic links to appropriate drivers into species distribution models could greatly improve predictions of climate change impacts on the biosphere (Trisos et al., 2020).

To assess the effects of weather anomalies on population growth rates, we collated information on global weather and the abundance, life history and phylogeny of the terrestrial mammals. We carried out all analyses using R version 4.0.5 (R Development Core Team, 2021). For all data on the terrestrial mammals, we standardised species names using the taxize package version 0.9.98 (Chamberlain, 2020) and matched using the Global Biodiversity Information Facility database (https://www.gbif.org/). All code used in the current study and additional descriptions of the analyses are archived in the Zenodo repository (https://doi.org/10.5281/zenodo.6620489), which was created from the following GitHub repository https://github.com/jjackson-eco/mammal_weather_lifehistory; Jackson, 2022 copy archived at swh:1:rev:cd6fb95ac8ae80c6889fe4f4785d17cab7d18375.

All data presented in the current manuscript is publicly available. All code and analyses are fully available and archived in the following Zenodo repository: https://doi.org/10.5281/zenodo.6620489, which was created from the folowing github repository: https://github.com/jjackson-eco/mammal_weather_lifehistory, copy archived at swh:1:rev:cd6fb95ac8ae80c6889fe4f4785d17cab7d18375.

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Decision letter after peer review:

Thank you for submitting your article "Life-history predicts global population responses to the weather in the terrestrial mammals" for consideration by eLife.

Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Bernhard Schmid as the Reviewing Editor and Christian Rutz as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Nigel Yoccoz (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this decision letter to help you prepare a revised submission.

Essential revisions:

1) Please try to improve the narrative by making it less exploratory (Reviewer #4). Clearly state your a priori hypotheses or at least explain why you expected the chosen life-history traits and environmental variables to be the most relevant and how they may interact and influence population dynamics. It may also be useful to explain why you expected robust prediction of absolute changes, but not for the direction of changes, and why this can still be useful for conservation biologists (Reviewer #4).

2) Please explain to which extent the greater absolute response of species to weather anomalies scales to a potentially larger absolute variation in population size of those species (Reviewer #2).

3) Please justify the selection of explanatory weather variables (Reviewers #3 and #4), e.g., by mentioning that fits with alternative variables were worse.

4) Perhaps try to analyze some subsets of the data to verify the robustness of the overall analysis (Reviewer #2).

5) Please discuss the limitations highlighted below by Reviewers #2-4, as well as the following points: traits were assumed constant between populations within species as were other characteristics of populations within species (e.g., marginality with regard to distribution range and thus "weather niche" (Reviewer #2), other traits (Reviewer #3) such as size at birth, sociality). Please provide a reason for why (or add corresponding analyses) biome (Reviewers #2 and #3) or site characteristics were not considered as explanatory variables (but rather subsumed in multilevel factors).

6) Please amend the title to read "Life-history predicts global population responses to weather in terrestrial mammals".

Reviewer #1 (Recommendations for the authors):

I must admit being a bit skeptical when it comes to large comparative analyses, as databases such as the Living Planet includes data of variable quality, and maximum longevity is, as you acknowledge, a far from ideal index for adult survival rate. Moreover, as you rightly point out, the available data are heavily biased towards some groups and biomes. Many analyses have been published using similar databases (e.g., Ono et al., 2019), so clearly some see value in such analyses. My concern is that because one has to "sell" the paper one avoids pointing out the limitations -- but alas this is not specific to your study.

One concern I have is how such analyses can be informed by the more detailed and accurate studies based on long-term monitoring of single populations (e.g., Gaillard et al., 2013). One knows that weather effects will vary among populations -- e.g., depending on where the population is in the climatic niche (see above) -- and such variation is crucial in predicting effects of weather and climate change. Large "meta-regressions" such as yours might be useful in summarizing the data available in one database, and you have done an excellent job analyzing these data, but given the far from random sample you have available, it is hard to assess how to make sense of the patterns observed. Perhaps using subsamples of high quality studies in different groups and biomes (if that is at all possible) to assess the predictive performance of your models may help.

You rightly discuss the limits of using maximum longevity as an index of a better measure such as adult survival rate. Would it be possible for the sample of species you have to at least assess the relationship between maximum longevity and adult survival rate? And for those species to repeat the analyses? The precision would be much lower as the sample size is likely to be small but you can at least assess the consistency of the relationships.

Regarding the effects of biomes, I did not understand how it was parameterized (e.g., l 465). It was included as a categorical predictor, right (figure S16 seems to indicate that)? So you have β_Biome[j] for j=1:number of biomes?

Adler, P. B., et al., 2011. Productivity Is a Poor Predictor of Plant Species Richness. Science 333:1750-1753.

Angert, A. L. 2009. The niche, limits to species' distributions, and spatiotemporal variation in demography across the elevation ranges of two monkeyflowers. Proceedings of the National Academy of Sciences of the United States of America 106 Suppl 2:19693-19698.

Gaillard, J. M., A. J. M. Hewison, F. Klein, F. Plard, M. Douhard, R. Davison, and C. Bonenfant. 2013. How does climate change influence demographic processes of widespread species? Lessons from the comparative analysis of contrasted populations of roe deer. Ecology Letters 16:48-57.

Ono, K., O. Langangen, and N. C. Stenseth. 2019. Improving risk assessments in conservation ecology. Nature Communications 10:7.

Reviewer #2 (Recommendations for the authors):

Jackson et al., present a global analysis of the effects of life history on the response of terrestrial mammal populations to weather, showing that litter size and longevity significantly alter how population's respond to anomalies in temperature and rainfall. The topic is highly interesting, and generally the manuscript is written in clear and concise way with some exceptions (see comments below). My main concern is about the timescales over which the weather events are calculated. As I understand it, these are anomalies away from the yearly expected value. This approach was of course used because the LPD data is recorded on an annual basis. However, there is a huge difference in the effect of slightly elevated temperatures across a whole year vs. one month with very high temperatures. The authors appear to have addressed this by looking at the yearly anomaly and the variance in the weather (annual weather variance) calculated across each year; however they are currently lacking some detail on exactly what this entailed (it is currently only mentioned in passing).

The authors have done a good job pulling all these analyses together into an accessible piece, and have provided all of the code to repeat these analyses.

Reviewer #3 (Recommendations for the authors):

1/ Overall framing and questions/hypotheses:

– Please justify more clearly, somewhere in the introduction, why looking at the weather is interesting for understanding future effects of climate change (see lines 87/88 and 111/112).

– In the introduction, the evidence from past work could be summarised more efficiently and concisely around key points (e.g., see lines 74-82, lines 96-104); thus the overall narrative/structure of the introduction can be improved.

– Beyond the three questions you ask, please explain what the underlying hypotheses are (e.g., for each question, adding a few words to explain what you expect: what are the spatial patterns you predict, what do you hypothesize would be the effect of life history, etc.)? In addition, the phrasing of the first question is a bit ambiguous with the term "consistent" (in my understanding, here you ask, across species and populations, whether the weather effects are significant, as in whether they differ from zero). Adding a specific hypothesis would also help to make this question clearer.

– Finally, please justify more why you look at the magnitude (absolute values) of the weather effects when investigating the influence of life-history on the responses, and why you don't investigate the directionality of the effects.

2/ Results

The results could be presented more efficiently and clearly overall. I would suggest including a few higher-level summaries throughout the manuscript to conclude on specific points.

3/ Discussion:

– Some of the limitations of the study should be developed in the discussion, in particular:

– The potential biases of the study (e.g., length of the time series): you mention that record length was significantly associated with temperature and precipitation effects. I would be good to mention whether your results are likely robust to these biases or otherwise what the expected effects on the results would be.

– The fact that you use absolute values (magnitude of the weather effects) for the life-history models, so that you can't conclude about the directionality of the effects. I suggest highlighting how your work helps predict future responses and inform conservation despite the fact that you didn't investigate the directionality of the effects (see lines 239/240; 243/244) (e.g., do your results imply that populations of species with fast life history should be more closely monitored irrespectively of whether they grow or decline?).

– It would be good for the discussion to make the key points stand out more.

4/ Methods

Life-history traits: It is important to include a statement about the degree of multicollinearity among the traits.

Essential revisions:

1) Please try to improve the narrative by making it less exploratory (Reviewer #4). Clearly state your a priori hypotheses or at least explain why you expected the chosen life-history traits and environmental variables to be the most relevant and how they may interact and influence population dynamics. It may also be useful to explain why you expected robust prediction of absolute changes, but not for the direction of changes, and why this can still be useful for conservation biologists (Reviewer #4).

We have addressed this point by thoroughly revising the introduction to include a specific set of hypotheses relating to both overall weather responses and interactions with species life-history L133-144. We also include a justification for why we included absolute changes L138-141, and why this is still important for conservation L258-262.

2) Please explain to which extent the greater absolute response of species to weather anomalies scales to a potentially larger absolute variation in population size of those species (Reviewer #2).

We have addressed this point with an extensive response to reviewer #1 (here reviewer #2). We feel that given our use of state-space models that explicitly model process noise (variation in population size), we have largely accounted for this variation. We now state this in the methods L531-535. However, we also argue that this variation is a core component of what we are trying to capture i.e. we expect that short-lived species have greater variability in population size as a result of their evolved response to the environment. We hope that with the more explicit hypotheses included here this is made clearer.

3) Please justify the selection of explanatory weather variables (Reviewers #3 and #4), e.g., by mentioning that fits with alternative variables were worse.

We have addressed this point by more explicitly highlighting our additional analyses on weather variance as well as weather anomalies. We have now included a clearer narrative of weather variance in the introduction L90-94, as well as explicit indication of these results L173-174. Our exploration of weather variance yielded near-identical results to that of the weather anomalies, and so we performed later analyses with weather anomalies.

4) Perhaps try to analyze some subsets of the data to verify the robustness of the overall analysis (Reviewer #2).

We have addressed this point by providing extensive additional analysis to explore the implications of our selection of life-history traits (by using more detailed life-history traits from structured population models) and our use of long-term records (by exploring both higher quality and lower quality subsets). These additional analyses are detailed in the Methods L401-402, L454-465 and results are included as supplementary figures Figure S25-27.

5) Please discuss the limitations highlighted below by Reviewers #2-4, as well as the following points: traits were assumed constant between populations within species as were other characteristics of populations within species (e.g., marginality with regard to distribution range and thus "weather niche" (Reviewer #2), other traits (Reviewer #3) such as size at birth, sociality). Please provide a reason for why (or add corresponding analyses) biome (Reviewers #2 and #3) or site characteristics were not considered as explanatory variables (but rather subsumed in multilevel factors).

We have addressed this comment primarily by including an explicit limitation section of the discussion L350-366 in which we describe the key limitations of the current study in detail. We hope that this in conjunction with our conservative and extensive modelling framework are convincing to address these limitations (although not fully, as pointed out by all reviewers). On the point about weather niche, we have now included additional discussion on within-species variation L330-340. Finally, we have amended our lack of clarity by specifying that biomes were explicitly included as explanatory variables L185-190.

6) Please amend the title to read "Life-history predicts global population responses to weather in terrestrial mammals".

This point has been addressed.

Reviewer #1 (Recommendations for the authors):

I must admit being a bit skeptical when it comes to large comparative analyses, as databases such as the Living Planet includes data of variable quality, and maximum longevity is, as you acknowledge, a far from ideal index for adult survival rate. Moreover, as you rightly point out, the available data are heavily biased towards some groups and biomes. Many analyses have been published using similar databases (e.g., Ono et al., 2019), so clearly some see value in such analyses. My concern is that because one has to "sell" the paper one avoids pointing out the limitations -- but alas this is not specific to your study.

We thank the reviewer for their honesty and scepticism for large comparative analyses. In general, we do agree that data quality and biases mean that rigor and conservatism in comparative analyses is crucial. Throughout the progression of this study, we have used conservative Bayesian inference with regularising priors, performed supplementary and confirmatory analyses, and ensured that we performed extensive data checks (please see supplementary code). However, we agree that acknowledging the key limitations of the study is crucial and was lacking in the previous version of the manuscript. In response to Reviewer #3 below, we have now included a dedicated limitations section in the discussion L350-366.

One concern I have is how such analyses can be informed by the more detailed and accurate studies based on long-term monitoring of single populations (e.g., Gaillard et al., 2013). One knows that weather effects will vary among populations -- e.g., depending on where the population is in the climatic niche (see above) -- and such variation is crucial in predicting effects of weather and climate change. Large "meta-regressions" such as yours might be useful in summarizing the data available in one database, and you have done an excellent job analyzing these data, but given the far from random sample you have available, it is hard to assess how to make sense of the patterns observed. Perhaps using subsamples of high quality studies in different groups and biomes (if that is at all possible) to assess the predictive performance of your models may help.

Reviewer #1 and Reviewer #3 both raise an excellent point on testing our findings for high quality subsets of the data. In particular, temporal replication is a component that we believe will give more indications of the robustness of our analyses. We have therefore run extensive additional analyses to explore the implications of data quality on our findings. Specifically, we repeated the full meta-regression framework, but included ≥5 years or ≥20 years of abundance data. The latter in particular enabled us to critically evaluate the assumptions of our key findings with respect to data quality. In both cases, in conjunction with our main findings we did not find evidence for directional patterns of weather responses. Posterior estimates temperature and precipitation anomalies are given in Author response image 1.

Author response image 1

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Interestingly, for both temperature and precipitation, models with ≥5 years of abundance data had greater estimates of phylogenetic covariance with respect to species-level variance. This is probably a result of reduced repeated observations.We then repeated life-history model selection for both data subsets. For temperature, we found that including life-history variables did not improve model predictive performance for records ≥5 years in length. Model selection results can be found in Author response table 1.

For precipitation, there was a slight increase in predictive performance for the model including life-history variables, however this was only a marginal change and both longevity and litter coefficients were moderate and substantially overlapped 0.

In summary, we found that while key directional patterns remained in the data subset with decreased numbers of observations, this was not reflected in life-history patterns, for which we conclude a more detailed subset of data was necessary to capture information on weather responses.

Repeated the analyses for the high-quality subset of data ≥20 years, overall the negative impact of longevity on absolute weather responses and positive impact of litter size was maintained. In this analysis, given a smaller subset of 54 species and 94 observations, we concluded that it was more pragmatic to ignore phylogenetic covariance and explore only within-species variance. In this repeated analysis, for both temperature and precipitation we found predictive support for life-history traits relative to the base model.

However, life-history coefficients were not as pronounced in this smaller subset. For temperature, there was still a strong positive association with litter size, but the longevity effect was reduced. In contrast, for precipitation the negative longevity effect remained but the litter size effect diminished. Posterior predictions of these patterns can be found in Author response image 2.

Author response image 2

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Overall, these additional analyses with both increased and decreased data quality revealed consistent patterns with our main findings. Importantly however, we suggest that ≥5 years of data are not sufficient to characterise annual responses to weather.In answer to the original point of the reviewer, rerunning analyses on a subset of increased quality (longer records) yielded consistent results in terms of life-history. Namely, traits relating to the fast-slow continuum were still associated with absolute responses to weather anomalies. We therefore conclude that our broad findings are robust. We have now included a brief description of this additional analysis L401-403 and the inclusion of a supplementary Figure S25.

You rightly discuss the limits of using maximum longevity as an index of a better measure such as adult survival rate. Would it be possible for the sample of species you have to at least assess the relationship between maximum longevity and adult survival rate? And for those species to repeat the analyses? The precision would be much lower as the sample size is likely to be small but you can at least assess the consistency of the relationships.

The reviewer rightly points out the issues with maximum longevity, which of course as a measure of the lifespan of the oldest recorded individual of a species, introduces a lot of noise when assessing species-level life history. We completely agree that in an ideal world we would have a fully formed life-table/structured population model with which we could assess other key life-history traits such as adult survival, life expectancy or generation time (Gaillard et al., 2005). The question is then whether the noise introduced by this biased measure is too much, given the research questions. Our goals were comparative here, which the reviewer concedes they have scepticism in, but that we feel are important to explore broad global patterns with available data. The goal of this study was to explore weather responses for as many mammal species as possible, for which we felt we needed life-history variables that gave us a comparable breadth of data. We reasoned that the broad metrics of maximum longevity and mean litter size were largely characteristic of key life-history axes for this number of species. However, as is rightly pointed out we did not initially explore this assumption explicitly.

Therefore, we have now performed several additional analyses to address these concerns, which can now be found in methods section L454-465 and with additional supplementary figures Figure S26 and Figure S27. We did not find that the choice of broad life-history metrics impacted our key results.

To answer in more detail here however, as is pointed out by the reviewer, this level of demographic detail is hard to come by, save for high-quality, individual-based, longitudinal studies, which the reviewer is extremely familiar with. We opted to use the COMADRE animal matrix population model database to extract more detailed demographic information for as many terrestrial mammal species as possible. After data checks (including only ergodic, reducible, primitive, non-NA matrices etc.) we were left with 37 species that had both detailed structured population models and the broad metrics of life-history we include. From these models, we calculated generation time (the mean number of years needed for a cohort to replace itself, a key fast-slow trait used in many studies, see Healy et al., (2019) for a recent example), life expectancy, and adult survival (here defined as the mean survival of adult stages in the matrix). First, we explored the collinearity between these life-history variables in the larger subset of 37 species, which is presented in Figure S26. This pairs plot gives the Pearson’s correlation coefficients for each pairwise set of log-transformed life history variables that we assessed here.

You will see that, in spite of the biases highlighted, the life-history variables included here are all highly correlated with one another, even for this small subset of 37 species. Specifically for Maximum Longevity, it has Pearson’s coefficients of >0.64 when correlated with more detailed life-history variables. This suggests that our choice of broad characteristics is appropriate for this comparative question.

As suggested, we then extended to include these life-history variables in our meta-regression framework. However, the number of species that had both sufficient life-history information and LPD population data and weather responses was 16 (10 times lower than the number of species included in the main findings of the manuscript). We hope you agree that this amount of data is not ideal to explore these patterns at a global scale. Therefore, we analysed this data in a much simpler hierarchical model framework rather than the full phylogenetically controlled analyses. Here, we simply regressed life history variables against absolute weather responses, including only a random effect of species and controlling for sample size. You can see the results of the posterior predictions in Figure S26. Posterior predictions are given with the 90% credible intervals for each life-history/weather effect combination.

While the sample size here prohibits a more detailed exploration of these patterns, the same negative relationships can be observed. The coefficients of these responses were all below -0.12. These reiterate our findings of increased absolute responses to weather in short-lived species.

Gaillard, J.M., Yoccoz, N.G., Lebreton, J.D., Bonenfant, C., Devillard, S., Loison, A. et al.,

(2005). Generation time: a reliable metric to measure life-history variation among mammalian populations. Am. Nat., 166, 119–123

Healy, K., Ezard, T.H.G., Jones, O.R. et al., Animal life history is shaped by the pace of life and the distribution of age-specific mortality and reproduction. Nat Ecol Evol 3, 1217–1224 (2019). https://doi.org/10.1038/s41559-019-0938-7

Regarding the effects of biomes, I did not understand how it was parameterized (e.g., l 465). It was included as a categorical predictor, right (figure S16 seems to indicate that)? So you have β_Biome[j] for j=1:number of biomes?

The reviewer Is correct, biome was included as a categorical predictor. This point has been addressed L189, with further explanations throughout the text explaining our use of biomes (following comments from other reviewers). Technically, the reference to the sub-script j is incorrect here, as this is in reference to a species (L555). However, the equation form described is correct and so we have added this additional information to equation 3 L564-567.

Adler, P. B., et al., 2011. Productivity Is a Poor Predictor of Plant Species Richness. Science 333:1750-1753.

Angert, A. L. 2009. The niche, limits to species' distributions, and spatiotemporal variation in demography across the elevation ranges of two monkeyflowers. Proceedings of the National Academy of Sciences of the United States of America 106 Suppl 2:19693-19698.

Gaillard, J. M., A. J. M. Hewison, F. Klein, F. Plard, M. Douhard, R. Davison, and C. Bonenfant. 2013. How does climate change influence demographic processes of widespread species? Lessons from the comparative analysis of contrasted populations of roe deer. Ecology Letters 16:48-57.

Ono, K., O. Langangen, and N. C. Stenseth. 2019. Improving risk assessments in conservation ecology. Nature Communications 10:7.

Reviewer #2 (Recommendations for the authors):

Jackson et al., present a global analysis of the effects of life history on the response of terrestrial mammal populations to weather, showing that litter size and longevity significantly alter how population's respond to anomalies in temperature and rainfall. The topic is highly interesting, and generally the manuscript is written in clear and concise way with some exceptions (see comments below). My main concern is about the timescales over which the weather events are calculated. As I understand it, these are anomalies away from the yearly expected value. This approach was of course used because the LPD data is recorded on an annual basis. However, there is a huge difference in the effect of slightly elevated temperatures across a whole year vs. one month with very high temperatures. The authors appear to have addressed this by looking at the yearly anomaly and the variance in the weather (annual weather variance) calculated across each year; however they are currently lacking some detail on exactly what this entailed (it is currently only mentioned in passing).

We apologise for the lack of detail when describing the effect of weather variance. The reviewer is absolutely correct, we explored the effect of weather variance on population growth in addition to weather anomalies. Generally, we aimed to explore the consequences of ‘extreme’ weather in the current study, hence the exploration of anomalies and variance in contrast to climate trends for example. As pointed out by reviewer #1, this focus on weather was not entirely clear in the introduction, and we have therefore more explicitly made the link to weather anomalies and weather variance in the new version L90-94. In Figure S23 we presented the posterior estimates for the effect of temperature (a) and precipitation (b) variance on population growth rates.

These results are near identical to the results for weather anomalies, with the same conclusion of no directional pattern of temperature or precipitation variance on population growth rates. The model for temperature does highlight an interesting pattern of increase phylogenetic covariance however.

In the updated manuscript, we have made this result clearer L173-174. Furthermore, we now explicitly refer to weather variance in the introduction, including a brief description, when outlining our methodology L120-122.

Other than this I have few significant comments -- the authors have done a good job pulling all these analyses together into an accessible piece, and have provided all of the code to repeat these analyses.

We thank the reviewer for their positive feedback, which is very much appreciated and rewarding given many of our goals on the paper.

Reviewer #3 (Recommendations for the authors):

1/ Overall framing and questions/hypotheses:

– Please justify more clearly, somewhere in the introduction, why looking at the weather is interesting for understanding future effects of climate change (see lines 87/88 and 111/112).

This point has been addressed in the Introduction with more information on investigating weather as opposed to climate L80-97. For the full response to this point please see the response to reviewer #1 above.

– In the introduction, the evidence from past work could be summarised more efficiently and concisely around key points (e.g., see lines 74-82, lines 96-104); thus the overall narrative/structure of the introduction can be improved.

In addition to addressing other points in the introduction, we have thoroughly reworked the sections mentioned in this comment. We aimed to make these specific points much more concise and clearer in narrative, which you will now find L68-151. We hope you agree that these improve the manuscript. However, given the additional changes to add hypotheses, justification for weather, and more detail on the method intro (Reviewer #2), we have restructured large sections of the introduction.

– Beyond the three questions you ask, please explain what the underlying hypotheses are (e.g., for each question, adding a few words to explain what you expect: what are the spatial patterns you predict, what do you hypothesize would be the effect of life history, etc.)?

We have now extensively reworked this section of the introduction to clearly highlight our main four hypotheses, L133-144. Generally, we expected no directional effect (following the winner/loser paradigm introduced in the second paragraph), but greater absolute responses in ‘fast’ species (paragraph 4 – following Morris et al., 2008 and Compagnoni et al., 2021). Given the demographic components underpinning population change, which are evolutionarily constrained (James et al., 2020), we initially expected phylogenetic autocorrelation. Finally, although spatial effects are less clear, we predicted that biomes with lower climate variability, where organisms are evolved in narrow climatic niches, would have larger impacts of weather anomalies. Please see introduction for full response.

In addition, the phrasing of the first question is a bit ambiguous with the term "consistent" (in my understanding, here you ask, across species and populations, whether the weather effects are significant, as in whether they differ from zero). Adding a specific hypothesis would also help to make this question clearer.

On reflection for this point, we agree that the term ‘consistent’ was not necessarily indicative of what we were attempting to communicate. And the reviewer rightly points out that we were aiming to ascertain whether responses were different from 0. However, we do not agree that the term significant is appropriate in the context of the current study given its heavy Bayesian focus, which may result in confusion with inference. Instead, we have opted to use the word directional in the manuscript, and have clarified what this means explicitly in the introduction L125-126 and Methods L550-551.

– Finally, please justify more why you look at the magnitude (absolute values) of the weather effects when investigating the influence of life-history on the responses, and why you don't investigate the directionality of the effects.

This choice to explore only the absolute magnitude of responses with respect to life-history was two-fold: We do not think there is good a-priori reasoning for a particular life-history to have positive/negative responses specifically (Morris et al., 2008), and this has been the approach in the previously published work of Compagnoni et al., (2021), for which absolute population coefficients were also explored with respect to weather (see also Le Coeur et al., 2021 for which absolute values of the long-term population growth rate were investigated with respect to interannual weather variance). While this reasoning was included in the discussion in our initial submission L591-593, we agree that this information is useful to place in the Introduction of the manuscript. Therefore we have now included this justification L138-141.

Le Coeur, C., Storkey, J., and Ramula, S. (2021). Population responses to observed climate variability across multiple organismal groups. Oikos, 130(3), 476-487.

2/ Results

The results could be presented more efficiently and clearly overall. I would suggest including a few higher-level summaries throughout the manuscript to conclude on specific points.

Following this suggestion, we have re-structured the Results section to clear highlight results at the opening of each paragraph, specifically at L165-166, L186-188, L211-212 and L232-234. We hope this presents the key findings more efficiently.

3/ Discussion:

– Some of the limitations of the study should be developed in the discussion, in particular:

We agree that acknowledging the limitations in the current study (which as all reviewers point out, is common to macroecological studies) is very important. Therefore, we have now included an additional section in the discussion highlighting these limitations explicitly L350-366. Further clarifications are included in response below, but please see the revised Discussion section for full details.

– The potential biases of the study (e.g., length of the time series): you mention that record length was significantly associated with temperature and precipitation effects. I would be good to mention whether your results are likely robust to these biases or otherwise what the expected effects on the results would be.

The reviewer raises an important point regarding the length of the time series, which is certainly important to acknowledge. Given that record length was controlled for in all analyses, and we did not have a-priori reasoning for expecting it to be associated with life-history, we conclude that our results are not impacted by this. Please see results L174-183 for this. However, we also now include this explicitly in the limitations paragraph of the discussion L350-366.

– The fact that you use absolute values (magnitude of the weather effects) for the life-history models, so that you can't conclude about the directionality of the effects. I suggest highlighting how your work helps predict future responses and inform conservation despite the fact that you didn't investigate the directionality of the effects (see lines 239/240; 243/244) (e.g., do your results imply that populations of species with fast life history should be more closely monitored irrespectively of whether they grow or decline?).

The reviewer is absolutely correct, and we agree about the monitoring interpretation. We have added this specific point to the first paragraph of the discussion L258-262. Specifically regarding the result however, we are not able to conclude on the directionality of weather effects with respect to life-history. Please see response above for full justification of this. Following this point however, we have added this point to the limitation section of the discussion L350-366.

– It would be good for the discussion to make the key points stand out more.

We have now made every effort to improve the clarity of the discussion. To achieve this, we have (1) included a ‘summary’ first paragraph L251-262 that highlights the study’s core results and implications, based on the comment of the reviewer above, (2) restructured the second paragraph L263-273 to make the key points stand out more clearly, (3) added summary sentences highlighting key results L263 and L297-298, (4) included a limitations paragraph to clearly highlight the study’s short-comings.

4/ Methods

Life-history traits: It is important to include a statement about the degree of multicollinearity among the traits.

We agree that the collinearity of the life-history variables is a very important aspect of this study to explore and account for. Allometric relationships in particular are very pervasive in macroecology and evolution and so investigating how lifespan and litter size influence population size independently of body size is critical.

To address this point explicitly, while this information was included on the Zenodo repository plot/lifehistory_raw/, we now include a statement of the collinearity in our life-history variables i.e. Pearson’s regression coefficients with adult body mass L459-465. However, in addition to these changes, we made further changes on the request of Reviewer #1 to incorporate more detailed demographic information. Therefore this section of the methods was thoroughly revised. For our full response on the appropriate use of life-history variables, please see the response to Reviewer #1 above.

Author details

1. John Jackson

   1. Interdisciplinary centre for population dynamics (CPop), Department of Biology, University of Southern Denmark, Odense, Denmark
   2. Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   john.jackson@zoo.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4563-2840

2. Christie Le Coeur

   Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0911-2506

3. Owen Jones

   Interdisciplinary centre for population dynamics (CPop), Department of Biology, University of Southern Denmark, Odense, Denmark

   Contribution

   Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

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