Contrasting pathways to tree longevity in gymnosperms and angiosperms

Domain: earth_science
Published: 2025-12-19
Editorial outcome: major_revision

Abstract
Tree longevity is thought to increase in growth-limiting, adverse environments, but a quantitative assessment of drivers of global variation in tree longevity is lacking. We assemble a global database of maximum longevity for 739 tree species and analyse associations between longevity and climate, soil, and species’ functional traits. Our results show two primary pathways towards long lifespans. The first is slow growth in resource-limited environments, consistent with the “adversity begets longevity” paradigm. The second pathway is through relief from abiotic constraints in productive environments. Despite notable exceptions, long-lived gymnosperms tend to follow the first path through slow growth in cold environments, whereas long-lived angiosperms tend to follow the second (“productivity”) path reaching maximum longevity generally in humid environments. For angiosperms, we identify two mechanisms for increased longevity under humid conditions. First, higher water availability increases species’ maximum tree height which is associated with greater longevities. Secondly, greater water availability increases stand density and inter-tree competition, limiting growth which may increase tree lifespan. The documented differences between gymnosperm and angiosperm longevity are likely rooted in intrinsic differences in hydraulic architecture that provide fitness advantages for gymnosperms under high abiotic stress, and for angiosperms under increased productivity or competition.

Introduction
Trees are widely revered as the longest-living group of organisms on Earth, including some species that can live for thousands of years1. Their lifespans control the time that carbon resides in tree stems, the main carbon reservoir of trees, and are thus closely related to the total carbon storage of forests2. Tree lifespan is also a key ecological and demographic trait that defines a species’ life history strategy, including its position along the fast-slow growth spectrum3–5, and its sensitivity to environmental variability6,7. Thus, to understand forest dynamics, and its response to global change, it is imperative to shed light on the drivers of tree lifespan among species, and across the globe. These insights can support predictions of future responses to global change, for example, by providing Earth System Models with more realistic estimates of tree turnover8,9. Yet, we lack systematic analyses of the environmental and climatic drivers, and species’ functional traits, associated with tree longevity.

Recent studies have provided strong evidence for increasing longevity toward high latitudes and altitudes10,11, confirming early observations that some of the longest-lived conifer trees such as 5000 year old bristlecone pines and 2000 year old junipers are usually found near tree lines, and at the coldest and/or driest places. These observations led early dendrochronologists to conclude that “adversity begets longevity”12. However, multi-millennia old trees are not just found in cold environments with short growing seasons, or sites with limiting water availability. They also grow in some of the most productive and humid temperate forests (e.g., Sequoia, Sequoiadendron), in flooded swamp forests (e.g., Taxodium13), and in African savannas (e.g., Adansonia14). These contrasting observations demonstrate that our understanding of the global environmental controls on tree longevity remains limited.

In addition, large differences exist among species growing in the same community. In tropical forests, longevity varies from a few decades for pioneers15 to several centuries for other species16,17. These differences arise from the diversification of species along different axes of demographic variation. One of these axes is the well-established fast-slow continuum of life-history strategies shaped by trade-offs between growth and survival18. Greater investment towards growth and resource acquisition traits are expected to result in lower tree lifespans3,19. Consistent with this trade-off, previous analyses have shown that faster growth is negatively associated with species’ longevity at the global scale11,20. However, very few studies have directly related species’ longevity to their traits. Most investigations instead have focused on the association between traits and demographic rates, such as tree mortality21,22. For example, wood density and tree mortality have repeatedly been shown to be negatively correlated21,23,24, but studies on relationships with longevity are less common25. In addition, mortality rates are far from uniform across a tree’s life history26, and mean mortality rates thus need not be correlated with maximum longevity across species and sites. Consequently, it remains unclear to what degree functional traits can be used to predict species’ genotypic or phenotypic variation in longevity, and a systematic quantitative analysis on the roles of environment, climate, and plant functional traits on species longevity is needed to shed light on the controls on growth-longevity trade-offs. Such analyses will also enhance our understanding of how climate change may affect tree longevity, and thus forest dynamics. Whether environmental drivers associated with longer lifespans among species (i.e. related to their adaptive traits) – and the focus of this study - also drive longer lifespans within species across habitats (i.e. related to their phenotypic responses) is poorly understood but is beyond the scope of the present work.

Here, we compile a new database of tree longevity or maximum life expectancy of 739 tree species. This dataset is based primarily on tree ring records complemented with radiocarbon dating, historical age records and growth projections from short-lived species, and tree species from under-represented biomes to capture the full spectrum of life history strategies. While conifers from cold sites are well represented (Supplementary Fig. 1b), our new dataset contains many tropical and short-lived (mostly broadleaved) species not included in previous analyses11,27,28. We use this database to assess the association of species’ longevity with global environmental gradients and functional traits, and aim to answer the following questions: (1) How is longevity associated with temperature, precipitation, and soil characteristics? (2) Can species’ traits predict variation in species’ longevity? And, (3) What are the relative roles of environment, traits and species growth rates on tree longevity? Previous studies showed that angiosperms and gymnosperms differ strongly in their growth strategies29, photosynthetic capacity30, hydraulic traits31–33 and longevity1,10,11,34, which we confirm in our analysis of the full dataset. Given that these taxonomic groups diverged over 300 million years ago35 and that they differ in their global distribution36, we primarily analyse the results for these groups separately to avoid any phylogenetic bias in the results. This represents the first effort aimed at disentangling the effects of climate, environment and species’ functional traits on tree species longevity.

Results
General patterns of variation in tree longevity
The oldest trees are typically found in mountain ranges at around 40° latitude such as the American Cordillera, Himalayas and Mediterranean mountains. However, trees over 1000 years old can also be found in tropical and subtropical regions (Supplementary Fig. 1a). We found that gymnosperm species live significantly longer (mean=717 years, s.e.=47 years) than angiosperm species (mean=296, se=12 years), and that there is a strong negative relationship between growth rate and longevity (Fig. 1a). When analysed by biome, we found that longevity increased from warm to cold climates (i.e., tropical < mediterranean < temperate mixed < boreal < temperate conifer biomes), and within (sub)tropical biomes from drier to wetter climates (dry grasslands and savannas & dry forest < moist forests, Fig. 1b). Species from tropical dry ecosystems (i.e., dry broadleaved forests, and grasslands, savannas and shrublands) had the lowest longevity, whereas coniferous temperate forest species displayed the highest longevity.

Covariation of species’ longevity with climate and soil
The effects of climate on tree longevity, at the global scale, differed between angiosperm and gymnosperms species (Fig. 2, Supplementary Tables 1, 2). Temperature had a greater influence on gymnosperm longevity (Fig. 2a), while precipitation had a stronger effect on angiosperm longevity (Fig. 2b). Longevity of gymnosperm species increased towards colder climates (Fig. 2a) and for species growing closer to the sub-arctic and alpine tree lines (Fig. 2c). The effect of mean annual temperature on tree longevity is in almost equal measures due to decreased growing season temperature and a shortening of the growing season length in colder climates (see Supplementary Table 2). Angiosperm longevity increased with precipitation metrics and climate moisture index (CMI) that takes potential evapotranspiration into account (Supplementary Table 1). The strongest correlation was found with precipitation during the driest quarter of the year (Fig. 2b). These climate relationships are robust when excluding species with lower confidence in their longevity estimates (Supplementary Fig. 3, Supplementary Table 3c). We further find that gymnosperms show a decrease in longevity with increasing site productivity, whereas angiosperms show a weak positive relationship with site potential net primary productivity (Fig. 2d).

Soil cation exchange capacity (CEC) and soil pH showed bivariate relationships with tree longevity in both groups (Supplementary Tables 1, 2). However, these effects on longevity are confounded by covariation with climate at the global scale. For example, in the angiosperm dataset, soil pH strongly covaried with precipitation and its effect on longevity disappeared when controlling for the effects of precipitation, temperature and CEC. Similarly, we found that a negative global relationship between CEC and temperature partially obscured a negative, but weak effect of temperature on longevity in angiosperms (Supplementary Table 2). That is, increased longevity due to colder temperatures at higher latitudes was partially offset by decreased longevity due to greater CEC at higher latitudes. For gymnosperms, we find no direct effect of CEC or soil pH after accounting for the effects of temperature and precipitation (Supplementary Table 2).

Covariation of traits and tree longevity
Many of the studied traits showed significant bivariate relationships with longevity across the full dataset, including both angiosperms and gymnosperms (Supplementary Table 1). However, most of these relationships disappeared when the data were analysed separately for angiosperms and gymnosperms. Of all the studied traits, the only traits that remained strong predictors for longevity within gymnosperms and angiosperms, were species’ radial growth (Fig. 1a) and species’ maximum tree height (Fig. 2f). For gymnosperms, no other functional traits were significantly related to longevity. For angiosperms we found significant, but weak, effects of leaf nitrogen (Fig. 1h) and wood density (Fig. 1e). Our results remained largely robust even when excluding growth projections from our analysis although the relationship with wood density for angiosperms disappeared (Supplementary Fig. 4). Within the two groups, we found no effects of hydraulic traits (P50, hydraulics safety margin-HSM, conduit size, and density, Supplementary Table 1).

As plant trait variation at the global scale was influenced by climate, we used Structural Equation Modelling (SEM) to disentangle the direct and indirect effects of climate and traits on species’ longevity. We included in this analysis key climate variables, i.e., temperature and precipitation during the driest quarter of the year, and those traits that showed associations with longevity, i.e., radial growth, maximum tree height and wood density. Results of the SEM are shown in Fig. 3 with detailed statistics in Table 1. About two third of the effect of precipitation on longevity in angiosperms was due to its influence on tree height and growth, while one third was due to the direct effect of precipitation (Table 1). Higher precipitation was associated with greater maximum tree height and reduced tree radial growth, both contributing to increasing longevity in angiosperms (Fig. 3a, Table 1). Temperature had a modest direct negative effect on longevity, while indirect effects of temperature via wood density and radial growth cancelled each other out. For gymnosperms, all of the effect of temperature was due to its influence on growth (i.e., increased tree growth at warmer sites reducing longevity) while our analysis did not detect a direct effect of temperature (Fig. 3b, Table 1). We find no significant effect of precipitation on gymnosperm longevity.

Association with the global plant trait spectrum
To overcome the limitation of the limited amount of trait data available for each species, we also perform the analyses with a more complete set of imputed trait data from ref. 37. Despite the additional uncertainty of these infilling approaches, this approach provides a more complete perspective on the association of longevity with a wider range of traits commonly used in global plant trait analyses. These data showed that for angiosperms longevity was associated with plant economics traits such as wood density, specific leaf area (SLA), and leaf nitrogen, and with the orthogonal axis of plant stature traits including plant height, seed mass, and conduit density (Supplementary Fig. 8). In contrast, in gymnosperms, there was no association with the plant economic traits (SLA and wood density) and the association with tree height was contrary to that expected (i.e., greater tree height resulting in lower longevity).

Evaluation of phylogenetic effects
We find that both groups show significant phylogenetic signals in longevity variation (Pagel’s λ for Gymnosperms=0.905, p<0.001; Pagel’s λ for Angiosperms: 0.46, p<0.001). We therefore checked whether our results are robust with regard to these phylogenetic relationships. To this end, we first repeated the correlation analysis for key variables shown in Fig. 2 whilst accounting for phylogenetic relationships between species and find that all remain significant with very little change in the correlation strength (see Table 2). The only correlations that switched to non-significant are those between angiosperm longevity and wood density (p=0.069) and potential site productivity (p=0.108). Secondly, we reanalysed the data using linear mixed effects models incorporating family as a random effect. This proves that relationships with climate hold up within families (i.e., standardized effect of temperature on gymnosperm longevity=−0.157, t-value=−7.23, p<0.001, n=205, standardized effect of precipitation during the driest quarter on angiosperm longevity=+0.107, t-value=6.43, p<0.001, n=534). Finally, we reanalysed the results using genus level means of longevity and climate. It confirms again that longevity-climate relationships remain robust within both groups (Supplementary Fig. 8a, b).

Methods
Longevity database
We compiled a new extended database of tree species longevity, that includes a total of 739 species, 205 gymnosperms and 534 angiosperms. Species longevity was defined as the highest age of the above ground stem tissue recorded for a given species. While below ground tissues or genets may be older than our definition of stem longevity, we did not include these ages. As we focus here on variation in tree longevity, or tree maximum lifespan, between species, we ignored variation within species arising from differences in climate58, soil20 and disturbance frequency91. Age estimates were obtained from a mixture of tree ring records, radiocarbon dating, growth projections and historical records sourced from original databases and literature (see Supplementary Table 3a for key datasets). For most species (78%), the highest age estimates were based on tree ring counts. Where possible we recorded the mean radial growth increment for each species, calculated as the average ring width across all sites or obtained from reported mean ring width or diameter growth from literature. Original tree ring data were compiled from publicly available ITRDB data, National Forest Inventories, and data from several tree ring laboratories (see Supplementary Table 3a). In total, we used tree ring records from over 530.000 trees and over 125.000 sites. Maximum ages derived from tree ring records were only included if the field sampling indicated that large trees from natural forests were targeted in the field campaign. Studies were excluded if they focussed purely on even-aged plantations, non-natural systems, non-native species, or if they seemed to consist of partially recorded or artificially truncated time series. Our estimates do not necessarily represent the oldest estimates available, as popular media often reports exaggerated age estimates, which were avoided by relying only on scientific dating methods from traceable sources. In general, tree ring records with less than 10 series were excluded, but we accepted age estimates based on fewer samples if these can reasonably be believed to represent ages close to species’ maxima such as published on Old lists for commonly sampled species (see Supplementary Table 3a). The final database was verified and checked by several authors on this article improving the database. Species names were checked against the World Flora Online (https://www.worldfloraonline.org/downloadData) using the R package WorldFlora, v1.14.592. We only included species listed on the Global Tree Search93, or those that fit the definition of a tree agreed by IUCN’s Global Tree Specialist Groups (GTSG): “a woody plant with usually a single stem growing to a height of at least two metres or if multi-stemmed with then at least one vertical stem five centimetres in diameter at breast height.” This database presents a significant extension compared to previous studies (i.e., 438 species11, 237 species94, 246 species34, <200 species10, 27).

To assess uncertainty on estimates of the maximum longevity for a species, we classified the age estimates for each species into four confidence categories according to the sampling methods, and number of sites and samples included (see Supplementary Table 3c for definitions). Estimates obtained from tree ring records containing less than 150 trees, or series, were classified as the lowest confidence (level 1). Radiocarbon dating on large trees, and growth projections or historical accounts were classified as moderate confidence (level 2). Tree ring records based on more than 150 samples and less than three sites, or those published on Old lists for commonly sampled species were classified as high confidence (level 3), and tree ring records from more than 3 sites and more than 150 series as very high confidence (level 4). A relatively large portion of species longevity estimates (45%) fell into the lowest confidence category (Supplementary Table 3c). While this could potentially bias our results, we find no evidence for significant changes in the results when this category was excluded from our analysis (see e.g. Supplementary Fig. 3) and we thus chose to present the main results including the maximum number of species. We also note that a relatively large portion of samples (22%) came from growth projections and/or radiocarbon dating, which included important functional groups such as fast-growing tropical pioneer species and extremely old, slow-growing species that generally lack reliable tree ring dating. These age estimates based on projections using short-term growth and mortality rates are subject to several potential biases due to limited data availability, variation in growth and mortality with tree size, and trade-offs between growth and mortality within species that may be poorly captured. However, our results remained largely robust even when excluding growth projections from our analysis although the relationship with wood density for angiosperms disappeared (Supplementary Fig. 4). In all, this provides confidence in our conclusions.

Study sites are well distributed across the globe (see Supplementary Fig. 1a) and included species are from all biomes (Supplementary Fig. 1b). However, the database does not equally represent all biomes and groups. Tree ring data, in particular those from the ITRDB, are overrepresented by old, mostly gymnosperm species growing close to tree lines as these are most valuable for dendro-climate reconstructions95. As a result, there is a geographic bias towards mountain ranges around 40° N latitude such as the American Cordillera, Himalayas, Alps. Tree ring studies further under represent tropical trees. To overcome these two biases, we included previously compiled tropical tree ring data11, and explicitly searched for additional age records for common short-lived species to capture the full spectrum of life history strategies. We note that this resulted in a relatively large set of tropical species growing at temperatures >25°C and somewhat unbalanced distribution of angiosperm species across the temperature axis (Fig. 1a), but we do not believe this influenced our results. We further note that our dataset sampled only a fraction (534) of the global estimated number of angiosperms species (>70.000)96. Filling this gap is a challenge, in particular for tropical angiosperms given the vast number of tropical species and their unreliable formation of tree rings. As a result, significant discussions remain regarding, for example, the longevity of slow-growing tropical shade tolerant species as radiocarbon dating in excess of 1000 years97 is still regarded uncertain17,98,99, and obtaining reliable estimates of tropical tree ages remains a major challenge. These shortcomings may raise questions as to what degree the small fraction of sampled angiosperms reflects the spectrum of life history strategies and could have led to biases in tree longevity estimates. To assess whether the life history strategies of the included species is different from the global diversity of strategies, we compared the range of wood densities and maximum tree height for our dataset to that available from global trait databases. This comparison included wood density as it is closely linked to life history variation along the fast-slow axis and other functional attributes of trees72,74,75 as well as tree height as a proxy for plant stature which is the second most important functional traits axis in trees37,77,78. This shows that both the average and range of wood density of our study are very similar to that of the globe for both angiosperm and gymnosperms (Supplementary Fig. 2a, b). It further shows very close agreement with tree height, although the functional trait dataset has a somewhat greater share of short statured trees for angiosperms (Supplementary Fig. 2c, d). This gives us confidence that our sample is a fair reflection of the global life history strategies of trees.

Environmental data
To assess the effects of climate, environment and soil properties on species’ longevity we extracted data from a variety of datasets. Firstly, climate and soil data were obtained, for most species, from the TreeGOER database (https://zenodo.org/records/10008994100. This database combines the Global Biodiversity Information Facility, GBIF, species occurrence data101 with 30-arcseconds resolution WorldClim 2.1102, aridity indices such as Climate Moisture Index (CMI)103, and SoilGrids 2.0 data104 to provide information on tree species’ mean climate and soil environments. For a small number of species (21 out of 739) not included in the TreeGOER database, we used the mean of the WorldClim 2.1 climate data from all sites for that species included in our database. In addition to standard climate and soil metrics, we also extracted growing season length and site level Net Primary Productivity (NPP) from CHELSA V2.1 dataset (https://chelsa-climate.org/downloads/)105, and calculated species means as the average across all sites included in our database.

For each species, we further calculated species mean proximity to the upper tree line, where tree growth is limited by cold temperatures. This was calculated as the mean of the difference between the actual elevation for each site and the potential upper tree line elevation for that sites estimated using an approximation of the relationship between latitude and potential tree line from Fig. 5 in Paulsen and Korner106. Lacking elevation site data were filled in using elevation data from WorldClim 2.1102.

Trait data
We extracted species trait data from various databases (see Supplementary Table 4). Maximum tree height was obtained from Tallo107, The Gymnosperm Database (www.conifers.org), and from Monumental trees.com. Wood density data came from the global wood density database108, CIRAD wood density109, complemented with several individual records provided by co-authors or accessed online. Conduit density and size were obtained from refs. 51, 63 and 110, and P50 and hydraulic safety margins from the Xylem Functional Traits database63, complemented with unpublished tropical data (Sunny & Barua, 2024). Leaf traits were obtained from GLOPNet leaf economics dataset111, and the seed mass data from TRY plant trait database_ requestNo30569112.

To include the maximum number of species and traits combinations in one single analysis, we extracted imputed trait data from ref. 37. These methods predict missing data using Bayesian hierarchical probabilistic matrix factorization algorithms (BHPMF) based on trait-trait correlations and trait variation (see ref. 37 for more information). We used all 17 plant functional traits of ref. 37 and log-transformed the data prior to analysis.

Analysis
We initially conducted a correlation analysis to evaluate the bivariate relationships between the log10-transformed longevity and various factors, including climate, environment, and traits. We then used multiple regression to assess the relative influence of climate variables, which co-vary at a global scale37. We used the Akaike Information Criteria (AIC) to select the set of environmental that best explained variation in longevity for angiosperms and gymnosperms modelled separately. We assessed collinearity between different variables by evaluating pairwise scatterplots between all variables and through the variance inflation factor (VIF). We avoided including closely correlated climate variables (e.g. growing season length and temperature, or temperature and proximity to tree line) within a single model and all VIF were lower than 5. To partition the contributions of different variables to explaining longevity, we used the lmg metric from the R package relaimpo, v2.2.7, as recommended by ref. 113.

We then assessed direct and indirect effects of climate and traits on maximum longevity using Structural Equation Models (SEMs). Previous studies showed that wood density114, leaf traits115 and tree height49,50 all vary along latitudinal gradients of climate and soil. SEM allowed separating direct climate and trait effects on longevity, from indirect effects of climate through climate’s effects on traits and radial growth. The included variables in this model were mean annual temperature, precipitation of the driest quarter, and wood density, radial growth (log10-transformed) and maximum tree height. We did not include other trait variables as these were usually available for much fewer species and/or showed poor bivariate relationships (see Supplementary Table 1). We used the R package lavaan, v0.6.19116 to fit the model, and report on conventional cutoff criteria117 to evaluate the model fit.

To obtain a more complete overview of trait – longevity relationships, we performed a principal component analysis using the R package FactoMineR, v2.12118 and 17 commonly used plant trait data from ref. 34. For this analysis all plant trait data were natural log-transformed. All analysis was performed in R 4.4.2119.

Evaluation of phylogenetic effects
Longevity is to some degree genetically controlled1, and evolutionary time between species and their phylogenetic relationships may thus affect our results. We therefore used phylogenetic relationships from ref. 120 to assess phylogenetic signals in both groups and re-analysed the main results accounting for phylogenetic relationships between species using phylogenetic generalised linear models (PGLS) from the R packages caper, v1.0.3121, phytools, v2.5.2122 and ape, v5.8.1123. We further checked whether climate effects on longevity could be driven by specific taxa selected for particular climates extremes. For example, the Pinaceae family contains some of the oldest species, and could be restricted to cold environments, driving the overall patterns. To check for these effects, we reanalysed the climate longevity relationships using linear mixed effects models from R package lme4124 incorporating family as a random effect. Finally, it could be argued that well represented genera (e.g. large genus of Pinus within the gymnosperms) could dominate the observed patterns. To check for this effect, we reanalysed the results using genus level means of longevity and climate.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors of this piece on tree longevity did a great job in revising the original text. The response letter impressed me and removes most of my concerns. The text was adjusted accordingly and I support publication.\nThe rest is opinion, and I respect the authors to retain theirs. What remains, is my uneasiness about placing those gymnosperm taxa well known for their great tree age and belonging to warm, humid climates into an exception category. In my view the cold = old conclusion results from attributing the same (statistical) weight to often single species ancient genera as to species in multispecies genera such as Pinus and Picea. At genus level, the cold = old hypothesis would most likely become falsified. So, I simply do not believe that cold climates select for longevity. These climates select for instance for pines, and some of these can get very old as well, but statistically, they dominate the analysis because of their many species (a within-gymnosperms phylogenetic bias). I did not see this issue so clearly in the first round, as I see it now. Instead of ‘cold’ (line 507), I rather prefer this statement copied from the revised text (line 194-195):\n“We further find that gymnosperms show a decrease in longevity with increasing site productivity, whereas angiosperms show a weak positive relationship with potential site productivity (Fig, 2d).”\n...leaving it open whether temperature, moisture, nutrients, soil chemistry are decisive. In Junipers it might be drought tolerance. In essence, it is a high rate of radial growth that selects against long life. Very important.\nAnother important statement is this one (line 274-275):\n“...the existence of relatively weak effects of functional traits on tree longevity, once phylogenetic differences between angiosperms and gymnosperms are taken into account”.\nI hope many people will get this message.\nI suggest to remove the word rainforest in line 447, it does not fit Sequoiadendron, nor Agathis, nor Fitzroya, and I doubt whether coastal redwoods would be ranked as rainforest taxa by experts. To my knowledge, the only rain forest in the US Westcoast is on the Olympia peninsula, where Thuja-giants dominate. Cryptomeria is certainly belonging to warm humid temperate. So, ‘Humid’ might do...\nSo, I am fine with the revision, the phylogenetic bias story (except for the above aspect) is treated adequately and so are trait syndromes and the novelty question that referee 2 rose. Was glad to see the omissions that removed doubtful aspects. It is not relevant here that I disbelieve in hydraulics exerting a decisive role (I think the ‘exceptions’ falsify that story, e.g. E. regnans, the 80 m E-Asia Dipterocarps), but this is a valid element of the discussion and not a result of the analysis.\nOffering the revision in tracking mode helped a lot in re-visiting this text.\nOverall this is a most valid analysis, and I am sure it will stir up discussion.\nNo need to consult with me again.\nChristian Körner", "reviewer_2": "This manuscript was transferred from NEE. The authors have responded all the concerns from both reviewers including me. However, their responses cannot convince me for the “novel findings” of this study, and my concerns still remain.\n1. Novelty. The authors summarized their novelty in their response to my review comment as “We identify two different ways for trees to become old. In addition to the previously identified “adversity pathway”, we find that species in humid, productive environments exhibit greater longevity (a “productivity pathway”). And we find that these pathways differ for angiosperms and gymnosperms”. Here the authors admit that the “adversity path way” was “previously identified”. Their new finding is “productivity pathway” (precipitation-productivity-longevity). However, the cited literature (reference 11, Locosselli, G. M. et al., 2020, PNAS) clearly stated that “we find consistent decreases in tree longevity with increasing aridity”. I therefore conclude that the novelty of the manuscript is very limited. The authors argued that they found angiosperms and gymnosperms follow different pathways. However, they set out a subgroup that are mainly temperate conifers with long lifespan. Therefore, they cannot fully distinguish between angiosperms and gymnosperms.\n2. Phylogeny and evolution. The authors admitted that “longevity is, at least to some degree, inherent to a plant species” in their responses. However, they stressed that “… is poorly understood but is beyond the scope of the present work” in the manuscript. The aim of this manuscript is to detect “climate-longevity relationships” “across species”. However, this may cause huge controversy, as also reminded by another reviewer for many times with many examples. They respond that “the species’ growth rates and longevity are all the outcome of environmental and climatic filtering of species and long evolutionary selection process that has resulted in an optimisation of their specific traits (including demographic traits such as growth and longevity) for that particular environment”, therefore, the authors have to take into consideration of the different “long evolutionary selection process” across species.\nIn their response, they stated “…….an optimisation of their specific traits (including demographic traits such as growth and longevity) for that particular environment”, but they did not admit that “The fact that we do not find strong relationships between longevity and traits within groups does not mean that our approach has failed”. Such kinds of contradictions can also be found in the manuscript, which makes the manuscript not logically organized.\n3. Climate change. The authors responded that “We realised this section was outside the scope of the article, and it was also criticised by Reviewer 1. We have now deleted this entire section”. But in the introduction, the following sentence remains: “These insights can support predictions of future responses to global change, for example, by providing Earth System Models with more realistic estimates of tree turnover” (Line 96-98). However, the climate and longevity relationships are mainly correlations, as shown in all the three figures in the main text. Do you really believe that your correlations between climate and longevity can contribute to Earth System Models with more realistic estimates of tree turnover?\n4. Conclusion. The authors responded that I had misunderstood their conclusion and emphasized in their responses that “when pooling the angiosperms and gymnosperms most traits were significantly related to longevity (first left columns), but these relationships disappeared, or became weaker, when the analysis was done per group (columns to the right). We go from 12 significant trait longevity relationships (when the two groups are pooled) to much fewer relationships for the groups separately (i.e., 4 for Angiosperms and 2 for conifers)”. Do you mean \"pooling the angiosperms and gymnosperms\" is better than separating them in explaining the role of traits? If so, why did you separate them?"}, "author_response": "Point-by-point response to reviewers.\nReviewer #1 (Remarks to the Author):\nThe authors of this piece on tree longevity did a great job in revising the original text. The\nresponse letter impressed me and removes most of my concerns. The text was\nadjusted accordingly and I support publication.\nThe rest is opinion, and I respect the authors to retain theirs. What remains, is my\nuneasiness about placing those gymnosperm taxa well known for their great tree age\nand belonging to warm, humid climates into an exception category. In my view the cold\n= old conclusion results from attributing the same (statistical) weight to often\nsingle species ancient genera as to species in multispecies genera such as Pinus\nand Picea. At genus level, the cold = old hypothesis would most likely become\nfalsified. So, I simply do not believe that cold climates select for longevity. These\nclimates select for instance for pines, and some of these can get very old as well, but\nstatistically, they dominate the analysis because of their many species (a within-gymnosperms\nphylogenetic bias). I did not see this issue so clearly in the first round, as I see it now.\nInstead of ‘cold’ (line 507), I rather prefer this statement copied from the revised text\n(line 194-195):\n“We further find that gymnosperms show a decrease in longevity with increasing site\nproductivity, whereas angiosperms show a weak positive relationship with potential\nsite productivity (Fig, 2d).”\n...leaving it open whether temperature, moisture, nutrients, soil chemistry are decisive.\nIn Junipers it might be drought tolerance. In essence, it is a high rate of radial growth\nthat selects against long life. Very important.\n>> We understand this concern, and we have checked how observed relationships hold\nup at genus level by plotting “genus level” mean longevity against the two key climate\nvariables (temperature and precipitation of the driest quarter, see Supplementary\nFigure 7).\na\n3000\n1000\n300\n-10 0 10 20 30\nMean temperature (degrees C)\nSupplementary Figure 7. Relationships between longevity and the two most important\nclimate, variables at genus level. For each genus we calculated the mean longevity and mean\ntemperature (a) and precipitation during the driest quarter (b). These results show that the key\npatterns are not driven simply by few widespread genera showing strong relationships with\nclimate but hold up at genus level.\nAnother important statement is this one (line 274-275):\n“...the existence of relatively weak effects of functional traits on tree longevity, once\nphylogenetic differences between angiosperms and gymnosperms are taken into\naccount”.\nI hope many people will get this message.\n>> We think readers will get this message as it is formulated clearly at the start of the\ndiscussion, and again at the end (i.e. we say , “In summary, our analyses showed\nthat most species-specific traits provided relatively weak relationships with species’\ntree longevity within both groups.”).\nI suggest to remove the word rainforest in line 447, it does not fit Sequoiadendron, nor\nAgathis, nor Fitzroya, and I doubt whether coastal redwoods would be ranked as\nrainforest taxa by experts. To my knowledge, the only rain forest in the US Westcoast is\non the Olympia peninsula, where Thuja-giants dominate. Cryptomeria is certainly\nbelonging to warm humid temperate. So, ‘Humid’ might do...\n>> We agree and have changed this to “humid forests”.\nSo, I am fine with the revision, the phylogenetic bias story (except for the above aspect)\nis treated adequately and so are trait syndromes and the novelty question that referee 2\nrose. Was glad to see the omissions that removed doubtful aspects. It is not relevant\nhere that I disbelieve in hydraulics exerting a decisive role (I think the ‘exceptions’ falsify\n)ry(\nytivegnol\nmumixaM\nb\na Angiosperm R = 0.35, p = 3.1e-08\nR = 0.074, p = 0.65\na Gymnosperm 3000\n1000\n300\n100\n30\n0 200 400 600\nPrecipitation driest quarter (mm)\nthat story, e.g. E. regnans, the 80 m E-Asia Dipterocarps), but this is a valid element of\nthe discussion and not a result of the analysis.\n>> Please see our additional work where we account for phylogeny.\nOffering the revision in tracking mode helped a lot in re-visiting this text.\nOverall this is a most valid analysis, and I am sure it will stir up discussion.\nNo need to consult with me again.\nChristian Körner\nReviewer #2 (Remarks to the Author):\nThis manuscript was transferred from NEE. The authors have responded all the\nconcerns from both reviewers including me. However, their responses cannot convince\nme for the “novel findings” of this study, and my concerns still remain.\n1. Novelty. The authors summarized their novelty in their response to my review\ncomment as “We identify two different ways for trees to become old. In addition to the\npreviously identified “adversity pathway”, we find that species in humid, productive\nenvironments exhibit greater longevity (a “productivity pathway”). And we find that\nthese pathways differ for angiosperms and gymnosperms”. Here the authors admit that\nthe “adversity path way” was “previously identified”. Their new finding is “productivity\npathway” (precipitation-productivity-longevity). However, the cited literature (reference\n11, Locosselli, G. M. et al., 2020, PNAS) clearly stated that “we find consistent\ndecreases in tree longevity with increasing aridity”. I therefore conclude that the novelty\nof the manuscript is very limited. The authors argued that they found angiosperms and\ngymnosperms follow different pathways. However, they set out a subgroup that are\nmainly temperate conifers with long lifespan. Therefore, they cannot fully distinguish\nbetween angiosperms and gymnosperms.\n>> We strongly contest this assertion. Both the distinction of the two pathways for the\ntwo groups and the identification of the mechanism using statistical attribution are\nnovel. The mentioned study by Locosselli et al. (2020) did not present separate analysis\nfor angiosperms and gymnosperms, and this is thus a new finding. The description of a\nsubgroup of temperate conifers deviating from this pattern does not invalidate the\ngeneral differences between groups as the main analysis of the gymnosperms\npresented in the manuscript included this subgroup. Thus the patterns are robust.\nAnother novelty that we mentioned in our response is the identification of mechanisms\ndriving tree longevity such as slow growth in conifers under low temperature, and\nincreased tree height and competition under humid conditions for angiosperms.\nWhile we cite some of the first studies proposing the adversity mechanism, no study\nbefore has shown this for a large dataset like the one presented here, and basically no\nstudy before has shown the mechanism(s) for the second pathway (mainly found in\nAngiosperms).\n2. Phylogeny and evolution. The authors admitted that “longevity is, at least to some\ndegree, inherent to a plant species” in their responses. However, they stressed that “…\nis poorly understood but is beyond the scope of the present work” in the manuscript.\nThe aim of this manuscript is to detect “climate-longevity relationships” “across\nspecies”. However, this may cause huge controversy, as also reminded by another\nreviewer for many times with many examples. They respond that “the\nspecies’ growth rates and longevity are all the outcome of environmental and climatic\nfiltering of species and long evolutionary selection process that has resulted in an\noptimisation of their specific traits (including demographic traits such as growth and\nlongevity) for that particular environment”, therefore, the authors have to take into\nconsideration of the different “long evolutionary selection process” across species.\nIn their response, they stated “…….an optimisation of their specific traits (including\ndemographic traits such as growth and longevity) for that particular environment”, but\nthey did not admit that “The fact that we do not find strong relationships between\nlongevity and traits within groups does not mean that our approach has failed”. Such\nkinds of contradictions can also be found in the manuscript, which makes the\nmanuscript not logically organized.\n>> We are not quite sure what the contradiction is, but we have now reanalysed the key\nresults taking fully into account phylogeny between species. While it shows that there is\na phylogenetic signal in tree longevity, the strength of the relationships remains the\nsame as the analysis presented in our original manuscript (see Supplementary Table 5).\nOur conclusions and findings thus remain robust. See also our arguments in the appeal\nletter.\n3. Climate change. The authors responded that “We realised this section was outside\nthe scope of the article, and it was also criticised by Reviewer 1. We have now deleted\nthis entire section”. But in the introduction, the following sentence remains: “These\ninsights can support predictions of future responses to global change, for example, by\nproviding Earth System Models with more realistic estimates of tree turnover” (Line 96-\n98). However, the climate and longevity relationships are mainly correlations, as shown\nin all the three figures in the main text. Do you really believe that your correlations\nbetween climate and longevity can contribute to Earth System Models with more\nrealistic estimates of tree turnover?\n>> This seems a minor comments, but we do believe that empirical estimates of tree\nturnover rates and their variation across biomes (as we show in our manuscript, see Fig,\n1b), can help Earth System models. This is what the modelling community themselves\nclearly state. For example, the study by Friend et al. (2014) that we cite, states that\n“Further work should focus on confronting the processes and emergent model\nbehavior responsible for these discontinuities with observational data. Spatial and\ntemporal variability in carbon residence time and tree mortality is an obvious\nplace to start, and although recent studies have identified important sources of relevant data\n(e.g., refs. 20 and 21), data on key processes such as mortality at large scales are\nrare.”\nWe believe it is not such a stretch to state this.\n4. Conclusion. The authors responded that I had misunderstood their conclusion and\nemphasized in their responses that “when pooling the angiosperms and gymnosperms\nmost traits were significantly related to longevity (first left columns), but these\nrelationships disappeared, or became weaker, when the analysis was done per group\n(columns to the right). We go from 12 significant trait longevity relationships (when the\ntwo groups are pooled) to much fewer relationships for the groups separately (i.e., 4 for\nAngiosperms and 2 for conifers)”. Do you mean \"pooling the angiosperms and\ngymnosperms\" is better than separating them in explaining the role of traits? If so, why\ndid you separate them?\n>> We mean that when pooling all data of angiosperms and gymnosperms we do find\nthat all traits (except for one) show significant correlations with longevity. One could\nindeed thus conclude that traits explain more longevity variation when data are pooled.\nWhy did we separate them? We separated them firstly because we know from\nliterature that the two groups have very different traits, growth strategies and\nlongevities, and this is thus not a new finding. Secondly, we wanted to avoid taxonomic\nbiases. We make this clear at the end of the introduction where we say “Previous\nstudies showed that angiosperms and gymnosperms differ strongly in their growth\nstrategies29, photosynthetic capacity30, hydraulic traits31-33 and longevity1,10,11, which\nwe confirm in our analysis of the full dataset. Given that these taxonomic groups\ndiverged over 300 million years ago34 and that they differ in their global distribution35,\nwe primarily analyse the results for these groups separately to avoid any\nphylogenetic bias in the results. “\nAdditional text to manuscript:\nFull text below and table and Supplementary figure 5 are added to the manuscript.\nEvaluation of phylogenetic effects\nLongevity is to some degree genetically controlled1, and evolutionary time between\nspecies and their phylogenetic relationships may thus affect our results. We used\nphylogenetic relationships from ref.118 to reveal significant phylogenetic signals in both\ngroups (Pagel’s l for Gymnosperms = 0.905, p<0.001; Pagel’s l for Angiosperms: 0.46,\np<0.001). We then performed additional checks to ensure our results are robust with regard to\nthese phylogenetic relationships. To this end, we first re-analysed the main results accounting\nfor phylogenetic relationships between species using phylogenetic generalised linear models\n(PGLS) from the R packages caper119, phytools2.0120 and ape 5.0121. This shows that core\nrelationships with climate, growth, and maximum tree height all remain significant with very\nlittle change in the correlation strength (see Supplementary table 5). The only correlation that\nswitched to marginally significant (p=0.069) is that between angiosperm longevity and wood\ndensity. Secondly, we checked whether climate effects on longevity could be driven by\nspecific taxa selected for particular climates extremes. For example, the Pinaceae family\ncontains some of the oldest species, and could be restricted to cold environments, driving the\noverall patterns. Reanalysis of the data using linear mixed effects models from R package\nlme4122 incorporating family as a random effect proves that relationships with climate (i.e.,\ntemperature for Gymnosperms and precipitation for angiosperms) hold up within families.\nFinally, it could be argued that well represented genera (e.g. large genus of Pinus within the\ngymnosperms) could dominate the observed patterns. To check for this effect, we reanalysed\nthe results using genus level means of longevity and climate. It confirms again that longevity\n– climate relationships remain robust for both groups (Supplementary Fig. 7a,b).\nSupplementary Table 5. Comparison of standard and phylogenetically controlled\ncorrelations for the key variables controlling variation in longevity (cf. Supplementary\nTable 1). Standard correlations are Pearson’s correlations, and phylogenetic controlled\ncorrelation were performed using phylogenetic generalised linear models (PGLS) (see\nmethods).\nAngiosperms Gymnosperms\nStandard correlation Phylogenetically Standard correlation Phylogenetically\ncontrolled controlled\nR p n R p n R p n R p n\nvalue value value value\nMean annual temperature 0.030 0.488 534 0.038 0.405 476 -0.378 0.000 205 -0.420 0.000 200\nPrecipitation driest quarter** 0.332 0.000 488 0.277 0.000 432 0.080 0.258 203 0.072 0.312 198\nMean radial growth* -0.564 0.000 429 -0.507 0.000 381 -0.567 0.000 184 -0.467 0.000 179\nMaximum tree height 0.351 0.000 325 0.294 0.000 291 0.200 0.005 198 0.261 0.000 193\nWood density 0.124 0.009 441 0.091 0.069 399 -0.081 0.333 144 0.123 0.146 140\n* These data were log10-transformed for the analysis; ** Correlations with precipitation and Climate moisture index exclude species that\nhave their main occurrence in floodplains or wetlands."}, {"round": 2, "reviewer_comments": {"reviewer_3": "This manuscript addresses important questions related to variation in tree longevity and its ecological drivers. I recognize that this work has already undergone prior rounds of peer review, which have helped resolve many technical details and improve clarity. The authors’ responses demonstrate significant effort to strengthen the manuscript, and I agree that the study presents novel insights, particularly the identification of two contrasting pathways to tree longevity across environmental gradients.\nNevertheless, I believe some conceptual uncertainties remain, which are inherently difficult to resolve fully but are worth considering. In addition, I would like to raise another of these difficult-to-address issues, which could have implications for the interpretation of the results. Given that gymnosperms tend to be more prevalent in environments subjected to freezing temperatures, I wonder whether similar relationships between temperature, precipitation, and longevity would emerge if gymnosperms and angiosperms were pooled and analyzed collectively, but separately for environments with and without freezing conditions. Such an approach could help disentangle whether the observed patterns are primarily driven by phylogenetic differences, environmental filtering, or their interaction.\nIn summary, I believe the manuscript, as it stands, contributes valuable and novel perspectives to ongoing research on variation in tree longevity.", "reviewer_4": "I provide comments in the attached document."}, "author_response": "Point-by-point response to reviewers\nReviewer #1 (Remarks to the Author):\nThe authors of this piece on tree longevity did a great job in revising the original text.\nThe response letter impressed me and removes most of my concerns. The text was\nadjusted accordingly and I support publication.\nThe rest is opinion, and I respect the authors to retain theirs. What remains, is my\nuneasiness about placing those gymnosperm taxa well known for their great tree age\nand belonging to warm, humid climates into an exception category. In my view the\ncold = old conclusion results from attributing the same (statistical) weight to often\nsingle species ancient genera as to species in multispecies genera such as Pinus\nand Picea. At genus level, the cold = old hypothesis would most likely become\nfalsified. So, I simply do not believe that cold climates select for longevity. These\nclimates select for instance for pines, and some of these can get very old as well, but\nstatistically, they dominate the analysis because of their many species (a within-\ngymnosperms phylogenetic bias). I did not see this issue so clearly in the first round,\nas I see it now. Instead of ‘cold’ (line 507), I rather prefer this statement copied from\nthe revised text (line 194-195):\n“We further find that gymnosperms show a decrease in longevity with increasing site\nproductivity, whereas angiosperms show a weak positive relationship with potential\nsite productivity (Fig, 2d).”\n...leaving it open whether temperature, moisture, nutrients, soil chemistry are\ndecisive. In Junipers it might be drought tolerance. In essence, it is a high rate of\nradial growth that selects against long life. Very important.\n>> We strongly agree that high growth selects against long life. To leave it more\nopen whether it is soil or climate, we now changed line 507 to “sites with adverse\ngrowing conditions (i.e., low temperature, short growing seasons, poor soils, etc.).”\nAs for potential bias due to disproporionare weight to specific taxa, we have now\nalso checked how observed relationships hold up at genus level by plotting “genus\nlevel” mean longevity against the two key climate variables (temperature and\nprecipitation of the driest quarter). This shows that the relationships are holding up at\ngenus level. We have added this figure to the supplementary info and also added\nfollowing text to the results section:\n“Finally, we reanalysed the results using genus level means of longevity and climate. It\nconfirms again that longevity-climate relationships remain robust within both groups\n(Supplementary Fig. 7a,b). “\nSupplementary Figure 7. Relationships between longevity and the two most important\nclimate, variables at genus level. For each genus we calculated the mean longevity and mean\ntemperature (a) and precipitation during the driest quarter (b). These results show that the key\npatterns are not driven simply by few widespread genera showing strong relationships with\nclimate but hold up at genus level.\nAnother important statement is this one (line 274-275):\n“...the existence of relatively weak effects of functional traits on tree longevity, once\nphylogenetic differences between angiosperms and gymnosperms are taken into\naccount”.\nI hope many people will get this message.\n>> We think readers will get this message as it is formulated clearly at the start of the\ndiscussion, and again at the end (ie we say , “In summary, our analyses showed that\nmost species-specific traits provided relatively weak relationships with species’ tree\nlongevity within both groups.”).\nI suggest to remove the word rainforest in line 447, it does not fit Sequoiadendron,\nnor Agathis, nor Fitzroya, and I doubt whether coastal redwoods would be ranked as\nrainforest taxa by experts. To my knowledge, the only rain forest in the US\nWestcoast is on the Olympia peninsula, where Thuja-giants dominate. Cryptomeria\nis certainly belonging to warm humid temperate. So, ‘Humid’ might do...\n>> We agree and have changed this to “humid forests”.\nSo, I am fine with the revision, the phylogenetic bias story (except for the above\naspect) is treated adequately and so are trait syndromes and the novelty question\nthat referee 2 rose. Was glad to see the omissions that removed doubtful aspects. It\nis not relevant here that I disbelieve in hydraulics exerting a decisive role (I think the\n‘exceptions’ falsify that story, e.g. E. regnans, the 80 m E-Asia Dipterocarps), but this\nis a valid element of the discussion and not a result of the analysis.\n>> We added a full analysis on the effects of phylogeny to the manuscript. See our\nresponse to comment#2 below of reviewer 2.\nOffering the revision in tracking mode helped a lot in re-visiting this text.\nOverall this is a most valid analysis, and I am sure it will stir up discussion.\nNo need to consult with me again.\nChristian Körner\nReviewer #2 (Remarks to the Author):\nThis manuscript was transferred from NEE. The authors have responded all the\nconcerns from both reviewers including me. However, their responses cannot\nconvince me for the “novel findings” of this study, and my concerns still remain.\n1. Novelty. The authors summarized their novelty in their response to my review\ncomment as “We identify two different ways for trees to become old. In addition to\nthe previously identified “adversity pathway”, we find that species in humid,\nproductive environments exhibit greater longevity (a “productivity pathway”). And we\nfind that these pathways differ for angiosperms and gymnosperms”. Here the authors\nadmit that the “adversity path way” was “previously identified”. Their new finding is\n“productivity pathway” (precipitation-productivity-longevity). However, the cited\nliterature (reference 11, Locosselli, G. M. et al., 2020, PNAS) clearly stated that “we\nfind consistent decreases in tree longevity with increasing aridity”. I therefore\nconclude that the novelty of the manuscript is very limited. The authors argued that\nthey found angiosperms and gymnosperms follow different pathways. However, they\nset out a subgroup that are mainly temperate conifers with long lifespan. Therefore,\nthey cannot fully distinguish between angiosperms and gymnosperms.\n>> We contest this assertion. Both the distinction of the two pathways for the two\ngroups and the identification of the mechanism using statistical attribution are novel.\nThe mentioned study by Locosselli et al. (2020) did not present separate analysis for\nangiosperms and gymnosperms, and this is thus a new finding. The description of a\nsubgroup of temperate conifers deviating from this pattern does not invalidate the\ngeneral differences between groups as the main analysis of the gymnosperms\npresented in the manuscript included this subgroup and thus the overall patterns are\nrobust.\nAnother novelty that we mentioned in our response is the identification of\nmechanisms driving tree longevity such as slow growth in conifers under low\ntemperature, and increased tree height and competition under humid conditions for\nangiosperms.\nWhile we cite some of the first studies proposing the adversity mechanism, no study\nbefore has shown this for a large dataset like the one presented here, and no study\nbefore has shown the mechanism(s) for the second pathway (mainly found in\nAngiosperms) in as we do here using robust statistical approaches (eg. structural\nequation modelling, SEM, see our Figure 3).\n2. Phylogeny and evolution. The authors admitted that “longevity is, at least to some\ndegree, inherent to a plant species” in their responses. However, they stressed that\n“… is poorly understood but is beyond the scope of the present work” in the\nmanuscript. The aim of this manuscript is to detect “climate-longevity relationships”\n“across species”. However, this may cause huge controversy, as also reminded by\nanother reviewer for many times with many examples. They respond that “the\nspecies’ growth rates and longevity are all the outcome of environmental and climatic\nfiltering of species and long evolutionary selection process that has resulted in an\noptimisation of their specific traits (including demographic traits such as growth and\nlongevity) for that particular environment”, therefore, the authors have to take into\nconsideration of the different “long evolutionary selection process” across species.\nIn their response, they stated “…….an optimisation of their specific traits (including\ndemographic traits such as growth and longevity) for that particular environment”, but\nthey did not admit that “The fact that we do not find strong relationships between\nlongevity and traits within groups does not mean that our approach has failed”. Such\nkinds of contradictions can also be found in the manuscript, which makes the\nmanuscript not logically organized.\n>> We are not quite sure what the contradiction is, but agree on the importance of\nphylogeny and now analysed to what degree that phylogyeny could have affected\nour results. This analysis shows a phylogenetic signal in tree longevity -as the\nreviewer suggested- and we thus reanalysed the key results taking into account\nphylogeny between species. This shows that the strength of the relationships\nremains largely the same as the analysis presented in our original manuscript, and\nshows that our conclusions and findings remain robust.\nWe have added these new analyses to the methods and results sections of the\nmanuscript and copy the text of these sections below.\nMETHODS:\nLongevity is to some degree genetically controlled1, and evolutionary time between\nspecies and their phylogenetic relationships may thus affect our results. We therefore used\nphylogenetic relationships from ref.2 to asses phylogenetic signals in both groups and re-\nanalysed the main results accounting for phylogenetic relationships between species using\nphylogenetic generalised linear models (PGLS) from the R packages caper3, phytools2.04 and\nape 5.05. We further checked whether climate effects on longevity could be driven by specific\ntaxa selected for particular climates extremes. For example, the Pinaceae family contains\nsome of the oldest species, and could be restricted to cold environments, driving the overall\npatterns. To check for these effects, we reanalysed the climate longevity relationships using\nlinear mixed effects models from R package lme46 incorporating family as a random effect.\nFinally, it could be argued that well represented genera (e.g. large genus of Pinus within the\ngymnosperms) could dominate the observed patterns. To check for this effect, we reanalysed\nthe results using genus level means of longevity and climate.\nRESULTS :\nWe find that both groups show significant phylogenetic signals in longevity variation\n(Pagel’s  for Gymnosperms = 0.905, p<0.001; Pagel’s  for Angiosperms: 0.46, p<0.001).\nWe therefore checked whether our results are robust with regard to these phylogenetic\nrelationships. To this end, we first repeated the correlation analysis for key variables shown\nin Figure 2 whilst accounting for phylogenetic relationships between species and find that all\nremain significant with very little change in the correlation strength (see Table 2). The only\ncorrelations that switched to non-significant are those between angiosperm longevity and\nwood density (p=0.069) and potential site productivity (p=0.108). Secondly, we reanalysed\nthe data using linear mixed effects models incorporating family as a random effect. This\nproves that relationships with climate hold up within families (i.e., standardized effect of\ntemperature on gymnosperm longevity = -0.157, t-value= -7.23, p< 0.001, n=205,\nstandardized effect of precipitation during the driest quarter on angiosperm longevity =\n+0.107, t-value = 6.43, p<0.001, n=534). Finally, we reanalysed the results using genus\nlevel means of longevity and climate. It confirms again that longevity-climate relationships\nremain robust within both groups (Supplementary Fig. 7a,b).\nTable 2. Standard and phylogenetically controlled correlations for the key variables\naffecting variation in longevity (cf. Supplementary Table 1). Standard correlations are\nPearson’s correlations and phylogenetic controlled correlations were performed using\nphylogenetic generalised linear models (PGLS) (see methods).\nAngiosperms Gymnosperms\nStandard correlation Phylogenetically Standard correlation Phylogenetically\ncontrolled controlled\nR p n R p n R p n R p n\nvalue value value value\nMean radial growth* -0.564 0.000 429 -0.507 0.000 381 -0.567 0.000 184 -0.467 0.000 179\nMean annual temperature 0.030 0.488 534 0.038 0.405 476 -0.378 0.000 205 -0.420 0.000 200\nPrecipitation driest quarter** 0.332 0.000 488 0.277 0.000 432 0.080 0.258 203 0.072 0.312 198\nProximity to tree line -0.071 0.108 519 0.048 0.302 462 0.397 0.000 201 0.429 0.000 196\nPotential Net Primary 0.115 0.009 517 0.075 0.108 460 -0.359 0.000 201 0.423 0.000 196\nProductivity\nWood density 0.124 0.009 441 0.091 0.069 399 -0.081 0.333 144 0.123 0.146 140\nMaximum tree height 0.351 0.000 325 0.294 0.000 291 0.200 0.005 198 0.261 0.000 193\nLeaf Mass per Area* -0.048 0.599 124 0.082 0.392 109 0.175 0.287 39 0.282 0.082 38\nNitrogen per Mass -0.284 0.001 124 0.220 0.020 109 0.009 0.957 40 0.002 0.991 39\n* These data were log10-transformed for the analysis; ** Correlations with precipitation and Climate moisture index exclude species that\nhave their main occurrence in floodplains or wetlands.\nSupplementary Figure 7. Relationships between longevity and the two most important climate,\nvariables at genus level. For each genus we calculated the mean longevity and mean\ntemperature (a) and precipitation during the driest quarter (b). These results show that the\nkey patterns are not driven simply by few widespread genera showing strong relationships\nwith climate but hold up at genus level.\n3. Climate change. The authors responded that “We realised this section was\noutside the scope of the article, and it was also criticised by Reviewer 1. We have\nnow deleted this entire section”. But in the introduction, the following sentence\nremains: “These insights can support predictions of future responses to global\nchange, for example, by providing Earth System Models with more realistic\nestimates of tree turnover” (Line 96-98). However, the climate and longevity\nrelationships are mainly correlations, as shown in all the three figures in the main\ntext. Do you really believe that your correlations between climate and longevity can\ncontribute to Earth System Models with more realistic estimates of tree turnover?\n>> This seems a minor comment, but we do believe that empirical estimates of tree\nturnover rates and their variation across biomes (as we show in our manuscript, see\nFig, 1b) can help Earth System models. This is what the modelling community\nthemselves clearly state. For example, the study by Friend et al. (2014) that we cite,\nstates that “Further work should focus on confronting the processes and emergent\nmodel behavior responsible for these discontinuities with observational data. Spatial\nand temporal variability in carbon residence time and tree mortality is an obvious\nplace to start, and although recent studies have identified important sources of\nrelevant data (e.g., refs. 20 and 21), data on key processes such as mortality at large\nscales are rare.”\nWe believe it is not such a stretch to state this, and thus prefer to keep it in the\nmanuscript.\n4. Conclusion. The authors responded that I had misunderstood their conclusion and\nemphasized in their responses that “when pooling the angiosperms and\ngymnosperms most traits were significantly related to longevity (first left columns),\nbut these relationships disappeared, or became weaker, when the analysis was\ndone per group (columns to the right). We go from 12 significant trait longevity\nrelationships (when the two groups are pooled) to much fewer relationships for the\ngroups separately (i.e., 4 for Angiosperms and 2 for conifers)”. Do you mean \"pooling\nthe angiosperms and gymnosperms\" is better than separating them in explaining the\nrole of traits? If so, why did you separate them?\n>> We mean that when pooling all data of angiosperms and gymnosperms we do\nfind that all traits (except for one) show significant correlations with longevity. One\ncould indeed thus conclude that traits are better in explaining longevity variation\nwhen data are pooled.\nWhy did we separate them? We separated them firstly because we know from\nliterature that the two groups have very different traits, growth strategies and\nlongevities, affecting the observed overall relationship (ie when both groups are\npooled). Secondly, we wanted to avoid taxonomic biases due to the long\nevolutionary differences between groups and their difference in biogeographic\ndistribution. We make this clear at the end of the introduction where we say\n“Previous studies showed that angiosperms and gymnosperms differ strongly in their\ngrowth strategies7, photosynthetic capacity8, hydraulic traits9-11 and longevity1,12,13,\nwhich we confirm in our analysis of the full dataset. Given that these taxonomic\ngroups diverged over 300 million years ago14 and that they differ in their global\ndistribution15, we primarily analyse the results for these groups separately to avoid\nany phylogenetic bias in the results. “\nAm. J. Bot. 105, 302-314 (2018).\nevolution in R. R package version 5, 1-36 (2013).\nmethods (and other things). PeerJ 12, e16505 (2024).\nevolutionary analyses in R. Bioinformatics 35, 526-528 (2019).\nUsing lme4. 2015 67, 48 (2015). https://doi.org/10.18637/jss.v067.i01\ngymnosperm persistence. Biol. J. Linn. Soc. 36, 227-249 (1989).\ncompetitive advantage of evergreen angiosperm trees over evergreen conifers in\nproductive habitats. New Phytol. 160, 329-336 (2003).\nphotosynthetic capacity during early angiosperm diversification. Ecol. Lett. 13, 175-\n183 (2010). https://doi.org/10.1111/j.1461-0248.2009.01410.x\nmargins and embolism reversal in stems and leaves: why are conifers and\nangiosperms so different? Plant Sci. 195, 48-53 (2012).\nangiosperm vessels. Am. J. Bot. 93, 1490-1500 (2006).\ne13907 (2022).\nlongevity with temperature. Proceedings of the National Academy of Sciences 117,\n33358-33364 (2020).\nUSA, 2014).\n(2023)."}, {"round": 3, "reviewer_comments": {"reviewer_3": "I greatly appreciate the additional analysis separating species into freezing versus frost-free environments, along with its thoughtful integration into the discussion. Importantly, the authors acknowledge that uncertainties remain regarding the extent to which observed differences are driven by environmental filtering versus intrinsic phylogenetic effects, which adds valuable nuance and depth to the manuscript.\nI am fully satisfied with the authors’ revisions and responses. I congratulate them on this significant contribution and look forward to the publication of the article, which will be an important resource to cite.", "reviewer_4": "This is a strong paper, and the final round of revisions was generally thorough and appropriate. I only have one final minor comment. The authors stated that their results were robust to the exclusion of the growth-based and mortality-based projections of tree longevity, but omitting these data actually caused the association between wood density and Angiosperm longevity to become non-significant (Supplementary Fig. 4e). This should be stated explicitly in the results (lines 200-201) so that it is clear for the reader that this association does not hold up when those data are excluded."}, "author_response": "Rebuttal letter\nREVIEWERS' COMMENTS\nReviewer #3 (Remarks to the Author):\nI greatly appreciate the additional analysis separating species into freezing versus frost-free\nenvironments, along with its thoughtful integration into the discussion. Importantly, the\nauthors acknowledge that uncertainties remain regarding the extent to which observed\ndifferences are driven by environmental filtering versus intrinsic phylogenetic effects, which\nadds valuable nuance and depth to the manuscript.\nI am fully satisfied with the authors’ revisions and responses. I congratulate them on this\nsignificant contribution and look forward to the publication of the article, which will be an\nimportant resource to cite.\nWe thank the reviewer for their comments.\nReviewer #4 (Remarks to the Author):\nThis is a strong paper, and the final round of revisions was generally thorough and\nappropriate. I only have one final minor comment. The authors stated that their results\nwere robust to the exclusion of the growth-based and mortality-based projections of tree\nlongevity, but omitting these data actually caused the association between wood density\nand Angiosperm longevity to become non-significant (Supplementary Fig. 4e). This should\nbe stated explicitly in the results (lines 200-201) so that it is clear for the reader that this\nassociation does not hold up when those data are excluded.\nWe thank the reviewer for pointing out this oversight and we have now stated this explicitly\nin the manuscript in the results section by adding “Our results remained largely robust\neven when excluding growth projections from our analysis although the relationship\nwith wood density for angiosperms disappeared (Supplementary Fig. 4).” And\nadding a comment to the methods section where the reason for the exclusion of\ngrowth projections is explained.\nThis was my first review of this manuscript. Overall, this was a very interesting manuscript that took a\nthoughtful approach to pursuing an important question. I found the responses to prior review both\nthorough and compelling, and I have no additional comments related to those critiques. I look forward\nto citing this study.\nTo inform my reading of these responses to review, I also thoroughly read the manuscript (which I quite\nenjoyed). My instructions were to avoid providing a full-review of the manuscript (which I did not), but\nmy reading of the text raised two major concerns. I realize that this manuscript has likely undergone\nintensive prior review and my role here was not to provide an in-depth review of the complete\nmanuscript, but I am sharing these concerns because (1) I think they are scientifically important and (2)\nif these concerns are not addressed, then readers like myself will be (hopefully unnecessarily) skeptical\nof the published results. I expect that it will be straightforward to address these concerns, particularly\nmajor comment #1, and addressing them will produce a product that is a more durable contribution to\nthe literature. I also highlight a couple very minor items that could be addressed.\nMajor comments\n1. Claims of causation do not appear to be supported (Lines 80-82, 202-216, 302-303, Fig. 3, and\nelsewhere). There are two major logical flaws in the conclusions listed in the abstract, which appear to\nexist because of how structural equation modeling was applied and interpreted. Fundamentally, the\nstudy does not justified why tree height and growth mechanistically cause differences in longevity, and I\nam not aware of data that would support a direct causal relationship between these variables. I break\ndown these issues around the two conclusions listed in the abstract, but they apply throughout the text.\nThese claims either need to be justified with data or the text should be reframed to highlight that these\nare statistical associations without directly causative effects.\nThe first unsupported claim in the abstract (line 80: “First, higher water availability increases maximum\ntree height allowing species to attain greater longevities”) is circular and it is missing a logical link. Trees\ncould just as reasonably be taller because they live longer, but the text claims that trees live longer\nbecause they are taller. Indeed, our metrics of maximum tree height are based on our field\nobservations, which are strongly sensitive to sample size; this is problematic because the number and\nmaximum height of very tall trees observed within a species will increase with their longevity (due to\nsurvivor bias and continued growth with survival), leading to greater observed height with greater\nlongevity. Additionally, there is no justification for why trees being taller would cause them to be older.\nIn simplistic terms, it is well known that taller individuals can be much younger than shorter individuals.\nThere must be an additional conceptual link for why taller trees mechanistically live longer for this claim\nto be supported.\nThe second claim highlighted in the abstract (line 81: “Secondly, greater water availability increases\nstand density and inter-tree competition, limiting growth thereby increasing tree lifespan.”) asserts that\nhigher growth directly causes lower survivorship. While high growth is correlated with higher mortality, I\nam not aware of any conceptual or empirical work demonstrating a direct mechanistic effect of growth\non mortality. Fundamentally, trees do not die because they were productive and increased in diameter.\nHigh growth may be correlated with tradeoffs against other traits that could cause susceptibility to an\nagent of mortality, but that is not the same as higher growth causing low survivorship. The study\nprovides some speculation about these connections on lines 306-316, but this text is speculative and key\nempirical connections are untested or uncertain (e.g., the fact that some shade-tolerant trees can\nsurvive for a long time with low growth in shaded conditions doesn’t mean that slow growth caused\nthem to live a long time).\nThe strong and empirically supported results in this study will carry more weight and inspire more\noverall confidence in this study if these unsupported claims of causation were supported by empirical\ndata or properly conveyed as speculation.\n2. I am concerned about the accuracy and reliability of the demography-based estimates of longevity.\nI have low confidence in the validity of the growth-based or mortality-based estimates of tree longevity\n(the methods text focuses on growth, but Table S3 says growth and mortality rates), particularly in\ntropical forests where this method appears to have been applied to a large proportion of species.\nFundamentally, we know (1) that growth and mortality rates change with tree size, and (2) our data on\nspecies-level growth and mortality rates is very limited for giant trees and, for most species, the\nimprecision of data for giant individuals is so high that they are unreliable. My concern is that if we know\nwe lack the data to properly characterize the growth and survivorship of giant trees (which we know will\nstrongly influence longevity), then we should suspect that we cannot accurately estimate the maximum\npotential longevity of these species. These problems will be exaggerated in tropical forests where\ndiversity is so high and sampling effort is so low that we lack sufficient species-specific demographic\ndata to be able to capture long-term trends.\nTo highlight this issue, I share some soon-to-be-published data comparing mortality rates from the only\nstudy site in the tropics with enough species-specific data (ca. 9 million years of tree-level monitoring\nwithin a single community) to estimate mortality rates for many giant tree taxa (N = 37 species in this\ncase). These data show a general positive correlation between species-specific mortality rates as giant\ntrees (y-axis: >50 cm in diameter in this case; points are species-level means with 95% confidence\nintervals) versus species-specific mortality rates for all trees <1cm in diameter within the same species\n(x-axis). However, for very long-lived taxa (annual mortality rates below 1.0 % year as giant trees,\nsuggesting mean survivorship of >100 years after reaching 50cm DBH), there is no predictive\nrelationship between their mortality as giant trees and their population-wide mortality rates. Similarly,\nspecies with the lowest population-wide mortality rates (~1%) exhibited >5-fold variation in their\nmortality rates as giant trees, meaning that true maximum longevity should differ enormously even\namong taxa with the lowest population-level mortality rates. If growth exhibits the same general pattern\nas mortality, then we might expect growth-based predictions of longevity for these longest-lived canopy\ntree species (and therefore for angiosperms in wet, warm, and productive locations) to chronically\nunderestimate their longevity. Overall, these patterns are concerning because – if the growth-based\nestimates of longevity are biased - then any climatic, phylogenetic (angiosperm versus gymnosperm), or\ngeographic biases in the distribution of growth-based estimates of longevity could thereby influence\nassociations with climate, phylogeny, and/or geography.\nI see three potential options to addressing this issue, although presumably there are alternative\nsolutions as well. First, this study does not provide details of exactly how the growth-based projections\nof longevity were performed or what steps were taken to ensure that they were robust to the potential\nissues described above. If appropriate steps were taken to ensure that these estimates were robust,\nthen the authors should add text detailing these reasons (I expect that there simply are not enough data\non size-dependent mortality and growth to properly test these trends, particularly in tropical regions).\nSecond, the authors could take the omission route they already employed for evaluating whether their\nresults were robust to the inclusion of the lowest confidence longevity data. Specifically, the authors\ncould re-run their analyses only including confidence levels 3 and 4 and evaluate the geographic\ncoverage of this reduced dataset. This would tell us whether the results of the study depend on our\nconfidence in the extrapolation of longevity from tree demography. Third, the authors could re-run the\nmodels on simulated data that assume either bias or imprecision to the growth-based projections of\nlongevity (for example, re-run the models 1,500 times with 500 iterations adding random noise to the\ngrowth-based longevity data, 500 adding random positive bias to these data, and 500 adding random\nnegative bias to these data), thereby quantifying how robust the results are to imprecision and bias in\nthese lower-confidence estimates. If the authors find that their original results are not always\nsupported, they could still draw similar conclusions, but with clear caveats that these conclusions\nrequire confidence in extrapolations based on limited data.\nMinor comments\nLines 77-78 (here and elsewhere). This study only provides information on a tiny fraction of\nangiosperms, with biased sampling in terms of both methods and geography. Consider revising the\nconclusions to be a bit more cautious and representative of the data. Specifically, we do not know how\n‘most long-lived angiosperms’ associate with any factor because we have not yet identified most of the\nlong-lived angiosperms in nature. It would be more accurate to discuss tendencies or associations of\nangiosperms, rather than speaking of them as a monolithic unit.\nLines 112-114. This statement overly simplifies the literature to the degree that it is misleading.\nSpecifically, it implies that variation in longevity is largely associated with the slow-fast continuum.\nHowever, the slow-fast continuum is almost entirely based on demographic differences among saplings\nand subcanopy trees, whereas many long-lived taxa ultimately persist for centuries in their local canopy.\nIt is well-established that additional axes of demographic variation (e.g., the stature-recruitment axis)\nalso play a major role in structuring community composition, assembly, and demography. Importantly,\nadditional axes like the stature-recruitment tradeoff are orthogonal to the slow-fast continuum and this\northogonal axis is driven by demographic differences associated with tree size, particularly large and\nlong-lived individuals that can fall at many locations along the slow-fast continuum (Ruger et al. 2018,\n2020). It would be more accurate to revise this text to emphasize that the slow-fast continuum is one of\nmultiple key axes of demographic variation that likely associated with longevity.\nRüger, N., L. S. Comita, R. Condit, D. Purves, B. Rosenbaum, M. D. Visser, S. Joseph Wright, and C. Wirth.\n2018. Beyond the fast–slow continuum: demographic dimensions structuring a tropical tree community.\n21 7:1075–1084.\nRüger, N., R. Condit, D. H. Dent, S. J. DeWalt, S. P. Hubbell, J. W. Lichstein, O. R. Lopez, C. Wirth, and C. E.\nFarrior. 2020. Demographic trade-offs predict tropical forest dynamics. Science 368:165.\nLine 320-321. The claim that there is global convergence in hydraulic safety margins should be omitted\nbecause this hypothesis was unsupported once confronted with more detailed sampling in the tropics.\nSpecifically, better data from tropical forests has shown that hydraulic safety margins are highly variable\namong sites, refuting the hypothesis that there is global convergence in hydraulic safety margins.\nTavares, J. V., et al. 2023. Basin-wide variation in tree hydraulic safety margins predicts the carbon\nbalance of Amazon forests. Nature 617:111–117.\nLine 506. Significantly should not have the ‘ly’\nLines 532-534. Wood density may be the best proxy available, but that does not suggest that wood\ndensity is actually a good proxy for the species-level life history strategies that determine longevity.\nWood density is a decent proxy for the slow-fast continuum, which is a tradeoff based on trends among\nunderstory trees. However, recent data from tropical forest demography has shown that\ntree survivorship as large individuals is related to a demographic axis (‘stature-recruitment’) that is\northogonal to the slow-fast continuum and largely unrelated to wood density (see the Ruger papers\ncited above). It is very likely that this orthogonal axis is more relevant to maximum tree age than the\nslow-fast continuum and wood density. The wood density-based result should give you confidence that\nyou have sampled a single axis of life history strategy (one that is largely associated with small-tree\nperformance), but it is not evidence that your sampling is representative of life history strategies that\ncontrol tree longevity."}]

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