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Species occurrence recordsWe compiled data on the distribution of 21,252 endemic species of any of the twelve megadiverse countries from four tetrapod (5,757) and four vascular plant groups (15,389) (amphibians, reptiles, birds, mammals, lycophytes, ferns, gymnosperms, and flowering plants). Species occurrence records were obtained from the Global Biodiversity Information Facility (GBIF)27, the International Union of Conservation of Nature (IUCN)28, and BirdLife60,61. We only modeled species with at least 25 unique records at a 5 arc-minute resolution (~10 km at the equator). In many cases, the processing of the IUCN polygons resulted in species with thousands of occurrence records. In these cases, we randomly chose a maximum of 500 records per species. The greater the number of observed records, more problems can be associated with spatial bias in the modeling62. In the case of records coming from IUCN polygons, more records require more computing time and these do not necessarily provide more information into the modeling given that their distribution is quite homogeneous.For tetrapods, we first explored the possibility of using occurrence records from GBIF, but data for megadiverse countries were scarce. Consequently, we decided to use the distribution polygons provided by the IUCN for amphibians, reptiles, and mammals (terrestrial and freshwater species)28, and the distribution polygons provided by BirdLife60. We based this decision on the fact that ecological niche modeling using IUCN polygons has been proven to give robust results20. For the IUCN polygons, we retained species that have been categorized as “extant”, “possibly extinct”, “probably extant”, “possibly extant”, and “presence uncertain”, discarding species considered to be “extinct”. In addition, we did not model species reported by the IUCN as “introduced”, “vagrant”, or those in the “assisted colonization” category; for mammals and birds, we only considered the distribution of “resident” species. Depending on the taxonomic group, and given the information available, we used different approaches to identify species endemic to any of twelve megadiverse countries: Australia, Brazil, China, Colombia, Ecuador, India, Indonesia, Madagascar, Mexico, Peru, Philippines, and Venezuela. For birds, we used BirdLife to identify species listed as “breeding endemic” and then choose the corresponding IUCN polygons. To identify the rest of endemic species in the other groups, we used a 0.08333° buffer around each country to select the IUCN polygons that fall completely within the country limits. We converted all selected species polygons into unique records at a 5 min resolution (~10 km at the equator).For vascular plants, we used geographic occurrence data obtained from the Global Biodiversity Information Facility by querying all records under “Tracheophyta” (we only considered “Preserved Specimens” in our search). Plants records were taxonomically homogenized and cleaned following the procedures described in ref. 63 using Kew’s Plants of the World database64 as the source of taxonomic information. Mostly, we identified endemic species as those with all occurrence records restricted to any given megadiverse country. For countries in which data for vascular plants were scarce or absent (e.g., India), we complemented occurrence information with polygons from the IUCN (although IUCN data for plants remains limited) following the procedure described for tetrapods.Climatic dataWe used the 19 bioclimatic variables available at WorldClim v.2 (Fick 2017) as the baseline (present-day) climatic conditions (1970–2000) (annual mean temperature, mean diurnal range, isothermality, temperature seasonality, the maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean annual range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter and precipitation of coldest quarter). From this baseline scenario, bioclimatic variables start to vary because of climate change. We used bioclimatic variables derived from the IPSL-CM5-LR ocean-atmospheric model under five scenarios: (i) the high-emissions RCP 8.5 W/m2; and (ii) melting scenarios consisting of four different experiments of freshwater discharge into the North Atlantic from Greenland’s meltwater (see DeFrance16 for details). We acknowledge that using a single GCM does not allow us to estimate inter-GCM variability in the resulting distribution models; however, the melting scenarios do only exist for IPSL-CM5-LR GCM. We applied as control scenario RCP 8.5 because melting scenarios would have been more complicated to support with lower emission scenarios. In addition, we are using well-designed opportunity experiments from ref. 11 and wanted to be consistent with their choice of RCP 8.5. Also, these experiments are based on CMIP5, which shows similar climate impact fingerprints than CMIP665. This might be explained by the fact that CMIP5 and CMIP6 are still relatively close, and that the main climatic effects of the AMOC are already well-represented by the climate dynamics in CMIP5.The four melting scenarios are equivalent to a sea-level rise of 0.5, 1.0, 1.5, and 3.0 meters above the current sea level, and these are named accordingly: Melting 0.5, Melting 1.0, Melting 1.5., and Melting 3.0. These AMOC scenarios are experiments that were superimposed to the RCP 8.5 scenario adding 0.11, 0.22, 0.34, and 0.68 Sv (1 Sv = 106 m3/s) coming from a freshwater release that starts in 2020 and finishes in 2070 (Anthoff et al.14). We obtained debiased bioclimatic variables11 under the five future scenarios for three consecutive time horizons: T1: 2030 (2030–2060); T2: 2050 (2050–2080); and T3: 2070 (2070–2100). The time horizons evaluated represent short, medium, and long terms in order to help decision-makers order conservation priorities.Ecological niche modelingAt their most basic, the algorithms used to construct species distribution models relate species occurrence records with climatic variables to create a climatic profile that can be projected onto other time periods and geographic regions66. The resulting models have proven useful in evaluating the impacts of climate change on biodiversity and to identify varying levels of vulnerability among species32,67,68. Here, we employed a multi-algorithm (ensemble) approach to construct species distribution models as implemented in the “biomod2” package67 in R69 (Supplementary Fig. 33). The underlying philosophy of ensemble modeling is that each model carries a true “signal” about the climate-occurrence relationships we aim to capture, but it also carries “noise” created by biases and uncertainties in the data and model structure32,67. By combining models created with different algorithms, ensemble models aim at capturing the true “signal” while controlling for algorithm-derived model differences; therefore, model uncertainty is accounted for during model construction (see Supplementary Material for further detail).Prior to modeling, we reduced the number of bioclimatic variables per species by estimating collinearity among present-day bioclimatic variables. We employed the “corrSelect” function of the package fuzzySim70 in R69, using a Pearson correlation threshold of 0.8 and variance inflation factors as criteria to select variables. Given the number of species evaluated and the ecological information scarcity, we did not select a set of variables based on ecological knowledge by each of the species modeled. Instead, for the variables pre-selection, we used the statistical approach described above that has been proven to give models with good performance71,72. We used seven algorithms with a good predictive performance (evaluated with the TSS and ROC statistics; Supplementary Fig. 1): Maxent (MAXENT.Phillips), Generalized Additive Models (GAM), Classification Trees Analysis (CTA), Artificial Neural Networks (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), and Random Forest (RF). Because occurrence datasets consisted of presence-only data, for each model, we randomly generated 10,000 pseudo-absences within the model calibration area; we gave presences and absences the same importance during the calibration process (BIOMOD’s prevalence = 0.5). For each species, we selected a calibration area (i.e., the accessible area or M)73 using a spatial intersection between a 4° buffer around species occurrences and the terrestrial ecoregions occupied by the species73 (Supplementary Fig. 33). The projected M (i.e., the area accessible for species in future scenarios) was defined using a 2° buffer around the present-day calibration area (M). By limiting the M, we incorporated information about dispersal and ecological limitations of each species into the modeling66. We did this to take into account a more realistic dispersal scenario given the velocity with which climatic changes are happening and because there are geographic and ecological barriers, which is the reason why we used ecoregions to limit our M. We assumed climatic niche conservatism across time; and inside the projected M we also assumed full dispersal. Consequently, inside the projected M, the evaluated species can win or lose suitable climatic conditions.We calibrated each algorithm using a random sample of 70% of occurrence records and evaluated the resulting models using the remaining 30% of records. To validate the predictive power of the ecological niche models, we used the True Skill Statistics (TSS) and the Receiver Operating Characteristics (ROC) and performed 10 replicates for every model, providing a tenfold internal cross-validation. To account for uncertainty, we constructed the ensemble models (seven algorithms × ten replicates) using a total consensus rule, where models from different algorithms were assembled using a weighted mean of replicates with an evaluation threshold of AUC  > 0.7 (Supplementary Fig. 1). However, as shown by the distribution of validation statistic in Supplementary Fig. 1, most ensemble models presented a very good predictive power (AUC  > 0.8). In some cases, modeling issues in some insular species required that we change the calibration area (M) to the entire country.We used the resulting ensemble models to project the potential distribution of each species under both current and future climatic conditions (Supplementary Fig. 34). We then examined the frequency in which different bioclimatic variables appeared to have the highest contribution during model construction for each species. The algorithms used (Maxent, GAM, CTA, ANN, SRE, FDA, and RF) identify these variables by iteratively testing combinations of all the available variables (i.e., those selected based on low correlation values) until reaching a set of variables that was most informative on the distribution of species; this set of variables had the highest predictive power of species occurrence. For every species, we retrieved the two variables with the largest model contribution (Supplementary Figs. 34 and 35).Species geographic rangeWe converted ensemble probability maps into binary maps of presence/absence using the TSS threshold; these binary maps reflect the distribution of climatic suitability of species, where values of 0 and 1 represent grid cells with non-suitable and suitable climates, respectively. In order to approximate the vulnerability of individual species to climate change, we estimated the temporal changes in the extent of the area of climatic suitability (geographic range) for every species relative to the present-day distribution. We estimated species’ geographic ranges by identifying and counting those grid cells with suitable climatic conditions (values of 1) in the present-day and under future scenarios. We then estimated the proportion of range changes through time, quantifying the proportion of grid cells either lost or gained for each species. This allowed us to estimate the proportion of species (by country and group) projected to have a complete loss of geographic ranges in the future.Species richness, differences in species richness, potential species hotspots (PSH), and temporal dissimilarityWe used binary maps to construct presence-absence matrices (PAM), which contain information on the presence (values of 1) or absence (values of 0) of species across grid cells. Using these PAMs, we estimated species richness (SR) as the sum of species present in each grid cell; to visualize SR across space, we generated 16 species richness maps corresponding to the present-day and the four future scenarios at each of the three temporal horizons. We used these maps to estimate and visualize temporal differences in species richness (ΔSR) over time by subtracting the estimated SR in the future from the current SR, for every grid cell; for visualization, we standardized SR per country to the range 0–1. We assumed full dispersal ability of species in all analyses, meaning that all suitable areas in the future had the same probability of being occupied, irrespective of the distance to the present-day distribution.By calculating species richness (SR) across grid cells, we defined Potential Species Hotspots (PSH) within each country as those grid cells with the highest levels of SR. For this, we defined the PSH by calculating the maximum present-day species richness (maxSR) observed in each country and then identified grid cells with richness values above a threshold of maxSR*0.6. Considering only those grid cells with a SR above this threshold, we estimated the geographic extent of PSH across time periods and scenarios and estimated changes to the extent of PSH relative to present-day conditions. Given that we use the threshold to define PSHs, we tested two additional thresholds (20 and 90%) to define and quantify the extent of PSHs. However, these additional results agree with the general trend. We chose not to base our threshold on the distribution of SR values (i.e., quantiles, median) due to the high proportion of grid cells with SR  More

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Urine spraying and scraping as potential scent-markingThe urine spraying and the scraping were reported in other felids6,20,21. In this study, only half of the other excretion instances were accompanied by sniffing, whereas almost all urine spraying and scraping events were accompanied by sniffing, indicating that these are scent-markings. The sniffing was also often observed immediately before urine spraying and scraping. Given the significant association of sniffing before excretion, especially with regard to the scraping, the presence or absence of a scent on the object was thought to be a trigger.Furthermore, during the scraping, liquid secretions thought to originate from the anal glands, were released. Domestic cats have scent glands in the anal sac22. The presence of secretions from the anal sac has also been confirmed in not only tigers, lions (Panthera leo), and bobcats (Lynx rufus), but also in cheetahs1,6,23; however, this study was the first to investigate their role in excretion. Generally, secretions are considered to be caused by health problems or estrus, but in this study, none of the individuals had health problems, and all secretions were observed only in males. Therefore, it was thought that the secretion was produced by the scent glands and contributed to a stronger smell than only urine and feces.Variations based on sexUrine spraying was observed only in adult males and females, and was more frequent in males, as reported in other felids4,5,6,9,24. In wild cheetahs, although urine spraying and scraping have been observed as scent-making, the frequency of scent-marking is known to be substantially higher in territorial than in non-territorial males and in females15,16,25, and the marking locations are concentrated in the core area of the male territories16. The territories of a single male cheetah or a male group are relatively small and exclusive, whereas the relatively large home ranges of non-territorial males (also known as “floaters”) overlap with each other and with those of females15,16. A male’s home range is also larger than that of a female15,16,26,27. Male cheetahs rarely encounter other males because they communicate via marking posts28. Given these reports, the frequent urine spraying by males may help prevent encounters between males. In addition, observations of captive cheetahs have shown a significantly positive correlation between urinary spraying frequency and fecal estradiol content in female cheetahs19. Therefore, as Cornhill and Kerley24 mentioned, female urine spraying is caused by estrus, and male urine spraying is intended as a home range marker for other males or as a sign for females.The action of scraping using the hind paws has been reported to occur in both males and females in servals, lions, tigers, black-footed cats, etc.2,5,6,7,29; however, this behavior was only observed in adult males in this study. Sunquist and Sunquist3 reported that female cheetahs also perform the scraping. In this study, we only recorded observations when the cheetahs were released in the outdoor enclosures, and not when they were in the indoor facilities. In 43.6% of the scraping events, the males excreted feces. During the observation period, the females defecated in the indoor facilities, and no defecation was observed in the outdoor enclosures. It is possible that no scraping action was observed among the females because defecation was not observed in the outdoor enclosure. In indoor facilities, the cheetahs were in a completely monopolized enclosure; hence, the females defecated in their own spaces. There was a difference in the defecation sites and frequency of scraping between the males and females; this was attributed to the sex difference in scent-marking.Differences in target height for each behaviorUrine spraying was frequently done on objects approximately 170 cm or higher, such as walls or fences, standing trees, and stumps, whereas scraping was observed on low-lying objects on the ground, such as a straw pile approximately 3 cm high and a fallen tree that was 10–50 cm high. In other words, the cheetah engaged in urine spraying and scraping depending on the object nearby. This might indicate the functional role of these behaviors. This is consistent with previous findings of urine spraying by tigers being more frequent in wooded forests than in grasslands, with few prominent objects, and scraping being more common in the latter6. In addition, in a study that investigated the place where the smell of the urine of domestic cats is likely to remain, the smell persisted for a long time on rough surfaces, areas covered with moss, and overhanging slopes30. Even for cheetahs living in the savanna woodlands, where there are comparatively fewer upright objects than in the habitat of felids living in the forest, increasing the chances of transmitting information via not only urine spraying but also by the scraping might be more important. On the other hand, in their natural habitat, there are some large carnivores like lions and leopards (Panthera pardus). Wild cheetahs tend not to visit the sites where such carnivores’ scent-mark is present31, suggesting that they might confine their marking to specific sites devoid of other carnivores’ scent. Further research is needed to determine how wild cheetahs use urine spraying and scraping. In this study, scraping was frequently observed even on tall stumps and rocks if they were within the cheetahs’ reach. Scraping by wild cheetahs has been also observed on trees32. Zoos other than Zoo C had few prominent horizontal objects. Therefore, the presence of straw piles, fallen trees, stumps, and rocks may have elicited the scraping.Differences in housing conditionsIn zoos C and D, where animals shared enclosures, the frequency of both urine spraying and scraping by males was higher than in the males in the monopolized enclosures. They possibly showed a more frequent scent-marking to strengthen their home range claims when sharing the exhibition space15. Regarding the scraping, Zoo C had at least four low and horizontal objects (straw piles, fallen tree, stones, and rocks), and scraping was frequently observed. As mentioned above, the placement of objects might have elicited the scraping.In this study, the frequency of urine spraying decreased when the submissive individual (Male 17) was released in the enclosure where the dominant individual (Male 13) was previously released. Among wild cheetahs, territorial males have been reported to mark their territories more often than non-territorial males17,25. Therefore, the difference in the number of markings is considered to be related to whether or not the target individual is within the territory, and it is highly possible that the dominant/submissive relationship between males at that location has an effect on marking.Function of scraping using hind pawsOther felid studies have reported scraping in tigers, pumas, jaguars, clouded leopards, and small felids6,10,20,21,32,33; however, there are fewer studies on different types of scraping. In certain species, such as jaguars and pumas, scraping using hind paws is more frequent than urine spraying33. From this study, the use of secretions was confirmed in the scraping, and it was considered to be a significant marking of the cheetah.The possible functions of scraping include: (1) dispersing the smell of excrement, (2) placing the smell of excrement on the hindlegs, (3) smearing the objects with excrement, and (4) adding the scent of the hind paws. Domestic cats are known to cover their feces with soil34; however, in this study, the cheetahs did not cover the feces with soil and were not observed to scrap only after excretion. Therefore, scraping using hind paws was not meant for concealing urine and/or feces. The results of this study suggest that the scrapings were mostly performed during and after excretion for any of the aforementioned functions. However, 43.2% of the observed scraping events were performed before excretion, and in these cases, the functions 1–3 did not apply, since we did not observe the feces being crushed by scraping the hind paws. As for function 4, domestic cats have sweat glands on the soles of their feet that are thought to retain their smell35. Therefore, the sweat glands on the soles of the feet of the cheetahs possible retain the smell of the hind paws as well. Schaller36 reported that among tigers, scraping on the grassland was exhibited by scratches in the grass and exposure of the ground, creating a visual effect. In the case of cheetahs, scraping may have the function of creating grooves and ridges on the ground to enhance the visual effect; however, the formation of grooves and ridges were not observed in this study. In certain cases, they scraped against trees and stones. Because trees and stones are not easily deformed, it is hard to say whether the visual effect was enhanced by scraping with their hind paws.Scraping has been reported in other felids; however, the movement of the hindlimbs is not uniform. For example, in the case of bobcats, behaviors such as kicking back on the ground with no surrounding objects and scattering of soil have been observed during scrapings20. The snow leopard slowly moves its hindlimbs on the ground near the rocks, exposing the ground; in fact, Schaller29 observed a tiger scraping its hind paws to create a pile of soil [37; Kinoshita, personal communication: Online Resource 3; Scraping of snow leopard]. The movement of urine spraying also varies among species. For example, bobcats sometimes squat and urinate on the ground20, and snow leopards rub their cheeks against the target object and then spray urine9, but cheetahs do not rub their face before urine spraying. Hence, even in the same behavior of “spraying/scraping,” the actions differ. Because felids are widely distributed in various environments, such differences in movements are possibly related to differences in habitat and behavioral functions.In conclusion, urine spraying and scraping using hind paws were considered scent-markings because they were more strongly associated with sniffing than other excretion. Both behaviors were also observed only in adults; however, urine spraying was confirmed in both sexes and was more frequent in males than in females, whereas scraping was observed only in males. Also, the frequencies of both behaviors were significantly higher in males kept in shared enclosures containing other individuals than in males kept in monopolized enclosures, while there was no difference in the frequencies among females. Hence, there were sex differences in these scent-markings possibly because of the difference in the sociality between the sexes even in captivity; the frequency of scent-markings was affected by the living environment including the number of target objects; urine spraying was frequently done on tall objects such as walls or fences, whereas scraping was more commonly done on low-lying objects near the ground, such as straw piles. To our knowledge, this study is the first to confirm that during the scraping a liquid other than feces and urine was secreted, presumably from the anal glands. Taken together, the results can serve to enhance our knowledge regarding the behavior of cheetahs, help improve management of these animals in captivity as well as breeding and animal welfare ex situ conservation, and help elucidate the kind of habitat that should be preserved for the in situ conservation of cheetahs. More

Plants increase their freezing resistance upon exposure to low temperatureThe freezing resistance (LT50 values) was found to vary ranging from − 6.9 °C (14-August-2017) to − 31.7 °C (04-November-2018) over the course of study period. The freezing resistance of leaves recorded during the 12 sampling time-points has been provided in Table 1 (also see39). The overlap of confidence intervals around the mean was examined for comparison of LT50 values for the different sampling time-points. Significant differences in freezing resistance were observed across the sampling time-points (Table 1). Leaves of R. anthopogon collected during summer [July and August (Air temperature and photoperiod was about 9.6 °C and 13 h day−1 respectively)] showed marginal resistance to freezing (LT50: − 7 °C) and thus, are more susceptible to freezing damage. Further, as the ambient air temperature and photoperiod decreased towards the end of growing season (i.e., October and November 2017 with air temperature and photoperiod of about − 1.1 °C and 10.5 h day−1 respectively), the plants acquired the highest freezing resistance (LT50: − 30 °C). Interestingly, a sharp increase in freezing resistance (− 29.4 °C) was observed in September 2018, when the daily mean air temperature decreased below 0 °C due to sudden snowfall (Supplementary Fig. S2). Comparison of LT50 values of all the leaf samples of R. anthopogon showed that cold de-acclimation occurred after the snowmelt during early spring in June (LT50: − 13.4 °C) with an increase in air temperature and photoperiod. These results demonstrated that R. anthopogon plants exhibit lowered freezing resistance during the warmer months [hence, these time-periods were referred as non-acclimation (NA)], progressively develop greater freezing resistance during the onset of winter season (hence, referred as cold acclimation) followed by an intermediate level of freezing resistance during the spring [hence, these time-periods were referred as de-acclimation (DA)].Table 1 The estimates of LT50, calculated by fitting sigmoidal curve to electrolyte leakage values of temperature treatments, recorded for leaves collected during the different sampling time-points (from August 22, 2017 to September 18, 2018).Full size tableDuring the acclimation period (i.e., late in the growing season), plants acquired the highest resistance to freezing (Fig. 1). The low electrolyte leakage (= high freezing resistance) observed during this period might be due to changes in cell wall properties (such as increase in lignification and suberization of cell walls), which provide resistance to diffusion of electrolytes from cells of the leaves to the extracellular water47. Moreover, high freezing resistance may also be attributed to high leaf toughness and sclerophyllous habit of this evergreen species48. Further, it was found that freezing resistance was the lowest during mid-summer period. This pattern could be explained by a trade-of between plant growth rates and freezing resistance, where warmer temperatures favour plant allocation to growth49. These observations corroborated well with earlier reports that showed a rapid increase in ‘freezing resistance’ during the transition from summer to early winter and vice versa50.Figure 1LT50 [black point (with solid fill) on the curve] calculated by fitting sigmoidal curve to relative electrolyte leakage (REL %) values recorded during the three different acclimation phases. GOF indicates ‘goodness of fit’ test values for the fitted sigmoidal curves.Full size imagePhotosynthetic rates are higher during non-acclimation and de-acclimation periodIt was found that PN of R. anthopogon varied in the range from 8.336 to 17.64 μmol(CO2)m−2 s−1 and E from 2.281 to 4.912 mol(H2O)m−2 s−1, throughout its growing season. The Gs of leaves was estimated to be in the range from 0.110 to 0.265 mol (H2O) m−2 s−1. WUE, a ratio of PN and E, varied between 52.21 and 87.68 (Table 2). The gas exchange parameters of R. anthopogon varied significantly among the sampling time-points [referred to here as different acclimation phases of the growing period of evergreen shrub (Fig. 2, Table 3)]. In particular, PN was significantly lower on 18-September-2018 (referred as cold acclimation phase), whereas it was higher on 31-August-2018 and 15-June-2018 (referred as NA and DA phases, respectively). Similarly, Gs of leaves was significantly lower during cold acclimation in comparison to the rest of the acclimation phases (i.e., NA and DA). Further, WUE was significantly higher during cold acclimation, while it was lower during both NA and DA (p ≤ 0.05) (Fig. 2).Table 2 Variability in leaf gas exchange parameters of R. anthopogon during the different acclimation phases (NA = Non-acclimation, LA = Late cold acclimation and DA = De-acclimation).Full size tableFigure 2Variability in leaf gas exchange parameters of R. anthopogon during the three acclimation phases [i.e., Non-acclimation (31 August, 2018), Cold acclimation (18 September, 2018) and De-acclimation (15 June, 2018)]. Different alphabets (a, b, c) represent statistically significant values (p  More