Ecology

Fossil record of insectsWe compiled all species-level fossil occurrences of insects using https://paleobiodb.org/ (PBDB) as a starting point (downloaded October 12, 2021). The dataset obtained from PBDB contained initially 5808 occurrences for a period ranging from the Asselian to the Rhaetian. The dataset was cleaned of synonyms, outdated combinations, nomina dubia, and other erroneous and doubtful records, based on revisions provided in the literature and/or on the expertise of the authors. After correction, including data addition from the literature, our dataset was composed of 3636 species (1784 genera, and 418 families) for 17,250 occurrences resulting from an in-depth study and curation of the entire bibliography of fossil insects, spanning from the Asselian (lowermost Permian) to the Rhaetian (uppermost Triassic). Although most of the taxa included in the datasets are nominal taxa (published and named), a few unnamed taxa (genera or species) that are considered separate from others were also included, although not formally named in the literature or not published yet. These unpublished taxa are identifiable by the notation ‘fam. nov.’ or ‘gen. nov.’ following their names.Occurrences used here are specimens originating from a given stratigraphic horizon assigned to a given taxon. The age of each occurrence is based on data from PBDB, corrected with a more precise age (generally stage, sometimes substage), and the age of each time bin boundaries relies on the stratigraphic framework proposed in the International Chronostratigraphic Chart (updated to correspond with the ICS 2022/0295). Similarly, the ages of some species assigned to the wrong stage were corrected. In fact, some species from the French Permian deposit of Lodève were initially considered to be of Artinskian age in PBDB but most species from this deposit originate from the Merifons member, which is of Kungurian age96.Our data compilation allows a robust integration of data before and after our period of interest (i.e. the lower Permian and all geologic stages after the Carnian) to encompass occurrences of genera that may survive until the Late Triassic and to generate a sufficient background for the model to correctly estimate the extinction events around the P/T boundary. Since we used different datasets, the differences between genus-level or family-level occurrence numbers are explained by the systematic placement of some specimens that can only be placed confidently in a family but not in a genus (Supplementary Table 1). Tentative species identifications originally placed with uncertainty (reported as ‘aff.’ or ‘?’) were always included at a higher taxonomic level. Uncertain generic attributions were integrated as occurrences at the family level (e.g. a fossil initially considered Tupus? is recorded as an occurrence of Meganeuridae). Our total dataset was subdivided into smaller datasets, which represent orders or other subclades of insects (e.g. Mecoptera, Holometabola and Polyneoptera). Note that all the ichnospecies—a species name assigned to trace fossils (e.g. resting trace, nest and leaf damage)— and insect eggs (e.g. Clavapartus latus, Furcapartus exilis and Monilipartus tenuis) were not included in the analyses97. To prevent potential issues regarding the diversification estimates for clades with poor delineation, we refrained from analysing several orders that serve as taxonomic ‘wastebaskets’ (e.g. Grylloblattodea). These groups are poorly defined, likely polyphyletic or paraphyletic, and not supported by apomorphic characters—e.g. the monophyly of the ‘Grylloblattodea’ (Grylloblattida Walker, 1914 plus numerous fossil families and genera of uncertain affinities) is not supported by any synapomorphy, nor the relationships within this group. The occurrences assigned to these orders were rather included in analyses conducted at a higher taxonomic level (at the Polyneoptera level in the case of the ‘Grylloblattodea’). The detail of the composition of all the datasets is given in Supplementary Table 14, and each dataset is available in Supplementary Data 1.Studying extinction should, when possible, rely on species-level diversity to better circumscribe extinction events at this taxonomic rank, which is primarily affected by extinction98,99,100. However, in palaeoentomology, species-level occurrence data may contain less information than genus-level data, mainly because species are most of the time only known from one deposit, resulting in reduced life span, and are also sometimes poorly defined. Insects are also less prone to long-lasting genera or species than other lineages, maybe because of the relatively short time between generations (allowing for rapid evolution) or because morphological characters are better preserved or more diagnostic than in other lineages (i.e. wing venation), allowing easier differentiation. Another argument for the use of genus-level datasets is the possibility to add occurrences represented by fossils that cannot be assigned at the species level because of poor preservation or an insufficient number of specimens/available characters. By extension, the genus life span provides clues as to survivor taxa and times of origination during periods of post-extinction or recovery. A genus encompassing extinction events indicates that at least one species of this genus crossed the extinction. To get the best signal and infer a robust pattern of insect dynamics around the P/T events, we have chosen to analyse our dataset at different taxonomic ranks (e.g. genus, family and order levels) to extract as much evidence as possible.To further support our choice to work at these different levels, most recent works aiming to decipher the diversification and extinction in insect lineages have worked using a combination of analyses21,22,26; this also applies to non-insect clades51,101,102. This multi-level approach should maximise our understanding of the Permo–Triassic events.Assessing optimal parameters and preliminary testsPrior to choosing the settings for the final analyses (see detail in Dynamics of origination and extinction), a series of tests were carried out to better evaluate the convergence of our analyses. First, we analysed our genus-level dataset with PyRate36 running for 10 million generations and sampling every 10,000 generations, on ten randomly replicated datasets using the reversible-jump Markov Chain Monte Carlo (RJMCMC) model37 and the parameters of PyRate set by default. As the convergence was too low, new settings were used, notably increasing the number of generations to 50 million generations and monitoring the MCMC mixing and effective sample size (ESS) each 10 million generations. We modified the minimal interval between two shifts (-min_dt option, testing 0.5, 1.5 and 2), and found no major difference in diversification patterns between our tests. We have opted for 50 million generations with a predefined time frame set for bins corresponding to the Permian and Triassic stages, and a minimum interval between two shifts of two Ma. These parameters allow for maintaining a short bin frame and high convergence values while correctly identifying periods of diversification and extinction. For each analysis, ten datasets were generated using the extract.ages function to randomly resample the age of fossil occurrences within their respective temporal ranges (i.e. resampled ages are randomly drawn between the minimum and the maximum ages of the geological stratum). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 after excluding the first 10% of the samples as a burn-in period. The parameters are considered convergent when their ESS are greater than 200.Dynamics of origination and extinctionWe carried out the analyses of the fossil datasets based on the Bayesian framework implemented in the programme PyRate36. We analysed the fossil datasets under two models: the birth–death model with constrained shifts (BDCS38) and the RJMCMC (-A 4 option37). These models allow for a simultaneous estimate for each taxon: (1) the parameters of the preservation process (Supplementary Fig. 17), (2) the times of origination (Ts) and extinction (Te) of each taxon, (3) the origination and extinction rates and their variation through time for each stage and (4) the number and magnitude of shifts in origination and extinction rates.All analyses were set with the best-fit preservation process after comparing (-PPmodeltest option) the homogeneous Poisson process (-mHPP option), the non-homogeneous Poisson process (default option), and the time-variable Poisson process (-qShift option). The preservation process infers the individual origination and extinction times of each taxon based on all fossil occurrences and on an estimated preservation rate, denoted q, expressed as expected occurrences per taxon per Ma. The time-variable Poisson process assumes that preservation rates are constant within a predefined time frame but may vary over time (here, set for bins corresponding to stages). This model is thus appropriate when rates over time are heterogeneous.We ran PyRate for 50 million MCMC generations and a sampling every 50,000 generations for the BDCS and RJMCMC models with time bins corresponding to Permian and Triassic stages (-fixShift option). All analyses were set with a time-variable Poisson process (-qShift option) of preservation and accounted for varying preservation rates across taxa using the Gamma model (-mG option), that is, with gamma-distributed rate heterogeneity with four rate categories36. As explained above, the minimal interval between two shifts (-min_dt option) was modified and a value of 2 was used. The default prior to the vector of preservation rates is a single gamma distribution with shape = 1.5 and rate = 1.5. We reduced the subjectivity of this parameter, and favoured a better adequation to the data, allowing PyRate to estimate the rate parameter of the prior from the data by setting the rate parameter to 0 (-pP option). Therefore, PyRate assigns a vague exponential hyper-prior to the rate and samples the rate along with all other model parameters. Similarly, because our dataset does not encompass the entire fossil record of insects, we assumed that a possible edge effect may interfere with our analyses, with a strong diversification during the lowermost Permian and, conversely a strong extinction during the uppermost Triassic. Because the RJMCMC and BDCS algorithms look for rate shifts, we constrained the algorithm to only search for shifts (-edgeShift option) within the following time range 295.0 to 204.5 Ma. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 after excluding the first 10% of the samples as a burn-in period. The parameters are considered convergent when their ESS are greater than 200.We then combined the posterior estimates of the origination and extinction rates across all replicates to generate rates through-time plots (origination, extinction, and net diversification). Shifts of diversification were considered significant when log Bayes factors were >6 in the RJMCMC model, while we considered shifts to be significant in the BDCS model when mean rates in a time bin did not overlap with the 95% credibility interval (CI) of the rates of adjacent time bins.We replicated all the analyses on ten randomly generated datasets of each clade and calculated estimates of the Ts and the Te as the average of the posterior samples from each replicate. Thus, we obtained ten posterior estimates of the Ts and Te for all taxa and we used these values to estimate the past diversity dynamics by calculating the number of living taxa at each time point. For all the subsequent analyses, we used the estimated Ts and Te of all taxa to test whether or not the origination and the extinction rate dynamics were correlated with particular abiotic factors, as suggested by the drastic changes in environmental conditions known during the Permo–Triassic. We used proxies for abiotic factors, such as global continental fragmentation or the dynamic of major clades of plants, and for biotic factors via species interaction within and between ecological guilds. This approach avoids re-modelling preservation and re-estimating times of origination and extinction, which reduces drastically the computational burden, while still allowing to account for the preservation process and the uncertainties associated with fossil ages. Similarly, the times of origination and extinction used in all the subsequent analyses were obtained while accounting for the heterogeneity of preservation, origination and extinction rates. To discuss the magnitude of the periods of extinction and diversification, we compared the magnitude of these events to the background origination and extinction rates (i.e. not during extinction or diversification peaks).The PyRate approach has proven to be robust following a series of tests and simulations that reflect commonly observed biases when modelling past diversity dynamics31,38. These simulations were based on datasets simulated under a range of potential biases (i.e. violations of the sampling assumptions, variable preservation rates, and incomplete taxon sampling) and reflecting the limitations of the fossil record. Simulation results showed that PyRate is able to correctly estimate the dynamics of origination and extinction rates, including sudden rate changes and mass extinction, even if the preservation levels are low (down to 1–3 fossil occurrences per species on average), the taxon sampling is partial (up to 80% missing) or if the datasets have a high proportion of singletons (exceeding 30% of the taxa in some cases). The strongest bias in birth–death rate estimates is caused by incomplete data (i.e. missing lineages) altering the distribution of taxa; a pervasive effect often mentioned for phylogeny-based models104,105,106. However, in the case of PyRate, the simulations confirm the absence of consistent biases due to an incomplete fossil record36. Finally, the recently implemented RJMCMC model was shown to be very accurate for estimating origination and extinction rates (i.e. more accurate than the BDCS model, the boundary-crossing and three-time methods) and is able to recover sudden extinction events regardless of the biases in the fossil dataset37.The severity of extinctions and survivorsFor each event—the Roadian–Wordian, the LPME, and the Ladinian–Carnian—we quantified the percentage of extinctions and survivors at the genus level. We used the Te and Ts from our RJMCMC analysis and computed the mean for the Te (Tem) and for the Ts (Tsm) of each genus. We then filtered our dataset to keep only the genera with a Tsm older than the upper boundary of the focal event, i.e., we only kept the genera that appeared before the end of the event. Then, we discarded the genera that have disappeared before the lower boundary of the focal event, i.e. Tem older that the lower boundary of the event. The remaining genera, which corresponds to all the genera (total) present during the crisis (Ttgen), can be classified into two categories, ‘survivor genera’ (Sgen), i.e. those that survived the crisis, and those that died: ‘extinct genera’ (Egen). The survivors have a Tem younger than the upper boundary of the focal event, while the ‘extinct genera’ died out during the event and have a Tem between the lower and upper boundaries of the event of interest. To obtain the percentage of survivors, we used the following formula: (Sgen/Ttgen) × 100. Similarly, the percentage of extinction is calculated as: (Egen/Ttgen) × 100.Age-dependent extinction modelWe assessed the effect of taxon age on the extinction probability by fitting the age-dependent extinction (ADE; -ADE 1 option) model50. This model estimates the probability for a lineage to become extinct as a function of its age, also named longevity, which is the elapsed time since its origination. It is recommended to run the ADE model over time windows with roughly constant origination and extinction rates, as convergence is difficult—but not impossible—to reach in extinction or diversification contexts50. We ran PyRate for 50 million MCMC generations with a sampling every 50,000 generations, with a time-variable Poisson process of preservation (-qShift option), while accounting for varying preservation rates across taxa using the Gamma model (-mG option). We replicated the analyses on ten randomised datasets and combined the posterior estimates across all replicates. We estimated the shape (Φ) and scale (Ψ) parameters of the Weibull distribution, and the taxon longevity in a million years. According to ref. 50, there is no evidence of age-dependent extinction rates if Φ = 1. However, the extinction rate is higher for young species and decreases with species age if Φ  1. Although ADE models are prone to high error rates when origination and extinction rates increase or decrease through time, simulations with PyRate have shown that fossil-based inferences are robust50. We investigated the effect of ADE during three different periods (-filter option) as follows: (1) between 264.28 Ma and 255 Ma (pre-decline), (2) between 254.5 Ma and 251.5 Ma (decline) and (3) between 234 Ma and 212 Ma (post-crisis). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Selection of abiotic and biotic variablesTo test correlations of insect diversification with environmental changes, we examined the link between a series of environmental variables and origination/extinction rates over a period encompassing the GEE, the LPME and the CPE but also for each extinction event. We focused on the role of nine variables, also called proxies, which have been demonstrated or assumed to be linked to extinctions and changes in insect diversity26,67.The variations in the atmospheric CO2 and O2 concentrations are thought to be correlated with the diversification of several insect lineages, including the charismatic giant Meganeuridae65,66,67. Because the increase of O2 concentration has likely driven the diversification of some insects, its diminution may have resulted in the extinction or decline of some lineages. Therefore, we investigated the potential correlation of the variations of this variable with insect dynamics using data from ref. 55. We extracted the data, with 1-million-year time intervals, spanning the Permo–Triassic.Similarly, the modification of CO2 concentration, notably its increase, is known to promote speciation in some modern insect groups107. Therefore, a similar effect may have occurred during the Permian and Triassic but remains to be tested. We based our analyses on the dataset of ref. 108. We used their cleaned dataset and extracted all verified values for the Permo–Triassic interval. Because the initial data (i.e. independent estimates) were made in various locations for the same age, different values of the CO2 concentration are provided. We incorporated all these values in our analysis, allowing PyRate to search for a correlation for each value of the CO2 concentration. We obtain a final correlation independent of the sampling location, in line with our large-scale analysis.The continental fragmentation, as approximated by plate tectonic change over time, has recently been proposed as a driver of Plecoptera dynamics26. Because the period studied encompasses a major geological event, the fragmentation of the supercontinent Pangea, we investigated the effect of continental fragmentation on insect diversification dynamics. We retrieved the index of continental fragmentation developed by ref. 69 using paleogeographic reconstructions for 1-million-year time intervals. This index approaches 1 when all plates are disjoined (complete plate fragmentation) and approaches 0 when the continental aggregation is maximal.Climate change (variations in warming and cooling periods) is a probable driver of diversification changes over the history of insects21,109. Temperature is likely directly linked with insect dynamics109 but also with their food sources, notably plants110. Because it was demonstrated that modification of temperature impacted floral assemblages110, we tested the correlation between temperature variations and the diversification dynamic of insects. Major trends in global climate change through time are typically estimated from relative proportions of different oxygen isotopes (δ18O) in samples of benthic foraminiferan shells111. We used the data from ref. 112, converted to absolute temperatures following the methodology described in Condamine et al.113 (see their section Global temperature variations through time). The resulting temperature data reflects planetary-scale climatic trends, with time intervals inferior to 1-million-year, which can be expected to have led to temporally coordinated diversification changes in several clades rather than local or seasonal fluctuations.The fluctuation in relative diversity of gymnosperms, non-Polypodiales ferns, Polypodiales ferns, spore-plants, and later the rise of angiosperms has likely driven the diversification of numerous insects57,60,61,114. Close interactions between insects and plants are well-recorded during the Permian and Triassic57,60,61. In fact, herbivorous insects are known to experience high selection pressure from bottom-up forces, resulting from interactions with their hosts or feeding plants30,72. Therefore, it appears crucial to investigate the effect of these modifications on the insects’ past dynamics. We used the data from ref. 38 for the different plant lineages (all with 1-million-year time intervals). All the datasets for these variables are available in the publications cited aside from each variable or in Supplementary Data 1.Multivariate birth–death modelWe used the multivariate birth–death (MBD) model to assess to what extent biotic and abiotic factors can explain temporal variation in origination and extinction rates55. The model is described in ref. 55, where origination and extinction rates can change through time in relation to environmental variables so that origination and extinction rates depend on the temporal variations of each factor. The strength and sign (positive or negative) of the correlations are jointly estimated for each variable. The sign of the correlation parameters indicates the sign of the resulting correlation. When their value is estimated around zero, no correlation is estimated. An MCMC algorithm combined with a horseshoe prior, controlling for over-parameterisation and for the potential effects of multiple testing, jointly estimates the baseline origination (λ0) and extinction (µ0) rates and all correlation parameters (Gλ and Gµ)55. The horseshoe prior is used to discriminate which correlation parameters should be treated as noise (shrunk around 0) and which represent a true signal (i.e. significantly different from 0). In the MBD model, a correlation parameter is estimated to quantify independently the role of each variable on the origination and the extinction.We ran the MBD model using 20 (for short intervals) or 50 million MCMC generations and sampling every 20,000 or 50,000 to approximate the posterior distribution of all parameters (λ0, µ0, nine Gλ, nine Gµ and the shrinkage weights of each correlation parameter, ωG). The MBD analyses used the Ts and the Te derived from our previous analyses under the RJMCMC model. The results of the MBD analyses were summarised by calculating the posterior mean and 95% CI of all correlation parameters and the mean of the respective shrinkage weights (across ten replicates), as well as the mean and 95% CI of the baseline origination and extinction rates. We carried out six analyses, over: (1) the Permo–Triassic (between 298.9 and 201.3 Ma); (2) the Roadian–Wordian (R/W) boundary (between 270 and 265 Ma), (3) the LPME (between 254.5 and 250 Ma), (4) the Ladinian–Carnian (L/C) boundary (between 240 to 234 Ma), (5) the Permian period (between 298.9 and 251.902 Ma) and (6) the Triassic period (between 251.902 and 201.3 Ma). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Multiple clade diversity-dependence modelTo assess the potential effect of diversity-dependence on the diversity dynamics of three or four insect guilds, we used the multiple clade diversity-dependence (MCDD) model in which origination and extinction rates are correlated with the diversity trajectory of other clades31. This model postulates that competitive interactions linked with an increase in diversity results in decreasing origination rates and/or increasing extinction rates. The MCDD model allows for testing diversity-dependence between genera of a given clade or between genera of distinct clades sharing a similar ecology.We estimated the past diversity dynamics for three (i.e. herbivores, predators, and a guild composed of generalists + detritivores/fungivores dubbed ‘others’) or four insect groups or guilds (i.e. herbivores, predators, generalists and detritivores/fungivores) by calculating the number of living species at every point in time based on the times of origination (Ts) and extinction (Te) estimated under the RJMCMC model (see above) (Supplementary Figs. 19–24). We defined our four insect groups with a cautious approach i.e. insect genera, families or orders for which nothing is known about the ecology or about the ecology of their close relatives were not considered for the analysis. For example, no diet was assigned to Diptera, Mecoptera or Glosselytrodea. The ecology of the Triassic Diptera and Permo–Triassic Mecoptera is difficult to establish because extant Diptera and Mecoptera have a wide diversity of ecology. Fossil Mecoptera are also putatively involved in numerous interactions with plants (species with elongated mouthparts), suggesting a placement in the herbivore group, while other species were likely predators. Therefore, we cannot decide to which group each species belongs. Similarly, nothing is known about the body and mouthparts of the Glosselytrodea, most of the time described based on isolated wings; we did not assign the order to any group. The definition and delineation of insect clades have also challenged the placement of several orders (e.g. ‘Grylloblattodea’) in one of our four groups. The order ‘Grylloblattodea’ is poorly delineated and mostly serves as a taxonomic ‘wastebasket’ to which it is impossible to assign a particular ecology. Finally, genera, species, or families not placed in a higher clade (e.g. Meshemipteron, Perielytridae) were not included in the analysis. Oppositely, the guilds ‘herbivores’ and ‘predators’ are well defined, and their ecology is evidenced by the morphology of their representatives and the principle of actualism. For example, the ecology of Meganeurites gracilipes (Meganeuridae) has been deeply studied, and its enlarged compound eyes, its sturdy mandibles with acute teeth, its tarsi and tibiae bearing strong spines, and the presence of a pronounced thoracic skewness are specialisations today found in dragonflies that capture their prey while in flight115. All Odonatoptera are well-known predator insects. The raptorial forelegs of the representatives of the order Titanoptera and their mouthparts with strong mandibles are linked with predatory habits81. The Palaeodictyopteroidea were herbivorous insects with long, beak-like, piercing mouthparts, and probably a sucking organ81,82. Most Hemiptera are confidently considered herbivorous insects by comparison with their extant representatives. For example, the Cicadomorpha or Sternorrhyncha are known to feed on plants and their fossil representatives likely possessed the same ecology because of similar morphologies116. Some hemipteran families (e.g. Nabidae) are predators and we cautiously distinguished herbivorous and carnivorous taxa among Hemiptera. The detail of the ecological assignations for the 1009 genera included in our analyses can be found in Supplementary Data 1 (Table MCCD).We calculated ten diversity trajectories from the ten replicated analyses under the RJMCMC model. The estimation of past species diversity might be biased by low preservation rates or taxonomic uncertainties. However, such trajectory curves are likely to provide a reasonably accurate representation of the past diversity changes in the studied clades, notably because the preservation during the Permian and Triassic period is relatively good for insects (i.e. no gaps).Our MCDD analyses comprise all the insect genera spanning from the lowermost Permian to the uppermost Triassic and were run and repeated on ten replicates (using the Te and Ts estimated under the RJMCMC model) with 50 million MCMC generations and a sampling frequency of 50,000. For each of the four insect groups, we computed the median and the 95% CI of the baseline origination and extinction rates (λi and µi), the within-group diversity-dependence parameters gλi and gµi, and the between-groups diversity dependence parameters gλij and gµij. The mean of the sampled diversity dependence parameters (e.g. gλij) was used as a measure of the intensity of the negative (if positive) or positive interactions (if negative) between each pair of groups. The interactions were considered significant when their median was different from 0 and the 95% CI did not overlap with 0. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.We cross-validated the result of the MCDD model using the MBD model. The MBD model can be used to run a multiple clade diversity-dependence analysis by providing the diversity trajectories of insect guilds as a continuous variable. These data are directly generated by PyRate using the lineages-through-time generated by the RJMCMC analyses (-ltt option). We ran the MBD model using 50 million MCMC generations and sampling every 50,000 to approximate the posterior distribution of all parameters (λ0, µ0, four Gλ, four Gµ and the shrinkage weights of each correlation parameter, ωG). We carried out three analyses, over the period encompassing the three extinction events (between 275 and 230 Ma): (1) for herbivores; (2) for predators; and (3) for ‘others’. For each analysis, the lineages-through-time data of the two other guilds are used as continuous variables to investigate a diversity dependence effect. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

SamplesWe examined 1,022 avian species (~ 10% recorded species) in this study, representing 418 genera, from 91 families (37% recorded families) and 29 orders (73% of all orders). Specimens were from the skin collection of the World Museum Liverpool, Tring Natural History Museum, Manchester Museum and Wollaton Hall Museum, all situated in the United Kingdom. All work was carried out in accordance with ethical regulations at Manchester Metropolitan University and with the permission of all aforementioned museums. Only the best-preserved adult specimens (no signs of cut off feathers or holes in the skin near the beak) were chosen for this study to ensure accurate measurements of bristle length, shape and presence, which should not be affected by the process of skin removal and specimen conservation. Species were randomly chosen, without targeting our sampling towards species known a priori to have bristles. Where possible, two specimens per species were measured (occurring in 82% of all species examined). Specimens of each sex were measured when present; however, this was not always possible since labelling was often inaccurate or missing. In total, the sample included 508 males, 412 females and 374 individuals of unknown sex. Both sexes were examined in 274 species and there was no difference whatsoever between the presence of bristles on male or female species (n = 97 with bristles present and n = 180 with bristles absent for both males and females). Length (Mann–Whitney U test, W = 37,962, N = 552, P = 0.94) and shape (Chi-square test, χ2 = 0, N = 552, df = 3, P = 1) of rictal bristles also did not significantly differ between males and females. Therefore, rictal bristles are likely to be sexually monomorphic and data for males and females was pooled for further analyses. Overall, rictal bristles were absent in 64% of species examined (n = 656) and just over a third of species (n = 366) had bristles present.Bristle descriptionsFacial bristles were initially identified by sight and touch in each specimen. Bristles were recorded as either present or absent from the upper rictal, lorial, lower rictal, narial and interramal regions (Fig. 1a). We use the term ‘rictal bristle’ here for bristles on both the upper rictal and/or the lorial region, since there was no clear differentiation and morphological differences between the bristles found in these regions forming a continuum of bristles above the edge of the beak. When present, rictal bristle shape was recorded as: (i) unbranched rictal bristles, (ii) rictal bristles with barbs only at the base (“Base”) and (iii) branched rictal bristles (“Branched”), i.e. barbs and barbules present along the bristle rachis (Fig. 1b). The three longest rictal bristles were measured on both sides of the head of each specimen using digital callipers, and these lengths were averaged to provide a mean length of rictal bristles per species. In species lacking rictal bristles, a length of “0” and a shape category of “Absent” was recorded.Ancestral reconstruction of facial bristle presenceFollowing Felice et al.19, a single consensus phylogenetic tree was generated from the Hackett posterior distribution of trees from Birdtree.org20 with a sample size of 10,000 post burn-in, using the TreeAnnotator utility in BEAST software21 with a burn-in of 0. Maximum Clade Credibility (MCC) with the option “-heights ca” was selected as the method of reconstruction. The common ancestor trees option (-heights ca) builds a consensus tree by summarising clade ages across all posterior trees. Both the consensus tree and posterior distribution of 10,000 trees were imported into RStudio v. 1.2.5 for R22,23 and pruned so that only species present in the dataset of this study remained in the phylogeny. Taxon names were modified where necessary to match those from the Birdtree.org (http://birdtree.org) species record. Negative terminal branches in our consensus tree were slightly lengthened to be positive using ‘edge.length[tree$edge.length  More

Pristine ecosystems such as mangrove forests protect against the effects of climate change.Credit: Karine Aigner/Nature Picture Library

The Paris climate agreement, signed in December 2015, ranks as one of the most momentous global treaties ever negotiated, setting a crucial goal to seek to limit warming to 1.5–2 °C above pre-industrial levels. At the time, the opening ceremony of the COP21 climate-change conference that led to the agreement also held the record for the largest number of world leaders ever to attend a United Nations event in a single day — more than 150. The two things are probably more than coincidence.Now biodiversity is hoping for its Paris moment. The long-delayed COP15 conference, starting on 7 December in Montreal, Canada, aims to seal a bold new international deal committing countries to precise targets to curb species loss and to protect and restore nature.Many factors suggest the time is ripe. The problem of biodiversity loss is more prominent than ever before. As ecologist Sandra Díaz wrote in Nature last week, researchers have assembled the strongest evidence base yet ahead of COP15, the Fifteenth Conference of the Parties to the Convention on Biological Diversity (S. Díaz Nature 612, 9; 2022). Initiatives such as the Dasgupta Review, commissioned by the UK government, have made plain that the protection of biodiversity is an economic necessity.
COP15 biodiversity plan risks being alarmingly diluted
There is also much greater public awareness of how pollution and habitat destruction threaten the health of ecosystems on which we depend for food, clean water and disease prevention, and a better understanding of nature’s crucial role in mitigating climate change — for example, by storing carbon in soils and trees — as well as in helping us to adapt to its impacts. Mangrove forests, for instance, are hugely effective in stopping influxes of seawater from tsunamis and sea-level rise.But when it comes to getting stalled negotiations motoring again, the scale of support by world leaders that was a feature of climate’s road to Paris is currently lacking.Change cannot come too soon. Nature is on the brink. Of 20 decadal targets to preserve nature that were set in Aichi, Japan, in 2010, not a single one had been fully met by 2020. That, coupled with underfunding and lack of regard for the rights of Indigenous peoples who steward much of the world’s remaining biodiversity, means more species than ever are at risk of extinction. Serious impacts on human wealth and health from biodiversity loss loom ever larger. Yet over the past three years, four difficult rounds of negotiations aiming to agree on a framework to replace Aichi have not yielded results. Hundreds of issues remain unresolved.
COVID delays are frustrating the world’s plans to save biodiversity
Many experts worry that the lacklustre progress made at COP27, the climate summit held last month in Sharm El-Sheikh, Egypt, augur badly for the biodiversity meeting. But there is also reason for hope. The agreement made at COP27 to establish a ‘loss and damage’ fund to compensate low- and middle-income countries (LMICs) for climate impacts indicates that richer nations are open to talking about funding, which has also been a major sticking point in biodiversity negotiations.Global funding for biodiversity is severely in the red. A UN estimate published last week suggests that only US$154 billion per year flows to ‘nature-based solutions’ from all sources, including government aid and private investment — a number the UN says needs to triple by 2030. Many LMICs — which are home to much of the world’s remaining biodiversity — would like rich nations to put fresh finance into a new multilateral fund. One option is that such a fund could compensate LMICs for bio-diversity loss and associated damages driven by the consumption of products in rich nations through international trade.A second major sticking point is how to fairly and equitably share the benefits of digital sequence information — genetic data collected from plants, animals and other organisms. Communities in biodiversity-rich regions where genetic material is collected have little control over the commercialization of the data, and no way to recoup financial or other benefits. A multipurpose fund for bio-diversity could provide a simple and effective way to share the benefits of these data and support other conservation needs of LMICs.Another reason to hope for a breakthrough is the forthcoming change in Brazil’s leadership. Conservation organizations such as the wildlife charity WWF have accused the world’s most biodiverse nation of deliberately obstructing previous negotiations, holding up agreement on targets such as protecting at least 30% of the world’s land and seas by 2030. But Brazil’s incoming president, Luiz Inácio Lula da Silva, has signalled that the environment is one of his top priorities. Although he does not take over until January 2023, he is thought to be sending an interim team of negotiators to Montreal.
Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
All negotiators face a Herculean task to get a deal over the line at COP15, with many issues in the text still unresolved and contested. What’s needed above all is global leadership to empower national negotiators to reach a strong deal, including a new fund of some kind for biodiversity. More than 90 heads of state and heads of government have signed a pledge to tackle the nature crisis. At the time of writing, only Justin Trudeau, the host nation’s prime minster, has confirmed that he is to attend in person.The no-shows send the wrong signal. It’s also true at the time of writing that neither Canada nor China — the original intended host of COP15 and still the meeting’s chair — has issued formal invitations. But leaders have regularly attended climate COPs for more than a decade. This shows in the ambition of climate agreements, if not in their implementation. Research communities and civil society must continue to pressure leaders to engage similarly with the biodiversity agenda. Otherwise, the world risks failing to grasp this opportunity to secure the kind of ambitious deal that nature — and humanity — desperately needs. More

Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).Article 

Google Scholar 
Frieler, K. et al. Understanding the weather signal in national crop-yield variability. Earths Future 5, 605–616 (2017).Article 

Google Scholar 
Vogel, E. et al. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010 (2019).Article 

Google Scholar 
Zscheischler, J. et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 1, 333–347 (2020).Article 

Google Scholar 
Ridder, N. N., Ukkola, A. M., Pitman, A. J. & Perkins-Kirkpatrick, S. E. Increased occurrence of high impact compound events under climate change. npj Clim. Atmos. Sci. 5, 3 (2022).Article 

Google Scholar 
Lesk, C. & Anderson, W. Decadal variability modulates trends in concurrent heat and drought over global croplands. Environ. Res. Lett. 16, 055024 (2021).Article 

Google Scholar 
Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D. & Diffenbaugh, N. S. Multidimensional risk in a nonstationary climate: joint probability of increasingly severe warm and dry conditions. Sci. Adv. 4, eaau3487 (2018).Article 

Google Scholar 
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).Article 

Google Scholar 
Lobell, D. B., Bänziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45 (2011).Article 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).Article 

Google Scholar 
Buckley, T. N. How do stomata respond to water status? New Phytol. 224, 21–36 (2019).Article 

Google Scholar 
Miralles, D. G., Gentine, P., Seneviratne, S. I. & Teuling, A. J. Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. NY Acad. Sci. 1436, 19–35 (2019).Article 

Google Scholar 
Mueller, B. & Seneviratne, S. I. Hot days induced by precipitation deficits at the global scale. Proc. Natl Acad. Sci. USA 109, 12398–12403 (2012).Article 

Google Scholar 
Cohen, I., Zandalinas, S. I., Huck, C., Fritschi, F. B. & Mittler, R. Meta-analysis of drought and heat stress combination impact on crop yield and yield components. Physiol. Plant 171, 66–76 (2021).Article 

Google Scholar 
Ostmeyer, T. et al. Impacts of heat, drought, and their interaction with nutrients on physiology, grain yield, and quality in field crops. Plant Physiol. Rep. 25, 549–568 (2020).Article 

Google Scholar 
Matiu, M., Ankerst, D. P. & Menzel, A. Interactions between temperature and drought in global and regional crop yield variability during 1961–2014. PLoS ONE 12, e0178339 (2017).Article 

Google Scholar 
Scheff, J., Mankin, J. S., Coats, S. & Liu, H. CO2-plant effects do not account for the gap between dryness indices and projected dryness impacts in CMIP6 or CMIP5. Environ. Res. Lett. 16, 034018 (2021).Article 

Google Scholar 
Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G. & Pitman, A. J. Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophys. Res. Lett. 47, e2020GL087820 (2020).Article 

Google Scholar 
Allan, R. P. et al. Advances in understanding large-scale responses of the water cycle to climate change. Ann. NY Acad. Sci. 1472, 49–75 (2020).Article 

Google Scholar 
Ault, T. R. On the essentials of drought in a changing climate. Science 368, 256–260 (2020).Article 

Google Scholar 
Fowler, H. J. et al. Anthropogenic intensification of short-duration rainfall extremes. Nat. Rev. Earth Environ. 2, 107–122 (2021).Article 

Google Scholar 
Raymond, C. et al. Understanding and managing connected extreme events. Nat. Clim. Change 10, 611–621 (2020).Article 

Google Scholar 
Mills, G. et al. Closing the global ozone yield gap: quantification and cobenefits for multistress tolerance. Glob. Chang. Biol. 24, 4869–4893 (2018).Article 

Google Scholar 
Pandey, P., Irulappan, V., Bagavathiannan, M. V. & Senthil-Kumar, M. Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front. Plant Sci. 8, 537 (2017).Article 

Google Scholar 
Couasnon, A. et al. Measuring compound flood potential from river discharge and storm surge extremes at the global scale. Nat. Hazards Earth Syst. Sci. 20, 489–504 (2020).Article 

Google Scholar 
Nguyen, L. T. T. et al. Flooding and prolonged drought have differential legacy impacts on soil nitrogen cycling, microbial communities and plant productivity. Plant Soil 431, 371–387 (2018).Article 

Google Scholar 
Medrano, H., Escalona, J. M., Bota, J., Gulías, J. & Flexas, J. Regulation of photosynthesis of C3 plants in response to progressive drought: stomatal conductance as a reference parameter. Ann. Bot. 89, 895–905 (2002).Article 

Google Scholar 
Scafaro, A. P. et al. Responses of leaf respiration to heatwaves. Plant Cell Environ. 44, 2090–2101 (2021).Article 

Google Scholar 
Atkin, O. K. & Tjoelker, M. G. Thermal acclimation and the dynamic response of plant respiration to temperature. Trends Plant Sci. 8, 343–351 (2003).Article 

Google Scholar 
Lukac, M., Gooding, M. J., Griffiths, S. & Jones, H. E. Asynchronous flowering and within-plant flowering diversity in wheat and the implications for crop resilience to heat. Ann. Bot. 109, 843–850 (2012).Article 

Google Scholar 
Coast, O., Murdoch, A. J., Ellis, R. H., Hay, F. R. & Jagadish, K. S. V. Resilience of rice (Oryza spp.) pollen germination and tube growth to temperature stress. Plant. Cell Environ. 39, 26–37 (2016).Article 

Google Scholar 
Li, Y., Guan, K., Schnitkey, G. D., Delucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Chang. Biol. https://doi.org/10.1111/gcb.14628 (2019).Article 

Google Scholar 
Tian, L. X. et al. How does the waterlogging regime affect crop yield? A global meta-analysis. Front. Plant Sci. 12, 634898 (2021).Article 

Google Scholar 
Langan, P. et al. Phenotyping for waterlogging tolerance in crops: current trends and future prospects. J. Exp. Bot. https://doi.org/10.1093/jxb/erac243 (2022).Article 

Google Scholar 
Tong, C. et al. Opportunities for improving waterlogging tolerance in cereal crops — physiological traits and genetic mechanisms. Plants 10, 1560 (2021).Article 

Google Scholar 
Colmer, T. D., Cox, M. C. H. & Voesenek, L. A. C. J. Root aeration in rice (Oryza sativa): evaluation of oxygen, carbon dioxide, and ethylene as possible regulators of root acclimatizations. New Phytol. 170, 767–778 (2006).Article 

Google Scholar 
Hattori, Y. et al. The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature 460, 1026–1030 (2009).Article 

Google Scholar 
Prasad, P. V. V., Pisipati, S. R., Momčilović, I. & Ristic, Z. Independent and combined effects of high temperature and drought stress during grain filling on plant yield and chloroplast EF-Tu expression in spring wheat. J. Agron. Crop Sci. 197, 430–441 (2011).Article 

Google Scholar 
Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E. & Mittler, R. Abiotic and biotic stress combinations. New Phytol. 203, 32–43 (2014).Article 

Google Scholar 
Hussain, H. A. et al. Interactive effects of drought and heat stresses on morpho-physiological attributes, yield, nutrient uptake and oxidative status in maize hybrids. Sci. Rep. 9, 3890 (2019).Article 

Google Scholar 
Mittler, R. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 11, 15–19 (2006).Article 

Google Scholar 
Choudhury, F. K., Rivero, R. M., Blumwald, E. & Mittler, R. Reactive oxygen species, abiotic stress and stress combination. Plant J. 90, 856–867 (2017).Article 

Google Scholar 
Van Der Wiel, K., Selten, F. M., Bintanja, R., Blackport, R. & Screen, J. A. Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions. Environ. Res. Lett. 15, 034050 (2020).Article 

Google Scholar 
Moore, C. E. et al. The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems. J. Exp. Bot. 72, 2822–2844 (2021).Article 

Google Scholar 
Fahad, S. et al. Crop production under drought and heat stress: plant responses and management options. Front. Plant Sci. 8, 1147 (2017).Article 

Google Scholar 
Zandalinas, S. I., Fritschi, F. B. & Mittler, R. Signal transduction networks during stress combination. J. Exp. Bot. 71, 1734–1741 (2020).Article 

Google Scholar 
Zhang, H. & Sonnewald, U. Differences and commonalities of plant responses to single and combined stresses. Plant J. 90, 839–855 (2017).Article 

Google Scholar 
Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017).Article 

Google Scholar 
Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E. & Raymond, C. A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep. 2, 242–259 (2016).Article 

Google Scholar 
Trenberth, K. E. & Shea, D. J. Relationships between precipitation and surface temperature. Geophys. Res. Lett. 32, L14703 (2005).Article 

Google Scholar 
Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & De Arellano, J. V. G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).Article 

Google Scholar 
Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).Article 

Google Scholar 
Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).Article 

Google Scholar 
Koster, R. D., Chang, Y., Wang, H. & Schubert, S. D. Impacts of local soil moisture anomalies on the atmospheric circulation and on remote surface meteorological fields during boreal summer: a comprehensive analysis over North America. J. Clim. 29, 7345–7364 (2016).Article 

Google Scholar 
Zhou, S. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Change 11, 38–44 (2021).Article 

Google Scholar 
Berg, A., Lintner, B., Findell, K. & Giannini, A. Soil moisture influence on seasonality and large-scale circulation in simulations of the West African monsoon. J. Clim. 30, 2295–2317 (2017).Article 

Google Scholar 
Lesk, C. et al. Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields. Nat. Food 2, 683–691 (2021).Article 

Google Scholar 
Wei, Z. et al. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophys. Res. Lett. 44, 2792–2801 (2017).Article 

Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

Google Scholar 
Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).Article 

Google Scholar 
Teuling, A. J. et al. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci. 3, 722–727 (2010).Article 

Google Scholar 
Raymond, C. et al. Increasing spatiotemporal proximity of heat and precipitation extremes in a warming world quantified by a large model ensemble. Environ. Res. Lett. 17, 035005 (2022).Article 

Google Scholar 
Raymond, C. et al. On the controlling factors for globally extreme humid heat. Geophys. Res. Lett. 48, e2021GL096082 (2021).Article 

Google Scholar 
Speizer, S., Raymond, C., Ivanovich, C. & Horton, R. M. Concentrated and intensifying humid heat extremes in the IPCC AR6 regions. Geophys. Res. Lett. 49, e2021GL097261 (2022).Article 

Google Scholar 
Ning, G. et al. Rising risks of compound extreme heat‐precipitation events in China. Int. J. Climatol. https://doi.org/10.1002/joc.7561 (2022).Article 

Google Scholar 
Thiery, W. et al. Warming of hot extremes alleviated by expanding irrigation. Nat. Commun. 11, 290 (2020).Article 

Google Scholar 
Mueller, N. D. et al. Global relationships between cropland intensification and summer temperature extremes over the last 50 years. J. Clim. 30, 7505–7528 (2017).Article 

Google Scholar 
Siebert, S., Ewert, F., Eyshi Rezaei, E., Kage, H. & Graß, R. Impact of heat stress on crop yield — on the importance of considering canopy temperature. Environ. Res. Lett. 9, 044012 (2014).Article 

Google Scholar 
Singh, D. et al. Distinct influences of land cover and land management on seasonal climate. J. Geophys. Res. Atmos. 123, 12017–12039 (2018).Article 

Google Scholar 
Luan, X. & Vico, G. Canopy temperature and heat stress are increased by compound high air temperature and water stress and reduced by irrigation — a modeling analysis. Hydrol. Earth Syst. Sci. 25, 1411–1423 (2021).Article 

Google Scholar 
Siebert, S., Webber, H., Zhao, G. & Ewert, F. Heat stress is overestimated in climate impact studies for irrigated agriculture. Environ. Res. Lett. 12, 054023 (2017).Article 

Google Scholar 
Sinha, R. et al. Differential regulation of flower transpiration during abiotic stress in annual plants. New Phytol. https://doi.org/10.1111/nph.18162 (2022).Article 

Google Scholar 
He, Y., Lee, E. & Mankin, J. S. Seasonal tropospheric cooling in Northeast China associated with cropland expansion. Environ. Res. Lett. 15, 034032 (2020).Article 

Google Scholar 
Alter, R. E., Douglas, H. C., Winter, J. M. & Eltahir, E. A. B. Twentieth century regional climate change during the summer in the Central United States attributed to agricultural intensification. Geophys. Res. Lett. 45, 1586–1594 (2018).Article 

Google Scholar 
Sánchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: a review. Glob. Chang. Biol. 20, 408–417 (2014).Article 

Google Scholar 
Prasad, P. V. V., Bheemanahalli, R. & Jagadish, S. V. K. Field crops and the fear of heat stress — opportunities, challenges and future directions. Field Crops Res. 200, 114–121 (2017).Article 

Google Scholar 
Schauberger, B. et al. Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun. 8, 13931 (2017).Article 

Google Scholar 
Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).Article 

Google Scholar 
Sadok, W. & Jagadish, S. V. K. The hidden costs of nighttime warming on yields. Trends Plant Sci. 25, 644–651 (2020).Article 

Google Scholar 
Troy, T. J., Kipgen, C. & Pal, I. The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10, 054013 (2015).Article 

Google Scholar 
Cook, B. I., Shukla, S. P., Puma, M. J. & Nazarenko, L. S. Irrigation as an historical climate forcing. Clim. Dyn. 44, 1715–1730 (2015).Article 

Google Scholar 
Li, Y. et al. Quantifying irrigation cooling benefits to maize yield in the US Midwest. Glob. Chang. Biol. 26, 3065–3078 (2020).Article 

Google Scholar 
Entekhabi, B. D. et al. The Soil Moisture Active Passive (SMAP). IEEE Proc. 98, 704–716 (2010).Article 

Google Scholar 
Ortiz-Bobea, A., Wang, H., Carrillo, C. M. & Ault, T. R. Unpacking the climatic drivers of US agricultural yields. Environ. Res. Lett. 14, 064003 (2019).Article 

Google Scholar 
Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).Article 

Google Scholar 
Proctor, J., Rigden, A., Chan, D. & Huybers, P. Accurate specification of water availability shows its importance for global crop production. Preprint at EarthArXiv https://doi.org/10.31223/X5ZS7P (2021).Article 

Google Scholar 
Carter, E. K., Melkonian, J., Riha, S. J. & Shaw, S. B. Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize. Environ. Res. Lett. 11, 094012 (2016).Article 

Google Scholar 
Hamed, R., Van Loon, A. F., Aerts, J. & Coumou, D. Impacts of compound hot-dry extremes on US soybean yields. Earth Syst. Dyn. 12, 1371–1391 (2021).Article 

Google Scholar 
Feng, S., Hao, Z., Zhang, X. & Hao, F. Probabilistic evaluation of the impact of compound dry-hot events on global maize yields. Sci. Total Environ. 689, 1228–1234 (2019).Article 

Google Scholar 
Haqiqi, I., Grogan, D. S., Hertel, T. W. & Schlenker, W. Quantifying the impacts of compound extremes on agriculture. Hydrol. Earth Syst. Sci. 25, 551–564 (2021).Article 

Google Scholar 
Zhu, P., Zhuang, Q., Archontoulis, S. V., Bernacchi, C. & Müller, C. Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration. Glob. Chang. Biol. 25, 2470–2484 (2019).Article 

Google Scholar 
Jin, Z. et al. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Chang. Biol. 22, 3112–3126 (2016).Article 

Google Scholar 
Filipa Silva Ribeiro, A., Russo, A., Gouveia, C. M., Páscoa, P. & Zscheischler, J. Risk of crop failure due to compound dry and hot extremes estimated with nested copulas. Biogeosciences 17, 4815–4830 (2020).Article 

Google Scholar 
Hsiao, J., Swann, A. L. S. & Kim, S. H. Maize yield under a changing climate: the hidden role of vapor pressure deficit. Agric. For. Meteorol. 279, 107692 (2019).Article 

Google Scholar 
Heinicke, S., Frieler, K., Jägermeyr, J. & Mengel, M. Global gridded crop models underestimate yield responses to droughts and heatwaves. Environ. Res. Lett. 17, 044026 (2022).Article 

Google Scholar 
Cook, B. I. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earths Future 8, e2019EF001461 (2020).Article 

Google Scholar 
He, Y., Hu, X., Xu, W., Fang, J. & Shi, P. Increased probability and severity of compound dry and hot growing seasons over world’s major croplands. Sci. Total Environ. 824, 153885 (2022).Article 

Google Scholar 
Wu, Y. et al. Global observations and CMIP6 simulations of compound extremes of monthly temperature and precipitation. GeoHealth 5, e2021GH000390 (2021).Article 

Google Scholar 
Zhang, Y., Hao, Z., Zhang, X. & Hao, F. Anthropogenically forced increases in compound dry and hot events at the global and continental scales. Environ. Res. Lett. 17, 024018 (2022).Article 

Google Scholar 
Chen, Y., Liao, Z., Shi, Y., Tian, Y. & Zhai, P. Detectable increases in sequential flood-heatwave events across China during 1961–2018. Geophys. Res. Lett. 48, e2021GL092549 (2021).
Google Scholar 
Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6, eaaw1838 (2020).Article 

Google Scholar 
Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture–temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).Article 

Google Scholar 
Garcia-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J. & Fischer, E. M. A review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Technol. 40, 267–306 (2010).Article 

Google Scholar 
Wegren, S. Food security and Russia’s 2010 drought. Eurasian Geogr. Econ. 52, 140–156 (2011).Article 

Google Scholar 
Christian, J. I., Basara, J. B., Hunt, E. D., Otkin, J. A. & Xiao, X. Flash drought development and cascading impacts associated with the 2010 Russian heatwave. Environ. Res. Lett. 15, 094078 (2020).Article 

Google Scholar 
Glotter, M. & Elliott, J. Simulating US agriculture in a modern Dust Bowl drought. Nat. Plants 3, 16193 (2016).Article 

Google Scholar 
Yuan, X., Wang, L. & Wood, E. F. Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bull. Am. Meteorol. Soc. 99, S86–S90 (2018).Article 

Google Scholar 
Ben-Ari, T. et al. Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France. Nat. Commun. 9, 1627 (2018).Article 

Google Scholar 
Gampe, D. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change 11, 772–779 (2021).Article 

Google Scholar 
Iizumi, T. & Ramankutty, N. Changes in yield variability of major crops for 1981–2010 explained by climate change. Environ. Res. Lett. 11, 034003 (2016).Article 

Google Scholar 
Brás, T. A., Seixas, J., Carvalhais, N. & Jagermeyr, J. Severity of drought and heatwave crop losses tripled over the last five decades in Europe. Environ. Res. Lett. 16, 065012 (2021).Article 

Google Scholar 
Lobell, D. B., Deines, J. M. & Di Tommaso, S. Changes in the drought sensitivity of US maize yields. Nat. Food 1, 729–735 (2020).Article 

Google Scholar 
Seneviratne, S. I. et al. Climate extremes, land–climate feedbacks and land-use forcing at 1.5 °C. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20160450 (2018).Article 

Google Scholar 
Pfleiderer, P., Schleussner, C. F., Kornhuber, K. & Coumou, D. Summer weather becomes more persistent in a 2 °C world. Nat. Clim. Change 9, 666–671 (2019).Article 

Google Scholar 
Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).Article 

Google Scholar 
Dai, A., Zhao, T. & Chen, J. Climate change and drought: a precipitation and evaporation perspective. Curr. Clim. Chang. Rep. 4, 301–312 (2018).Article 

Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).Article 

Google Scholar 
Lesk, C., Coffel, E. & Horton, R. Net benefits to US soy and maize yields from intensifying hourly rainfall. Nat. Clim. Change 10, 819–822 (2020).Article 

Google Scholar 
Goulart, H. M. D., Van Der Wiel, K., Folberth, C., Balkovic, J. & Van Den Hurk, B. Weather-induced crop failure events under climate change: a storyline approach. Earth Syst. Dyn. https://doi.org/10.5194/esd-2021-40 (2021).Article 

Google Scholar 
Franke, J. A. et al. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change. Glob. Chang. Biol. 28, 167–181 (2022).Article 

Google Scholar 
Jägermeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873–885 (2021).Article 

Google Scholar 
Waha, K. et al. Multiple cropping systems of the world and the potential for increasing cropping intensity. Glob. Environ. Chang. 64, 102131 (2020).Article 

Google Scholar 
Zhu, T., Fonseca De Lima, C. F. & De Smet, I. The heat is on: how crop growth, development, and yield respond to high temperature. J. Exp. Bot. 72, 7359–7373 (2021).
Google Scholar 
Lizaso, J. I. et al. Impact of high temperatures in maize: phenology and yield components. Field Crops Res. 216, 129–140 (2018).Article 

Google Scholar 
Rezaei, E. E., Siebert, S. & Ewert, F. Intensity of heat stress in winter wheat — phenology compensates for the adverse effect of global warming. Environ. Res. Lett. 10, 024012 (2015).Article 

Google Scholar 
Liu, K. et al. Climate change shifts forward flowering and reduces crop waterlogging stress. Environ. Res. Lett. 16, 094017 (2021).Article 

Google Scholar 
Bagley, J. et al. The influence of photosynthetic acclimation to rising CO2 and warmer temperatures on leaf and canopy photosynthesis models. Global Biogeochem. Cycles https://doi.org/10.1002/2014GB004848 (2015).Article 

Google Scholar 
Hossain, M. A. et al. Heat or cold priming-induced cross-tolerance to abiotic stresses in plants: key regulators and possible mechanisms. Protoplasma 255, 399–412 (2018).Article 

Google Scholar 
Wolz, K. J., Wertin, T. M., Abordo, M., Wang, D. & Leakey, A. D. B. Diversity in stomatal function is integral to modelling plant carbon and water fluxes. Nat. Ecol. Evol. 1, 1292–1298 (2017).Article 

Google Scholar 
Ainsworth, E. A. & Long, S. P. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Glob. Chang. Biol. 27, 27–49 (2021).Article 

Google Scholar 
Toreti, A. et al. Narrowing uncertainties in the effects of elevated CO2 on crops. Nat. Food 1, 775–782 (2020).Article 

Google Scholar 
Myers, S. S. et al. Climate change and global food systems: potential impacts on food security and undernutrition. Annu. Rev. Public Health 38, 259–277 (2017).Article 

Google Scholar 
Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).Article 

Google Scholar 
Houshmandfar, A., Fitzgerald, G. J., Armstrong, R., Macabuhay, A. A. & Tausz, M. Modelling stomatal conductance of wheat: an assessment of response relationships under elevated CO2. Agric. For. Meteorol. 214–215, 117–123 (2015).Article 

Google Scholar 
Chavan, S. G., Duursma, R. A., Tausz, M. & Ghannoum, O. Elevated CO2 alleviates the negative impact of heat stress on wheat physiology but not on grain yield. J. Exp. Bot. 70, 6447–6459 (2019).Article 

Google Scholar 
Gray, S. B. et al. Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean. Nat. Plants 2, 16132 (2016).Article 

Google Scholar 
Coffel, E. D. et al. Future hot and dry years worsen Nile basin water scarcity despite projected precipitation increases. Earths Future 7, 967–977 (2019).Article 

Google Scholar 
Mishra, V., Thirumalai, K., Singh, D. & Aadhar, S. Future exacerbation of hot and dry summer monsoon extremes in India. npj Clim. Atmos. Sci. 3, 10 (2020).Article 

Google Scholar 
Bevacqua, E., Zappa, G., Lehner, F. & Zscheischler, J. Precipitation trends determine future occurrences of compound hot–dry events. Nat. Clim. Change 12, 350–355 (2022).Article 

Google Scholar 
Seager, R. et al. Climate variability and change of Mediterranean-type climates. J. Clim. 32, 2887–2915 (2019).Article 

Google Scholar 
Vogel, M. M., Hauser, M. & Seneviratne, S. I. Projected changes in hot, dry and wet extreme events’ clusters in CMIP6 multi-model ensemble. Environ. Res. Lett. 15, 094021 (2020).Article 

Google Scholar 
Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).Article 

Google Scholar 
Byrne, M. P. Amplified warming of extreme temperatures over tropical land. Nat. Geosci. 14, 837–841 (2021).Article 

Google Scholar 
McDermid, S. S. et al. Disentangling the regional climate impacts of competing vegetation responses to elevated atmospheric CO2. J. Geophys. Res. Atmos. 126, e2020JD034108 (2021).Article 

Google Scholar 
Swann, A. L. S., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).Article 

Google Scholar 
Ali, H., Fowler, H. J., Lenderink, G., Lewis, E. & Pritchard, D. Consistent large-scale response of hourly extreme precipitation to temperature variation over land. Geophys. Res. Lett. https://doi.org/10.1029/2020GL090317 (2021).Article 

Google Scholar 
Dai, A., Rasmussen, R. M., Liu, C., Ikeda, K. & Prein, A. F. A new mechanism for warm-season precipitation response to global warming based on convection-permitting simulations. Clim. Dyn. 55, 343–368 (2020).Article 

Google Scholar 
Fishman, R. More uneven distributions overturn benefits of higher precipitation for crop yields. Environ. Res. Lett. 11, 024004 (2016).Article 

Google Scholar 
Shortridge, J. Observed trends in daily rainfall variability result in more severe climate change impacts to agriculture. Clim. Chang. 157, 429–444 (2019).Article 

Google Scholar 
Guan, K., Sultan, B., Biasutti, M., Baron, C. & Lobell, D. B. What aspects of future rainfall changes matter for crop yields in West Africa? Geophys. Res. Lett. 42, 8001–8010 (2015).Article 

Google Scholar 
Byrne, M. P. & O’Gorman, P. A. Trends in continental temperature and humidity directly linked to ocean warming. Proc. Natl Acad. Sci. USA 115, 4863–4868 (2018).Article 

Google Scholar 
Coffel, E. D., Horton, R. M. & De Sherbinin, A. Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century. Environ. Res. Lett. 13, 014001 (2018).Article 

Google Scholar 
Matthews, T. Humid heat and climate change. Prog. Phys. Geogr. 42, 391–405 (2018).Article 

Google Scholar 
McKinnon, K. A. & Poppick, A. Estimating changes in the observed relationship between humidity and temperature using noncrossing quantile smoothing splines. J. Agric. Biol. Environ. Stat. 25, 292–314 (2020).Article 

Google Scholar 
Parsons, L. A. et al. Global labor loss due to humid heat exposure underestimated for outdoor workers. Environ. Res. Lett. 17, 014050 (2022).Article 

Google Scholar 
Ridder, N. N., Pitman, A. J. & Ukkola, A. M. Do CMIP6 climate models simulate global or regional compound events skillfully? Geophys. Res. Lett. 48, e2020GL091152 (2021).Article 

Google Scholar 
Hao, Z., Aghakouchak, A. & Phillips, T. J. Changes in concurrent monthly precipitation and temperature extremes. Environ. Res. Lett. 8, 034014 (2013).Article 

Google Scholar 
Zhang, B. & Soden, B. J. Constraining climate model projections of regional precipitation change. Geophys. Res. Lett. 46, 10522–10531 (2019).Article 

Google Scholar 
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).Article 

Google Scholar 
Butler, E. E., Mueller, N. D. & Huybers, P. Peculiarly pleasant weather for US maize. Proc. Natl Acad. Sci. USA 115, 11935–11940 (2018).Article 

Google Scholar 
Lombardozzi, D. L. et al. Simulating agriculture in the Community Land Model Version 5. J. Geophys. Res. Biogeosci. 125, e2019JG005529 (2020).Article 

Google Scholar 
Puma, M. J. & Cook, B. I. Effects of irrigation on global climate during the 20th century. J. Geophys. Res. Atmos. 115, D16120 (2010).Article 

Google Scholar 
Coffel, E. D., Lesk, C., Winter, J. M., Osterberg, E. C. & Mankin, J. S. Crop–climate feedbacks boost US maize and soy yields. Environ. Res. Lett. 17, 024012 (2022).Article 

Google Scholar 
Mueller, N. D. et al. Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Change 6, 317–322 (2016).Article 

Google Scholar 
Zaveri, E. & B. Lobell, D. The role of irrigation in changing wheat yields and heat sensitivity in India. Nat. Commun. 10, 4144 (2019).Article 

Google Scholar 
DeLucia, E. H. et al. Are we approaching a water ceiling to maize yields in the United States? Ecosphere 10, e02773 (2019).Article 

Google Scholar 
Cook, B. I. et al. Divergent regional climate consequences of maintaining current irrigation rates in the 21st century. J. Geophys. Res. Atmos. 125, e2019JD031814 (2020).Article 

Google Scholar 
Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. USA 115, 6644–6649 (2018).Article 

Google Scholar 
Liu, W. et al. Future climate change significantly alters interannual wheat yield variability over half of harvested areas. Environ. Res. Lett. 16, 094045 (2021).Article 

Google Scholar 
Wang, X. et al. Global irrigation contribution to wheat and maize yield. Nat. Commun. 12, 1235 (2021).Article 

Google Scholar 
Rosa, L., Chiarelli, D. D., Rulli, M. C., Dell’Angelo, J. & D’Odorico, P. Global agricultural economic water scarcity. Sci. Adv. 6, eaaz6031 (2020).Article 

Google Scholar 
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).Article 

Google Scholar 
Livneh, B. & Badger, A. M. Drought less predictable under declining future snowpack. Nat. Clim. Change 10, 452–458 (2020).Article 

Google Scholar 
Elliott, J. et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl Acad. Sci. USA 111, 3239–3244 (2014).Article 

Google Scholar 
Jägermeyr, J. et al. Integrated crop water management might sustainably halve the global food gap. Environ. Res. Lett. 11, 025002 (2016).Article 

Google Scholar 
Rosa, L. et al. Potential for sustainable irrigation expansion in a 3 °C warmer climate. Proc. Natl Acad. Sci. USA 117, 29526–29534 (2020).Article 

Google Scholar 
Gleeson, T., Wada, Y., Bierkens, M. F. P. & Van Beek, L. P. H. Water balance of global aquifers revealed by groundwater footprint. Nature 488, 197–200 (2012).Article 

Google Scholar 
Bhattarai, N. et al. The impact of groundwater depletion on agricultural production in India. Environ. Res. Lett. 16, 085003 (2021).Article 

Google Scholar 
Nie, W. et al. Irrigation water demand sensitivity to climate variability across the contiguous United States. Water Resour. Res. 57, e2020WR027738 (2021).Article 

Google Scholar 
Wu, W.-Y. et al. Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers. Nat. Commun. 11, 3710 (2020).Article 

Google Scholar 
Jain, M. et al. Groundwater depletion will reduce cropping intensity in India. Sci. Adv. 7, eabd2849 (2021).Article 

Google Scholar 
Kerr, R. B., Hasegawa, T. & Lasco, R. Food, fibre and other ecosystem products. In IPCC WGII Sixth Assessment Report 11–13 Ch. 5 (IPCC, 2022).Zandalinas, S. I. & Mittler, R. Plant responses to multifactorial stress combination. New Phytol. 234, 1161–1167 (2022).Article 

Google Scholar 
Barrett, C. B. et al. Bundling innovations to transform agri-food systems. Nat. Sustain. 3, 974–976 (2020).Article 

Google Scholar 
Peng, B. & Guan, K. Harmonizing climate-smart and sustainable agriculture. Nat. Food 2, 853–854 (2021).Article 

Google Scholar 
Zabel, F. et al. Large potential for crop production adaptation depends on available future varieties. Glob. Chang. Biol. 27, 3870–3882 (2021).Article 

Google Scholar 
Challinor, A. J., Koehler, A.-K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).Article 

Google Scholar 
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).Article 

Google Scholar 
Vogel, E. & Meyer, R. Climate Change, Climate Extremes, and Global Food Production — Adaptation in the Agricultural Sector. Resilience: The Science of Adaptation to Climate Change (Elsevier Inc., 2018).Lal, R. Soil health and carbon management. Food Energy Secur. 5, 212–222 (2016).Article 

Google Scholar 
Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food 2, 54–65 (2021).Article 

Google Scholar 
Baldos, U. L. C. & Hertel, T. W. The role of international trade in managing food security risks from climate change. Food Secur. 7, 275–290 (2015).Article 

Google Scholar 
Deguines, N. et al. Large-scale trade-off between agricultural intensification and crop pollination services. Front. Ecol. Environ. 12, 212–217 (2014).Article 

Google Scholar 
Vyas, S., Dalhaus, T., Kropff, M., Aggarwal, P. & Meuwissen, M. P. M. Mapping global research on agricultural insurance. Environ. Res. Lett. 16, 103003 (2021).Article 

Google Scholar 
Hazell, P. & Varangis, P. Best practices for subsidizing agricultural insurance. Glob. Food Sec. 25, 100326 (2020).Article 

Google Scholar 
Funk, C. et al. Recognizing the famine early warning systems network over 30 years of drought early warning science advances and partnerships promoting global food security. Bull. Am. Meteorol. Soc. 100, 1011–1027 (2019).Article 

Google Scholar 
Reichstein, M., Riede, F. & Frank, D. More floods, fires and cyclones — plan for domino effects on sustainability goals. Nature 592, 347–349 (2021).Article 

Google Scholar 
Müller, C. et al. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environ. Res. Lett. 16, 034040 (2021).Article 

Google Scholar 
Hao, Z., Hao, F., Xia, Y., Singh, V. P. & Zhang, X. A monitoring and prediction system for compound dry and hot events. Environ. Res. Lett. 14, 114034 (2019).Article 

Google Scholar 
Benami, E. et al. Uniting remote sensing, crop modelling and economics for agricultural risk management. Nat. Rev. Earth Environ. 2, 140–159 (2021).Article 

Google Scholar 
Famine Early Warning System Network. East Africa seasonal monitor. FEWS https://fews.net/sites/default/files/documents/reports/EAST_AFRICA_Seasonal_Monitor_20_May_2022_1.pdf (2022).Becker-Reshef, I. et al. The GEOGLAM crop monitor for AMIS: assessing crop conditions in the context of global markets. Glob. Food Sec. 23, 173–181 (2019).Article 

Google Scholar 
GEOGLAM Crop Monitor. Special report: unprecedented 4th consecutive poor rainfall season for the Horn of Africa. Crop Monitor https://cropmonitor.org/documents/SPECIAL/reports/Special_Report_20220523_East_Africa.pdf (2022).Geange, S. R. et al. The thermal tolerance of photosynthetic tissues: a global systematic review and agenda for future research. New Phytol. 229, 2497–2513 (2021).Article 

Google Scholar 
Reynolds, M. P. et al. Harnessing translational research in wheat for climate resilience. J. Exp. Bot. 72, 5134–5157 (2021).Article 

Google Scholar 
Makondo, C. C. & Thomas, D. S. G. Climate change adaptation: linking indigenous knowledge with western science for effective adaptation. Environ. Sci. Policy 88, 83–91 (2018).Article 

Google Scholar 
Sharafi, L., Zarafshani, K., Keshavarz, M., Azadi, H. & Van Passel, S. Farmers’ decision to use drought early warning system in developing countries. Sci. Total Environ. 758, 142761 (2021).Article 

Google Scholar 
Fischer, K. Why new crop technology is not scale-neutral — A critique of the expectations for a crop-based African Green Revolution. Res. Policy 45, 1185–1194 (2016).Article 

Google Scholar 
Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).Article 

Google Scholar 
Glauber, J., Baldwin, K., Antón, J. & Ziebinska, U. Design principles for agricultural risk management policies. OECD Food Agric. Fish. Pap. https://doi.org/10.1787/1048819f-en (2021).Article 

Google Scholar 
Annan, F. & Schlenker, W. Federal crop insurance and the disincentive to adapt to extreme heat. Am. Econ. Rev. 105, 262–266 (2015).Article 

Google Scholar 
Deryugina, T. & Konar, M. Impacts of crop insurance on water withdrawals for irrigation. Adv. Water Resour. 110, 437–444 (2017).Article 

Google Scholar 
Agrimonti, C., Lauro, M. & Visioli, G. Smart agriculture for food quality: facing climate change in the 21st century. Crit. Rev. Food Sci. Nutr. 61, 971–981 (2021).Article 

Google Scholar 
Sloat, L. L. et al. Climate adaptation by crop migration. Nat. Commun. 11, 1243 (2020).Article 

Google Scholar 
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).Article 

Google Scholar 
Willmott, C. J. & Matsuura, K. Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950–1999). University of Delaware http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts.html (2000).Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations — the CRU TS3.10 dataset. Int. J. Clim. 34, 623–642 (2014).Article 

Google Scholar 
Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).Article 

Google Scholar 
Beyer, R. M., Hua, F., Martin, P. A., Manica, A. & Rademacher, T. Relocating croplands could drastically reduce the environmental impacts of global food production. Commun. Earth Environ. 3, 49 (2022).Article 

Google Scholar  More

Parties to the Convention on Biological Diversity will meet this month to finalize the post-2020 Global Biodiversity Framework and the text for the stand-alone target on gender equality (Target 22). This target aims to reshape conservation policy and practice to make them more inclusive, equitable and effective.
Competing Interests
The authors declare no competing interests. More

Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 
PubMed 

Google Scholar 
– Potts, S. G., et al. Summary for Policymakers of the Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination, and food production (eds. Potts, S. G. et al.). 36 pages. (Bonn, Germany, 2016).Dolezal, A. G. et al. Interacting stressors matter: Diet quality and virus infection in honeybee health. R. Soc. Open Sci. 6, 181803 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Annoscia, D. et al. Neonicotinoid Clothianidin reduces honeybee immune response and contributes to Varroa mite proliferation. Nat. Commun. 11, 1–7 (2020).Article 

Google Scholar 
Macías-Macías, J. O. et al. Nosema ceranae causes cellular immunosuppression and interacts with thiamethoxam to increase mortality in the stingless bee Melipona colimana. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Michener, C. D. Pot-honey. In Pot-Honey: A Legacy of Stingless Bees (eds Vit, P. et al.) 3–17 (Springer, 2013).Chapter 

Google Scholar 
Rosa, C. A. et al. Yeast communities associated with stingless bees. FEMS Yeast Res. 4, 271–275 (2003).Article 
PubMed 

Google Scholar 
Menezes, C., Vollet-Neto, A. & Fonseca, V. L. I. An advance in the in vitro rearing of stingless bee queens. Apidologie 44, 491–500 (2013).Article 

Google Scholar 
Morais, P. B., Calaça, P. S. S. T. & Rosa, C. A. Microorganisms associated with stingless bees. In Pot-Honey Bees (eds Vit, P. et al.) 173–186 (Springer, 2013).Chapter 

Google Scholar 
Menegatti, C. et al. Paenibacillus polymyxa associated with the stingless bee Melipona scutellaris produces antimicrobial compounds against entomopathogens. J. Chem. Ecol. 44, 1158–1169 (2018).Article 
PubMed 

Google Scholar 
Paludo, C. R. et al. Stingless bee larvae require fungal steroid to pupate. Sci. Rep. 8, 1122321 (2018).Article 

Google Scholar 
Paludo, C. R. et al. Microbial community modulates growth of symbiotic fungus required for stingless bee metamorphosis. PLoS ONE 14, e0219696 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hamzah, S. A., Zawawi, N. & Sabri, S. A review on the association of bacteria with stingless bees. Sains Malays. 49, 1853–1863 (2020).Article 

Google Scholar 
de Paula, G. T., Menezes, C., Pupo, M. T. & Rosa, C. A. Stingless bees and microbial interactions. Curr. Opin. Insect Sci. 44, 41–47 (2020).Article 
PubMed 

Google Scholar 
Menezes, C. et al. A Brazilian social bee must cultivate fungus to survive. Curr. Biol. 25, 2851–2855 (2015).Article 
PubMed 

Google Scholar 
– Flechtmann, C. H. W. & de Camargo, C. A. Acari associated with stingless bees (Meliponidae, Hymenoptera) from Brazil. in Proceedings of the 4th International Congress of Acarology, Saalfelden (Austria)/edited by Edward Piffl (Budapest, Akademiai Kiado,1979).Dorigo, A. S. et al. In vitro larval rearing protocol for the stingless bee species Melipona scutellaris for toxicological studies. PLoS ONE 14, e0213109 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Rosa-Fontana, A., Dorigo, A. S., Galaschi-Teixeira, J. S., Nocelli, R. C. F. & Malaspina, O. What is the most suitable native bee species from the neotropical region to be proposed as model-organism for toxicity tests during the larval phase?. Environ. Pollut. 265, 114849 (2020).Article 
PubMed 

Google Scholar 
Miotelo, L., Dos Reis, A. L. M., Malaquias, J. B., Malaspina, O. & Roat, T. C. Apis mellifera and Melipona scutellaris exhibit differential sensitivity to thiamethoxam. Environ. Pollut. 268, 115770 (2021).Article 
PubMed 

Google Scholar 
Rosa, A. E., André, H. & Flechtmann, C. H. W. Acari domun meliponirarum brasiliensium habitantes. Proctotydaeus alvearii 45(1–2), 79–83 (1985).
Google Scholar 
Da-Costa, T., dos Santos, C. F., Rodighero, L. F., Ferla, N. J. & Blochtein, B. Mite diversity is determined by the stingless bee host species. Apidologie 52(5), 950–959. https://doi.org/10.1007/s13592-021-00878-2 (2021).Article 

Google Scholar 
de Rosa, A. S. et al. Consumption of the neonicotinoid thiamethoxam during the larval stage affects the survival and development of the stingless bee Scaptotrigona aff. depilis. Apidologie 47, 729–738 (2016).Article 

Google Scholar 
Wu, J. Y., Anelli, C. M. & Sheppard, W. S. Sub-lethal effects of pesticide residues in brood comb on worker honeybee (Apis mellifera) development and longevity. PLoS One 6, e14720 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Tavares, D. A., Roat, T. C., Carvalho, S. M., Silva-Zacarin, E. C. M. & Malaspina, O. In vitro effects of thiamethoxam on larvae of Africanized honeybee Apis mellifera (Hymenoptera: Apidae). Chemosphere 135, 370–378 (2015).Article 
PubMed 

Google Scholar 
Biani, N. B., Mueller, U. G. & Wcislo, W. T. Cleaner mites: sanitary mutualism in the miniature ecosystem of neotropical bee nests. Am. Nat. 173, 841–847 (2009).Article 
PubMed 

Google Scholar 
Gilliam, M., Roubik, D. W. & Lorenz, B. J. Microorganisms associated with pollen, honey, and brood provisions in the nest of a stingless bee Melipona fasciata. Apidologie 21, 89–97 (1990).Article 

Google Scholar 
Rebelo, K. S., Ferreira, A. G. & Carvalho-Zilse, G. A. Physicochemical characteristics of pollen collected by Amazonian stingless bees. Ciência Rural 46, 927–932 (2016).Article 

Google Scholar 
Mohammad, S. M., Mahmud-Ab-Rashid, N.-K. & Zawawi, N. Stingless bee-collected pollen (bee bread): Chemical and microbiology properties and health benefits. Molecules 26, 957 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
da Cruz Landim, C. (2009). Abelhas. Unesp.Rosa, A. S. et al. Quantification of larval food and its pollen content in the diet of stingless bees: Subsidies for toxicity bioassays studies. Braz. J. Biol. 75(3), 771–772. https://doi.org/10.1590/1519-6984.22314 (2015).Article 
PubMed 

Google Scholar 
Vollet-Neto, A., Maia-Silva, C., Menezes, C. & Imperatriz-Fonseca, V. L. Newly emerged workers of the stingless bee Scaptotrigona aff. depilis prefer stored pollen to fresh pollen. Apidologie 48, 204–210 (2017).Article 

Google Scholar 
Hartfelder, K. & Engels, W. The composition of larval food in stingless bees: evaluating nutritional balance by chemosystematic methods. Insect. Soc. 36, 1–14 (1989).Article 

Google Scholar 
Costa, R. A. C. & da Cruz-Landim, C. Distribution of acid phosphatases in the hypopharyngeal glands from workers, queens, and males of a Brazilian stingless bee Scaptotrigona postica Latreille: An ultrastructural cytochemical study. Histochem. J. 33, 653–662 (2001).Article 
PubMed 

Google Scholar 
de Moraes, R. L. M. S., Brochetto-Braga, M. R. & Azevedo, A. Electrophoretical studies of proteins of the hypopharyngeal glands and of the larval food of Melipona quadrifasciata anthidioides Lep. (Hymenoptera, Meliponinae). Insect. Soc. 43, 183–188 (1996).Article 

Google Scholar 
Fernandes-da-Silva, P. G., Muccillo, G. & Zucoloto, F. S. Determination of minimum quantity of pollen and nutritive value of different carbohydrates for Scaptotrigona depilis Moure (Hymenoptera, Apidae). Apidologie 24, 73–79 (1993).Article 

Google Scholar 
Fernandes-da-Silva, P. G. & Serrão, J. E. Nutritive value and apparent digestibility of bee-collected and bee-stored pollen in the stingless bee, Scaptotrigona postica Latr. (Hymenoptera, Apidae, Meliponini). Apidologie 31, 39–45 (2000).Article 

Google Scholar 
Crailsheim, K. & Stolberg, E. Influence of diet, age and colony condition upon intestinal proteolytic activity and size of the hypopharyngeal glands in the honeybee (Apis mellifera L.). J. Insect Physiol. 35, 595–602 (1989).Article 

Google Scholar 
Oliveira, R. A., Roat, T. C., Carvalho, S. M. & Malaspina, O. Side-effects of thiamethoxam on the brain and midgut of the africanized honeybee Apis mellifera (Hymenopptera: Apidae). Environ. Toxicol. 29, 1122–1133 (2014).Article 
PubMed 

Google Scholar 
Christen, V., Schirrmann, M., Frey, J. E. & Fent, K. Global transcriptomic effects of environmentally relevant concentrations of the neonicotinoids clothianidin, imidacloprid, and thiamethoxam in the brain of honeybees (Apis mellifera). Environ. Sci. Technol. 52, 7534–7544 (2018).Article 
PubMed 

Google Scholar 
Moreira, D. R. et al. Toxicity and effects of the neonicotinoid thiamethoxam on Scaptotrigona bipunctata Lepeletier, 1836 (Hymenoptera: Apidae). Environ. Toxicol. 33, 463–475 (2018).Article 
PubMed 

Google Scholar 
Tavares, D. A., Roat, T. C., Silva-Zacarin, E. C. M., Nocelli, R. C. F. & Malaspina, O. Exposure to thiamethoxam during the larval phase affects synapsin levels in the brain of the honeybee. Ecotoxicol. Environ. Saf. 169, 523–528 (2019).Article 
PubMed 

Google Scholar 
Roat, T. C. et al. Using a toxicoproteomic approach to investigate the effects of thiamethoxam into the brain of Apis mellifera. Chemosphere 258, 127362 (2020).Article 
PubMed 

Google Scholar 
Caesar, L. et al. The virome of an endangered stingless bee suffering from annual mortality in southern Brazil. J. Gen. Virol. 100, 1153–1164 (2019).Article 
PubMed 

Google Scholar 
Guimarães-Cestaro, L. et al. Occurrence of virus, microsporidia, and pesticide residues in three species of stingless bees (Apidae: Meliponini) in the field. Sci. Nat. 107, 1–14 (2020).Article 

Google Scholar 
Teixeira, É. W. et al. European Foulbrood in stingless bees (Apidae: Meliponini) in Brazil: Old disease, renewed threat. J. Invertebr. Pathol. 172, 107357 (2020).Article 
PubMed 

Google Scholar 
Alberoni, D., Gaggìa, F., Baffoni, L. & Di Gioia, D. Beneficial microorganisms for honeybees: problems and progresses. Appl. Microbiol. Biotechnol. 100, 9469–9482 (2016).Article 
PubMed 

Google Scholar 
Manley, R., Boots, M. & Wilfert, L. Emerging viral disease risk to pollinating insects: ecological, evolutionary, and anthropogenic factors. J. Appl. Ecol. 52, 331–340 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Manley, R. et al. Knock- on community impacts of a novel vector: spillover of emerging DWV- B from Varroa- infested honeybees to wild bumblebees. Ecol. Lett. 22, 1306–1315 (2019).PubMed 
PubMed Central 

Google Scholar 
Graystock, P., Blane, E. J., McFrederick, Q. S., Goulson, D. & Hughes, W. O. H. Do managed bees drive parasite spread and emergence in wild bees?. Int. J. Parasitol. Parasit. Wildl. 5, 64–75 (2016).Article 

Google Scholar 
Requier, F. et al. The conservation of native honeybees is crucial. Trends Ecol. Evol. 34, 789–798 (2019).Article 
PubMed 

Google Scholar 
Test No. 237: Honey Bee (Apis Mellifera) Larval Toxicity Test, Single Exposure. (2013). OECD. https://doi.org/10.1787/9789264203723-enMoral, R. A., Hinde, J. & Demétrio, C. G. Half-normal plots and overdispersed models in R: the hnp package. J. Stat. Softw. 81(1), 1–23 (2017).
Google Scholar 
– Kassambara, A. Survminer. GitHub repository. https://github.com/kassambara/survminer (2020).- Therneau, T., Crowson, C., & Atkinson, E. Multi-state models and competing risks. CRAN-R https://cran.r-project.org/web/packages/survival/vignettes/compete (2020). More

Banerjee, A. K. et al. Setting the priorities straight-Species distribution models assist to prioritize conservation targets for the mangroves. Sci. Total Environ. 806, 150937 (2022).Article 
CAS 

Google Scholar 
Duke, N. C. et al. A world without mangroves?. Science 317(5834), 41–42 (2007).Article 
CAS 

Google Scholar 
Friess, D. A. Ecosystem services and disservices of mangrove forests: Insights from historical colonial observations. Forests 7(9), 183 (2016).Article 

Google Scholar 
Hu, W. et al. Mapping the potential of mangrove forest restoration based on species distribution models: A case study in China. Sci. Total Environ. 748, 142321 (2020).Article 
CAS 

Google Scholar 
Blankespoor, B., Dasgupta, S. & Lange, G. M. Mangroves as a protection from storm surges in a changing climate. Ambio 46(4), 478–491 (2017).Article 

Google Scholar 
FAO. TheWorld’s Mangroves 1980–2005. Food and Agriculture Organization of the United Nations, Rome. http://www.fao.org/3/a1427e/a1427e00.htm. (2007).Abd-El Monsef, H., Hassan, M. A. & Shata, S. Using spatial data analysis for delineating existing mangroves stands and siting suitable locations for mangroves plantation. Comput. Electron. Agric. 141, 310–326 (2017).Article 

Google Scholar 
Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297. https://doi.org/10.1038/ngeo1123 (2011).Article 
CAS 

Google Scholar 
Aheto, D. W. et al. Community-based mangrove forest management: Implications for local livelihoods and coastal resource conservation along the Volta estuary catchment area of Ghana. Ocean Coast. Manag. 127, 43–54 (2016).Article 

Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Chang. 26, 152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002 (2014).Article 

Google Scholar 
Stephanie, S. R. et al. Conservation and restoration of mangroves: Global status, perspectives, and prognosis. Ocean Coast. Manag. 154, 72–82. https://doi.org/10.1016/j.ocecoaman.2018.01.009 (2018).Article 

Google Scholar 
Friess, D. A. et al. Mangroves give cause for conservation optimism, for now. Curr. Biol. 30, R153–R154 (2020).Article 
CAS 

Google Scholar 
Valiela, I., Bowen, J. L. & York, J. K. Mangrove forests: One of the world’s threatened major tropical environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 51, 807–815. https://doi.org/10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2 (2001).Article 

Google Scholar 
Feller, I. C. et al. Biocomplexity in mangrove ecosystems. Ann. Rev. Mar. Sci. 2, 395–417 (2010).Article 
CAS 

Google Scholar 
Polidoro, B. A. et al. The loss of species: Mangrove extinction risk and geographic areas of global concern. PLoS ONE 5, e10095 (2010).Article 

Google Scholar 
IUCN. Global Assessments of Mangrove Losses and Degradation, 2016; https://www.iucn.org/sites/dev/files/content/documents/mangroveloss-brief-4pp-19.10.low_.pdf.Sreelekshmi, S., Nandan, S. B., Kaimal, S. V., Radhakrishnan, C. K. & Suresh, V. R. Mangrove species diversity, stand structure and zonation pattern in relation to environmental factors—a case study at Sundarban delta, east coast of India. Reg. Stud. Mar. Sci. 35, 101111 (2020).
Google Scholar 
Sahana, M. et al. Assessing coastal island vulnerability in the Sundarban Biosphere Reserve, India, using geospatial technology. Environ. Earth Sci. 78(10), 1–22 (2019).Article 

Google Scholar 
FSI. India State of Forest Report. Forest Survey of India, Dehradun (2017).Ellison, A. M., Mukherjee, B. B. & Karim, A. Testing patterns of zonation in mangroves: Scale dependence and environmental correlates in the Sundarbans of Bangladesh. J. Ecol. 88(5), 813–824 (2000).Article 

Google Scholar 
Sahana, M., Rehman, S., Sajjad, H. & Hong, H. Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India. CATENA 189, 104450 (2020).Article 

Google Scholar 
Sahana, M. & Sajjad, H. Vulnerability to storm surge flood using remote sensing and GIS techniques: A study on Sundarban Biosphere Reserve, India. Rem. Sens. Appl. Soc. Env. 13, 106–120 (2019).
Google Scholar 
Chowdhury, M. Q. et al. Nature and periodicity of growth rings in two Bangladeshi mangrove species. IAWA J. 29(3), 265–276 (2008).Article 

Google Scholar 
Sarker, S. K., Reeve, R., Thompson, J., Paul, N. K. & Matthiopoulos, J. Are we failing to protect threatened mangroves in the Sundarbans world heritage ecosystem?. Sci. Rep. 6(1), 1–12 (2016).Article 

Google Scholar 
Iftekhar, M. S. & Saenger, P. Vegetation dynamics in the Bangladesh Sundarbans mangroves: A review of forest inventories. Wetlands Ecol. Manage. 16(4), 291–312 (2008).Article 

Google Scholar 
Siddiqi, N. A. In Mangrove forestry in Bangladesh, Institute of Forestry and Environmental Sciences. University of Chittagong, Chittagong, Bangladesh 201 (2001).Lewis, R. R. III. Ecological engineering for successful management and restoration of mangrove forests. Ecol. Eng. 24(4), 403–418 (2005).Article 

Google Scholar 
Peterson, T. A., Papeş, M. & Eaton, M. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography 30, 550–560. https://doi.org/10.1111/j.0906-7590.2007.05102.x (2007).Article 

Google Scholar 
Stockwell, D. & Peters, D. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158. https://doi.org/10.1080/136588199241391 (1999).Article 

Google Scholar 
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009. https://doi.org/10.1111/j.1461-0248.2005.00792.x (2005).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Feng, Z. et al. Dynamics ofmangrove forests in Shenzhen Bay in response to natural and anthropogenic factors from 1988 to 2017. J. Hydrol. 591, 125271. https://doi.org/10.1016/j.jhydrol.2020.125271 (2020).Article 

Google Scholar 
Kaky, E. & Gilbert, F. Using species distribution models to assess the importance of Egypt’s protected areas for the conservation of medicinal plants. J. Arid Environ. 135, 140–146. https://doi.org/10.1016/j.jaridenv.2016.09.001 (2016).Article 

Google Scholar 
Pecchi, M. et al. Species distribution modelling to support forest management A literature review. Ecol. Model. 411, 108817 (2019).Article 

Google Scholar 
Spiers, J. A., Oatham, M. P., Rostant, L. V. & Farrell, A. D. Applying species distribution modelling to improving conservation-based decisions: A gap analysis of Trinidad and Tobago’s endemic vascular plants. Biodivers. Conserv. 27, 2931–2949 (2018).Article 

Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159 (2009).Article 

Google Scholar 
Fois, M., Cuena-Lombraña, A., Fenu, G. & Bacchetta, G. Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions. Ecol. Model. 385, 124–132 (2018).Article 

Google Scholar 
Gilani, H., Goheer, M. A., Ahmad, H. & Hussain, K. Under predicted climate change: Distribution and ecological niche modelling of six native tree species in Gilgit-Baltistan, Pakistan. Ecol. Indic. 111, 106049 (2020).Article 

Google Scholar 
Ellison, A. M., Felson, A. J. & Friess, D. A. Mangrove rehabilitation and restoration as experimental adaptive management. Front. Mar. Sci. 7, 327. https://doi.org/10.3389/fmars.2020.00327 (2020).Article 

Google Scholar 
Ellison, A. M. Mangrove restoration: Do we know enough?. Restor. Ecol. 8(3), 219–229 (2000).Article 

Google Scholar 
Brown, B., Fadillah, R., Nurdin, Y., Soulsby, I., & Ahmad, R. CASE STUDY: Community Based Ecological Mangrove Rehabilitation (CBEMR) in Indonesia. In From small (12–33 ha) to medium scales (400 ha) with pathways for adoption at larger scales ( > 5000 ha). SAPI EN. S. Surveys and Perspectives Integrating Environment and Society 7.2 (2014).Rodríguez-Rodríguez, J. A., Mancera-Pineda, J. E. & Tavera, H. Mangrove restoration in Colombia: Trends and lessons learned. For. Ecol. Manage. 496, 119414 (2021).Article 

Google Scholar 
Romañach, S. S. et al. Conservation and restoration
of mangroves: Global status, perspectives, and prognosis. Ocean Coast Manag. 154, 72–82. https://doi.org/10.1016/j.ocecoaman.2018.01.009 (2018).Article 

Google Scholar 
Sulochanan, B. et al. Water and sediment quality parameters of the restored mangrove ecosystem of Gurupura River and natural mangrove ecosystem of Shambhavi River in Dakshina Kannada, India. Marine Pollution Bulletin 176, 113450. https://doi.org/10.1016/j.marpolbul.2022.113450 (2022).Lovelock, C. E., Barbier, E. & Duarte, C. M. Tackling the mangrove restoration challenge. PLoS Biol. 20(10), e3001836 (2022).Article 
CAS 

Google Scholar 
Lovelock, C. E. & Brown, B. M. Land tenure considerations are key to successful mangrove restoration. Nature Ecol. Evol. 3(8), 1135–1135 (2019).Article 

Google Scholar 
Su, J., Friess, D. A. & Gasparatos, A. A meta-analysis of the ecological and economic outcomes of mangrove restoration. Nat. Commun. 12(1), 1–13 (2021).Article 

Google Scholar 
Lee, S. Y., Hamilton, S., Barbier, E. B., Primavera, J. & Lewis, R. R. Better restoration policies are needed to conserve mangrove ecosystems. Nature Ecol. Evol. 3(6), 870–872 (2019).Article 

Google Scholar 
Chakraborty, S., Sahoo, S., Majumdar, D., Saha, S. & Roy, S. Future Mangrove suitability assessment of Andaman to strengthen sustainable development. J. Clean. Prod. 234, 597–614 (2019).Article 

Google Scholar 
Charrua, A. B., Bandeira, S. O., Catarino, S., Cabral, P. & Romeiras, M. M. Assessment of the vulnerability of coastal mangrove ecosystems in Mozambique. Ocean Coast. Manag. 189, 105145 (2020).Article 

Google Scholar 
Hu, W. et al. Predicting potential mangrove distributions at the global northern distribution margin using an ecological niche model: Determining conservation and reforestation involvement. For. Ecol. Manage. 478, 118517 (2020).Article 

Google Scholar 
Rodríguez-Medina, K., Yañez-Arenas, C., Peterson, A. T., Euán Ávila, J. & Herrera-Silveira, J. Evaluating the capacity of species distribution modeling to predict the geographic distribution of the mangrove community in Mexico. PLoS ONE 15(8), e0237701 (2020).Article 

Google Scholar 
Wang, Y. et al. Simulating spatial change of mangrove habitat under the impact of coastal land use: Coupling MaxEnt and Dyna-CLUE models. Sci. Total Environ. 788, 147914 (2021).Article 
CAS 

Google Scholar 
Gopal, B. & Chauhan, M. Biodiversity and its conservation in the Sundarban mangrove ecosystem. Aquat. Sci. 68(3), 338–354 (2006).Article 

Google Scholar 
Sahana, M., Rehman, S., Paul, A. K. & Sajjad, H. Assessing socio-economic vulnerability to climate change-induced disasters: Evidence from Sundarban Biosphere Reserve, India. Geol. Ecol. Landsc. 5(1), 40–52 (2021).Article 

Google Scholar 
Giri, C. et al. Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia. J. Biogeogr. 35(3), 519–528 (2008).Article 

Google Scholar 
Giri, C., Pengra, B., Zhu, Z., Singh, A. & Tieszen, L. L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 73(1–2), 91–100 (2007).Article 

Google Scholar 
Islam, S. N. & Gnauck, A. Effects of salinity intrusion in mangrove wetlands ecosystems in the Sundarbans: An alternative approach for sustainable management. Wetlands Monitor. Modell. Manag. 2007, 315 (2007).
Google Scholar 
Hazra, S., Ghosh, T., DasGupta, R. & Sen, G. Sea level and associated changes in the Sundarbans. Sci. Cult. 68(9/12), 309–321 (2002).
Google Scholar 
Purkait, B. Coastal erosion in response to wave dynamics operative in Sagar Island, Sundarban delta, India. Front. Earth Sci. China 3(1), 21–33 (2009).Article 

Google Scholar 
World Bank (2014). Building resilience for sustainable development of the Sundarbans: Strategy report (No. 20116; World Bank Other Operational Studies). The World Bank Group. https://ideas.repec.org/p/wbk/wboper/20116.html.Das, M. A. H. U. A. Impact of commercial coastal fishing on the environment of Sundarbans for sustainable development. Asian Fish. Sci. 22(1), 157–167 (2009).
Google Scholar 
Hoq, M. E. An analysis of fisheries exploitation and management practices in Sundarbans mangrove ecosystem, Bangladesh. Ocean Coast. Manag. 50(5–6), 411–427 (2007).Article 

Google Scholar 
Census of India (2011). Primary census abstract, census of India. The government of India, Registrar General and Census Commissioner of India, Ministry of Home Affairs, New Delhi, India. https://censusindia.gov.in/nada/index.php/catalog/41021Chowdhury, A. & Maiti, S. K. Assessing the ecological health risk in a conserved mangrove ecosystem due to heavy metal pollution: A case study from Sundarbans Biosphere Reserve, India. Hum. Ecol. Risk Assess. Int. J. 22(7), 1519–1541 (2016).Article 
CAS 

Google Scholar 
Hajra, R. et al. Unravelling the association between the impact of natural hazards and household poverty: Evidence from the Indian Sundarban delta. Sustain. Sci. 12(3), 453–464 (2017).Article 

Google Scholar 
Sahana, M. & Sajjad, H. Assessing Influence of Erosion and Accretion on Landscape Diversity in Sundarban Biosphere Reserve, Lower Ganga Basin: A Geospatial Approach. In Quaternary Geomorphology in India, (eds Das, B. et al.) (Springer, Cham, 2019). https://doi.org/10.1007/978-3-319-90427-6_10 (2018).Chaudhuri, A. B., Choudhury, A., Hussain, Z., & Acharya, G. Mangroves of the Sundarbans. Vol. I. India, The IUCN Wetlands Programme 247 (IUCN, 1994).GBIF.org. GBIF Occurrence Download, 2018. https://www.gbif.org/. Avicennia marina: https://doi.org/10.15468/dl.vmlooq and R. mucronata: https://doi.org/10.15468/dl.ewnqnm (accessed March 2019).Mandal, R. N. & Naskar, K. R. Diversity and classification of Indian mangroves: A review. Trop. Ecol. 49(2), 131–146 (2008).
Google Scholar 
Mandal, A. K., & Nandi, N. C. Fauna of Sundarban mangrove ecosystem, west Bengal, India, Vol. 3 (Zoological Survey of India, 1989).Mitra, A. & Pal, S. The Oscillating Mangrove Ecosystem and the Indian Sundarbans (WWF-India-WBSO, 2002).Naskar, K., & Guha Bakshi, D. N. Mangrove Swamps of the Sundarbans (Naya Prokash, 1987).Barik, J. & Chowdhury, S. True mangrove species of Sundarbans delta, West Bengal, eastern India. Check list 10(2), 329–334. https://doi.org/10.15560/10.2.329 (2014).IUCN 2018. The IUCN Red List of Threatened Species. Version 2018. 2018. Electronic database accessible, accessed 15 Nov 2018; http://www.iucnredlist.org.Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).MATH 

Google Scholar 
Cavanaugh, K. C. et al. Climate-driven regime shifts in a mangrove–salt marsh ecotone over the past 250 years. Proc. Natl. Acad. Sci. 116(43), 21602–21608 (2019).Article 
CAS 

Google Scholar 
Naskar, K. & Mandal, R. Ecology and Biodiversity of Indian Mangroves, Vol. 1 (Daya Books, 1999).Figueiredo, F. O. et al. Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species. J. Biogeogr. 45(1), 190–200 (2018).Article 

Google Scholar 
Booth, T. H., Nix, H. A., Busby, J. R. & Hutchinson, M. F. BIOCLIM: The first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Divers. Distrib. 20(1), 1–9 (2014).Article 

Google Scholar 
Asbridge, E., Lucas, R., Ticehurst, C. & Bunting, P. Mangrove response to environmental change in Australia’s Gulf of Carpentaria. Ecol. Evol. 6(11), 3523–3539 (2016).Article 

Google Scholar 
He, Q. & Silliman, B. R. Climate change, human impacts, and coastal ecosystems in the Anthropocene. Curr. Biol. 29(19), R1021–R1035. https://doi.org/10.1016/j.cub.2019.08.042 (2019).Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: Use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186(2), 251–270 (2005).Article 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R (Cambridge University Press, 2017).Book 

Google Scholar 
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
STR Annual Report. In Conservator of Forest & Field Director, Sundarban Tiger Reserve. Canning, West Bengal, India: Directorate of Forests, Government of West Bengal (2013–2014).Segurado, P. & Araujo, M. B. An evaluation of methods for modelling species distributions. J. biogeogr. 31(10), 1555–1568. https://doi.org/10.1111/j.1365-2699.2004.01076.x (2004).Kadmon, R., Farber, O. & Danin, A. A systematic analysis of factors affecting the performance of climatic envelope models. Ecol. Appl. 13(3), 853–867. https://doi.org/10.1890/1051-0761(2003)013[0853:ASAOFA]2.0.CO;2 (2003).Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. distribut. 14(5), 763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x (2008).Simard, M. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat. Geosci. 12(1), 40–45 (2019).Article 
CAS 

Google Scholar 
Hoguane, A. M., Hill, A. E., Simpson, J. H. & Bowers, D. G. Diurnal and tidal variation of temperature and salinity in the Ponta Rasa mangrove swamp, Mozambique. Estuar. Coast. Shelf S. 49(2), 251–264. https://doi.org/10.1006/ecss.1999.0499 (1999).  Article 
CAS 

Google Scholar 
Sanders, C. J. et al. Are global mangrove carbon stocks driven by rainfall? J. Geophys. Res. Biogeosci. 121(10), 2600–2609. https://doi.org/10.1002/2016JG003510 (2016).Srivastava, J., Farooqui, A. & Seth, P. Pollen-vegetation relationship in surface sediments, Coringa mangrove ecosystem, India: palaeoecological applications. Palynology 43(3), 451–466. https://doi.org/10.1080/01916122.2018.1458755 (2019).Nandy, P., Das, S., Ghose, M. & Spooner-Hart, R. Effects of salinity on photosynthesis, leaf anatomy, ion accumulation and photosynthetic nitrogen use efficiency in five Indian mangroves. Wetlands Ecol. Manage. 15(4), 347–357 (2007).Article 
CAS 

Google Scholar 
Washington, W., Kathiresan, K. & Bingham, B. L. Biology of mangroves and mangrove ecosystems. Adv. Mar. Biol. 2001, 40 (2001).
Google Scholar 
Blasco, F., Aizpuru, M. & Gers, C. Depletion of the mangroves of Continental Asia. Wetlands Ecol. Manage. 9(3), 255–266 (2001).Article 

Google Scholar 
Datta, D. & Deb, S. Forest structure and soil properties of mangrove ecosystems under management scenarios: Experiences from the intensely humanized landscape of Indian Sunderbans. Ocean Coast. Manag. 140, 22–33 (2017).Article 

Google Scholar 
Wahid, S. M., Babel, M. S. & Bhuiyan, A. R. Hydrologic monitoring and analysis in the Sundarbans mangrove ecosystem, Bangladesh. J. Hydrol. 332(3–4), 381–395 (2007).Article 

Google Scholar 
Iftekhar, M. S. & Islam, M. R. Degeneration of Bangladesh’s Sundarbans mangroves: A management issue. Int. For. Rev. 6(2), 123–135 (2004).
Google Scholar 
Saenger, P. Mangrove Ecology, Silviculture, and Conservation (Kluwer Academic Publishers, 2002).Book 

Google Scholar 
Feka, Z. N. Sustainable management of mangrove forests in West Africa: A new policy perspective?. Ocean Coast. Manag. 116, 341–352. https://doi.org/10.1016/j.ocecoaman.2015.08.006 (2015).Article 

Google Scholar 
Giri, S. et al. A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique. J. Coast Conserv. 18, 359–367. https://doi.org/10.1007/s11852-014-0322-3 (2014).Article 

Google Scholar 
Moschetto, F. A., Ribeiro, R. B. & De Freitasa, D. M. Urban expansion, regeneration and socioenvironmental vulnerability in a mangrove ecosystem at the southeast coastal of São Paulo, Brazil. Ocean Coast. Manag. 24, 105418. https://doi.org/10.1016/j.ocecoaman.2020.105418 (2020).Article 

Google Scholar 
Tuholskea, C., Tane, Z., López-Carra, D., Roberts, D. & Cassels, S. Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Appl. Geogr. 88, 84–93. https://doi.org/10.1016/j.apgeog.2017.08.018 (2017).Article 

Google Scholar 
Kantharajan, G. et al. Vegetative structure and species composition of mangroves along the Mumbai coast, Maharashtra, India. Reg. Stud. Mar. Sci. 19, 1–8 (2018).
Google Scholar 
Marcinko, C. L. et al. The development of a framework for the integrated assessment of SDG trade-offs in the Sundarban Biosphere Reserve. Water 13(4), 528 (2021).Article 

Google Scholar 
Sahana, M. et al. Assessing Wetland ecosystem health in Sundarban Biosphere Reserve using pressure-state-response model and geospatial techniques. Remot. Sens. Appl. Soc. Environ. 26, 100754. https://doi.org/10.1016/j.rsase.2022.100754 (2022).Saha, S., & Choudhury, A. Vegetation Analysis of Restored And Natural Mangrove Forest In Sagar Island, Sundarbans, East Coast of India. Indian J. Mar. Sci. 24, 133–136. http://nopr.niscpr.res.in/bitstream/123456789/37297/1/IJMS%2024%283%29%20133-136.pdf (1995).Balke, T. & Friess, D. A. Geomorphic knowledge for mangrove restoration: A pantropical categorization. Earth Surf. Process. Landf. 41, 231–239. https://doi.org/10.1002/esp.3841 (2016).Article 

Google Scholar 
Alongi, D. M. Mangrove forests of timor-leste: Ecology, degradation and vulnerability to climate change. In Mangrove ecosystems of Asia 199–212 (Springer, 2014).Biswas, S. R., Mallik, A. U., Choudhury, J. K. & Nishat, A. A unified framework for the restoration of Southeast Asian mangroves—bridging ecology, society and economics. Wetlands Ecol. Manage. 17(4), 365–383 (2009).Article 

Google Scholar 
Dubey, S. K., Censkowsky, U., Roy, M., Chand, B. K., & Dey, A. Framework for rapid evaluation of a mangrove restoration site: A case study from Indian Sundarban. In Sabkha Ecosystems 363–378 (Springer, 2019).Islam, M. M. & Shamsuddoha, M. Coastal and marine conservation strategy for Bangladesh in the context of achieving blue growth and sustainable development goals (SDGs). Environ. Sci. Pol. 87, 45–54. https://doi.org/10.1016/j.envsci.2018.05.014 (2018).Article 

Google Scholar 
Bosire, J., Celliers, L., Groeneveld, J., Paula, J. & Schleyer, M.H. Regional State of the Coast Report-Western Indian Ocean. UNEP-Nairobi Convention and WIOMSA 546 (2015).Owuor, M. A., Mulwa, R., Otieno, R., Icely, J. & Newton, A. Valuing mangrove biodiversity and ecosystem services: A deliberative choice experiment in Mida Creek, Kenya. Ecosyst. Serv. 40, 101040. https://doi.org/10.1016/j.ecoser.2019.101040 (2019).Article 

Google Scholar 
Barwell, L. et al. (2018). Regional
State of the Coast Report Western Indian Ocean. The United Nations Environment
Programme/Nairobi Convention Secretariat. https://wedocs.unep.org/handle/20.500.11822/9700?show=fullde Jesús Arce-Mojica, T., Nehren, U., Sudmeier-Rieux, K., Miranda, P. J. & Anhuf, D. Nature-based solutions (NbS) for reducing the risk of shallow landslides: where do we stand? Int. J. disaster risk reduct. 41, 101293. https://doi.org/10.1016/j.ijdrr.2019.101293 (2019).Bardhan, M. An empirical study on mangrove restoration in Indian Sundarbans—a community-based environmental approach. In Modern Cartography Series, vol. 10 387–405 (Academic Press, 2021).Kumar, M. C., Bholanath, M. & Debashis, S. Study on utility and revival through community approach in sundarbans mangrove. Int. J. Soc. Sci. https://doi.org/10.5958/2321-5771.2014.00101.X (2014).Article 

Google Scholar 
Chakraborty, S. K., Giri, S., Chakravarty, G. & Bhattacharya, N. Impact of eco-restoration on the biodiversity of Sundarbans Mangrove Ecosystem, India. Water Air Soil Pollut. Focus 9(3), 303–320 (2009).Article 

Google Scholar 
Paulson Institute. Research report on mangrove protection and restoration strategy in China, 2020; https://paulsoninstitute.org.cn/wpcontent/uploads/2020/06/%E4%B8%AD%E5%9B%BD%E7%BA%A2%E6%A0%91%E6%9E%97%E4%BF%9D%E6%8A%A4%E4%B8%8E%E6%81%A2%E5%A4%8D%E6%88%98%E7%95%A5%E7%A0%94%E7%A9%B6%E6%8A%A5%E5%91%8A%E2%80%94%E6%91%98%E8%A6%81%E7%89%88.pdf.Fan, H. Q. & Wang, W. Q. Some thematic issues for mangrove conservation in China. J. Xiamen Univ. Nat. Sci 56, 323–330. https://doi.org/10.6043/j.issn.0438-0479.201612003 (2017).Article 

Google Scholar 
Wang, W., Fu, H., Lee, S. Y., Fan, H. & Wang, M. Can strict protection stop the decline of mangrove ecosystems in China? Fromrapid destruction to rampant degradation. Forests 11, 55. https://doi.org/10.3390/f11010055 (2020).Article 

Google Scholar 
Roy, A. K. D. & Alam, K. Participatory forest management for the sustainable management of the sundarbans mangrove forest. Am. J. Env. Sci. 8(5), 549–555. https://doi.org/10.3844/ajessp.2012.549.555 (2012).Article 

Google Scholar 
Selvam, V. et al. In Toolkit for establishing coastal bioshield. M. S. Swaminathan Research Foundation, Centre for Research on Sustainable Agriculture and Rural Development (2005).Raju, J. S. S. N. Xylocarpus (Meliaceae): A less-known mangrove taxon of the Godavari estuary, India. Curr. Sci. 84(7), 879–881. https://www.currentscience.ac.in/Volumes/84/07/0879.pdf (2003).
Google Scholar 
Siddiqui, A. H. & Khair, A. Infestation status of heart rot disease of pasur (Xylocarpus mekongensis), tree in the sundarbans. Indian For. 138(2), 165–168 (2012).
Google Scholar 
Iqbal, M. & Hossain, M. Tourists’ willingness to pay for restoration of Sundarbans Mangrove forest ecosystems: A contingent valuation modeling study. Env. Dev. Sustain. 2022, 1–22 (2022).
Google Scholar 
Ekka, A. & Pandit, A. Willingness to pay for restoration of natural ecosystem: A study of Sundarban mangroves by contingent valuation approach. Indian J. Agric. Econ. 67, 902 (2012).
Google Scholar 
Datta, D., Chattopadhyay, R. N. & Guha, P. Community based mangrove management: A review on status and sustainability. J. Env. Manag. 107, 84–95. https://doi.org/10.1016/j.jenvman.2012.04.013 (2012).Article 

Google Scholar 
Ghosh, A., Schmidt, S., Fickert, T. & Nusser, M. The Indian Sundarban mangrove forests: History, utilization, conservation strategies and local perception. Diversity 7(2), 149–169. https://doi.org/10.3390/d7020149 (2015).Article 
CAS 

Google Scholar 
Ranjan, R. Optimal mangrove restoration through community engagement on coastal lands facing climatic risks: The case of Sundarbans region in India. Land Use Policy 81, 736–749 (2019).Article 

Google Scholar 
Dutta, M., Roy, S. & Nibirh, S. Joint forest management and forest protection committees: Negotiation systems and the design of incentives—a case study of West Bengal. Electron. J. https://doi.org/10.2139/ssrn.2245965 (2001).Article 

Google Scholar 
McKee, K. L., Rooth, J. E. & Feller, I. C. Mangrove recruitment after forest disturbance is facilitated by herbaceous species in the Caribbean. Ecol. Appl. 17(6), 1678–1693 (2007).Article 

Google Scholar 
Begam, M. et al. Native salt-tolerant grass species for habitat restoration, their acclimation and contribution to improving edaphic conditions: A study from a degraded mangrove in the Indian Sundarbans. Hydrobiologia 803(1), 373–387 (2017).Article 
CAS 

Google Scholar 
Donnelly, M. & Walters, L. Trapping of Rhizophora mangle propagules by coexisting early successional species. Estuaries Coasts 37, 1562–1571 (2014).Article 

Google Scholar 
Ren, H. et al. Sonneratia apetala Buch. Ham in the mangrove ecosystems of China: An invasive species or restoration species?. Ecol. Eng. 35(8), 1243–1248 (2009).Article 

Google Scholar 
Cheong, S.-M. et al. Coastal adaptation with ecological engineering. Nature Clim. Change 3, 787–791. https://doi.org/10.1038/nclimate1854 (2013).Article 

Google Scholar  More

The categorisation system was developed through an iterative procedure including a careful evaluation at each stage. This was necessary because each of three rounds yielded substantial feedback from the expert taggers, identifying issues to be resolved and proposing improvements to the system. This process led to a much clearer understanding of the structure of sensitive data resources and a wider agreement on definitions to be applied in the tagging process. In addition, the allocation of exactly one tag per category improved during the development for many categories, indicating that the selection process was straightforward for most resources and categories. As a result, the categorisation system could be simplified and the structure improved, appropriately representing a trans-disciplinary effort. This may also be important from the user perspective. At the end of the day, the system should be so intuitive that the users searching for terms would have the same logic as the experts entered the tags.To be beneficial for the domain of LS, the categorisation system and the toolbox requires broad community approval38,39. In the project, we began the approval process with nominated experts from 6 LS RIs, embedded in a larger working group of the H2020-funded project EOSC-Life, covering 13 LS RIs. Though this can be seen as a useful starting point, the toolbox obviously needs community approval at a much larger scale. As the categorisation system is specifying a part of essential metadata for resources about sensitive data, it will be relevant to the FAIR Digital Objects (FDO) Forum for a « resources in the life sciences » FDO. The categorisation system can be used to derive FDO attributes and values for such FDOs. FDOs for the sensitive data itself, when levels of sensitivity and specific access protocols need to be specified is an interesting possible extension, and the categorisation system could support as a backbone information for access governance and technical choices. FDOs are to be “machine actionable”, so desirable mappings between different categorisation systems will be operationalisable. New European projects such as FAIRCORE4EOSC (https://faircore4eosc.eu/), FAIR-IMPACT (https://fair-impact.eu/) and other projects working on pragmatic semantic improvements for FAIR appliance will provide possibilities for registering metadata schemas and mappings that should reuse interdisciplinary approaches in the heterogeneous field of life sciences.The RDA has established and is maintaining a Metadata Standards Catalogue (MSC) (https://rdamsc.bath.ac.uk/mapping-index,5). An appropriate goal for the categorisation system would be to be included in this catalogue, after further refinement and alignment with other vocabularies addressing sensitive data in the life sciences. In any case, the work on the categorisation system can contribute to discussions on methodologies for aligning metadata schemas across scientific domains, while the categorisation system itself can be seen as an important contribution to the process of developing the most useful and appropriate cross-disciplinary terms and categories for describing sensitive data. We keep in mind that similar approaches have been applied via long and iterative processes in other scientific domains, such as understanding and predicting the evolution of climate (essential climate variables, https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables) and essential biodiversity variables for mapping and monitoring species populations40. There are biases and gaps in the existing system that need to be tackled in the future. The initial content of the toolbox demonstrator, consisting of 110 resources related to sensitive data, has been primarily selected by four RIs with a focus on clinical and biomedical research (BBMRI, EATRIS, ECRIN, Euro-Bioimaging). Other areas and sensitive data types, such as environmental, classified, and proprietary data are under-represented, as are some disciplines, such as zoology, ecology, plant and mycological sciences, and microbiology. This indicates a need for a broader coverage of resources linked to sensitive data in the future work. Another question that needs to be investigated is how interoperable the categorisation system is with other domains outside the LS that systematically deal with sensitive data, for example, the Social Science and Humanities41). In addition, systematic data on the usability/user-friendliness of the toolbox from a broad sample of potential users from the life sciences are needed. Initial and informal evaluation of these aspects by the experts involved so far has been very positive but is clearly limited in scale and needs to be supplemented by more evidence.There are major challenges to the sharing of sensitive data, including interoperability, accessibility, and governance. The primary objective of the toolbox is to improve discoverability of resources and digital objects linked to the sharing and re-use of sensitive data (F in FAIR)4. The systematic application of a standardised typology for resources about sensitive data, as defined by the categorisation system, helps to better structure, and organise the issues and results in metadata enrichment (F4, R1.3 of the FAIR principles in Supplementary, Table S1). The toolbox alone will not be enough for the ‘I’ of the FAIR principles, but it may become a useful backbone for building more interoperable classification systems for sensitive data resources.It is perhaps more common to base findability on a tagging system using keywords (plus title text). That is, for example, how PubMed works—it does not categorise resources, it adds MESH terms to them (https://pubmed.ncbi.nlm.nih.gov/). Another option would have been to try to derive keywords from text or title. In our case, a categorisation system with pre-defined dimensions and pre-listed tags was preferred by the expert group. Keywords, in isolation, suffer from several disadvantages:

(a)

A range of equivalent terms may be used to mean the same thing – making searching for that concept difficult, requiring multiple ‘Or’ statements.

(b)

They may have multiple meanings (polysemy) especially if “drawn from”, or “applied to”, a wide range of scientific disciplines.

(c)

The different aspects of the resource covered by keywords, i.e., the types or dimensions of keyword applied, may be inconsistent and / or incomplete.

The categorisation system, on the other hand, guarantees that all 7 validated dimensions required are used in the tagging process and that the tags selected are standardised and defined. The toolbox categories also aid browsing of results by enabling sequential filtering using the categories and tags.In addition, there is a useful link between developing community approved categories for metadata, in this case for characterising resources dealing with sensitive data, and community understood (but implicit) ontologies used in the same area. Categories and ontologies can complement each other—without a common underlying ontology, metadata terms can be interpreted inconsistently, and without defining metadata categories, ontologies may remain implicit and inconsistent. We found, for example, that discussions on the best categorisation to use for scientific disciplines, or data types, exposed the implicit (and different) ontologies being used by different people and is based on the personal views of those in the group. Those would have been obviously rooted in and / or influenced by the language and working assumptions of their discipline(s), and their roles and experiences, (current and previous). That will be more and more the case with interdisciplinary research development and development in research careers. Developing categories in metadata can therefore play an important role in describing, understanding and, ultimately, harmonising the implicit ontologies scientists use in thinking about the area of sensitive data.In the development of the categorisation system, existing ontologies, classifications, and terminologies were taken into consideration (Table 2). However, many more have relationships to the categorisation system. An example is the Subject Resource Application Ontology (SRAO), an application ontology describing subject areas/academic disciplines used within FAIRsharing records by curators and the user community42. A first crosswalk has demonstrated considerable agreement between the toolbox category “research field” and subsections of SRAO42 and EDAM15. The toolbox has been registered as a resource (database) at FAIRsharing, a curated, informative, and educational resource on data and metadata standards, inter-related to databases and data policies (https://fairsharing.org/3577). It is planned to create a collection group of resources (standards, databases, policies) in FAIRsharing linked to the toolbox and the underlying categorisation system. This will also cover relationships to ontologies and classifications.There is a need to explore the applicability of the toolbox to specific domains. One example could be the European Joint Programme on Rare Diseases (EJP RD), where resources are made progressively FAIR at the record level to support innovative basic, translational and clinical research (https://www.ejprarediseases.org/coordinated-access-data-services/fairification-support/). The goal is to identify, refine and expose core standards for dataset interoperability, asset (data, sample, subject) discovery, and responsible data sharing, concentrating on data level rather than resource level information. Knowledge exchange between EJP RD and the toolbox could be of benefit in exploring the complementary of both approaches in adequately characterising resources linked to sensitive data and thus improving data discoverability.The first pilot study demonstrated major variation in tagging of resources if independent taggers are assessing the same resource (inter-observer variation). The example of BBMRI has shown that this variation can be considerably reduced if adequate training is performed; which in return is resource intense. Thus, to arrive at a valid and reliable tagging process, there is a necessity for adequate training and support to reduce inter-observer variation. Specific training sets and training programs as well as intercalibration tools need to be developed and implemented and approved by the community.Another option to be explored should be AI—or ML-algorithms to support automatic (or at least semi-automatic) tagging of resources. It is not easy to use AI/ML in this field due to the multilingualism and the misinterpretation of terms. Often there are different meanings between scientific disciplines and a common backbone for the application of AI/ML is difficult to achieve. It is necessary to come to a common understanding between people involved in the assessment of resources related to sensitive data in all life sciences. Nevertheless, the toolbox can become of major importance for research and application of AI/ML techniques in this field. It may serve as a resource for AI/ML to better find resources in the field by serving as a kind of gold standard to compare with. Another promising approach would be to consider a knowledge graph as an intelligent representation. For the categorisation system the approach could be used to interlink categories to a resource (e.g., “source related to sensitive data” has “geographical scope”) and to link individual tags between categories if possible (e.g., “clinical research data” result from “clinical research”). This would give a richer representation of the knowledge behind the categorisation system and the option to be integrated in existing approaches (e.g., OpenAIRE, https://www.openaire.eu/). Therefore, we will consider knowledge graphs as an intelligent knowledge representation of the categorisation system in the future.A major challenge will be the transition of the toolbox demonstrator to a mature toolbox and ultimately its maintenance, extension, and sustainability. Development of the toolbox demonstrator has been financed by EOSC-Life, but this project will end in 2023. Discussion on sustainability has been initiated with several life-science infrastructures (e.g., BBMRI, EATRIS, ECRIN and ELIXIR, another European Life-Science Infrastructure). Key aspects of sustainability that need to be considered are maintenance of the toolbox portal and tagging tool and of the toolbox content including expert time for tagging as well as human resources to maintain the system. Different approaches are under evaluation: an organization considering the resource core to its operations and taking full responsibility, or a joint ownership across multiple organisations (e.g., multiple RIs) or a community taking responsibility, either funded by future grants or through in-kind contributions from motivated research parties/individuals. Further costs to be covered will include system maintenance, input from a toolbox manager, tagging of resources by experts, as well as advertisement to the envisioned user groups, hardware costs and costs for debugging and major extension of functionality if needed. More