More stories

  • in

    Large size in aquatic tetrapods compensates for high drag caused by extreme body proportions

    Drag coefficients of plesiosaurs, ichthyosaurs and modern cetaceansAt equal Reynolds numbers (same body length and same flow velocity), the total drag coefficients of plesiosaurs (Cd) are higher than the estimated values for ichthyosaurs and modern cetaceans (Fig. 1a). The limbless bodies, however, display similar Cd in all three groups and are even lower-than-average in the long-necked plesiosaurs, indicating that the limbs are responsible for the observed high Cd. The limbs of plesiosaurs contribute to more than 20% of their total drag coefficient: up to 32.2% in the basal Meyerasaurus and averaging 25% in derived plesiosaurs, with no major differences between plesiosaur morphotypes. In parvipelvian ichthyosaurs the contribution of the limbs to Cd is 11.2–15.6%, compared to 8.7–14.3% in modern cetaceans. Some of the living taxa we include provide a functional reference for this analysis. Our computed drag coefficient for the bottlenose dolphin model (Cd = 0.00413 at Re = 107) for example, is consistent with the estimates from a gliding living dolphin33 (Cd = 0.0034 at Re = 9.1 × 106) and other static CFD simulations34 (Cd = 0.00413 at Re = 107). It is worth noting that these values are, as expected, lower than estimates obtained from kinematic models, as motion is not accounted for35. In a former study, drag coefficients for a plesiosaur (Cryptoclidus), two ichthyosaurs and various cetaceans were obtained from rigid models in water tanks36. However, the pressure drag component (Cp) was likely overestimated due to the proximity of the models to the air–water interface, and thus are not directly comparable to ours.Fig. 1: Comparison of the drag coefficient of derived plesiosaurs, ichthyosaurs and cetaceans.a Total drag coefficient computed for the full models including the limbs (‘body + limbs’, circles) and the limbless models (‘body’, squares). Average (point) and range (bar) shown for calculations at Re = 5 × 106–107. The derived short-necked plesiosaurs are highlighted in orange; the parvipelvian ichthyosaurs in blue and the extant cetaceans in red. A basal plesiosaur included as a reference is highlighted in purple. b Representative two-dimensional plots of the flow velocity magnitude at Re = 5 × 106 (inlet velocity of 5 ms−1) in lateral view. For dorsal view see Supplementary Fig. 1. Images of Tursiops and the three ichthyosaurs modified from Gutarra et al.29.Full size imageIn all models across the various clades, velocity plots display a stagnation point at the anterior tip of the model, a thin velocity gradient along the body corresponding to the boundary layer, an area of higher velocity around the greatest diameter and a low velocity wake behind the body, characteristic features of a fully developed external flow (Fig. 1b, Supplementary Fig. 1). The acceleration of flow results in areas of low pressure (Supplementary Fig. 2), while high pressure areas are observed where stagnation occurs. Our CFD methodology has been previously validated against experimental data from slender torpedo-like shapes26 and has been shown to provide a reliable distribution of internal drag components29 essential when dealing with streamlined bodies35. In all our simulations, the proportion of frictional and pressure drag was consistent with the expected values for slender geometries31: most of the drag originated from skin friction with a minor pressure drag component (Supplementary Fig. 2). The relatively larger limbs of plesiosaurs (Supplementary Table 1) produce a small increase in skin friction (Supplementary Fig. 2a), but a large increase in the pressure drag coefficient (Supplementary Fig. 2b), indicating that the latter largely explains differences in total drag coefficient between the groups. These effects might be explained by the low local Reynolds number of the flippers (resulting from a small chord length) producing high local Cd relative to the rest of the body31, alongside interference drag (i.e. drag caused by the interaction of flow fields where limbs and body meet), which might be higher for larger flippers.Effect of body shape and body size on drag-related costs of steady swimmingWhen comparing morphologies at the same volume (proxy for body mass) and the same velocity, to focus on the effect of shape alone, derived plesiosaurs produce on average 30% more drag than parvipelvian ichthyosaurs and modern cetaceans (Fig. 2a, Supplementary Table 3; two-sample t-tests p  0.05). In these conditions, the drag-related costs of steady swimming of plesiosaurs fall within the range observed in both modern cetaceans and ichthyosaurs. Normalised against a 2.85 m-long Tursiops, the COTdrag for derived plesiosaurs ranges from 0.42, estimated for the large elasmosaur Thalassomedon, to 1.41 in the medium-sized Dolichorhynchops. In the parvipelvians, COTdrag spans from 0.33 estimated for the large Temnodontosaurus, to 1.76 in a 2.5 m-long Stenopterygius. Cetaceans show a smaller lower limit, because they include the largest animal in our sample, a 16 m-long humpback whale, with a COTdrag of 0.13 compared to Tursiops. The estimated cetacean upper COTdrag limit is 1.54 for a 1.9 m Tursiops. On the other hand, comparisons of the total drag power (Pdrag, i.e., the non-mass normalised version of COTdrag) for the same speed of 1 ms−1 (Fig. 3), show a different trend. Pdrag is highest for Megaptera, higher than in any fossil taxa included in this study, and is lowest in Tursiops. Thalassomedon is comparable both in total drag power and COTdrag to the killer whale. Similarly, the thalassophonean pliosaurid Liopleurodon matches the elasmosaurian Hydrotherosaurus in having a similarly low mass-normalised COTdrag but requiring about 4× more total drag power than Tursiops. Smaller forms like the polycotylid Dolichorhynchops and the thunnosaurian Ophthalmosaurus resemble the extant bottlenose dolphin in having a relatively high COTdrag and low total power.Fig. 3: Comparative plot of mass-normalised drag power and total drag power.Values of mass-normalised drag power (i.e., drag per unit of volume or COTdrag calculated as in Fig. 2b) in grey, and non-mass-normalised total drag power, in black, for an array of derived plesiosaurs, parvipelvian ichthyosaurs and modern cetaceans compared at the same inlet velocity of 1 ms−1. Error bars represent minimum and maximum values accounting for taxon body size variation (see Supplementary Data). Values are normalised to the results for Tursiops.Full size imageThus, in contrast to the volume-normalised simulations, differences between animals at their life-size scale are mainly influenced by size. For example, medium-sized plesiosaurs and ichthyosaurs, such as Dolichorhynchops and Ophthalmosaurus, have values of COTdrag close to that of a dolphin, while large plesiosaurs like Thalassomedon are more like the parvipelvian ichthyosaur Temnodontosaurus and a modern Orcinus. It is worth noting that the inflow velocity of 1 ms−1, is a reference velocity used for comparative purposes, and is not equivalent to the optimal cruising speed (i.e. speed at which COT is minimum16). This parameter is known to vary little in nature, with most vertebrates displaying values of preferred speed between 1–2 ms−1 regardless of body size40,41,42, which means it is reasonable to assume all tested taxa, regardless of their size, were able to swim at this velocity. Using a different reference velocity (2 ms−1) has no effect on the relative values of drag per unit of volume and the mass-normalised drag power (Supplementary Fig. 3; Supplementary Data). A reduction of mass-normalised drag-related costs of cruising as body size increases is selectively advantageous, as energy savings can be used to extend foraging and mating range, increase swimming speed and fuel other activities42,43.Our analysis shows that for highly aquatic tetrapods, size dominates over shape in affecting the drag-related costs of steady locomotion. This is because COTdrag (i.e., the balance of drag to volume) is highly sensitive to surface/volume proportion (Fig. 2f), and so is much influenced by isometry in streamlined animals.Interplay between neck anatomy and body size in plesiosaur dragSimulations at constant Reynolds number (i.e., comparing models at same total length and same flow velocity), show that necks up to 5× the length of the trunk do not increase substantially the total drag coefficient. Longer neck ratios up to 7× were found to impact the drag coefficient by as little as 3% (Fig. 4a). We estimated a 4–10% increase in skin friction drag coefficient for neck ratios of 3–7×, but also a comparable reduction in pressure drag resulting in almost no change in the total drag coefficient. A previous CFD-based study also found no differences in drag coefficient between plesiosaur models with variable neck proportions20, but further comparison is not possible because of great differences in the order of magnitude of Cd, the use of a different scaling reference area and the lack of information on skin and pressure drag20. Here, we have shown that long necks produce only a small increase in skin friction, although not as great as previously speculated25,30, and this is nullified by reduced pressure drag.Fig. 4: Influence of neck length and its interaction with body size on the drag-related costs of swimming in plesiosaurs.a Total drag coefficient and skin friction drag coefficient for an array of hypothetical plesiosaurs with varying neck ratios computed at Re = 5 × 106 (same total length and inflow velocity). b Drag per unit of trunk volume computed for the same array of models scaled at the same trunk length and tested at the same speed of 1 ms−1. The hypothetical models were created by modifying the length in the model of the basal plesiosaur Meyerasaurus victor which has a neck ratio of 0.87×. The limits of the trunk (which extends along the torso and includes the edges of the pectoral and pelvic girdles) are shown in red in the rendered models. c Three-dimensional models of a wide array of plesiosaurs, in dorsal view, at their life-size dimensions, showing the differences in body proportions and sizes. The limits of the trunk in the models (defined as in b) are coloured by group. Basal plesiosaurs are highlighted in purple. Among the derived groups, thalassophonean plesiosaurs (derived pliosaurid plesiosaurs) are highlighted in light orange, polycotylid plesiosaurs in dark orange and elasmosaurid plesiosaurs in green. d Scatterplot of trunk length (cm) and neck ratio showing the relative drag per unit of trunk volume as a gradient of colour for each taxon analysed and for the plot area in between (contour lines represent the interpolated values of drag per unit of volume). e Plot of the relative drag per unit of trunk volume versus the trunk length showing results highlighted by group. Line plots at the right-hand side show the range for each group. The D/Vtr and the trunk length show a significant negative correlation (Pearson’s correlation coefficient calculated with log-transformed variables, p = 2.28 × 10−7, R2 = −0.92). A small version of the fitted power curve (regression equation (y=69.76{x}^{-0.94})) is shown on the right upper corner. The grey area around the curve represents a confidence interval of 95%. All values in b, d and e are normalized to the results for the Meyerasaurus model.Full size imageNext, we explored the impact of neck proportions on drag-related costs of swimming in simulations where the size factor is removed. We found that if trunk dimensions are kept constant while the neck is enlarged, the drag per unit of trunk volume does not change appreciably for neck ratios up to 2×. However, longer neck proportions did impact resistive forces. This was moderate for a 3× ratio, with 12% more drag per unit of trunk volume, but became more substantial for longer necks, with 22%, 35% and 59% excess drag for necks of 4×, 5× and 7× respectively (Fig. 4b). This means that elasmosaurine elasmosaurs, with necks commonly 3–4× the length of the trunk23 might have experienced higher drag than other plesiosaurs of similar trunk dimensions.To test if the ‘long neck effect’ remains when body size is accounted for, we compared the relative amount of drag-per-unit-trunk-volume (D/Vtr) in a wide sample of plesiosaurs (Fig. 4c) at life-size scale for a constant velocity of 1 ms−1, including three species with neck ratios above 2×: Styxosaurus (2.76×), Hydrotherosaurus (3.18×) and Albertonectes (3.72×), the last being the elasmosaur with the longest reported neck44. Our results show great variability in D/Vtr. Small-bodied plesiosaurs such as Plesiosaurus, Meyerasaurus and Dolichorhynchops generated up to six times more D/Vtr than the largest plesiosaurs, Kronosaurus and Aristonectes (Fig. 4d, e). Comparisons per group show that both basal plesiosaurs and derived polycotylids, the groups with the smallest specimens, produced generally higher D/Vtr. Moreover, we did not find substantial differences between elasmosaurs and thalassophonean pliosauroids (Fig. 4e, Supplementary Table 4; all two-sample t-tests p  > 0.05). Both groups had similarly low ranges of D/Vtr regardless of neck length, lower on average than in polycotylids. These results stand even if we exclude Aristonectes, which belongs to the aristonectines, an elasmosaur subfamily with reduced neck length23,45. Further comparisons by morphotype show no significant differences between short-necked pliosauromorphs (here arbitrarily including plesiosaurs with neck ratios below 2×) and long-necked plesiosauromorphs (Supplementary Table 4, all two-sample t-tests p  > 0.05). The highest values of D/Vtr occur in animals with trunk lengths of 100 cm or less, followed by a steep decrease between 100–150 cm and a steadier decrease in longer trunks. This indicates a strong negative correlation between trunk dimensions and D/Vtr (Pearson’s product-moment correlation between the log-transformed variables, adjusted r2 = −0.92, p = 2.28 × 10−7). The curve that best describes this relationship is the power equation, D/Vtr = 69.76 × Trunk length−0.944 (Fig. 4e), an almost inversely proportional relationship, consistent with the streamlined nature of these animals for which skin friction drag is dominant.Polycotylids and thalassophonean pliosaurs, both derived pliosauromorph plesiosaurs9,21, share the same general body proportions9,21,46, but the latter had larger bodies and therefore needed less power in relation to their muscles to move at the same speed. Elasmosaurs on the other hand, despite their disparate morphologies, were no different from thalassophonean pliosaurs in their drag-related costs of forward swimming (Fig. 4c–e) and therefore they were likely to have been equally efficient cruisers.Earlier research suggested that, even if long necks did not add extra drag during forward swimming, speed in elasmosaurs would have been limited to avoid added drag when their necks bent20. However, when the neck is bent in living forms, the course of swimming changes, as does the flow direction, but the body remains streamlined in the direction of incoming flow. For example, sea lions perform non-powered turns initiated by the head in which the body glides smoothly in a curved position, limiting deceleration47. Further biomechanical research is needed to understand the role of plesiosaur necks in manoeuvrability and other aspects of swimming performance, as well as how these were influenced by shape and flexibility. The well-established idea that long-necked plesiosaurs were sluggish, slow swimmers7,30 is thus not supported here, not because long necks did not increase drag20, but because body size overrode this drag excess.Long necks evolved in large-bodied plesiosaurs: implications for dragWe analysed trends of body size and neck proportion in a wider sample of sauropterygians, including plesiosaurian and non-plesiosaurian Triassic sauropterygians. Long necks (neck ratio > 3×) occur in taxa with trunk lengths > 150 cm, whereas most sauropterygians had neck ratios of ≤ 2× (Fig. 5a). The great plasticity of body proportions of sauropterygians before and after their transition to a pelagic lifestyle after the Triassic has been well documented21,23,46, but this is the first time that neck and body size have been explored in the context of swimming performance for such a wide sample. We show that overall, sauropterygians and particularly plesiosaurs, mainly explored neck morphologies with little or no effect on drag costs and did not enter morphospaces that were suboptimal for aquatic locomotion (i.e., corresponding to small trunks with long necks; Fig. 5a). In fact, ancestral state reconstruction for trunk length shows that the ancestor of elasmosaurs was likely around 180 cm long and had a relatively short neck with a ratio smaller than 2× (Fig. 5b, c). This indicates that large trunks preceded neck elongation in elasmosaurs and suggests that extreme proportions might have been favoured by a release of hydrodynamic constraints.Fig. 5: Evolutionary trends of neck proportions and body size in Sauropterygia and their implications for the drag-related costs of swimming.a Bivariate plot of the length of trunk and the neck ratio of 79 sauropterygian taxa. Polygons in different colours show area occupied by the main sauropterygian groups. The functional trends describing the effect of each axis are based on results from flow simulations. On the top of this graph, a univariate plot shows the distribution and mean values of trunk length for each group. b, c Phenograms showing the disparity of trunk length (b) and neck ratio (c) in sauropterygians through time. The branches corresponding to basal Plesiosauria (including Rhomaleosauridae and Plesiosauridae), thalassophonean pliosaurs, polycotylids and elasmosaurs are highlighted (colour coding as in a). d, e Sauropterygian trees showing the evolutionary rates for trunk length (d) and neck ratio (e) represented by colour gradient (see Supplementary Fig. 5 for an alternative analysis to 5d using the log10-transformed trunk length). Consensus trees show average results from analyses of 20 cal3-dated trees (see Supplementary Figs. 4 and 6 for analysis on Hedman-dated trees). Rates are based on the mean scalar evolutionary rate parameter.Full size imageWe next explored evolutionary rates of relative neck length and trunk length in sauropterygians. The pattern of trunk length evolution is consistent with a heterogeneous rates model, not a homogeneous Brownian motion model (log Bayes Factor48 (BF)  > 5 in 100% of the sampled trees and > 10 in 92.5%, Supplementary Table 5). Analysis of non-transformed trunk data shows that through the evolution of Sauropterygia, there was a general increase in trunk length with some higher rates, in Triassic nothosauroids, Jurassic rhomaleosaurids and Cretaceous aristonectine elasmosaurs (Fig. 5d; Supplementary Fig. 4a). Additionally, analysis of the log10-transformed trunk data highlights variation in the small-to-medium size ranges and reveals high rates in Triassic eosauropterygians (Supplementary Figs. 5 and 6). The largest trunks evolved independently in two groups, thalassophonean pliosaurids and elasmosaurid plesiosauroids, with no evidence of high rates in the former. In the plesiosauroids, rates are not particularly high in the basal branches, but they are very high in derived aristonectines, and rates for the whole clade were significantly higher than the background rate in 40% of randomisation tests (Supplementary Fig. 7 and Table 6). A progressive increase in body mass over evolutionary time has been described for various clades of aquatic mammals49 and seems to be a common hallmark of the aquatic adaptation to marine pelagic lifestyles in secondarily aquatic tetrapods44. Whether body size reaches a plateau as is the case in cetaceans49 and what constraints influence the evolutionary patterns of size in plesiosaurs remains unexplored. Against this general trend, some derived plesiosaurs, such as polycotylids, saw a reduction in body size, which might have been related to pressures on niche selection, such as adaptation to specific prey, the need for higher manoeuvrability or other ecological factors. As shown earlier, small sizes require lower amounts of total power for a given speed, and therefore would be favoured if for example food resources were limited. This suggests that, in spite of the energy advantages of large size in terms of reduced mass-specific drag29 and metabolic rates49,50, which make it a common adaptation to the pelagic mode of life, other constraints limiting very large sizes were also at work50,51.A heterogeneous evolutionary rates model for neck proportion is also strongly supported (log BF  > 5 in 100% of the sampled trees and > 10 in 45%, Supplementary Table 5). Fast rates are consistently seen at the base of Pistosauroidea (including some Triassic forms and plesiosaurs) and, interestingly, also within elasmosaurs (Fig. 5e; Supplementary Fig. 4b). The neck proportions of elasmosaurs were found to evolve at a faster pace than the background rate in 90% of analyses (randomisation test p-value < 0.001 in 80% and < 0.01 in 10% of the sampled trees; Supplementary Fig. 7 and Table 6). Very fast rates in elasmosaurs are concentrated in the most derived branches (i.e., Euelasmosauridia from the late Upper Cretaceous52) and represent both rapid neck elongation in elasmosaurines and rapid neck shortening in weddellonectians (i.e., aristonectines and closely related taxa52). Additionally, various other independent instances of relative shortening of the neck occurred during the evolution of Sauropterygia, most notably in placodonts, pliosaurs and polycotylids, but these are not associated with high rates.Our findings contrast with a previous study23 which did not identify any significant evolutionary rate shifts in the neck ratio across Sauropterygia. Here we use a larger number of taxa and a different model fitting approach, which might account for these discrepancies. The association between very long necks and large trunks, along with our flow simulations results and the evidence of high rates in the elongation of necks in elasmosaurines (Fig. 5e), suggests that neck elongation was facilitated by large body sizes. The question remains why neck ratios did not evolve longer than 4×. According to our data, hydrodynamic constraints might have operated against the selection of such long necks. However, it is possible that the primary function for which they were selected, which is still debated30,53, did not require necks with those characteristics. Neck anatomy is likely to be the result of a compromise between different functions/constraints, one of them being hydrodynamic, as shown by the results presented herein. More

  • in

    How diverse ecosystems remain stable

    May, R. M. Nature 238, 413–414 (1972).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yonatan, Y., Amit, G., Friedman, J. & Bashan, A. Nat. Eco. Evo., https://doi.org/10.1038/s41559-022-01745-8 (2022).Yodzis, P. Nature 289, 674–676 (1981).Article 

    Google Scholar 
    Winemiller, K. O. Am. Nat. 134, 960–968 (1989).Article 

    Google Scholar 
    James, A. et al. Am. Nat. 185, 680–692 (2015).Article 
    PubMed 

    Google Scholar 
    Schmid-Araya, J. M. et al. J. Anim. Ecol. 71, 1056–1062 (2002).Article 

    Google Scholar 
    Bashan, A. et al. Nature 534, 259–262 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Human Microbiome Project Consortium. Nature 486, 207–214 (2012).Article 

    Google Scholar 
    Moitinho-Silva, L. et al. Gigascience 6, 1–7 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Dunne, J. A., Williams, R. J. & Martinez, N. D. Proc. Natl Acad. Sci. USA 99, 12917–12922 (2002).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grilli, J., Barabás, G., Michalska-Smith, M. J. & Allesina, S. Nature 548, 210–213 (2017).CAS 
    Article 
    PubMed 

    Google Scholar  More

  • in

    Evolution of coastal forests based on a full set of mangrove genomes

    He, Z. et al. Speciation with gene flow via cycles of isolation and migration: insights from multiple mangrove taxa. Natl Sci. Rev. 6, 275–288 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhou, R. et al. Population genetics of speciation in nonmodel organisms: I. Ancestral polymorphism in mangroves. Mol. Biol. Evol. 24, 2746–2754 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    Xu, S. et al. Genome-wide convergence during evolution of mangroves from woody plants. Mol. Biol. Evol. 34, 1008–1015 (2017).CAS 
    PubMed 

    Google Scholar 
    He, Z. et al. Convergent adaptation of the genomes of woody plants at the land–sea interface. Natl Sci. Rev. 7, 978–993 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lyu, H., He, Z., Wu, C.-I. & Shi, S. Convergent adaptive evolution in marginal environments: unloading transposable elements as a common strategy among mangrove genomes. New Phytol. 217, 428–438 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Xu, S. et al. The origin, diversification and adaptation of a major mangrove clade (Rhizophoreae) revealed by whole-genome sequencing. Natl Sci. Rev. 4, 721–734 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Feng, X. et al. Molecular adaptation to salinity fluctuation in tropical intertidal environments of a mangrove tree Sonneratia alba. BMC Plant Biol. 20, 178 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Feng, X. et al. Genomic insights into molecular adaptation to intertidal environments in the mangrove Aegiceras corniculatum. New Phytol. 231, 2346–2358 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Angelini, C. et al. A keystone mutualism underpins resilience of a coastal ecosystem to drought. Nat. Commun. 7, 12473 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Atwood, T. B. et al. Global patterns in mangrove soil carbon stocks and losses. Nat. Clim. Change 7, 523–528 (2017).CAS 
    Article 

    Google Scholar 
    Barbier, E. B. et al. Coastal ecosystem-based management with nonlinear ecological functions and values. Science 319, 321–323 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

    Google Scholar 
    Hensel, M. J. S. & Silliman, B. R. Consumer diversity across kingdoms supports multiple functions in a coastal ecosystem. Proc. Natl Acad. Sci. USA 110, 20621–20626 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tomlinson, P. B. The Botany of Mangroves 2nd edn (Cambridge Univ. Press, 2016).Rovai, A. S. et al. Global controls on carbon storage in mangrove soils. Nat. Clim. Change 8, 534–538 (2018).CAS 
    Article 

    Google Scholar 
    Alongi, D. M. Carbon sequestration in mangrove forests. Carbon Manag. 3, 313–322 (2012).CAS 
    Article 

    Google Scholar 
    Grant, K. M. et al. Sea-level variability over five glacial cycles. Nat. Commun. 5, 5076 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Guo, Z. et al. Extremely low genetic diversity across mangrove taxa reflects past sea level changes and hints at poor future responses. Glob. Change Biol. 24, 1741–1748 (2018).Article 

    Google Scholar 
    Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sollars, E. S. A. et al. Genome sequence and genetic diversity of European ash trees. Nature 541, 212–216 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhao, S. et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat. Genet. 45, 67–71 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Duke, N. C. in Mangrove Ecosystems: A Global Biogeographic Perspective (eds Rivera-Monroy, V. H. et al.) 17–53 (Springer, 2017).Ellison, A. M., Farnsworth, E. J. & Merkt, R. E. Origins of mangrove ecosystems and the mangrove biodiversity anomaly. Glob. Ecol. Biogeogr. 8, 95–115 (1999).
    Google Scholar 
    Gee, C. T. The mangrove palm Nypa in the geologic past of the new world. Wetl. Ecol. Manag. 9, 181–203 (2001).Article 

    Google Scholar 
    Germeraad, J. H., Hopping, C. A. & Muller, J. Palynology of tertiary sediments from tropical areas. Rev. Palaeobot. Palynol. 6, 189–348 (1968).Article 

    Google Scholar 
    Graham, A. Paleobotanical evidence and molecular data in reconstructing the historical phytogeography of Rhizophoraceae. Ann. Missouri Bot. Gard. 93, 325–334 (2006).Article 

    Google Scholar 
    Mazer, S. J. & Tiffney, B. H. Fruits of Wetherellia and Palaeowetherellia (?Euphorbiaceae) from Eocene sediments in Virginia and Maryland. Brittonia 34, 300–333 (1982).Muller, J. Fossil pollen records of extant angiosperms. Bot. Rev. 47, 1–142 (1981).Article 

    Google Scholar 
    Srivastava, J. & Prasad, V. Evolution and paleobiogeography of mangroves. Mar. Ecol. 40, e12571 (2019).Hu, M.-J. et al. Chromosome-scale assembly of the Kandelia obovata genome. Hortic. Res. 7, 75 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jin, Y. & Qian, H. V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).Article 

    Google Scholar 
    Zachos, J. C., Dickens, G. R. & Zeebe, R. E. An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. Nature 451, 279–283 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Handley, L., Crouch, E. M. & Pancost, R. D. A New Zealand record of sea level rise and environmental change during the Paleocene–Eocene Thermal Maximum. Palaeogeogr. Palaeoclimatol. Palaeoecol. 305, 185–200 (2011).Article 

    Google Scholar 
    Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Saintilan, N. et al. Thresholds of mangrove survival under rapid sea level rise. Science 368, 1118–1121 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lu, J. & Wu, C.-I. Weak selection revealed by the whole-genome comparison of the X chromosome and autosomes of human and chimpanzee. Proc. Natl Acad. Sci. USA 102, 4063–4067 (2005).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lynch, M. et al. Perspective: spontaneous deleterious mutation. Evolution 53, 645–663 (1999).Article 
    PubMed 

    Google Scholar 
    Ohta, T. Slightly deleterious mutant substitutions in evolution. Nature 246, 96–98 (1973).CAS 
    Article 
    PubMed 

    Google Scholar 
    Ohta, T. The nearly neutral theory of molecular evolution. Annu. Rev. Ecol. Syst. 23, 263–286 (1992).Article 

    Google Scholar 
    Liu, X. & Fu, Y. X. Exploring population size changes using SNP frequency spectra. Nat. Genet. 47, 555–559 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu, X. & Fu, Y.-X. Stairway Plot 2: demographic history inference with folded SNP frequency spectra. Genome Biol. 21, 280 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Krauss, K. W. et al. How mangrove forests adjust to rising sea level. New Phytol. 202, 19–34 (2014).Article 
    PubMed 

    Google Scholar 
    Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526, 559–563 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Frederiksen, N. O. Review of Early Tertiary Sporomorph Paleoecology (American Association of Stratigraphic Palynologists Foundation, 1985).Smith, D. E., Harrison, S., Firth, C. R. & Jordan, J. T. The early Holocene sea level rise. Quat. Sci. Rev. 30, 1846–1860 (2011).Article 

    Google Scholar 
    Bouillon, S. et al. Mangrove production and carbon sinks: a revision of global budget estimates. Glob. Biogeochem. Cycles 22, GB2013 (2008).Article 
    CAS 

    Google Scholar 
    Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297 (2011).CAS 
    Article 

    Google Scholar 
    Hamilton, S. E. & Friess, D. A. Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012. Nat. Clim. Change 8, 240–244 (2018).CAS 
    Article 

    Google Scholar 
    Hutchison, J., Manica, A., Swetnam, R., Balmford, A. & Spalding, M. Predicting global patterns in mangrove forest biomass. Conserv. Lett. 7, 233–240 (2014).Article 

    Google Scholar 
    Ouyang, X. & Lee, S. Y. Improved estimates on global carbon stock and carbon pools in tidal wetlands. Nat. Commun. 11, 317 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Richards, D. R., Thompson, B. S. & Wijedasa, L. Quantifying net loss of global mangrove carbon stocks from 20 years of land cover change. Nat. Commun. 11, 4260 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sanders, C. J. et al. Are global mangrove carbon stocks driven by rainfall? J. Geophys. Res. Biogeosci. 121, 2600–2609 (2016).Article 

    Google Scholar 
    Alongi, D. M. Carbon cycling and storage in mangrove forests. Ann. Rev. Mar. Sci. 6, 195–219 (2014).Article 
    PubMed 

    Google Scholar 
    Valiela, I., Bowen, J. L. & York, J. K. Mangrove forests: one of the world’s threatened major tropical environments. Bioscience 51, 807–815 (2001).Article 

    Google Scholar 
    Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 19, 11–15 (1987).
    Google Scholar 
    Yang, G., Zhou, R., Tang, T. & Shi, S. Simple and efficient isolation of high-quality total RNA from Hibiscus tiliaceus, a mangrove associate and its relatives. Prep. Biochem. Biotechnol. 38, 257–264 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wang, O. et al. Efficient and unique cobarcoding of second-generation sequencing reads from long DNA molecules enabling cost-effective and accurate sequencing, haplotyping, and de novo assembly. Genome Res. 29, 798–808 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu, B. et al. Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects. Preprint at https://arxiv.org/abs/1308.2012v2 (2013).Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chin, C.-S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xiao, C.-L. et al. MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nat. Methods 14, 1072–1074 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Chin, C.-S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 27, 737–746 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 4, 30 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tarailo‐Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics 25, 4.10.1–4.10.14 (2009).Article 

    Google Scholar 
    Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. USA 117, 9451–9457 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).CAS 
    Article 
    PubMed 

    Google Scholar 
    Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878–2879 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Birney, E. Genewise and genomewise. Genome Res. 14, 988–995 (2004).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kent, W. J. BLAT—The BLAST-Like Alignment Tool. Genome Res. 12, 656–664 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227–245 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Katoh, K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yang, Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    Reis, M. Dos & Yang, Z. Approximate likelihood calculation on a phylogeny for Bayesian estimation of divergence times. Mol. Biol. Evol. 28, 2161–2172 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. GGTREE: an package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

    Google Scholar 
    Sanderson, M. J. r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19, 301–302 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).Article 
    PubMed 

    Google Scholar 
    Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Liang, Y. et al. Chromosome level genome assembly of Andrographis paniculata. Front. Genet. 11, 701 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, L. et al. The water lily genome and the early evolution of flowering plants. Nature 577, 79–84 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Huang, X. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42, 961–967 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Miller, K. G. et al. The Phanerozoic record of global sea-level change. Science 310, 1293–1298 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    Marçais, G. et al. MUMmer4: a fast and versatile genome alignment system. PLoS Comput. Biol. 14, e1005944 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Narasimhan, V. et al. BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data. Bioinformatics 32, 1749–1751 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hudson, R. R. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).CAS 
    Article 
    PubMed 

    Google Scholar  More

  • in

    Expanding ocean food production under climate change

    United Nations. World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/WP/248 (UN-DESA, 2017).Costello, C. et al. The future of food from the sea. Nature 588, 95–100 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (2019).FAO. Mapping Supply and Demand for Animal-Source Foods to 2030 (2011).Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    DeFries, R. S., Rudel, T., Uriarte, M. & Hansen, M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nat. Geosci. 3, 178–181 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    Rockström, J. et al. Future water availability for global food production: the potential of green water for increasing resilience to global change. Water Resour. Res. 45, W00A12 (2009).Article 

    Google Scholar 
    IPCC. IPCC Special Report on Climate Change and Land (2019).Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    FAO. The State of World Fisheries and Aquaculture 2020: Sustainability in Action (2020).Bryndum‐Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).ADS 
    Article 

    Google Scholar 
    Cheung, W. W. L., Dunne, J., Sarmiento, J. L. & Pauly, D. Integrating ecophysiology and plankton dynamics into projected maximum fisheries catch potential under climate change in the Northeast Atlantic. ICES J. Mar. Sci. 68, 1008–1018 (2011).Article 

    Google Scholar 
    Froehlich, H. E., Gentry, R. R. & Halpern, B. S. Global change in marine aquaculture production potential under climate change. Nat. Ecol. Evol. 2, 1745–1750 (2018).PubMed 
    Article 

    Google Scholar 
    Handisyde, N., Telfer, T. C. & Ross, L. G. Vulnerability of aquaculture-related livelihoods to changing climate at the global scale. Fish Fish. 18, 466–488 (2017).Article 

    Google Scholar 
    Szuwalski, C. S. & Hollowed, A. B. Climate change and non-stationary population processes in fisheries management. ICES J. Mar. Sci. 73, 1297–1305 (2016).Article 

    Google Scholar 
    Pinsky, M. L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Gaines, S. D. et al. Improved fisheries management could offset many negative effects of climate change. Sci. Adv. 4, eaao1378 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Free, C. M. et al. Realistic fisheries management reforms could mitigate the impacts of climate change in most countries. PLoS ONE 15, e0224347 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Clapp, J. Food self-sufficiency: making sense of it, and when it makes sense. Food Policy 66, 88–96 (2017).Article 

    Google Scholar 
    Barange, M., Bahri, T., Beveridge, M. & Cochrane, K. L. Impacts of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options. Fisheries and Aquaculture Technical Paper No. 627 (FAO, 2018).Lester, S. E. et al. Marine spatial planning makes room for offshore aquaculture in crowded coastal waters. Nat. Commun. 9, 945 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cottrell, R. S., Blanchard, J. L., Halpern, B. S., Metian, M. & Froehlich, H. E. Global adoption of novel aquaculture feeds could substantially reduce forage fish demand by 2030. Nat. Food 1, 301–308 (2020).Article 

    Google Scholar 
    Hua, K. et al. The future of aquatic protein: implications for protein sources in aquaculture diets. One Earth 1, 316–329 (2019).ADS 
    Article 

    Google Scholar 
    Chavanne, H. et al. A comprehensive survey on selective breeding programs and seed market in the European aquaculture fish industry. Aquacult. Int. 24, 1287–1307 (2016).Article 

    Google Scholar 
    Troell, M., Jonell, M. & Henriksson, P. J. G. Ocean space for seafood. Nat. Ecol. Evol. 1, 1224–1225 (2017).PubMed 
    Article 

    Google Scholar 
    European Union. Commission Regulation (EC) No 710/2009 of 5 August 2009 Amending Regulation (EC) No 889/2008 laying down detailed rules for the implementation of Council Regulation (EC) No 834/2007, as regards laying down detailed rules on organic aquaculture animal and seaweed production. http://data.europa.eu/eli/reg/2009/710/oj (2009).Golden, C. D. et al. Aquatic foods to nourish nations. Nature 598, 315–320 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Davies, I. P. et al. Governance of marine aquaculture: pitfalls, potential, and pathways forward. Mar. Policy 104, 29–36 (2019).Article 

    Google Scholar 
    Gentry, R. R. et al. Exploring the potential for marine aquaculture to contribute to ecosystem services. Rev. Aquacult. 12, 499–512 (2020).Article 

    Google Scholar 
    Troell, M. et al. Ecological engineering in aquaculture — potential for integrated multi-trophic aquaculture (IMTA) in marine offshore systems. Aquaculture 297, 1–9 (2009).Article 

    Google Scholar 
    Froehlich, H. E., Jacobsen, N. S., Essington, T. E., Clavelle, T. & Halpern, B. S. Avoiding the ecological limits of forage fish for fed aquaculture. Nat. Sustain. 1, 298–303 (2018).Article 

    Google Scholar 
    Øverland, M., Mydland, L. T. & Skrede, A. Marine macroalgae as sources of protein and bioactive compounds in feed for monogastric animals. J. Sci. Food Agric. 99, 13–24 (2019).PubMed 
    Article 
    CAS 

    Google Scholar 
    Besson, M. et al. Environmental impacts of genetic improvement of growth rate and feed conversion ratio in fish farming under rearing density and nitrogen output limitations. J. Clean. Prod. 116, 100–109 (2016).Article 

    Google Scholar 
    Froehlich, H. E., Runge, C. A., Gentry, R. R., Gaines, S. D. & Halpern, B. S. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proc. Natl Acad. Sci. USA 115, 5295–5300 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Aguilar-Manjarrez, J., Soto, D., Brummett, R. E. Aquaculture Zoning, Site Selection and Area Management under the Ecosystem Approach to Aquaculture (FAO, 2017).Soto, D. et al. In Impacts Of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options Ch. 26 (FAO, 2018).Darwin, C. The Variation of Animals and Plants Under Domestication (John Murray, 1868).Gjedrem, T., Robinson, N. & Rye, M. The importance of selective breeding in aquaculture to meet future demands for animal protein: a review. Aquaculture 350–353, 117–129 (2012).Article 

    Google Scholar 
    Antonello, J. et al. Estimates of heritability and genetic correlation for body length and resistance to fish pasteurellosis in the gilthead sea bream (Sparus aurata L.). Aquaculture 298, 29–35 (2009).Article 

    Google Scholar 
    Saillant, E., Dupont-Nivet, M., Haffray, P. & Chatain, B. Estimates of heritability and genotype–environment interactions for body weight in sea bass (Dicentrarchus labrax L.) raised under communal rearing conditions. Aquaculture 254, 139–147 (2006).Article 

    Google Scholar 
    Klinger, D. H., Levin, S. A. & Watson, J. R. The growth of finfish in global open-ocean aquaculture under climate change. Proc. R. Soc. B 284, 20170834 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Salayo, N. D., Perez, M. L., Garces, L. R. & Pido, M. D. Mariculture development and livelihood diversification in the Philippines. Mar. Policy 36, 867–881 (2012).Article 

    Google Scholar 
    Boyce, D. G., Lotze, H. K., Tittensor, D. P., Carozza, D. A. & Worm, B. Future ocean biomass losses may widen socioeconomic equity gaps. Nat. Commun. 11, 2235 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sumaila, U. R. et al. Benefits of the Paris Agreement to ocean life, economies, and people. Sci. Adv. 5, eaau3855 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development (United Nations, 2017).Hilborn, R. et al. Effective fisheries management instrumental in improving fish stock status. Proc. Natl Acad. Sci. USA 117, 2218–2224 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Costello, C. et al. Global fishery prospects under contrasting management regimes. Proc. Natl Acad. Sci. USA 113, 5125–5129 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ye, Y. & Gutierrez, N. L. Ending fishery overexploitation by expanding from local successes to globalized solutions. Nat. Ecol. Evol. 1, 0179 (2017).Article 

    Google Scholar 
    Leape, J. et al. Technology, Data and New Models for Sustainably Managing Ocean Resources (World Resources Institute, 2020).Anderson, C. R. et al. Scaling up from regional case studies to a global harmful algal bloom observing system. Front. Mar. Sci. 6, 250 (2019).Article 

    Google Scholar 
    Dunn, D. C., Maxwell, S. M., Boustany, A. M. & Halpin, P. N. Dynamic ocean management increases the efficiency and efficacy of fisheries management. Proc. Natl Acad. Sci. USA 113, 668–673 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    FAO. Aquaculture Development: 7. Aquaculture Governance and Sector Development (2017).Oyinlola, M. A., Reygondeau, G., Wabnitz, C. C. C., Troell, M. & Cheung, W. W. L. Global estimation of areas with suitable environmental conditions for mariculture species. PLoS ONE 13, e0191086 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Jackson, A. Fish in-fish out ratio explained. Aquacult. Eur. 34, 5–10 (2009).
    Google Scholar 
    Tacon, A. G. J. & Metian, M. Feed matters: satisfying the feed demand of aquaculture. Rev. Fish. Sci. Aquacult. 23, 1–10 (2015).Article 

    Google Scholar 
    Tacon, A. G. J. & Metian, M. Global overview on the use of fish meal and fish oil in industrially compounded aquafeeds: trends and future prospects. Aquaculture 285, 146–158 (2008).CAS 
    Article 

    Google Scholar 
    World Bank. Population, Total (2020); https://data.worldbank.org/indicator/SP.POP.TOTLEdwards, P., Zhang, W., Belton, B. & Little, D. C. Misunderstandings, myths and mantras in aquaculture: its contribution to world food supplies has been systematically over reported. Mar. Policy 106, 103547 (2019).Article 

    Google Scholar 
    Roberts, P. Conversion Factors for Estimating the Equivalent Live Weight of Fisheries Products (The Food and Agriculture Organization of the United Nations, 1998).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Kaschner, K. et al. AquaMaps: Predicted Range Maps for Aquatic Species https://www.aquamaps.org/ (2019).García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).ADS 
    Article 

    Google Scholar 
    Cashion, T., Le Manach, F., Zeller, D. & Pauly, D. Most fish destined for fishmeal production are food-grade fish. Fish Fish. 18, 837–844 (2017).Article 

    Google Scholar 
    Froehlich, H. E., Gentry, R. R. & Halpern, B. S. Synthesis and comparative analysis of physiological tolerance and life-history growth traits of marine aquaculture species. Aquaculture 460, 75–82 (2016).Article 

    Google Scholar 
    Thorson, J. T., Munch, S. B., Cope, J. M. & Gao, J. Predicting life history parameters for all fishes worldwide. Ecol. Appl. 27, 2262–2276 (2017).PubMed 
    Article 

    Google Scholar 
    Froese, R. & Pauly, D. FishBase http://www.fishbase.org (2021).Palomares, M. & Pauly, D. SeaLifeBase http://www.sealifebase.org (2019).FAO. Cultured Aquatic Species (2019).Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).ADS 
    Article 

    Google Scholar 
    Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).ADS 
    Article 

    Google Scholar 
    Song, Z. et al. Centuries of monthly and 3-hourly global ocean wave data for past, present, and future climate research. Sci. Data 7, 226 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gentry, R. R. et al. Mapping the global potential for marine aquaculture. Nat. Ecol. Evol. 1, 1317–1324 (2017).PubMed 
    Article 

    Google Scholar 
    Barton, A. et al. Impacts of coastal acidification on the Pacific Northwest shellfish industry and adaptation strategies implemented in response. Oceanography 25, 146–159 (2015).Article 

    Google Scholar 
    Froehlich, H. E., Smith, A., Gentry, R. R. & Halpern, B. S. Offshore aquaculture: I know it when I see it. Front. Mar. Sci. 4, 154 (2017).Article 

    Google Scholar 
    World Bank. Adjusted Net National Income per Capita (Current US$) (2019); https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.CDWorld Bank. Pump Price for Diesel Fuel (US$ per liter) (2019); https://data.worldbank.org/indicator/EP.PMP.DESL.CDPiburn, J. wbstats: programmatic access to the World Bank API. R package v.1.0.4 https://cran.r-project.org/web/packages/wbstats/index.html (2018).Rubino, M. (ed.) Offshore Aquaculture in the United States: Economic Considerations, Implications & Opportunities NOAA Technical Memorandum NMFS F/SPO-103 (US Department of Commerce, 2008).Jackson, A. & Newton, R. Project to Model the Use of Fisheries By-products in the Production of Marine Ingredients, with Special Reference to the Omega 3 Fatty Acids EPA and DHA (Institute Of Aquaculture, University Of Stirling And IFFO, 2016). More

  • in

    Changes to the gut microbiota of a wild juvenile passerine in a multidimensional urban mosaic

    Szulkin, M. et al. How to quantify urbanization when testing for urban evolution?. Urban Evol. Biol. https://doi.org/10.1093/oso/9780198836841.003.0002 (2020).Article 

    Google Scholar 
    Slabbekoorn, H. Songs of the city: Noise-dependent spectral plasticity in the acoustic phenotype of urban birds. Anim. Behav. https://doi.org/10.1016/j.anbehav.2013.01.021 (2013).Article 

    Google Scholar 
    Christiansen, N. A., Fryirs, K. A., Green, T. J. & Hose, G. C. The impact of urbanisation on community structure, gene abundance and transcription rates of microbes in upland swamps of Eastern Australia. PLoS ONE https://doi.org/10.1371/journal.pone.0213275 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alberti, M. et al. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1606034114 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McFall-Ngai, M. M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1218525110 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol. Rev. https://doi.org/10.1111/j.1574-6976.2008.00123.x (2008).Article 
    PubMed 

    Google Scholar 
    Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: A call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2448 (2019).Article 

    Google Scholar 
    Jarrett, C., Powell, L. L., McDevitt, H., Helm, B. & Welch, A. J. Bitter fruits of hard labour: diet metabarcoding and telemetry reveal that urban songbirds travel further for lower-quality food. Oecologia https://doi.org/10.1007/s00442-020-04678-w (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zollinger, S. A. et al. Traffic noise exposure depresses plasma corticosterone and delays offspring growth in breeding zebra finches. Conserv. Physiol. https://doi.org/10.1093/conphys/coz056 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sprau, P., Mouchet, A. & Dingemanse, N. J. Multidimensional environmental predictors of variation in avian forest and city life histories. Behav. Ecol. https://doi.org/10.1093/beheco/arw130 (2017).Article 

    Google Scholar 
    Teyssier, A. et al. Inside the guts of the city: Urban-induced alterations of the gut microbiota in a wild passerine. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2017.09.035 (2018).Article 
    PubMed 

    Google Scholar 
    Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE https://doi.org/10.1371/journal.pone.0220926 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fuirst, M., Veit, R. R., Hahn, M., Dheilly, N. & Thorne, L. H. Effects of urbanization on the foraging ecology and microbiota of the generalist seabird Larus argentatus. PLoS ONE https://doi.org/10.1371/journal.pone.0209200 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Phillips, J. N., Berlow, M. & Derryberry, E. P. The effects of landscape urbanization on the gut microbiome: An exploration into the gut of urban and rural white-crowned sparrows. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00148 (2018).Article 

    Google Scholar 
    Berlow, M., Phillips, J. N. & Derryberry, E. P. Effects of urbanization and landscape on gut microbiomes in white-crowned sparrows. Microb. Ecol. https://doi.org/10.1007/s00248-020-01569-8 (2020).Article 
    PubMed 

    Google Scholar 
    Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell https://doi.org/10.1016/j.cell.2014.05.052 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Knutie, S. A., Wilkinson, C. L., Kohl, K. D. & Rohr, J. R. Early-life disruption of amphibian microbiota decreases later-life resistance to parasites. Nat. Commun. 8, 1–8 (2017).CAS 
    Article 

    Google Scholar 
    Sudyka, J., Di Lecce, I., Wojas, L., Rowiński, P. & Szulkin, M. Nest-boxes alter the reproductive ecology of urban cavity-nesters in a species-dependent way. https://doi.org/10.32942/OSF.IO/WP9MN.
    Maziarz, M., Broughton, R. K. & Wesołowski, T. Microclimate in tree cavities and nest-boxes: Implications for hole-nesting birds. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2017.01.001 (2017).Article 

    Google Scholar 
    Thompson, M. J., Capilla-Lasheras, P., Dominoni, D. M., Réale, D. & Charmantier, A. Phenotypic variation in urban environments: mechanisms and implications. Trends Ecol. Evol. 37, 171–182 (2022).CAS 
    Article 

    Google Scholar 
    Salmón, P. et al. Continent-wide genomic signatures of adaptation to urbanisation in a songbird across Europe. Nat. Commun. 12, 1–14 (2021).ADS 
    Article 

    Google Scholar 
    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. https://doi.org/10.1186/s13059-014-0550-8 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sackey, B. A., Mensah, P., Collison, E. & Sakyi-Dawson, E. Campylobacter, Salmonella, Shigella and Escherichia coli in live and dressed poultry from metropolitan Accra. Int. J. Food Microbiol. https://doi.org/10.1016/S0168-1605(01)00595-5 (2001).Article 
    PubMed 

    Google Scholar 
    Benskin, C. M. W. H., Wilson, K., Jones, K. & Hartley, I. R. Bacterial pathogens in wild birds: A review of the frequency and effects of infection. Biol. Rev. https://doi.org/10.1111/j.1469-185X.2008.00076.x (2009).Article 
    PubMed 

    Google Scholar 
    Hansell, M. & Overhill, R. Bird nests and construction behaviour. Bird Nests Constr. Behav. https://doi.org/10.1017/cbo9781139106788 (2000).Article 

    Google Scholar 
    Siddiqui, S. H., Khan, M., Kang, D., Choi, H. W. & Shim, K. Meta-analysis and systematic review of the thermal stress response: Gallus gallus domesticus show low immune responses during heat stress. Front. Physiol. 13, 31 (2022).Article 

    Google Scholar 
    Sepulveda, J. & Moeller, A. H. The effects of temperature on animal gut microbiomes. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.00384 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kohl, K. D. & Yahn, J. Effects of environmental temperature on the gut microbial communities of tadpoles. Environ. Microbiol. https://doi.org/10.1111/1462-2920.13255 (2016).Article 
    PubMed 

    Google Scholar 
    Teyssier, A. et al. Diet contributes to urban-induced alterations in gut microbiota: Experimental evidence from a wild passerine. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2019.2182 (2020).Article 

    Google Scholar 
    Benskin, C. M. W. H., Rhodes, G., Pickup, R. W., Wilson, K. & Hartley, I. R. Diversity and temporal stability of bacterial communities in a model passerine bird, the zebra finch. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2010.04892.x (2010).Article 
    PubMed 

    Google Scholar 
    Garrett, W. S. et al. Enterobacteriaceae Act in concert with the gut microbiota to induce spontaneous and maternally transmitted colitis. Cell Host Microbe https://doi.org/10.1016/j.chom.2010.08.004 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Videvall, E. et al. Early-life gut dysbiosis linked to juvenile mortality in ostriches. BMC Microbiome 8, 1–13 (2020).Article 

    Google Scholar 
    Hooper, L. V. & MacPherson, A. J. Immune adaptations that maintain homeostasis with the intestinal microbiota. Nat. Rev. Immunol. https://doi.org/10.1038/nri2710 (2010).Article 
    PubMed 

    Google Scholar 
    Borre, Y. E. et al. Microbiota and neurodevelopmental windows: Implications for brain disorders. Trends Mol. Med. https://doi.org/10.1016/j.molmed.2014.05.002 (2014).Article 
    PubMed 

    Google Scholar 
    Jones, E. L. & Leather, S. R. Invertebrates in urban areas: A review. Eur. J. Entomol. https://doi.org/10.14411/eje.2012.060 (2012).Article 

    Google Scholar 
    Wilkin, T. A., King, L. E. & Sheldon, B. C. Habitat quality, nestling diet, and provisioning behaviour in great tits Parus major. J. Avian Biol. https://doi.org/10.1111/j.1600-048X.2009.04362.x (2009).Article 

    Google Scholar 
    Pollock, C. J., Capilla-Lasheras, P., McGill, R. A. R., Helm, B. & Dominoni, D. M. Integrated behavioural and stable isotope data reveal altered diet linked to low breeding success in urban-dwelling blue tits (Cyanistes caeruleus). Sci. Rep. https://doi.org/10.1038/s41598-017-04575-y (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davidson, G. L. et al. Diet induces parallel changes to the gut microbiota and problem solving performance in a wild bird. Sci. Rep. https://doi.org/10.1038/s41598-020-77256-y (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bodawatta, K. H. et al. Flexibility and resilience of great tit (Parus major) gut microbiomes to changing diets. Anim. Microbiome 2021(3), 1–14 (2021).
    Google Scholar 
    Baniel, A. et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 9, 1–20 (2021).Article 

    Google Scholar 
    Sullam, K. E. et al. Environmental and ecological factors that shape the gut bacterial communities of fish: A meta-analysis. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2012.05552.x (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martiny, J. B. H. et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro1341 (2006).Article 
    PubMed 

    Google Scholar 
    Lucass, C., Eens, M. & Müller, W. When ambient noise impairs parent-offspring communication. Environ. Pollut. https://doi.org/10.1016/j.envpol.2016.03.015 (2016).Article 
    PubMed 

    Google Scholar 
    Kight, C. R. & Swaddle, J. P. How and why environmental noise impacts animals: An integrative, mechanistic review. Ecol. Lett. https://doi.org/10.1111/j.1461-0248.2011.01664.x (2011).Article 
    PubMed 

    Google Scholar 
    Cui, B., Gai, Z., She, X., Wang, R. & Xi, Z. Effects of chronic noise on glucose metabolism and gut microbiota-host inflammatory homeostasis in rats. Sci. Rep. https://doi.org/10.1038/srep36693 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Campo, J. L., Gil, M. G. & Dávila, S. G. Effects of specific noise and music stimuli on stress and fear levels of laying hens of several breeds. Appl. Anim. Behav. Sci. https://doi.org/10.1016/j.applanim.2004.08.028 (2005).Article 

    Google Scholar 
    Injaian, A. S., Taff, C. C. & Patricelli, G. L. Experimental anthropogenic noise impacts avian parental behaviour, nestling growth and nestling oxidative stress. Anim. Behav. https://doi.org/10.1016/j.anbehav.2017.12.003 (2018).Article 

    Google Scholar 
    Cui, B. et al. Effects of chronic noise exposure on the microbiome-gut-brain axis in senescence-accelerated prone mice: Implications for Alzheimer’s disease. J. Neuroinflammation https://doi.org/10.1186/s12974-018-1223-4 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wei, L. et al. Constant light exposure alters gut microbiota and promotes the progression of steatohepatitis in high fat diet rats. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.01975 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chatelain, M. et al. Replicated, urban-driven exposure to metallic trace elements in two passerines. Sci. Rep. 11, 1–10 (2021).Article 

    Google Scholar 
    Chatelain, M. et al. Urban metal pollution explains variation in reproductive outputs in great tits and blue tits. Sci. Total Environ. 776, 145966 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Rosenfeld, C. S. Gut dysbiosis in animals due to environmental chemical exposures. Front. Cell. Infect. Microbiol. 7, 396 (2017).Article 

    Google Scholar 
    Sommer, F. & Bäckhed, F. The gut microbiota-masters of host development and physiology. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro2974 (2013).Article 
    PubMed 

    Google Scholar 
    Tomiałojć, L. & Wesołowski, T. Diversity of the Białowieza forest avifauna in space and time. J. Ornithol. https://doi.org/10.1007/s10336-003-0017-2 (2004).Article 

    Google Scholar 
    Corsini, M. et al. Growing in the city: Urban evolutionary ecology of avian growth rates. Evol. Appl. https://doi.org/10.1111/eva.13081 (2021).Article 
    PubMed 

    Google Scholar 
    Teyssier, A., Lens, L., Matthysen, E. & White, J. Dynamics of gut microbiota diversity during the early development of an avian host: Evidence from a cross-foster experiment. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01524 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tremblay, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis (Lond 1859). 147, 17–24 (2005).Article 

    Google Scholar 
    Corsini, M., Marrot, P. & Szulkin, M. Quantifying human presence in a heterogeneous urban landscape. Behav. Ecol. https://doi.org/10.1093/beheco/arz128 (2019).Article 

    Google Scholar 
    Corsini, M., Dubiec, A., Marrot, P. & Szulkin, M. Humans and tits in the city: Quantifying the effects of human presence on great tit and blue tit reproductive trait variation. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00082 (2017).Article 

    Google Scholar 
    Kyba, C. C. M. et al. High-resolution imagery of earth at night: New sources, opportunities and challenges. Remote Sens. https://doi.org/10.3390/rs70100001 (2015).Article 

    Google Scholar 
    Maraci, Ö. et al. The gut microbial composition is species-specific and individual-specific in two species of estrildid finches, the Bengalese finch and the zebra finch. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.619141 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Engel, K. et al. Individual- and species-specific skin microbiomes in three different estrildid finch species revealed by 16S amplicon sequencing. Microb. Ecol. https://doi.org/10.1007/s00248-017-1130-8 (2017).Article 
    PubMed 

    Google Scholar 
    Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics https://doi.org/10.1093/bioinformatics/btr507 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal https://doi.org/10.14806/ej.17.1.200 (2011).Article 

    Google Scholar 
    Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01541-09 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics https://doi.org/10.1093/bioinformatics/btq461 (2010).Article 
    PubMed 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1219 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
    Google Scholar 
    Clarke, K. R., Gorley, R., Somerfield, P. & Warwick, R. Change in Marine Communities: an Approach to Statistical Analysis and Interpretation 3rd edn (Prim. Plymouth, 2014).Shannon, C. E. The mathematical theory of communication. MD Comput. https://doi.org/10.2307/410457 (1997).Article 
    PubMed 

    Google Scholar 
    Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. https://doi.org/10.1016/0006-3207(92)91201-3 (1992).Article 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

    Google Scholar 
    Fox, J. et al. The car Package. R (2012).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. https://doi.org/10.1111/j.2041-210x.2009.00001.x (2010).Article 

    Google Scholar 
    DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Book 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE https://doi.org/10.1371/journal.pone.0061217 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
    MATH 

    Google Scholar 
    Whittaker, R. H. Vegetation of the Siskiyou mountains Oregon and California. Ecol. Monogr. https://doi.org/10.2307/1948435 (1960).Article 

    Google Scholar 
    Paulson, J. metagenomeSeq: Statistical analysis for sparse high-throughput sequencing. Bioconductor.Jp (2014).Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. https://doi.org/10.2307/1942268 (1957).Article 

    Google Scholar 
    Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01996-06 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oksanen, J. et al. Package ‘vegan’ Title Community Ecology Package Version 2.5-6. cran.ism.ac.jp (2019).Anderson, M. J. & Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. https://doi.org/10.1046/j.1442-9993.2001.01070.x (2001).Article 

    Google Scholar 
    Clarke, K. R. & Ainsworth, M. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps092205 (1993).Article 

    Google Scholar 
    QGIS Development Team. QGIS Geographic Information System (Open Source Geospatial Foundation, 2019).
    Google Scholar  More

  • in

    Apparent absence of avian malaria and malaria-like parasites in northern blue-footed boobies breeding on Isla Isabel

    Atkinson, C. T. & Van Riper, C. Pathogenicity and epizootiology of avian haematozoa: Plasmodium, Leucocytozoon, and Haemoproteus. Bird-Parasite Interact. 2, 19–48 (1991).
    Google Scholar 
    Sorci, G. & Moller, A. P. Comparative evidence for a positive correlation between haematozoan prevalence and mortality in waterfowl. J. Evol. Biol. 10, 731–741 (1997).
    Google Scholar 
    Merino, S., Moreno, J., Sanz, J. J. & Arriero, E. Are avian blood parasites pathogenic in the wild? A medication experiment in blue tits (Parus caeruleus). Proc. Biol. Sci. 267, 2507–2510 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Asghar, M. et al. Hidden costs of infection: Chronic malaria accelerates telomere degradation and senescence in wild birds. Science 347, 436–438 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Quillfeldt, P., Arriero, E., Martínez, J., Masello, J. F. & Merino, S. Prevalence of blood parasites in seabirds – A review. Front. Zool. 8, 26 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Piersma, T. Do global patterns of habitat use and migration strategies co-evolve with relative investments in immunocompetence due to spatial variation in parasite pressure?. Oikos 80, 623 (1997).
    Google Scholar 
    Mendes, L., Piersma, T., Lecoq, M., Spaans, B. & Ricklefs, R. E. Disease-limited distributions? Contrasts in the prevalence of avian malaria in shorebird species using marine and freshwater habitats. Oikos 109, 396–404 (2005).
    Google Scholar 
    Martínez-Abraín, A., Esparza, B. & Oro, D. Lack of blood parasites in bird species: Does absence of blood parasite vectors explain it all?. Ardeola 51, 225–232 (2004).
    Google Scholar 
    Campioni, L. et al. Absence of haemosporidian parasite infections in the long-lived Cory’s shearwater: Evidence from molecular analyses and review of the literature. Parasitol. Res. 117, 323–329 (2018).PubMed 

    Google Scholar 
    Osorio-Beristain, M. & Drummond, H. Non-aggressive mate guarding by the blue-footed booby: A balance of female and male control. Behav. Ecol. Sociobiol. 43, 307–315 (1998).
    Google Scholar 
    Nelson, J. B. Pelicans, Cormorants and Their Relatives: The Pelecaniformes (Oxford University Press, 2006).
    Google Scholar 
    Kim, S. Y., Torres, R., Domínguez, C. A. & Drummond, H. Lifetime philopatry in the blue-footed booby: A longitudinal study. Behav. Ecol. 18, 1132–1138 (2007).
    Google Scholar 
    Drummond, H. & Rodríguez, C. Viability of booby offspring is maximized by having one young parent and one old parent. PLoS ONE 10, e0133213 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Lee-Cruz, L. et al. Prevalence of Haemoproteus sp. in Galápagos blue-footed boobies: Effects on health and reproduction. Parasitol. Open 2 (2016).Santiago-Alarcon, D., Palinauskas, V. & Schaefer, H. M. Diptera vectors of avian Haemosporidian parasites: Untangling parasite life cycles and their taxonomy. Biol. Rev. 87, 928–964 (2012).PubMed 

    Google Scholar 
    Bond, J. G. et al. Diversity of mosquitoes and the aquatic insects associated with their oviposition sites along the Pacific coast of Mexico. Parasit. Vectors 7, 41 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Ibañez-Bernal, S. Informe Final del Proyecto Actualización del Catálogo de Autoridad Taxonómica del Orden Diptera (Insecta) de México CONABIO (JE006). (2017).Levin, I. I. et al. Hippoboscid-transmitted Haemoproteus parasites (Haemosporida) infect Galapagos Pelecaniform birds: Evidence from molecular and morphological studies, with a description of Haemoproteus iwa. Int. J. Parasitol. 41, 1019–1027 (2011).PubMed 

    Google Scholar 
    Madsen, V. et al. Testosterone levels and gular pouch coloration in courting magnificent frigatebird (Fregata magnificens): Variation with age-class, visited status and blood parasite infection. Horm. Behav. 51, 156–163 (2007).CAS 
    PubMed 

    Google Scholar 
    Clark, G. W. & Swinehart, B. Avian haematozoa from the offshore islands of northern Mexico. Wildl. Dis. 5, 111–112 (1969).CAS 
    PubMed 

    Google Scholar 
    Quillfeldt, P. et al. Hemosporidian blood parasites in seabirds—A comparative genetic study of species from Antarctic to tropical habitats. Naturwissenschaften 97, 809–817 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Merino, S. et al. Infection by haemoproteus parasites in four species of frigatebirds and the description of a new species of Haemoproteus (Haemosporida: Haemoproteidae). J. Parasitol. 98, 388–397 (2012).PubMed 

    Google Scholar 
    Svensson, L. M. E. & Ricklefs, R. E. Low diversity and high intra-island variation in prevalence of avian Haemoproteus parasites on Barbados, Lesser Antilles. Parasitology 136, 1121–1131 (2009).PubMed 

    Google Scholar 
    Loiseau, C. et al. Spatial variation of haemosporidian parasite infection in african rainforest bird species. J. Parasitol. 96, 21–29 (2010).PubMed 

    Google Scholar 
    Madsen, V. Female Mate Choice in the Magnificent Frigatebird (Fregata magnificens) (Universidad Nacional Autónoma de México, 2004).
    Google Scholar 
    Super, P. E. & van Riper, C. A comparison of avian hematozoan epizootiology in two California coastal scrub communities. J. Wildl. Dis. 31, 447–461 (1995).CAS 
    PubMed 

    Google Scholar 
    CONANP. Programa de Conservación y Manejo del Parque Nacional Isla Isabel. (2005).Ancona, S., Drummond, H., Rodríguez, C. & Zúñiga-Vega, J. J. Long-term population dynamics reveal that survival and recruitment of tropical boobies improve after a hurricane. J. Avian Biol. 48, 320–332 (2017).
    Google Scholar 
    Martínez-de la Puente, J., Martinez, J., Rivero-de Aguilar, J., Herrero, J. & Merino, S. On the specificity of avian blood parasites: Revealing specific and generalist relationships between haemosporidians and biting midges. Mol. Ecol. 20, 3275–3287 (2011).PubMed 

    Google Scholar 
    Bastien, M., Jaeger, A., Le Corre, M., Tortosa, P. & Lebarbenchon, C. Haemoproteus iwa in Great Frigatebirds (Fregata minor) in the Islands of the Western Indian Ocean. PLoS ONE 9, e97185 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maa, T. C. Records of Hippoboscidae (diptera) from the Central Pacific. J. Med. Ent. 3, 325–328 (1968).
    Google Scholar 
    Levin, I. I. & Parker, P. G. Comparative host–parasite population genetic structures: Obligate fly ectoparasites on Galapagos seabirds. Parasitology 140, 1061–1069 (2013).CAS 
    PubMed 

    Google Scholar 
    Ramos-González, A. Hábitat y Edad de los Bobos de Patas Azules: Factores Importantes Para la Paternidad y Abundancia de Garrapatas. Primera edición. 88. (Universidad Nacional Autónoma de México, 2019). Print ISBN 978-607-30-1489-2.Bensch, S. et al. Contaminations contaminate common databases. Mol. Ecol. Resour. 21, 355–362 (2021).CAS 
    PubMed 

    Google Scholar 
    Taylor, S. A., Maclagan, L., Anderson, D. J. & Friesen, V. L. Could specialization to cold-water upwelling systems influence gene flow and population differentiation in marine organisms? A case study using the blue-footed booby, Sula nebouxii. J. Biogeogr. 38, 883–893 (2011).
    Google Scholar 
    Kalbe, M. & Kurtz, J. Local differences in immunocompetence reflect resistance of sticklebacks against the eye fluke Diplostomum pseudospathaceum. Parasitology 132, 105–116 (2006).CAS 
    PubMed 

    Google Scholar 
    Martin, L. B., Gilliam, J., Han, P., Lee, K. & Wikelski, M. Corticosterone suppresses cutaneous immune function in temperate but not tropical house sparrows Passer domesticus. Gen. Comp. Endocrinol. 140, 126–135 (2005).CAS 

    Google Scholar 
    Becker, D. J. et al. Macroimmunology: The drivers and consequences of spatial patterns in wildlife immune defence. J. Anim. Ecol. 89, 972–995 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Ting, J. et al. Malaria parasites and related haemosporidians cause mortality in cranes: A study on the parasites diversity, prevalence and distribution in Beijing Zoo. Malar. J. 17, 234 (2018).
    Google Scholar 
    Grilo, M. L. et al. Malaria in penguins – Current perceptions. Avian Pathol. 45, 393–407 (2016).CAS 
    PubMed 

    Google Scholar 
    Jovani, R. & Tella, J. L. Parasite prevalence and sample size: misconceptions and solutions. Trends Parasitol. 22, 214–218 (2006).PubMed 

    Google Scholar 
    Bensch, S. et al. Temporal dynamics and diversity of avian malaria parasites in a single host species. J. Anim. Ecol. 76, 112–122 (2007).MathSciNet 
    PubMed 

    Google Scholar 
    Lachish, S., Knowles, S. C., Alves, R., Wood, M. J. & Sheldon, B. C. Infection dynamics of endemic malaria in a wild bird population: Parasite species-dependent drivers of spatial and temporal variation in transmission rates. J. Anim. Ecol. 80, 1207–1216 (2011).PubMed 

    Google Scholar 
    Lopes, V. L. et al. High fidelity defines the temporal consistency of host-parasite interactions in a tropical coastal ecosystem. Sci. Rep. 10, 16839 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Valkiunas, G. et al. A comparative analysis of microscopy and PCR-based detection methods for blood parasites. J. Parasitol. 94, 1395–1401 (2008).CAS 
    PubMed 

    Google Scholar 
    Santiago-Alarcon, D. et al. Parasites in space and time: A case study of haemosporidian spatiotemporal prevalence in urban birds. Int. J. Parasitol. 49, 235–246 (2019).PubMed 

    Google Scholar 
    Ancona, S., Sánchez-Colón, S., Rodríguez, C. & Drummond, H. E. Niño in the warm tropics: Local sea temperature predicts breeding parameters and growth of blue-footed boobies. J. Anim. Ecol. 80, 799–808 (2011).PubMed 

    Google Scholar 
    Drummond, H., Torres, R. & Krishnan, V. V. Buffered development: Resilience after aggressive subordination in infancy. Am. Nat. 161, 794–807 (2003).PubMed 

    Google Scholar 
    Merino, S. & Potti, J. High prevalence of hematozoa in nestlings of a passerine species, the pied flycatcher (Ficedula hypoleuca). Auk 112, 1041–1043 (1995).
    Google Scholar 
    Gutiérrez-López, R. et al. Low prevalence of blood parasites in a long-distance migratory raptor: The importance of host habitat. Parasit. Vectors 8, 189 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Hellgren, O., Waldenström, J. & Bensch, S. A new PCR assay for simultaneous studies of Leucocytozoon, Plasmodium, and Haemoproteus from avian blood. J. Parasitol. 90, 797–802 (2004).CAS 
    PubMed 

    Google Scholar 
    Bensch, S. et al. Host specificity in avian blood parasites: A study of Plasmodium and Haemoproteus mitochondrial DNA amplified from birds. Proc. Biol. Sci. 267, 1583–1589 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Variations in leaf water status and drought tolerance of dominant tree species growing in multi-aged tropical forests in Thailand

    Stibig, H. J., Achard, F., Carboni, S., Raši, R. & Miettinen, J. Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences 11, 247–258. https://doi.org/10.5194/bg-11-247-2014 (2014).ADS 
    Article 

    Google Scholar 
    Wilcove, D. S., Giam, X., Edwards, D. P., Fisher, B. & Koh, L. P. Navjot’s nightmare revisited: Logging, agriculture, and biodiversity in Southeast Asia. Trends Ecol. Evol. 28, 531–540. https://doi.org/10.1016/j.tree.2013.04.005 (2013).Article 
    PubMed 

    Google Scholar 
    Zeng, Z. et al. Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century. Nat. Geosci. 11, 556–562. https://doi.org/10.1038/s41561-018-0166-9 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Imai, N., Furukawa, T., Tsujino, R., Kitamura, S. & Yumoto, T. Correction: Factors affecting forest area change in Southeast Asia during 1980–2010. PLoS ONE 13, e0199908. https://doi.org/10.1371/journal.pone.0199908 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684. https://doi.org/10.1016/j.foreco.2009.09.001 (2010).Article 

    Google Scholar 
    McDowell, N. G. et al. Multi-scale predictions of massive conifer mortality due to chronic temperature rise. Nat. Clim. Change 6, 295–300. https://doi.org/10.1038/nclimate2873 (2015).ADS 
    Article 

    Google Scholar 
    Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295. https://doi.org/10.1038/nature12350 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Barbeta, A. et al. The combined effects of a long-term experimental drought and an extreme drought on the use of plant-water sources in a Mediterranean forest. Global Change Biol. 21, 1213–1225. https://doi.org/10.1111/gcb.12785 (2015).ADS 
    Article 

    Google Scholar 
    Mueller, R. C. et al. Differential tree mortality in response to severe drought: Evidence for long-term vegetation shifts. J. Ecol. 93, 1085–1093. https://doi.org/10.1111/j.1365-2745.2005.01042.x (2005).Article 

    Google Scholar 
    Carnicer, J. et al. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc. Natl. Acad. Sci. USA 108, 1474–1478. https://doi.org/10.1073/pnas.1010070108 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shaw, J. D., Steed, B. E. & DeBlander, L. T. Forest Inventory and Analysis (FIA) annual inventory answers the question: What is happening to pinyon-juniper woodlands?. J. For. 103, 280–285 (2005).
    Google Scholar 
    Lebrija-Trejos, E., Pérez-García, E. A., Meave, J. A., Poorter, L. & Bongers, F. Environmental changes during secondary succession in a tropical dry forest in Mexico. J. Trop. Ecol. 27, 477–489. https://doi.org/10.1017/s0266467411000253 (2011).Article 

    Google Scholar 
    Lee, Y. K. et al. Differences of tree species composition and microclimate between a mahogany(swietenia macrophyllaking) plantation and a secondary forest in Mt. Makiling, Philippines. For. Sci. Technol. 2, 1–12. https://doi.org/10.1080/21580103.2006.9656293 (2006).CAS 
    Article 

    Google Scholar 
    Lebrija-Trejos, E., Perez-Garcia, E. A., Meave, J. A., Bongers, F. & Poorter, L. Functional traits and environmental filtering drive community assembly in a species-rich tropical system. Ecology 91, 386–398. https://doi.org/10.1890/08-1449.1 (2010).Article 
    PubMed 

    Google Scholar 
    Heithecker, T. D. & Halpern, C. B. Edge-related gradients in microclimate in forest aggregates following structural retention harvests in western Washington. For. Ecol. Manag. 248, 163–173. https://doi.org/10.1016/j.foreco.2007.05.003 (2007).Article 

    Google Scholar 
    Marthews, T. R., Burslem, D. F. R. P., Paton, S. R., Yangüez, F. & Mullins, C. E. Soil drying in a tropical forest: Three distinct environments controlled by gap size. Ecol. Model. 216, 369–384. https://doi.org/10.1016/j.ecolmodel.2008.05.011 (2008).Article 

    Google Scholar 
    Pineda-Garcia, F., Paz, H. & Meinzer, F. C. Drought resistance in early and late secondary successional species from a tropical dry forest: The interplay between xylem resistance to embolism, sapwood water storage and leaf shedding. Plant Cell Environ. 36, 405–418. https://doi.org/10.1111/j.1365-3040.2012.02582.x (2013).Article 
    PubMed 

    Google Scholar 
    Bretfeld, M., Ewers, B. E. & Hall, J. S. Plant water use responses along secondary forest succession during the 2015–2016 El Nino drought in Panama. New Phytol. 219, 885–899. https://doi.org/10.1111/nph.15071 (2018).Article 
    PubMed 

    Google Scholar 
    Matheny, A. M. et al. Contrasting strategies of hydraulic control in two codominant temperate tree species. Ecohydrology https://doi.org/10.1002/eco.1815 (2016).Article 

    Google Scholar 
    Pineda-Garcia, F., Paz, H., Meinzer, F. C. & Angeles, G. Exploiting water versus tolerating drought: Water-use strategies of trees in a secondary successional tropical dry forest. Tree Physiol. 36, 208–217. https://doi.org/10.1093/treephys/tpv124 (2016).Article 
    PubMed 

    Google Scholar 
    Powell, T. L. et al. Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees. Global Change Biol. 23, 4280–4293. https://doi.org/10.1111/gcb.13731 (2017).ADS 
    Article 

    Google Scholar 
    Ruiz-Benito, P. et al. Climate- and successional-related changes in functional composition of European forests are strongly driven by tree mortality. Global Change Biol. 23, 4162–4176. https://doi.org/10.1111/gcb.13728 (2017).ADS 
    Article 

    Google Scholar 
    Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539. https://doi.org/10.1038/s41586-018-0240-x (2018).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Sevanto, S., McDowell, N. G., Dickman, L. T., Pangle, R. & Pockman, W. T. How do trees die? A test of the hydraulic failure and carbon starvation hypotheses. Plant Cell Environ. 37, 153–161. https://doi.org/10.1111/pce.12141 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    McDowell, N. et al. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought?. New Phytol. 178, 719–739. https://doi.org/10.1111/j.1469-8137.2008.02436.x (2008).Article 
    PubMed 

    Google Scholar 
    Rowland, L. et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528, 119–122. https://doi.org/10.1038/nature15539 (2015).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Lazar, T., Taiz, L. & Zeiger, E. Plant physiology. 3rd edn. Ann. Bot. 91, 750–751. https://doi.org/10.1093/aob/mcg079 (2003).Article 
    PubMed Central 

    Google Scholar 
    Steppe, K. The potential of the tree water potential. Tree Physiol. 38, 937–940. https://doi.org/10.1093/treephys/tpy064 (2018).Article 
    PubMed 

    Google Scholar 
    Johnson, D., Katul, G. G. & Domec, J. C. Catastrophic hydraulic failure and tipping points in plants. Plant Cell Environ. (2022).Adams, H. D. et al. A multi-species synthesis of physiological mechanisms in drought-induced tree mortality. Nat. Ecol. Evol. 1, 1285–1291. https://doi.org/10.1038/s41559-017-0248-x (2017).Article 
    PubMed 

    Google Scholar 
    Skelton, R. P., West, A. G. & Dawson, T. E. Predicting plant vulnerability to drought in biodiverse regions using functional traits. Proc. Natl. Acad. Sci. USA 112, 5744–5749. https://doi.org/10.1073/pnas.1503376112 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Domec, J.-C. et al. Conversion of natural forests to managed forest plantations decreases tree resistance to prolonged droughts. For. Ecol. Manag. 355, 58–71. https://doi.org/10.1016/j.foreco.2015.04.012 (2015).Article 

    Google Scholar 
    Maherali, H., Pockman, W. T. & Jackson, R. B. Adaptive variation in the vulnerability of woody plants to xylem cavitation. Ecology 85, 2184–2199. https://doi.org/10.1890/02-0538 (2004).Article 

    Google Scholar 
    Barros, F. V. et al. Hydraulic traits explain differential responses of Amazonian forests to the 2015 El Nino-induced drought. New Phytol. 223, 1253–1266. https://doi.org/10.1111/nph.15909 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bittencourt, P. R. L. et al. Amazonia trees have limited capacity to acclimate plant hydraulic properties in response to long-term drought. Global Change Biol. 26, 3569–3584. https://doi.org/10.1111/gcb.15040 (2020).ADS 
    Article 

    Google Scholar 
    Nolf, M. et al. Stem and leaf hydraulic properties are finely coordinated in three tropical rain forest tree species. Plant Cell Environ. 38, 2652–2661. https://doi.org/10.1111/pce.12581 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Trueba, S. et al. Vulnerability to xylem embolism as a major correlate of the environmental distribution of rain forest species on a tropical island. Plant, Cell Environ. 40, 277–289. https://doi.org/10.1111/pce.12859 (2017).CAS 
    Article 

    Google Scholar 
    Zhu, S. D., Chen, Y. J., Fu, P. L. & Cao, K. F. Different hydraulic traits of woody plants from tropical forests with contrasting soil water availability. Tree Physiol. 37, 1469–1477. https://doi.org/10.1093/treephys/tpx094 (2017).Article 
    PubMed 

    Google Scholar 
    Chen, Y. J. et al. Physiological regulation and efficient xylem water transport regulate diurnal water and carbon balances of tropical lianas. Funct. Ecol. 31, 306–317. https://doi.org/10.1111/1365-2435.12724 (2016).Article 

    Google Scholar 
    Tan, F.-S. et al. Hydraulic safety margins of co-occurring woody plants in a tropical karst forest experiencing frequent extreme droughts. Agr. Forest Meteorol. https://doi.org/10.1016/j.agrformet.2020.108107 (2020).Article 

    Google Scholar 
    Markesteijn, L., Iraipi, J., Bongers, F. & Poorter, L. Seasonal variation in soil and plant water potentials in a Bolivian tropical moist and dry forest. J. Trop. Ecol. 26, 497–508. https://doi.org/10.1017/s0266467410000271 (2010).Article 

    Google Scholar 
    Mitchell, P. J., Veneklaas, E. J., Lambers, H. & Burgess, S. S. Leaf water relations during summer water deficit: Differential responses in turgor maintenance and variation in leaf structure among different plant communities in south-western Australia. Plant Cell Environ. 31, 1791–1802. https://doi.org/10.1111/j.1365-3040.2008.01882.x (2008).Article 
    PubMed 

    Google Scholar 
    Baltzer, J. L., Davies, S. J., Bunyavejchewin, S. & Noor, N. S. M. The role of desiccation tolerance in determining tree species distributions along the Malay-Thai Peninsula. Funct. Ecol. 22, 221–231. https://doi.org/10.1111/j.1365-2435.2007.01374.x (2008).Article 

    Google Scholar 
    Kursar, T. A. et al. Tolerance to low leaf water status of tropical tree seedlings is related to drought performance and distribution. Funct. Ecol. 23, 93–102. https://doi.org/10.1111/j.1365-2435.2008.01483.x (2009).Article 

    Google Scholar 
    Engelbrecht, B. M. J., Tyree, M. T. & Kursar, T. A. Visual assessment of wilting as a measure of leaf water potential and seedling drought survival. J. Trop. Ecol. 23, 497–500. https://doi.org/10.1017/s026646740700421x (2007).Article 

    Google Scholar 
    Blackman, C. J. et al. Drought response strategies and hydraulic traits contribute to mechanistic understanding of plant dry-down to hydraulic failure. Tree Physiol. 39, 910–924. https://doi.org/10.1093/treephys/tpz016 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bucci, S. J. et al. Mechanisms contributing to seasonal homeostasis of minimum leaf water potential and predawn disequilibrium between soil and plant water potential in Neotropical savanna trees. Trees 19, 296–304. https://doi.org/10.1007/s00468-004-0391-2 (2004).Article 

    Google Scholar 
    Prado, C. H. B. A., Wenhui, Z., Cardoza Rojas, M. H. & Souza, G. M. Seasonal leaf gas exchange and water potential in a woody cerrado species community. Braz. J. Plant Physiol. 16, 7–16. https://doi.org/10.1590/s1677-04202004000100002 (2004).Article 

    Google Scholar 
    Fetcher, N., Oberbauer, S. F. & Strain, B. R. Vegetation effects on microclimate in lowland tropical forest in Costa Rica. Int. J. Biometeorol. 29, 145–155. https://doi.org/10.1007/bf02189035 (1985).ADS 
    Article 

    Google Scholar 
    McCarthy, J. Gap dynamics of forest trees: A review with particular attention to boreal forests. Environ. Rev. 9, 1–59. https://doi.org/10.1139/a00-012 (2001).Article 

    Google Scholar 
    Zhu, S.-D. & Cao, K.-F. Hydraulic properties and photosynthetic rates in co-occurring lianas and trees in a seasonal tropical rainforest in southwestern China. Plant Ecol. 204, 295–304. https://doi.org/10.1007/s11258-009-9592-5 (2009).Article 

    Google Scholar 
    Sperry, J. S., Hacke, U. G., Oren, R. & Comstock, J. P. Water deficits and hydraulic limits to leaf water supply. Plant Cell Environ. 25, 251–263. https://doi.org/10.1046/j.0016-8025.2001.00799.x (2002).Article 
    PubMed 

    Google Scholar 
    Choat, B., Sack, L. & Holbrook, N. M. Diversity of hydraulic traits in nine Cordia species growing in tropical forests with contrasting precipitation. New Phytol. 175, 686–698. https://doi.org/10.1111/j.1469-8137.2007.02137.x (2007).Article 
    PubMed 

    Google Scholar 
    Vinya, R. et al. Xylem cavitation vulnerability influences tree species’ habitat preferences in miombo woodlands. Oecologia 173, 711–720. https://doi.org/10.1007/s00442-013-2671-2 (2013).ADS 
    Article 
    PubMed 

    Google Scholar 
    Vander Willigen, C., Sherwin, H. W. & Pammenter, N. W. Xylem hydraulic characteristics of subtropical trees from contrasting habitats grown under identical environmental conditions. New Phytol. 145, 51–59. https://doi.org/10.1046/j.1469-8137.2000.00549.x (2000).Article 

    Google Scholar 
    Domec, J. C. et al. Diurnal and seasonal variation in root xylem embolism in neotropical savanna woody species: Impact on stomatal control of plant water status. Plant Cell Environ. 29, 26–35. https://doi.org/10.1111/j.1365-3040.2005.01397.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Barnard, D. M. et al. Climate-related trends in sapwood biophysical properties in two conifers: Avoidance of hydraulic dysfunction through coordinated adjustments in xylem efficiency, safety and capacitance. Plant Cell Environ. 34, 643–654. https://doi.org/10.1111/j.1365-3040.2010.02269.x (2011).Article 
    PubMed 

    Google Scholar 
    Rosner, S., Heinze, B., Savi, T. & Dalla-Salda, G. Prediction of hydraulic conductivity loss from relative water loss: New insights into water storage of tree stems and branches. Physiol. Plant. 165, 843–854. https://doi.org/10.1111/ppl.12790 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Markesteijn, L., Poorter, L., Paz, H., Sack, L. & Bongers, F. Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. Plant Cell Environ. 34, 137–148. https://doi.org/10.1111/j.1365-3040.2010.02231.x (2011).Article 
    PubMed 

    Google Scholar 
    Cartwright, J. M., Littlefield, C. E., Michalak, J. L., Lawler, J. J. & Dobrowski, S. Z. Topographic, soil, and climate drivers of drought sensitivity in forests and shrublands of the Pacific Northwest, USA. Sci. Rep. 10, 18486. https://doi.org/10.1038/s41598-020-75273-5 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Choat, B., Ball, M. C., Luly, J. G. & Holtum, J. A. M. Hydraulic architecture of deciduous and evergreen dry rainforest tree species from north-eastern Australia. Trees 19, 305–311. https://doi.org/10.1007/s00468-004-0392-1 (2004).Article 

    Google Scholar 
    Krober, W., Zhang, S., Ehmig, M. & Bruelheide, H. Linking xylem hydraulic conductivity and vulnerability to the leaf economics spectrum–a cross-species study of 39 evergreen and deciduous broadleaved subtropical tree species. PLoS ONE 9, e109211. https://doi.org/10.1371/journal.pone.0109211 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brockelman, W. Y., Nathalang, A. & Maxwell, J. F. Mo Singto Forest Dynamics Plot: Flora and Ecology (National Science and Technology Development Agency, 2017).
    Google Scholar 
    Zhang, Q. W., Zhu, S. D., Jansen, S., Cao, K. F. & McCulloh, K. Topography strongly affects drought stress and xylem embolism resistance in woody plants from a karst forest in Southwest China. Funct. Ecol. 35, 566–577. https://doi.org/10.1111/1365-2435.13731 (2020).Article 

    Google Scholar 
    Ishida, A. et al. Seasonal variations of gas exchange and water relations in deciduous and evergreen trees in monsoonal dry forests of Thailand. Tree Physiol. 30, 935–945. https://doi.org/10.1093/treephys/tpq025 (2010).Article 
    PubMed 

    Google Scholar 
    Nardini, A., Battistuzzo, M. & Savi, T. Shoot desiccation and hydraulic failure in temperate woody angiosperms during an extreme summer drought. New Phytol. 200, 322–329. https://doi.org/10.1111/nph.12288 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755. https://doi.org/10.1038/nature11688 (2012).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Brodribb, T. J. Progressing from “functional” to mechanistic traits. New Phytol. 215, 9–11. https://doi.org/10.1111/nph.14620 (2017).Article 
    PubMed 

    Google Scholar 
    Oliveira, R. S. et al. Embolism resistance drives the distribution of Amazonian rainforest tree species along hydro-topographic gradients. New Phytol. 221, 1457–1465. https://doi.org/10.1111/nph.15463 (2019).Article 
    PubMed 

    Google Scholar 
    Popradit, A. et al. Anthropogenic effects on a tropical forest according to the distance from human settlements. Sci. Rep. 5, 14689. https://doi.org/10.1038/srep14689 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hérault, B. & Gourlet-Fleury, S. In Climate Change and Agriculture Worldwide (ed. Torquebiau, E.) 183–196 (Springer, 2016).Chapter 

    Google Scholar 
    Elliott, S. et al. Selecting framework tree species for restoring seasonally dry tropical forests in northern Thailand based on field performance. For. Ecol. Manag. 184, 177–191. https://doi.org/10.1016/s0378-1127(03)00211-1 (2003).Article 

    Google Scholar 
    Vieira, D. L. M. & Scariot, A. Principles of natural regeneration of tropical dry forests for restoration. Restor. Ecol. 14, 11–20. https://doi.org/10.1111/j.1526-100X.2006.00100.x (2006).Article 

    Google Scholar 
    Hérault, B. & Piponiot, C. Key drivers of ecosystem recovery after disturbance in a neotropical forest. For. Ecosyst. 5, 2. https://doi.org/10.1186/s40663-017-0126-7 (2018).Article 

    Google Scholar 
    Davies, S. J. et al. ForestGEO: Understanding forest diversity and dynamics through a global observatory network. Biol. Conserv. 253, 108907. https://doi.org/10.1016/j.biocon.2020.108907 (2021).Article 

    Google Scholar 
    Chanthorn, W. et al. Viewing tropical forest succession as a three-dimensional dynamical system. Theor. Ecol. 9, 163–172. https://doi.org/10.1007/s12080-015-0278-4 (2015).Article 

    Google Scholar 
    Chanthorn, W., Hartig, F. & Brockelman, W. Y. Structure and community composition in a tropical forest suggest a change of ecological processes during stand development. For. Ecol. Manag. 404, 100–107. https://doi.org/10.1016/j.foreco.2017.08.001 (2017).Article 

    Google Scholar 
    Rodtassana, C. et al. Different responses of soil respiration to environmental factors across forest stages in a Southeast Asian forest. Ecol. Evol. 11, 15430–15443. https://doi.org/10.1002/ece3.8248 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tor-ngern, P. et al. Variation of leaf-level gas exchange rates and leaf functional traits of dominant trees across three successional stages in a Southeast Asian tropical forest. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2021.119101 (2021).Article 

    Google Scholar 
    Zhu, S. D., Song, J. J., Li, R. H. & Ye, Q. Plant hydraulics and photosynthesis of 34 woody species from different successional stages of subtropical forests. Plant Cell Environ. 36, 879–891. https://doi.org/10.1111/pce.12024 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Martin-StPaul, N. K. et al. How reliable are methods to assess xylem vulnerability to cavitation? The issue of “open vessel” artifact in oaks. Tree Physiol. 34, 894–905. https://doi.org/10.1093/treephys/tpu059 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Ennajeh, M., Simoes, F., Khemira, H. & Cochard, H. How reliable is the double-ended pressure sleeve technique for assessing xylem vulnerability to cavitation in woody angiosperms?. Physiol. Plant. 142, 205–210. https://doi.org/10.1111/j.1399-3054.2011.01470.x (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pérez-Harguindeguy, N. et al. Corrigendum to: New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 64, 715–716. https://doi.org/10.1071/bt12225_co (2016).Article 

    Google Scholar 
    Ewers, F. W. & Fisher, J. B. Techniques for measuring vessel lengths and diameters in stems of woody plants. Am. J. Bot. 76, 645–656. https://doi.org/10.1002/j.1537-2197.1989.tb11360.x (1989).Article 

    Google Scholar 
    Gao, H. et al. Vessel-length determination using silicone and air injection: Are there artifacts?. Tree Physiol. 39, 1783–1791. https://doi.org/10.1093/treephys/tpz064 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Sperry, J. S. & Saliendra, N. Z. Intra- and inter-plant variation in xylem cavitation in Betula occidentalis. Plant Cell Environ. 17, 1233–1241. https://doi.org/10.1111/j.1365-3040.1994.tb02021.x (1994).Article 

    Google Scholar 
    Melcher, P. J. et al. Measurements of stem xylem hydraulic conductivity in the laboratory and field. Methods Ecol. Evol. 3, 685–694. https://doi.org/10.1111/j.2041-210X.2012.00204.x (2012).Article 

    Google Scholar 
    Edwards, W. R. N. & Jarvis, P. G. Relations between water content, potential and permeability in stems of conifers. Plant Cell Environ. 5, 271–277. https://doi.org/10.1111/1365-3040.ep11572656 (1982).Article 

    Google Scholar 
    Sperry, J. S. & Ikeda, T. Xylem cavitation in roots and stems of Douglas-fir and white fir. Tree Physiol. 17, 275–280. https://doi.org/10.1093/treephys/17.4.275 (1997).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pammenter, N. W. & Vander Willigen, C. A mathematical and statistical analysis of the curves illustrating vulnerability of xylem to cavitation. Tree Physiol. 18, 589–593. https://doi.org/10.1093/treephys/18.8-9.589 (1998).Article 
    PubMed 

    Google Scholar 
    Domec, J.-C. & Gartner, B. L. Cavitation and water storage capacity in bole xylem segments of mature and young Douglas-fir trees. Trees 15, 204–214. https://doi.org/10.1007/s004680100095 (2001).Article 

    Google Scholar  More

  • in

    A global reptile assessment highlights shared conservation needs of tetrapods

    We used the IUCN Red List criteria34,35 and methods developed in other global status-assessment efforts36,37 to assess 10,078 reptile species for extinction risk. We additionally include recommended Red List categories for 118 turtle species38, for a total of 10,196 species covered, representing 89% of the 11,341 described reptile species as of August 202039.Data compilationWe compiled assessment data primarily through regional in-person and remote (that is, through phone and email) workshops with species experts (9,536 species) and consultation with IUCN Species Survival Commission Specialist Groups and stand-alone Red List Authorities (442 species, primarily marine turtles, terrestrial and freshwater turtles, iguanas, sea snakes, mainland African chameleons and crocodiles). We conducted 48 workshops between 2004 and 2019 (Supplementary Table 1). Workshop participants provided information to complete the required species assessment fields (geographical distribution, population abundance and trends, habitat and ecological requirements, threats, use and trade, literature) and draw a distribution map. We then applied the Red List criteria34 to this information to assign a Red List category: extinct, extinct in the wild, critically endangered, endangered, vulnerable, near threatened, least concern and data deficient. Threatened species are those categorized as critically endangered, endangered and vulnerable.TaxonomyWe used The Reptile Database39 as a taxonomic standard, diverging only to follow well-justified taxonomic standards from the IUCN Species Survival Commission40. We could not revisit new descriptions for most regions after the end of the original assessment, so the final species list is not fully consistent with any single release of The Reptile Database.Distribution mapsWhere data allowed, we developed distribution maps in Esri shapefile format using the IUCN mapping guidelines41 (1,003 species). These maps are typically broad polygons that encompass all known localities, with provisions made to show obvious discontinuity in areas of unsuitable habitat. Each polygon is coded according to species’ presence (extant, possibly extant or extinct) and origin (native, introduced or reintroduced)41. For some regions covered in workshops (Caucasus, Southeast Asia, much of Africa, Australia and western South America), we collaborated with the Global Assessment of Reptile Distributions (GARD) (http://www.gardinitiative.org/) to provide contributing experts with a baseline species distribution map for review. Although refined maps were returned to the GARD team, not all of these maps have been incorporated into the GARD.Habitat preferencesWhere known, species habitats were coded using the IUCN Habitat Classification Scheme (v.3.1) (https://www.iucnredlist.org/resources/habitat-classification-scheme). Species were assigned to all habitat classes in which they are known to occur. Where possible, habitat suitability (suitable, marginal or unknown) and major importance (yes or no) was recorded. Habitat data were available for 9,484 reptile species.ThreatsAll known historical, current and projected (within 10 years or 3 generations, whichever is the longest; generation time estimated, when not available, from related species for which it is known; generation time recorded for 76.3% of the 186 species categorized as threatened under Red List criteria A and C1, the only criteria using generation length) threats were coded using the IUCN Threats Classification Scheme v.3.2 (https://www.iucnredlist.org/resources/threat-classification-scheme), which follows a previously published study42. Where possible, the scope (whole ( >90%), majority (50–90%), minority (30%), rapid ( >20%), slow but notable ( More