Ecology

In a paper in Nature, Terrer et al.1 reveal an unexpected trade-off between the effects of rising atmospheric carbon dioxide levels on plant biomass and on stocks of soil carbon. Contrary to the assumptions encoded in most computational models of terrestrial ecosystems, the accrual of soil carbon is not positively related to the amount of carbon taken up by plants for biomass growth when CO2 concentrations increase. Instead, the authors show that carbon accumulates in soils when there is a small boost in plant biomass growth in response to CO2, and declines when the growth of biomass is high. Terrer et al. propose that associations of plants with mycorrhizal soil fungi are a key factor in this relationship between the above- and below-ground responses to elevated CO2 levels.
Read the paper: A trade-off between plant and soil carbon storage under elevated CO2
Rising levels of atmospheric CO2 are thought to have driven an increase in the amount of carbon absorbed globally by land ecosystems over the past few decades, a phenomenon known as the CO2 fertilization effect2. This occurs because, at the scale of leaves, higher CO2 levels enhance photosynthesis and the efficiency with which resources (water, light and nutrients such as nitrogen) are used to assimilate CO2 and support biomass growth3. Evidence supporting the existence of the CO2 fertilization effect has been observed in experiments in which the atmosphere around plants or plant communities is enriched with CO2. But at the level of whole ecosystems, responses to CO2 enrichment are more difficult to track, because the effects are diluted throughout a chain of connected processes. Constraining estimates of the response of the global land carbon sink to rising CO2 levels therefore remains a major challenge (see go.nature.com/3vgvhj).Changes in soil carbon are inherently difficult to detect, and studies that assess the effects of elevated CO2 levels on soil-carbon stocks have been equivocal4. Terrer and colleagues set out to investigate these effects by carrying out a meta-analysis of 108 CO2-enrichment experiments. The authors estimate that, in these studies, soil-carbon stocks increased in non-forest sites but remained almost unchanged in forests. By evaluating the effects of multiple environmental variables, the authors found that, surprisingly, the best explanation for the observed patterns is that the changes in soil carbon stocks are inversely related to the changes in above-ground plant biomass: high accumulation of carbon in biomass was associated with soil-carbon loss, whereas low biomass accumulation was associated with soil-carbon gain. This relationship was evident only in experiments in which no nutrients had been added to the studied systems, leading the authors to propose that plant nutrient-acquisition strategies are responsible — which, in turn, depend on the mycorrhizal soil fungi associated with the plants.
Soils linked to climate change
A previous study reported5 that only a small increase in above-ground biomass occurs in CO2-enriched plants that associate with a particular family of mycorrhizae (arbuscular mycorrhizae; AM). AM-associated plants benefit from the fungi’s extensive network of hyphae (branching filaments that aid vegetative growth), which support the plants’ uptake of nitrogen from the soil solution. However, AM have only a limited ability to ‘mine’ nitrogen from organic matter in the soil. The availability of soil nitrogen therefore limits the increase of biomass growth of AM-associated plants in response to elevated CO2 levels. By contrast, plant species that associate with a different group of soil fungi (the ectomycorrhizae; ECM) exhibit a greater increase in above-ground biomass in CO2-enrichment studies, because some of their carbon is allocated to ECM that can mine for nitrogen5. Mining for nutrients by ECM is, however, thought to accelerate the decomposition of organic matter in soil.Terrer et al. now find that AM-associated plants produce a bigger increase in soil-carbon stocks in CO2-enrichment experiments than do ECM-associated plants. The authors suggest that this is because AM-associated plants allocate more carbon to fine roots and to compounds exuded by the roots, resulting in soil-carbon accrual (Fig. 1a). By contrast, nutrient acquisition by ECM-associated plants results in increased turnover — and therefore loss — of soil organic matter (Fig. 1b). Overall, this would lead to an ecosystem-scale trade-off between the amount of carbon sequestered in plants and that sequestered in soil, in a CO2-enriched atmosphere.

Figure 1 | Proposed effects of elevation of atmospheric carbon dioxide levels. Terrer et al.1 suggest that associations of plants with different types of mycorrhizal soil fungi affect plant and soil responses to increases in atmospheric carbon dioxide levels. a, Plants that associate with arbuscular mycorrhizal fungi (grasses and some trees, in this study) do not ‘mine’ nitrogen (N, a nutrient) from the soil, and therefore do not produce much extra above-ground biomass when CO2 levels rise. Instead, they allocate carbon to fine roots and to root-exuded substances, resulting in soil-carbon accrual. Carbon dioxide produced from the respiration of soil microorganisms returns carbon to the atmosphere. b, Plants that associate with ectomycorrhizal fungi (only trees in this study) mine the soil for nitrogen, the uptake of which supports a bigger increase in biomass growth than in a. However, nutrient mining increases the rate of decomposition of organic matter in soil. The amount of carbon in the soil therefore decreases in response to elevated CO2 levels; microbial soil respiration is greater than in a.

Most Earth-system models that account for land carbon-cycling processes assume that rising levels of atmospheric CO2 will increase plant growth, thus producing more plant litter and thereby increasing stocks of soil carbon6. The authors compared the changes in soil carbon and above-ground plant biomass predicted by various models, both in simulations of six open-air CO2-enrichment experiments, and in global simulations of historical and future increases in atmospheric CO2. None of the models reproduced the negative relationship between carbon sequestration by soil and growth in plant biomass that was observed in the current study.Terrer and co-workers’ findings thus provide another urgent warning that current climate models overestimate the amount of carbon that will be sequestered by land ecosystems as atmospheric CO2 levels increase — not only because the models largely ignore the effects of nutrient limitations, but also because they overestimate the amount of carbon that could be sequestered in soil, particularly in forest ecosystems7. But the new study also reveals that grasslands, shrublands and other ecosystems that already have high soil-carbon stocks have great potential to accumulate more soil carbon as CO2 levels increase. These results thus add weight to previous calls to protect existing soil-carbon stocks to mitigate the effects of climate change8.
Carbon dioxide loss from tropical soils increases on warming
There are some limitations to the set of CO2-enrichment experiments included in Terrer and colleagues’ meta-analysis. The experiments are biased towards temperate systems, and most of the forests studied are associated with ECM, whereas the grasslands are all AM-associated. The authors did not find that the type of ecosystem had a substantial effect on the observed responses to CO2, but it remains to be seen whether the reported trade-off between above- and below-ground carbon sequestration for AM- compared with ECM-associated plants applies to forests alone9. Further experiments, especially in tropical ecosystems, are now needed to address these issues.Tropical ecosystems are large contributors to the global terrestrial carbon sink10, but they are notoriously under-studied. Field observations are scarce and few manipulation experiments — such as CO2 enrichment or nutrient additions — have been carried out in these ecosystems11,12. Below-ground processes are particularly challenging to assess in the tropics, where the effects of multiple nutrient scarcities often come into play12. Terrer and colleagues’ study provides a promising framework that can be elaborated to describe diverse plant–soil interactions in various terrestrial ecosystems in the future.CO2-enrichment experiments generally last for just a few years, or just over a decade at most13. Such timescales are unlikely to capture the effects of elevated CO2 levels on plant mortality, plant-species composition and soil-carbon turnover time, all of which can affect the sequestration of carbon by ecosystems in different ways in the longer term. Mechanistic understanding gained from experiments about the coupling between carbon and nutrient cycling can, however, be integrated into computational models. And this will allow us to constrain estimates of the size of the terrestrial carbon sink in the coming decades. The interactions between plants and their associated soil fungi, as well as other crucial below-ground agents and processes such as microbial communities, are already stirring up modelling efforts14,15. Terrer and colleagues’ study now invites researchers to test hypotheses about the processes that drive coordinated above- and below-ground responses to rising CO2 levels. Such studies could be a real step forwards in our understanding of the fate of the terrestrial carbon sink. More

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Demographic dataTo address our hypotheses, we used matrix population models (MPMs) or integral projection models (IPMs) from the COMPADRE Plant Matrix Database (v. 5.0.156) and the PADRINO IPM Database57, which we amended with a systematic literature search. First, we selected density-independent models from COMPADRE and PADRINO which described the transition of a population from 1 year to the next. Among these, we selected studies with at least six annual transition matrices, to balance the needs of adequate yearly temporal replicates and sufficient sample size for a quantitative synthesis. This yielded data from 48 species and 144 populations.We then performed a systematic literature search for studies linking climate drivers to structured population models in the form of either MPMs or IPMs. We performed this search on ISI Web of Science for studies published between 1997 and 2017. We used a Boolean expression containing keywords related to plant form, structured demographic models, and environmental drivers (Supplementary Methods). We only considered studies linking macro-climatic drivers to natural populations (e.g., transplant experiments and studies focused on local climatic factors such as soil moisture, light due to treefall gaps, etc. were excluded). Finally, we used the same criteria used to filter studies in COMPARE and PARDINO, by selecting studies with at least six, density-independent, annual projection models. This search brought two additional species, belonging to three additional populations, which we entered in the COMPADRE database.One of the studies we excluded from the literature search because it contained density-dependent IPMs, also provided raw data with high temporal replication (14–32 years of sampling) for 12 species from 15 populations58. Therefore, we re-analyzed these freely available data to produce density-independent MPMs that were directly comparable to the other studies in our dataset (Supplementary Methods).The resulting dataset consisted of 46 studies, 62 species, 162 populations, and a total of 3761 MPMs and 52 IPMs (Supplementary Data 1). The analyzed plant populations were tracked for a mean of 16 (median of 12) annual transitions. To our knowledge, this is the largest open-access dataset of long-term structured population projection models. However, this dataset is taxonomically and geographically biased. Specifically, among our 62 species, this dataset contains 54 herbaceous perennials (11 of which graminoids), and eight woody species: five shrubs, two trees, and one woody succulent (Opuntia imbricata). Moreover, almost all of these studies were conducted in North America and Europe (Supplementary Fig. 1), in temperate biomes that are cold, dry, or both cold and dry (Supplementary Fig. 1, inset). Our geographic and taxonomic bias reflects the rarity of long-term plant demographic data in general. This dearth of long-term demographic data is particularly evident in the tropics. The ForestGEO network59 is an exception to this rule, but to date, no matrix population models or integral projection models using these data have been published.We used the MPMs and IPMs in this dataset to calculate the response variable of our analyses: the yearly asymptotic population growth rate (λ). This measure is one of the most widely used summary statistics in population ecology60, as it integrates the response of multiple interacting vital rates. Specifically, λ reflects the population growth rate that a population would attain if its vital rates remained constant through time61. This metric therefore distills the effect of underlying vital rates on population dynamics, free of other confounding factors (e.g., transient dynamics arising from population structure62). We calculated λ of each MPM or IPM with standard methods61,63. Because our MPMs and IPMs described the demography of a population transitioning from one year to the next, our λ values were comparable in time units. Finally, we identified and categorized any non-climatic driver associated with these MPMs and IPMs. Data associated with 21 of our 62 species explicitly quantified a non-climatic driver (e.g., grazing, neighbor competition), for a total of 60 of our 162 populations. Of the datasets associated with these species, 19 included discrete drivers, and only three included a continuous driver.Climatic dataTo test the effect of temporal climatic variation on demography, we gathered global climatic data. We downloaded 1 km2 gridded monthly values for maximum temperature, minimum temperature, and total precipitation between 1901 and 2016 from CHELSAcruts64, which combines the CRU TS 4.0165, and CHELSA66 datasets. Gridded climatic data are especially suited to estimate annual climatic means45. These datasets include values from 1901 to 2016, which are necessary to cover the temporal extent of all 162 plant populations considered in our analysis. For our temperature analyses, we calculated the mean monthly temperature as the mean of the minimum and maximum monthly temperatures. We used monthly values to calculate the time series of mean annual temperature and total annual precipitation at each site. We then used this dataset to calculate our annual anomalies for each census year, defined as the 12 months preceding a population census. Our annual anomalies are standardized z-scores. For example, if X is a vector of 40 yearly precipitation or temperature values, E() calculates the mean, and σ() calculates the standard deviation, we compute annual anomalies as A = [X − E(X)]/σ(X). Therefore, an anomaly of one refers to a year where precipitation or temperature was one standard deviation above the 40-year mean. In other words, anomalies represent how infrequent annual climatic conditions are at a site. Specifically, if we assume that A values are normally distributed, values exceeding one and two should occur every 6 and 44 years, respectively. We used 40-year means because the minimum number of years suggested to calculate climate averages is 3067.Z-scores are commonly used in global studies on vegetation responses to climate8,68, and they reflect the null hypothesis that species are adapted to the climatic variation at their respective sites. Across our populations, the standard deviations of annual precipitation and temperature anomalies change by 300% and 60%, respectively (Supplementary Fig. 2). Thus, a z-score of one refers to a precipitation anomaly of 50 or 160 mm and to a temperature anomaly of 0.5 or 0.8 °C. Our null hypothesis posits that species are adapted to these conditions, regardless of the absolute magnitude of the standard deviation in annual climatic anomalies. If this null hypothesis were true, each species would respond similarly to z-scores. Z-scores are more easily interpreted when calculated on normally distributed variables. We found our temperature and precipitation z-scores were highly skewed (skewness above 1) only in, respectively, 2 (for temperature) and three (for precipitation) of our 162 populations. We concluded that this degree of skewness should not bias our z-scores substantially.To test how the response of plant populations to climate changes based on biome we used two proxies of water and temperature limitation. For each study population, we computed a proxy for water limitation, water availability index (WAI), and temperature limitation using mean annual temperature. To compute these metrics, we downloaded data at 1 km2 resolution for mean annual potential evapotranspiration, mean annual precipitation, and mean annual temperature referred to the 1970–2000 period. We obtained potential evapotranspiration data from the CGIAR-CSI consortium (http://www.cgiar-csi.org/). This dataset calculates potential evapotranspiration using the Hargreaves method69. We obtained mean annual precipitation and mean annual temperature from Worldclim70. Here, we used WorldClim rather than CHELSA climatic data because the CGIAR-CSI potential evapotranspiration data were computed from the former. We calculated the WAI values at each of our sites by subtracting mean annual potential evapotranspiration from the mean annual precipitation. Such proxy is a coarse measure of plant water availability that ignores information such as soil characteristics and plant rooting depth. However, WAI is useful to compare water availability among disparate environments, so that it is often employed in global analyses68,71. As our proxy of temperature limitation, we use mean annual temperature. While growing degree days would be a more mechanistic measure of temperature limitation48, this requires daily weather data. However, we could not find a global, downscaled, daily gridded weather dataset to calculate this metric.The overall effect of climate on plant population growth rateTo test H1, we estimated the overall effect sizes of responses to anomalies in temperature, precipitation, and their interaction with a linear mixed-effect model.$${mathrm{log}}left( lambda right) = alpha + beta P + eta T + theta P{mathrm{x}}T + varepsilon$$
(1)
where log(λ) is the log of the asymptotic population growth rate of plant population P is precipitation, T is temperature. We included random population effects on the intercept and the slopes to account for the nonindependence of measurements within populations. We then compared the mean absolute effect size of precipitation, temperature, and their interaction. This final model did not include a quadratic term of temperature and precipitation because these additional terms led to convergence issues. This likely occurred because single data sets did not include enough years of data.Population-level effect of climate on plant population growth ratesTo test our remaining three hypotheses, we carried out meta-regressions where the response variable was the slope (henceforth “effect size”) of climatic anomalies on the population growth rate for each of our populations. Before carrying out our meta-regression, we first estimated the effect size of our two climatic anomalies on the population growth rate of each population separately. We initially fit population-level and meta-regression simultaneously, in a hierarchical Bayesian framework. However, these Bayesian models shrunk the uncertainty of the noisiest population–level relationships, resulting in unrealistically strong meta-regressions. We, therefore, chose to fit population models separately, resulting in more conservative results.For each population, we fit multiple regressions with an autoregressive error term, and we evaluated the potential for nonlinear effects in the datasets longer than 14 years. We fit multiple regressions because temperature and precipitation anomalies were negatively correlated, so that fitting separate models for temperature and precipitation would yield biased results72. We fit an autoregressive error term because density dependence and autocorrelated climate anomalies can produce autocorrelated plant population growth rates. The form of our baseline model was$${rm{log}}(lambda )_y = alpha + beta _pP_y + beta _tT_y + varepsilon _y$$
(2)
$$varepsilon _y = rho varepsilon _{y – 1} + eta _y$$
(3)
The model in Eq. 2 is a linear regression relating each log(λ) data point observed in year y, to the corresponding precipitation (P) and temperature (T) anomalies observed in year y, via the intercept α, the effect sizes, β, and an error term, εy, which depends on white noise, ηy, and on the correlation with the error term of the previous year, ρ. When multiple spatial replicates per each population were available each year, we estimated the ρ autocorrelation value separately for each replicate. This happened in the few cases when a study contained contiguous populations, with no ecologically meaningful (e.g., habitat) differences.We compared the baseline model in Eqs. 2 and 3 to models including a quadratic climatic effect and non-climatic covariates. We estimated quadratic climatic effects only for time series longer than 14 years. We choose this threshold because when using a model selection approach to select a quadratic or linear regression model, the recommended minimum sample size is between 8 and 25 data points73. We fit models including a quadratic effect of temperature, precipitation, or both (Supplementary Table 1).Finally, we also tested whether non-climatic covariates could bias the effects of climate on log(λ) estimated in our analysis. Such bias, either upwards or downwards, could result in the case non-climatic co-variates interacted with climate. For example, harvest can have multiplicative, rather than additive effects on the climate responses of forest understory herbs74. We tested for an interaction between a covariate and climate anomaly in 17 of the 21 studies that included a non-climatic covariate. In the remaining three studies, discrete covariates corresponded with the single populations. Because Eqs. 2 and 3 is fit on separate populations, it implicitly accounted for these covariates. For the 17 studies above, we fit a linear effect of the non-climatic covariate and its interaction with one of the two linear climatic anomalies. Thus, including the linear model in Eqs. 2 and 3, the nonlinear models, and the covariate interaction models, we tested up to six alternative models for each one of our populations (Supplementary Table 1). We selected the best model according to the Akaike Information Criterion corrected for small sample sizes (AICc75). We carried out these and subsequent analyses in R version 3.6.176.In the populations for which AICc selected one of the model alternatives to the baseline in Eqs. 2 and 3, we calculated the effect size of climate by adding the effect of the new terms to the linear climatic terms. For example, when a quadratic precipitation model was selected, we calculated the effect size of precipitation as β = βp + βp2. For models including an interaction between temperature and a non-climatic covariate, we evaluated the effect of the interaction at the mean value of the covariate. Therefore, we calculated the effect size as β = βt + βxE(Ci) for continuous covariates. For categorical variables, we calculated the effect size as βp + βx0.5: that is, we calculated the mean effect size between the two categories. We quantified the standard error of the resulting effect sizes by adding the standard errors of the two terms.The effect of biome on the response of plants to climateWe used a simulation procedure to run two meta-regressions to test for the correlation between the effect size of climate drivers on λ, and our measures of water or temperature limitation. These meta-regressions accounted for the uncertainty, measured as the standard error, in the effect sizes of climate drivers. We represented the effect of biome using a proxy of water (WAI) and temperature (mean annual temperature) limitation. For each of our 162 populations, the response data of this analysis were the effect sizes (βp or βt values) estimated by Eqs. 2 and 3 or their modifications in case a quadratic or non-climatic covariate model were selected. In these meta-regressions, the weight of each effect size was inversely proportional to its standard error. To test H2 and H3 on how water and temperature limitation should affect the response of populations to climate, we used linear meta-regressions. These two hypotheses tested both the sign and magnitude of the effect of climate. Therefore, we used the effect sizes as a response variable which could take negative or positive values. As predictors, we used population-specific WAI (H2, only for effect sizes quantifying the effect of precipitation), and mean annual temperature (H3, only for effect sizes quantifying the effect of temperature). The null hypothesis of these meta-regressions is that plant species are adapted to the climatic variation at their respective sites. Such an adaptation implies that a precipitation z-score of one should produce effects on log(λ) of similar magnitude and sign across different climates. This should happen across average climatic values that are connected to substantially different absolute climatic anomalies (Supplementary Fig. 2). On the other hand, our hypotheses posit that at low WAI and MAT values, species are more responsive to z-scores than expected under the null hypothesis.We performed these two meta-regressions by exploiting the standard error of each effect size. We simulated 1000 separate datasets where each effect size was independently drawn from a normal distribution whose mean was the estimated β value, and the standard deviation was the standard error of this β. These simulated datasets accounted for the uncertainty in the β values. We fit 1000 linear models, extracting for each its slope, βmeta. Each one of these slopes had in turn an uncertainty, quantified by its standard error, σmeta. For each βmeta, we then drew 1000 values from a normal distribution with mean βmeta and standard deviation σmeta. We used the resulting 1 × 106 values to estimate the confidence intervals of βmeta. This procedure assumes that the distribution of βmeta values is normally distributed. We performed one-tailed hypothesis tests, considering meta-regression slopes significant when over 95% of simulated values were below zero.The effect of generation time on the response of plants to climateTo test H4 on how the generation time of a species should mediate its responses to climate, we used a gamma meta-regression. We fitted gamma meta-regressions because our response variables were the absolute effect sizes of precipitation and temperature anomalies, |β|, which are bounded between 0 and infinity. To test H4, we therefore fit gamma meta-regressions with a log link, using |β| values as response variable and generation time (T) as predictor. We calculated T directly from the MPMs and IPMs (Supplementary Methods). We log-transformed T to improve model fit. We carried out these meta-regressions using the same simulation procedure described for testing H2 and H3. We also carried out one-tailed hypothesis tests, by verifying whether 95% of βmeta values were below zero.The effect of plant types on estimates of climate effectsWe verified whether certain plant types could bias our results by subdividing our species as graminoids, herbaceous perennials, ferns, woody species (shrubs and trees), and succulents. We ran ANOVA tests to verify whether the effect sizes of precipitation and temperature anomalies differed between plant types. We then tested for significant differences in pairwise contrasts between plants types by running Tukey’s honestly significant difference tests. We carried out these tests on the average effects of climate, without accounting for differences in parameter uncertainty. If Tukey’s test identified significant differences among plant types, we ran additional tests of H2–H4 excluding the plant type, or plant types, whose response to climate differed.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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The geographic area covered by the WOODIV database is the Euro-Mediterranean region, as defined by Médail et al.1. The northern Mediterranean region was selected following the definition of terrestrial ecoregions of the world by Olson et al.13. The study area covers all or part of the following countries and islands: Albania, Croatia, Cyprus, France, Greece, Italy, Malta, Montenegro, Portugal, Slovenia, Southern Macedonia, and Spain, including the Balearic archipelago, Corsica, Sardinia, Sicily, and Crete.We focused on the 245 tree taxa (210 species and 35 subspecies) identified in the Euro-Mediterranean checklist from Médail et al.1. These taxa belong to 33 families and 64 genera and include 46 endemics (as defined by Médail et al.1, i.e. range-restricted taxa in and outside of the study area).Observed occurrence dataWe collected tree occurrence data (at the species or subspecies level) from 23 sources: national databases and floras, regional databases, and publications (Table 1). Some records still unpublished were specifically provided at the grid level for this project by experts for southern Macedonia, Malta, Montenegro, and Sicily (four sources, Table 1).Table 1 Sources of the occurrence records, giving the name of the dataset (Source name; ined. if unpublished), the Type of data (records with geographic coordinates (records), records at the grid level (gridded records), or atlas-type (atlas) data), and the Countries/Islands covered by the source.Full size tableWhen considering the subspecies level, the WOODIV database lacks the occurrences of 11 sub-species among the 35 listed by Médail et al.1. When aggregated at the species level (to match the taxonomic resolution of the functional and phylogenetic data which are available at the species level only), the WOODIV database lacks only the occurrences of 3 of the 210 species from the Médail et al.1 checklist (n = 207; Table 2; Supplementary Table 2): Pyrus elaeagrifolia Pall., which occurs in Albania and Macedonia (and in northeastern Greece but outside the Mediterranean biome), P. syriaca Boiss. and Tamarix passerinoides Desv., which occur in Cyprus and in Sardinia, respectively.Table 2 Summary of the availability of data in the WOODIV database: total number of species among the 210 species from the Médail et al.1 checklist with (1) observed occurrences; (2) functional traits data, including the detail of the number of species with available data for 4 traits: adult plant height (Height), seed mass (SeedMass), specific leaf area (SLA) and wood density (SSD) (see “Functional data” section); and, (3) genetic data including the detail of the number of species with available data for 3 DNA-regions: matK, rbcL and psbA-trnH (see “Genetic data” section).Full size tableAlso, due to the taxonomic heterogeneity of the different data sources, we recommend aggregating the occurrences of certain tree taxa at the species’ group level (see sections Data Records and Usage Notes): i.e. to aggregate Pinus uncinata DC. and P. mugo Turra into P. mugo aggr., Juniperus deltoides R.P.Adams and J. oxycedrus L. into J. oxycedrus aggr. and Alnus lusitanica Vít, Douda & Mandák., A. rohlenae Vít, Douda & Mandák, and A. glutinosa (L.) Gaertn. into A. glutinosa aggr. The WOODIV database thus contains reliable occurrences of 200 species and three aggregated species (n = 203; Table 2; Supplementary Table 2).The raw dataset obtained from gathering occurrences from all sources included a total of 1,248,701 occurrence records distributed across the participating countries.The raw occurrence data were aggregated at a resolution of 10 × 10 km in line with an INSPIRE14 compliant 10 × 10 km grid (SCR 4258). This gridding procedure provided a way to standardize data from different sources. We selected this spatial grain because it was the finest resolution available for some countries of the study area (e.g. Slovenia, Croatia, Greece). Sources of occurrence data with a resolution coarser than 10 × 10 km (e.g. Atlas Florae Europaeae15) were not considered. The considered area includes 10,042 grid cells with at least one occurrence record (Fig. 1a). The occurrence dataset provided by the WOODIV database, i.e. aggregated records for species considered as native in the given grid cell using the 10 × 10 km grid (removal of duplicate species within a grid cell) includes 140,279 occurrences.Fig. 1Geographic scope of the WOODIV database, spatial distribution, and validation of trees occurrences. (a) Number of species within a 10 × 10 km grid cell based on modelled occurrence data for the 171 modelled species, with the addition of the occurrence data of the 21 small-range species; and, within grid cells of Atlas Flora Europaeae (AFE; 50x50km) (b) Number of species with presences recorded in AFE but not in the WOODIV dataset on the 104 species present both in the AFE and WOODIV data; and, (c) Number of species with presences recorded in the WOODIV dataset but not in AFE on the 104 species present both in the AFE and WOODIV data.Full size imageModelled occurrence dataThe WOODIV database provides modelled occurrences of the species from the Médail et al.1 checklist. From the 10 × 10 km gridded observed occurrence data, we modelled the distribution of each species across the Euro-Mediterranean area using Species Distribution Models (SDM). SDM statistically relate species occurrence records to environmental variables to predict the potential distribution of species16.Due to the extent of the study area, we only related species occurrence to climate gradients17. Bioclimatic variables were extracted from the CHELSA database V1.218 available at a resolution of 30 arc‐sec (http://chelsa‐climate.org/) and then averaged to a 10 × 10 km resolution. The selection of the environmental predictors for niche modeling is a source of uncertainty in model predictions that can be reduced with sound statistical methods and ecological knowledge of the target species19. We also focused on proximal predictors that directly influence species distribution and selected a low number of predictive variables to reduce the issues of model overfitting and multicollinearity20. We selected four bioclimatic variables that previous studies had reported to be relevant predictors of the distribution of plant species, especially in environments such as those that characterize the Mediterranean Basin21,22,23,24: “Minimum temperature of the coldest month” (Bio06, in °C) quantifies potentially lethal frost events and more generally, stress due to low temperatures; “Total annual precipitation” (Bio12, in mm) approximates average water availability; “Precipitation of the driest month” (Bio14, in mm) describes the extremes associated with drought events and stress due to low water availability, and “Temperature seasonality” (Bio04, no dimension) describes the variability of temperature during the year. All selected predictors showed VIF (variance inflation factor25) values below 5, indicating that a given predictor was not correlated with any linear combinations of the other predictors (VIF Bio04 = 1.68, VIF Bio06 = 2.06, VIF Bio12 = 1.53, and VIF Bio14 = 2.07).We related species occurrence to these four bioclimatic variables using the Random Forest algorithm26. As only presence data are archived in the WOODIV database, we randomly sampled a number of pseudo-absences equal to the number of observed occurrences27. This random selection of pseudo-absences was repeated 10 times for each species. When comparing the floras, occurrence data in the Italian Peninsula, Sardinia and/or Sicily were highly unrepresentative of the distribution of some species (n = 84; see Supplementary Table 3). To overcome this potential bias in the models, we did not include these regions in the model calibration step (Supplementary Table 3). The model was projected in these areas after having tested the similarity in the variables between the projection dataset (Italy, Sicily, and Sardinia) and the fitting dataset (the rest of the study area). Indeed, when model predictions are projected into regions not analyzed in the fitting data, it is necessary to measure the similarity between the new environments and those in the training sample28, as models are not so reliable when predicting outside their domain29. Similarity analyses computed using ExDet30 indicated that all covariables in the projected area are within the univariate range of the fitting area and that there is no change in correlation between covariables (NT1 and NT2 = 0).Each of these 10 datasets (per species) was then randomly split into two datasets to evaluate model performance on pseudo-independent data31: 70% of the data was used to calibrate models and the 30% remaining data was used to evaluate model performance using the True Skill Statistic (TSS32) and the Area Under the Curve (AUC) of the receiver-operating characteristic (ROC) plot33 metrics. This split-sample step was repeated 10 times resulting in 100 models per species.For each of the 171 modelled species, a mean model (from the 100 replicates) was then used to predict potential species distribution. Predicted probabilities of occurrence were finally converted into presence/absence using the threshold maximizing the TSS. We fitted all models under the R environment R Core team34 and the package biomod235,36.The WOODIV database provides modelled occurrences of each of the 171 species for each 10 × 10 km grid cell (Fig. 1a). Thirty-two species with less than 10 occurrence records were not modelled (Supplementary Table 3). Among these 32 species, 21 are small-ranged species whose distribution is limited to a few grid cells (Supplementary Table 3). The observed occurrence records for these 21 species can be considered as representative of their distribution and we therefore recommend using the non-modelled records for these species for analyses. The occurrences of the remaining 11 species should be considered unrepresentative of their distribution.Functional dataFour functional traits were considered in this project: adult plant height (Height), seed mass (SeedMass), specific leaf area (SLA), and wood density (StemSpecDens). These traits have been proposed to reflect a global spectrum of plant strategies37,38: height is a commonly measured proxy for individual size and reflects several aspects including resource acquisition, competitive ability, or dispersal capacity. SeedMass represents the trade-off between fecundity, seed survival, and dispersal. SLA (the ratio between leaf area and dry mass) is correlated to photosynthetic capacity and leaf life span and is an indirect measure of the return on investments in carbon gain compared to water loss. StemSpecDens is a key component of woody plant growth linked to the mechanical support of the stem and its growth rate.We compiled the values for these traits at the species level for the trees from the Médail et al.1 checklist, referring mostly to 2 databases: TRY9 and BROT 2.039. Supplementary values were obtained from more specific databases (Global Wood Density Database40, Kew Seed Information Database41) or from the scientific literature and atlas42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61. In total, 92% of the entries were extracted from TRY, 7% from BROT 2.0 and the remaining were retrieved from the other sources. The original ID of records from the TRY and BROT databases is provided in order to make it possible to refer to the complete observation if a user needs to have some contextual information.The WOODIV database lacks all traits data for only 6 of the 210 species from the checklist (Table 2, Supplementary Table 2): Alnus lusitanica Vít, Douda & Mandák, Alnus rohlenae Vít, Douda & Mandák, Malus dasyphylla Borkh., Quercus infectoria Olivier, Tamarix arborea Ehrenb. ex Bunge and, Tamarix passerinoides Del. ex Desf.Adult plant height and seed mass data were available for more than 75% of the 210 species (Table 2; Fig. 2a), whereas wood density and specific leaf area were available for only around 50%. The WOODIV database includes all four trait values for 41% of the 210 species (Fig. 2b; Supplementary Table 2), three trait values for 56% more species.Fig. 2Prevalence of traits and genetic data among the 210 species from Médail et al.1 checkist: (a) For each of the four considered functional traits (adult plant height (Height), seed mass (SeedMass), wood density (SSD) and specific leaf area (SLA)), percentage of the 210 species with existing data; (b) Percentage of the 210 species for which none to four functional traits data are available; (c) For each of the three considered DNA regions (matK, rbcL and psbA-trnH), percentage of the 210 species with existing data (in grey species with only one available sequence for the considered region, in black species with consensus sequence for that region); and, (d) Percentage of the 210 species for which none to three DNA regions data are available.Full size imageThe database provides an R script that can be used to estimate missing trait values using the taxonomic classification if needed.Genetic dataThree different DNA regions from the plastid genome corresponding to the most commonly used DNA barcode regions62,63,64 were considered in this project: the ribulose-bisphosphate/carboxylase Large-subunit gene (rbcL), the maturase-K gene (matK), and the psbA-trnH intergenic spacer (trnH).In a first step, we collected all sequences from GenBank (https://www.ncbi.nlm.nih.gov/genbank/) for the three DNA regions available for the species from the Médail et al.1 checklist at the species level: rbcL: n = 650 sequences for 146 species, matK: n = 644 sequences for 127 species, trnH: n = 493 sequences for 129 species). To fill the gaps, we obtained DNA from fresh samples collected in the field or gathered from herbarium specimens (Supplementary Table 4). DNA extraction and sequencing were performed at INRA-URFM, Avignon (France) and the National Research Council (IBBR-CNR), Florence (Italy) (rbcL: n = 233 for 125 species, matK: n = 162 for 91 species, trnH: n = 200 for 120 species). Methods used for DNA isolation and Sanger sequencing are described by Albassatneh et al.65. When more than one sequence was available for a given DNA region/species, a sequence alignment was performed to check data quality and a taxon-consensus sequence was generated. Consensus sequences were built using the IUPAC-IUB ambiguity66 code for a total of 119 (rbcL), 109 (matK), and 110 species (trnH), respectively (Fig. 2c). All newly created sequences were uploaded to GenBank.The WOODIV database lacks the DNA-region sequences data of only 6 of the 210 species from the Médail et al.1 checklist (Table 2, Fig. 2d): Alnus lusitanica Vít, Douda & Mandák, Cytisus aeolicus Guss., Celtis planchoniana K.I. Chr., Salix appendiculata Vill., Tamarix hampeana Boiss. & Heldr. and, Tamarix minoa J.L. Villar, Turland, Juan, Gaskin, M.A. Alonso & M.B. Crespo.PhylogenyThe WOODIV database provides a phylogram including the 204 species for which at least one piece of DNA-region sequence data was available (Supplementary Table 2) and phylograms including the 210 species from the Medail et al.1 list (Supplementary Fig. 1).Uneven taxon sampling focused on a single biogeographic area such as ours, can bias phylogenetic inferences67. Our goal here is to provide DNA sequence data that can be readily re-used to estimate, e.g. comparable phylogenetic diversity indices, not phylogenetic inferences per se. To illustrate our DNA-sequences data and to facilitate their use for future analyses (to calculate phylogenetic diversity for example), we constructed a molecular phylogeny encompassing the 204 Euro-Mediterranean tree species. Each gene was independently aligned using the MAFFT program68 and parsed using the program Gblocks69 to exclude the segments characterized by several variable positions or gaps from final alignments. An appropriate substitution model of sequence evolution was selected for each of the three plastid DNA regions using the Akaike Information Criterion (AIC) as implemented in the JModeltest 2 program70. The optimal substitution model identified was the same for all three sequences: GTR + I + G. We obtained a concatenated matrix with 1615 aligned bases. We used the Maximum Likelihood analysis71 as implemented in the RAxML V8 program72. The DNA sequence matrix of 1615 sites was analyzed using three partitions with the GTRGAMMAI model (GTR + Gamma substitution model + proportion of invariant sites). We searched for the optimal tree, running at least 20 independent maximum likelihood analyses; full analyses also consisted of 100 bootstrap replicates72.For users who would like to work on the complete pool of 210 tree species, we also built a 210 species phylogram including all Euro-Mediterranean trees. The six missing species for which no DNA-region sequence was available were added to the phylogenetic tree using the Simulation with Uncertainty for Phylogenetic Investigating (SUNPLIN) method73, with 100 replicates. The geometric median tree was computed from the set of 100 replicates with the medTree function from the R package treespace74. Both the median tree and the set of 100 replicates are provided in the WOODIV database, together with the molecular tree with 204 species. More

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