Global hotspots of butterfly diversity in a warming world

Global hotspots of butterfly diversity in a warming world | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Global hotspots of butterfly diversity in a warming world Stefan Pinkert, Nina Farwig, Akito Kawahara, Walter Jetz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4437399/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2025 Read the published version in Nature Ecology & Evolution → Version 1 posted You are reading this latest preprint version Abstract Insects and their many ecosystem functions are in decline and threatened by climate change 1 , 2 , yet lack of globally comprehensive information limits the understanding and management of this crisis 3 . Here we use butterflies as a global model insect system 4 – 7 and uncover a strong coincidence of their diversity and threat. Integrating comprehensive phylogenetic and geographic range data for 12,119 species, we find that global centers of butterfly richness, rarity, and phylogenetic diversity are unusually concentrated in tropical and sub-tropical mountain systems. Mountains 8 hold 3.5 times more butterfly hotspots (top 5%) than lowlands and two thirds of the species are primarily mountain-dwelling. Only a small portion (14%-54%) of these diversity centers overlap with those of ants, terrestrial vertebrates and vascular plants, and this spatial coincidence rapidly decreases above 2,000 m elevation where butterflies are uniquely concentrated. The geographically restricted temperature conditions of these mountain locations now put butterflies at extreme risk from global warming. We project that 64% of butterflies’ temperature niche space in tropical realms will erode by 2070. Our study identifies critical conservation needs for butterflies and illustrates how the consideration of global insect systems is key for assessing and managing biodiversity loss in a rapidly warming world. Biological sciences/Ecology/Conservation biology Biological sciences/Ecology/Biodiversity Biological sciences/Zoology/Entomology Biological sciences/Ecology/Climate-change ecology Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Main Understanding the distribution of biodiversity on Earth is the prerequisite for effective conservation and for mitigating the loss of species and their functions under rapid environmental change 9–13 . Recent work has highlighted the range of implications arising from an uneven geographic distribution of biodiversity, such as vast differences in countries’ conservation responsibilities 14 and heterogenous representation in protected areas 15,16 . These geographic differences in cause and consequence extend to, and are often exacerbated for, the functional and phylogenetic aspects of biodiversity 17 , which are recognized as central for supporting ecosystem resilience and preserving critical evolutionary heritage 18–20 . The same motivation for a more comprehensive understanding extends to different taxa. For terrestrial vertebrates, the thus far dominant model system for global ecology and conservation 10,13,21,22 , prior work has documented marked differences in diversity patterns of endothermic and ectothermic taxa. While birds and mammals (endotherms) share 75% of their richness and rarity hotspots, their respective overlap with amphibians and reptiles (ectotherms) is not strong 20,21 . The extent to which these differences apply to plants and invertebrates remains poorly understood. This is especially true for insects, despite their essential ecosystem functions 2,5 , outstanding diversity, and alarming decline 3 . For ants, species richness and rarity hotspots are uniquely concentrated in regions with dry conditions that reflect the xeric preference and advantages of the groups’ social organization 23 . These recognized differences exemplify the precarious foundation of the current knowledgebase for global biodiversity at large scales and leave insect biodiversity poorly represented. The uneven distribution of biodiversity takes on an additional weight when hotspots cluster into distinct portions of the climate space that are particularly strongly impacted by anthropogenic change 22,24 . One prominent case are mountains. Owing to their historical role in supporting species’ survival and speciation through long-term climate dynamics and their own isolation, many mountain systems are centers of biodiversity 25–27 . Over 40% of terrestrial vertebrate species are mountain endemics 28 , but despite the strong implication for conservation whether and how this extends to insects remains unknown. At the same time, temperature-induced up-slope shifts and losses in habitat combined with strong geographic isolation put mountain biodiversity in great peril 29–32 . Impacts from warming might be particularly severe for insects due to their known strong temperature sensitivity 33–36 , but to date a global assessment of the geographic coincidence of diversity, rarity, and climate change threats for an insect system does not exist. Here we address these issues using butterflies as global insect system. Given their ecological importance 5 , high surrogacy for insect diversity 4 , and their uniquely comprehensive distributional and phylogenetic information 6,7 , butterflies offer pivotal insights into complementary priorities for insect conservation. To achieve global coverage, we mobilized, modelled, and validated species distribution information for 12,119 species based on over 8 million occurrence records and newly digitized expert range maps (Supplementary Figs. 1, 2 and Table 1). Richness, rarity and phylogenetic diversity Across our 110 km-resolution analysis grid, butterfly species richness (SR) increases toward lower latitudes and higher elevation (Fig. 1a), peaking near the equator and at approximately 2500 m mean elevation (see methods and supplement for additional results addressing data gap species). In contrast, average range rarity (RR), defined as the average inverse of species’ global range sizes per assemblage – a continuous measure of endemism, bimodally peaks at around 20° and 40° latitude and 3500 m elevation (Fig. 1b). Notably in most realms, both metrics show limited congruence, particularly for peaks in narrow-ranged species, which are usually of greatest conservation concern (Spearman’s rank SR-RR: ρ = 0.40, n = 12,515). These differences become more evident in an assessment of global hotspots, defined as the top 5% of assemblages of the six major biogeographical realms (Fig. 2). Only 10% of richness and range rarity hotspots are shared globally, with no overlap in the Indomalayan and Afrotropic realms and as few as 2% in the Afrotropic and 4% in the Palearctic realms. This suggests that the hotspot disparity previously documented for vertebrates 10,11,13 , and hence the limited value of richness hotspots for identifying places of greatest global conservation concern, also extends to butterflies. A recently completed comprehensive phylogenetic framework 7 enables us to consider the geography of butterfly phylogenetic diversity. Assessments of the phylogenetic aspect of insect biodiversity at large scale are still sparse, but offer important opportunities to determine the degree of congruence with traditional diversity measures 20 . We find that phylogenetic diversity (PD, defined as deviation from a realm-level null expectation) peaks at about 20° latitude and at elevations of 3000 m and 4000 m (Fig 1c). This PD only weakly correlates with species richness and range rarity (SR-PD: ρ = 0.17, RR-PD: ρ = 0.29; n = 12,515), and respective global hotspots show only limited congruence (SR-PD: 29%; RR-PD: 15%). The mismatch is particularly strong in the central Neotropics, south-east Palearctic and central Indomalaya (Fig. 2), all representing global centers of PD. These centers of PD (Fig. 1c; see also Supplementary Fig. 3 for an alternative PD measure) match the previously argued hotspots of deep lineage diversification and potential evolutionary origin of butterflies based on phylogeographical reconstructions 7 . The limited congruence between PD and other measures of diversity we uncover, highlights the importance of geographically targeted efforts to protect the evolutionary heritage of butterflies. Cross-taxon congruence Research on global priorities for biodiversity conservation has offered important insights, but remains mostly limited to terrestrial vertebrates 10,11,13,20 . Because of their ectothermic physiology and thus direct dependence on ambient temperature 34,37 , we expected butterfly richness patterns to be more similar with those of amphibians, reptiles, and ants than with birds and mammals. Within ectotherm taxa, we expect the diversity of butterflies and ants to be more congruent with one another than with vertebrates, given that insects are orders of magnitude smaller and differ in both scale of habitat selection and physiological rates from vertebrates 34,35 . Due to the known co-evolutionary association 7 , we also predict a particularly high association between the richness patterns of butterflies and plants. Given a much lesser dominance of a minority of wide-ranging species in driving range rarity 38 and a stronger signature of idiosyncratic biogeographic histories, we expect overall weaker associations for this measure than for species richness patterns 33 . We find that across the assessed 110 km-resolution assemblages, the species richness of butterflies is moderately positively associated with those of plants, mammals, birds, and amphibians (all ρ > 0.70, n = 12,515), but less so with that of ants (0.67) and reptiles (ρ = 0.55; Fig. 3). This suggests that differences in thermal strategies or ecophysiology play a limited role for driving richness differences at this scale. Instead, the environmental drivers of immigration and survival that ultimately underpin richness patterns appear shared among taxa. While the exact contributions of evolutionary, abiotic, and biotic factors shaping cross-taxon richness similarities remain largely unknown 33 , our results for insects, vertebrates, and plants emphasize the potential to derive generalities of broad relevance for biodiversity 28,39,40 . Range rarity hotspots harbor many species not occurring anywhere else, and their recognition is thus of extraordinary conservation concern. Compared to geographical congruence in species richness, cross-taxon similarity in range rarity patterns is much weaker (all ρ > 0.50, n = 12,515), and particularly poor with amphibians (ρ = 0.39). Even stronger cross-taxon mismatches emerge when comparing the top 5% diversity centers per realm and taxon (Fig. 3, 4). For these hotspots, overlap among taxa is generally poor, varying from 19% to 36% for species richness and 14% to 33% for range rarity. Notably, over 41% of the butterfly range rarity hotspots – approximately 4% of the land surface – are not shared with a single terrestrial vertebrate group (Fig. 4). Only 16% of butterfly range rarity hotspots overlap with a currently recognized rarity hotspot of ants. The hotspots areas distinct to butterflies include parts of the SW US, W Madagascar, S Asian highlands and select other locations. These results indicate that priority areas identified based on vertebrates miss critical places needed to safeguard the diversity of insects. Advancing the information foundation and conservation actions for these newly recognized butterfly hotspots should be of highest urgency 17,20 (Fig. 4). The unique role of mountains Owing to their geographically isolated nature, strong topographic heterogeneity, and rapid environmental turnover, mountains are well-recognized for their role as catalysts for speciation and for offering refugia for species survival 27,28,41,42 . All of these factors are expected to contribute to the large number of butterfly species found to be concentrated in, or endemic to, mountain regions. In addition, a broad range of insects possesses physiological adaptations to cold conditions 43–46 . We thus expected mountains to play an important role in supporting exceptionally high levels of species richness and range rarity of butterflies compared to vertebrates. Our results confirm several of these expectations. Although mountains represent only 38% of the world’s terrestrial surface outside the polar regions at our study grain, they harbor 72% and 76% of global hotspots of species richness and range rarity, respectively (Figs. 2, 4; odds ratio of 4.29 and 5.24, respectively). This proportion is lower for phylogenetic diversity (59%), but still more than twice as high than expected by chance alone (odd ratio of 2.35). On the species level, these differences are reflected in the much greater portion of butterflies’ geographic ranges in mountains than elsewhere (violin plots in Fig. 2). The importance of mountains differs among realms, with generally lower mountain association in tropical compared to temperate realms (all hotspots combined: 1.43 to 7.09; bar plots in Fig. 2). Our findings corroborate existing work recognizing the evolutionary and conservation significance of mountains for biodiversity 26–28,42 , but also highlight that their role might be even greater than previously thought. Greater concentrations of insect compared to vertebrate diversity toward higher elevations have been documented at local scales 47 , but at broad scales remain untested. We find that the concentration of butterfly diversity in mountains substantially exceeds that of almost all globally studied taxa, especially so for range rarity (Fig. 5a). Toward higher average elevations, butterfly richness decreases weakly, and butterfly rarity increases much more strongly than found in ants and terrestrial vertebrates. Above 2000 m, plant and butterfly richness remain relatively high, and butterfly range rarity markedly exceeds that of other taxa. We attribute this to several factors, including physiological adaptations such as color- and size-based thermoregulation of butterflies 43,46 supporting short windows of activity 48 even in cold, high-elevation settings. The remarkably strong association of butterflies with mountains seems to be further driven by their strong co-evolution with plants 7 and its impact on resource availability in colder climates 49 . Specifically, grasslands and open habitats above the tree line are known to support mountain-top endemics, including Erebia and Parnassius species highly specialized to grasses (mostly Poaceae) and other alpine plants (e.g. Sedum ) 50,51 . For instance, in the European Alpes alone at least 30% (1,489) of all European Lepidoptera (butterflies and moths) species occur and 15% (220) are endemic to the alpine region 51 . This extraordinary concentration in narrow parts of geographical and environmental space 39 highlights a precarious situation of butterflies in a rapidly changing world. Projected erosion of butterfly niches Through their role as buffers for past climatic change, their diversity of microclimatic conditions, and their isolation, mountains have repeatedly served as fundamental refugium for terrestrial global biodiversity 26–28,31,33,42 . During the projected upcoming period of rapid global warming, the same attributes have been hypothesized to convert mountain habitats from safe havens 26 to graves, especially in combination with growing land-use pressures 52–54 . To gauge this threat, we assess the relative temperature exposure and niche erosion of butterfly hotspots in a future warmer world using ensemble predictions of change in mean annual temperature between now and 2070 (see methods). Assessing the availability of temperatures within realms while accounting for the inherently small portion of hotspot assemblages 55 reveals that even minor warming can have severe impacts on hotspots of species richness, range rarity, and phylogenetic diversity (Fig. 6). For example, the rare cold temperatures of Afrotropical richness hotspots are predicted to erode by 60% despite a comparatively minor temperature increase of 2.6°C (RCP 8.5). This temperature niche loss is five times greater than for Afrotropical non-hotspots, which are mainly encompassing warmer conditions. Similar trends underpin other realms, especially in the tropics. Butterfly hotspot temperature niche loss ranges from 13% to 64% (mean: 31%) for species richness, from 6% to 14% (mean: 11%) for range rarity, and from 9% to 60% (mean: 33%) for PD, respectively (Fig. 6a, Extended Data Fig. 4). In almost all cases, the niche loss is greater for hotspots compared to non-hotspots (Fig. 6a) even though non-hotspots usually experience greater projected increase in absolute temperature (Extended Data Fig. 5). However, because temperature regimes of biodiversity hotspots are distinct and within a realm geographically much rarer compared to non-hotspots, they are more susceptible to niche loss (Fig. 6 and Extended Data Fig. 2, 3). For butterfly hotspots, we found a negative relationship between projected warming and resulting niche loss (RCP 4.5 and 8.5: Spearman’s ρ = –0.43 and –0.53; P = 0.072 and 0.023; Fig. 6c and Supplementary Fig. 4). Together, these findings reveal that under the anticipated rapid global warming, mountains do not function as safe havens, but instead might be traps for butterfly biodiversity. Assessments of climate change impacts often neglect niche availability, for instance because they are based on absolute climate changes in focal areas 56–58 , or rely on simple, binary scenarios of whether or not species might track their niches 59,60 . Our results suggest that quantifying geographic niche availability is crucial for understanding threats to mountain biodiversity, due to the nuanced and gradual decline of specific temperature regimes at upslope locations following warming. These trends are mirrored by local population declines at highest elevations despite concurrent up-slope range shifts in the last decades 29,61 . However, we note that additional dispersal constraints not accounted for in our analysis might cause yet greater niche losses in island endemics (e.g. range rarity hotspots in Australasia and Indomalaya) and in regions where the latitudinal orientation of mountain ranges hinders northward shifts (Fig. 6 and Extended Data Fig. 4). Despite a growing recognition of their worldwide populations declines 3 , less than 1% of all insects and under 8% of butterflies have so far been assessed for their global threat status). Our study uncovered that the global conservation hotspots for butterflies vary strongly across different aspects of diversity, show limited overlap with other taxa, and are unusually strongly exposed to global warming. This threat arises from butterflies’ strong concentration at higher elevations that differs markedly from other species groups assessed globally to date. Mountains play a pivotal role for this species group yet due to their geographically rare and isolated environmental conditions are now bound to become ecological dead ends. There is an urgent need for targeted conservation strategies that address the connectivity, protection, and restoration of mountain areas where identified rarity and threat most strongly coincide. Our findings sound a clarion call for a more comprehensive global biogeographic knowledgebase to ensure the recognition of imminent threats to biodiversity and guide effective biodiversity conservation and management. Methods All analyses were conducted in R v.3.5.1. Distributional data Despite the rich natural history records for butterflies, the limited availability of expert range maps thus far hampered global-scale assessments of their diversity and conservation prioritization. We therefore used a broad spectrum of approaches and types of distributional data to fill this knowledge gap. Specifically, globally comprehensive country-level distribution information was used in cleaning all available occurrence records and digitized records from publications to model species’ distributions and generate ecoregional ranges of species (for sources see Supplementary Table 1). This information was complemented with digitized regional expert range maps. All data were taxonomically harmonized using the most up-to-date taxonomic reference 6 , 62 . We considered Hesperiidae, Lycaenidae, Nymphalidae, Papilionidae, Pieridae, Riodinidae, but excluded Hedylidae (American moth-butterflies) due to their limited species diversity, unique ecology among butterflies, and poor representation in literature 63 . We preferred species distribution models due to their high accuracy and resolution and used expert range maps only for species with fewer than five records or those with SDMs of poor accuracy. In the absence of expert range maps, records for species with fewer than five records or those with SDMs of poor quality were intersected with terrestrial ecoregions. The final analyses were based on 6,650 species distribution models, expert range maps for additional 881 species and ecoregional range maps for additional 5,137 species, covering a total of 65% of all butterfly species 6 . All distribution data were resampled to the same grid of assemblages with an approximate size 110 km × 110 km as a compromise between the high accuracy of species distribution models (here 1 km-resolution) and the rather low accuracy of range maps 64 . Data integration Occurrence records. For generating SDMs and ecoregional ranges, we used 8,160,747 taxonomically harmonized and spatially cleaned records of 10,565 species (original data retrieved from gbif.org February 9, 2021; data and download filters at DOI: 10.15468/dl.92t83z ) 6 . Cleaning steps for these data, included the removal of duplicated records, of non-terrestrial records and of spatial outliers. To improve the accuracy of occurrence records based on expert knowledge, we employed a validation step based on country-level occurrences from a near-complete checklist 6 . In addition, we included 11,839 digitized records of 593 species from literature for the Iran and Cambodia for which only ecoregional range maps were generated due to their lower spatial accuracy (Supplementary Fig. 2 and Table 1). Species distribution models. Spatially explicit and cleaned occurrences recorded after the year 1970 were thinned at a 10 km-resolution 65 . These records and twelve biologically relevant environmental variables 66 , were used to build Maximum Entropy-based species distribution models 67 with functions of the R-package dismo . Five variables that describe annual and seasonality trends in temperature and precipitation were retrieved from chelsa.org (Bio1, Bio4, Bio10, Bio12, Bio15; CHELSA v2 current condition records 68 , 69 ). Species distribution models also included the coefficient of variation in elevation and average elevation 70 , mean annual EVI (Enhanced Vegetation Index), Winter EVI and Summer EVI 71 , and the standard deviation of interannual variation in MODIS-based cloud cover 72 downloaded from EarthEnv.org. In addition, as a proxy for paleoclimatic stability we used the standard deviation of mean annual temperatures across the Pleistocene calculated based on climate simulations in 10-thousand-year intervals 73 . All variables were cropped to the same extent and if necessary resampled to a 1 km-resolution. MaxEnt models were fitted using 10,000 randomly sampled background points as suggested by ref. 74 and default settings. Note that the inclusion of more variables than occurrence records is unproblematic for MaxEnt SDMs as they evaluate predictive gain and incorporate an overfitting penalty for each predictor 67 . Models were evaluated on a held-out test set consisting of a fifth of the original presences and sampled pseudo-absences. The raw MaxEnt output of species’ habitat suitability at 1 km-resolution was then converted to binary data, by using the 95% quantile of the suitability values extracted from the underlying occurrences records as presence threshold. Finally, these binary distribution data were masked with terrestrial ecoregions 75 (retrieved from OneEarth.org) that intersected with occurrence records of the species 66 . 336 species distribution models with poor accuracy as determined by a low area under the curve (i.e. an AUC lower than 0.5) were visually checked by experts. 51 of them were discarded because the predictions were evaluated as unrealistic. The median AUC of the remaining 6,650 species distribution models was 0.93 (mean: 0.87). The final binary species distribution maps were reassigned to our equal-area grid 76 and only range fragments overlapping more than 50% with an assemblage were considered presences. Expert and ecoregional range maps. 10,314 vector distribution maps for 5,303 butterfly species from 35 field guides were georeferenced, quality controlled, taxonomically harmonized 6 , 62 and spatially merged (Supplementary Table 1). However, for many species both sufficient records for SDMs as well as expert range maps were lacking, and these data shortfall was most severe in tropical regions and taxa (Supplementary Fig. 6). We therefore adopted a regionalization scheme commonly used in global-scale conservation for these species, if at least one record was available. This scheme, the ecoregions of the world, represents a downscaling of biogeographical realms based on expert synthesis and species turnover 75 . The 5,137 generated ecoregional range maps 66 were highly spatially and taxonomically complementary to SDMs and expert range maps, particularly for tropical regions and taxa (Supplementary Fig. 2). We preferred ecoregional range maps over alternative approaches, such as alpha hulls and geographical buffers, as they have been shown to be more concordant with expert range maps and SDMs, particularly for data-poor species and because they span ecologically meaningful extents rather than being based on spatial proximity alone 66 . Both expert and ecoregional range maps were reassigned to our equal-area grid and only range fragments overlapping more than 50% with an assemblage were considered presences. Diversity aspects. Species richness (SR) was calculated as the count of co-occurring species per assemblage. As a measure of endemism, we calculated the range rarity (‘RR’) as the mean of the inverse occupancy (total count of assemblages globally/ count of assemblages occupied by a species) of co-occurring species. Our choice of this measure, in contrast to the one employed in pioneering studies on vertebrates and plants 9 , 10 , was driven by the absence of established thresholds for endemic species and regions crucial for butterfly conservation. Additionally, range rarity proved to be less depended on species richness 38 , making it a more effective complement for the conservation of biogeographical unique and highly threatened species 11 . To also incorporate concentrations of distantly related species into our prioritization, we calculated the phylogenetic distinctiveness of co-occurring butterfly species (hereafter ‘phylogenetic diversity’ or ‘PD’ for simplicity). We used a recently published genus-level phylogeny 7 and the taxonomic names of all accepted butterfly species 6 , to add all butterfly species for which distribution data was available at their respective genus and randomly resolved the intra-genus relationships with functions of the R-package phytools 77 . Species were not added if they were represented by only one taxon in the phylogeny or if the genus to which they belong was paraphyletic. Note that this phylogenetic tree included only 11,143 species, while species richness and range rarity were calculated based on the distribution data for 12,119 species. For the calculation of phylogenetic diversity, the tree was pruned to include only the regional (realm-specific) species pool. Phylogenetic diversity was calculated as the standardized effect sizes of mean pairwise distances (MPD) across co-occurring species, i.e., the observed MPD minus the random MPD divided by the standard deviation of the random MPD values, to account for the effect of species richness 78 . Random MPD represents a null model expectation of the phylogenetic composition of an assemblage based on the average of 10,000 calculations of the MPD for the same number, but a randomly drawn sample, of species per assemblage. As for range rarity the choice of this measure was driven by its empirical independence of species richness and range rarity, compared to, for instance, Faith’s phylogenetic diversity (Supplementary Fig. 3) or phylogenetic endemism 20 , and consequently its greater emphasis on unique phylogenetic aspects. We restricted the species pool to the respective biogeographical realms to accommodate species turnover resulting from past and present geographical dispersal barriers. Using Chao’s dissimilarity index with species’ range size as weight, we confirmed that the selected realm definition 75 effectively delimited highly dissimilar species pools (Supplementary Table 2). Only Oceania was included in the Australasian realm due to its small extent and a similar species composition of the two realms. Hotspots of diversity. Hotspots of diversity patterns for all taxa were arbitrarily defined as the 5% assemblages with the highest SR, RR, and PD per realm, respectively. PD was transformed to positive values for this purpose [PD+(min(PD*-1))]. We identified hotspots at the realm scale, instead of the global scale, to ensure accurate representation of the three complementary aspects of diversity within and between regions 20 , while minimizing the impact of potential sampling biases. Leveraging coarse, but near-complete country-level distribution data, we document the species coverage of our high-resolution data (Supplementary Figs. 5, 6). With this information on data shortfalls, we also assessed the robustness of our realm-level hotspot definition. Specifically, we multiplied the respective diversity estimate per assemblage by the inverse of the country-specific species coverage and compared our core hotspots with those derived based on the weighted diversity estimates (Supplementary Fig. 8). For instance, while the species richness for a given assemblage that falls into a country with 100% species coverage remains the same that of an assemblage with a lower coverage in a data-poor country is upweighted by the factor 1.67 (1/0.60 in the most extreme case) before hotspots are defined. This allowed an evaluation of the robustness of hotspot locations for all diversity aspects, based on the assumption that the magnitude, but not the relative within-country variation of diversity, will change with the addition of species. Thus, greater emphasis is placed on data-poor countries across each realm. We thereby confirm that only 19% of the combined hotspots of SR, RR and PD do not overlap with any of the coverage weighted ones (17% for SR, 11% for RR, and 37% for PD, separately; Supplementary Fig. 8). Strongest mismatches highlight potential shifts in PD hotspot locations from lowland Japan to highlands in southern China and from lowlands of Borneo to highlands of New Guinea. However, note that our measure of PD is richness-corrected and should be hence robust to the sampling of additional species that by chance alone add phylogenetic diversity to an assemblage. Following ref. 10 , we also evaluated the influence of different hotspot definitions in percent top-ranking assemblages by recalculating overlap of diversity aspects for the top 1–100% assemblages in 1%-steps. This analysis showed that the increase in congruence among the three diversity aspects is proportional to the increase in area until approximately 20% of all assemblages are considered (Supplementary Fig. 9). For butterflies both congruence and overlap were compared between SR, RR, and PD. To assess to congruence and hotspot overlap of butterfly diversity with terrestrial vertebrate diversity, expert range maps for 32,851 amphibians ( https://www.iucnredlist.org/resources/spatial-data-download ), reptiles 21 , mammals 79 , and birds 80 were retrieved, and then resampled to our grid. We also leveraged ant richness and rarity data based on 14,324 (sub-)species 23 as well as a prediction of plant richness based on more than 10,000 species (maximum regional estimate) 81 . Layers of plant and ant diversity data had a two times finer resolution and were therefore averaged per intersecting cell of our grid. Although thus far, this ant and plant data have not been explicitly factored into the establishment of conservation priorities, they serve as valuable benchmarks for comparing elevational gradients and assessing the prevalence of hotspots in mountainous regions. This is particularly important due to the co-evolution of butterflies with plants 7 and the potential ecological parallels among insects 23 . Hotspots of all taxa were calculated in the same way as for butterflies, yielding in total 626 hotspot assemblage for each vertebrate taxon-diversity aspect combination, but fewer for ants and plants (622 and 592, respectively) due a slightly lower spatial coverage of the underlying data. Mountain diversity To evaluate the likelihood that a hotspot was located in mountains, we took into account that mountain regions cover a smaller proportion (38% at our grain) of the terrestrial area of the Earth and that hotspot regions represent only 5% of all assemblages within each realm, as per our definition. We intersected our grid with the most up-to-date classification of mountains from a global inventory (GMBA) 8 , considering cells as mountainous if they were covered by more than 5% of the area. We chose this threshold to exclude smaller mountain fragments, but also accommodate the rather coarse resolution of the distribution data. For each realm and diversity aspect the proportion of hotspot areas in mountains was divided by the proportion of hotspot areas in lowlands. For instance, the likelihood of range rarity hotspots being located in mountains is 5.24 times greater (472 of 4,696 grid cells ≈ 10.0%) than that for lowlands (150 of 7,819 grid cells ≈ 1.9%). For species-level analyses, the proportion of species’ ranges in mountains was calculated based on those species that occur in each respective type of hotspot and realm. As the regional baseline for this measure, we calculated the proportion of grid cells that are mountains on the total number of grid cells per realm. Species whose ranges cover a higher proportion of mountains than the baseline (disproportionately more mountain) were considered primarily mountain-dwelling. Temperature difference and niche loss under global change scenarios For analysis of the impact of temperature changes on species assemblages in hotspots and non-hotspots, we generated ensembles of mean annual temperature across predictions from complementary climate models for the period of 2061–2080. These models provided predictions for two representative carbon pathways, one with net carbon emissions of zero until 2050 and one with continued emissions until the end of this century (RCP 4.5 and 8.5 scenario), as defined by the Intergovernmental Panel on Climate Change 82 . We focused on mean annual temperature as it is the most important and general determinant of species’ distributions and diversity patterns 28 , 33 , 59 . The selection of eight models (CESM1-BGC, CESM1-CAM5, CMCC-CM, FIO-ESM, INMCM4, IPSL-CM5A, MIROC5, MPI-ESM-MR) from climatologies available at chelsa.org (v1.2 CMIP5) followed ref. 83 in using predictions with low interdependence. Temperature values were averaged across predictions for each of the two RCP’s and aggregated to mean values per grid cell. The temperature difference was calculated from these ensembles by subtracting the current mean annual temperature from that of future temperature ensembles (Extended Data Fig. 1 ). Absolute temperature differences are a common measure to identify regions with high potential threat to biodiversity under future climate change (i.e. climate change exposure). However, here we argue that even small difference in temperature can have drastic impacts on biodiversity as certain climates and particularly hotspot climates might be rarer than those of non-hotspots. To address this issue, we calculated the loss of the current temperature niche of hotspots and their corresponding non-hotspots for each realm and diversity aspect based on density overlap in a Bayesian framework 55 . Frequency distributions were converted to densities with a sample size of 1,000 samples and the default probability region size of 95% was used for 1,000 repeated calculations (Extended Data Fig. 4 ). Overlap was calculated as the probability that a given current temperature of an assemblage is found in the probability region of future temperatures, and the non-overlapping part of the current conditions we denominate the ‘temperature niche loss’ (see Fig. 6 c). Unlike other niche overlap metrics, this probabilistic measure explicitly accounted for uncertainty in the niche regions definition and for an uneven number of compared entities (i.e. in our case grid cells). Note that the direction of comparison matters for this calculation, and we did not discuss the gain in temperature niche (compared future with current temperature) because novel very warm climates will not maintain current biodiversity. In addition to that of hotspots and non-hotspots, we also calculated the niche loss for a random subset of 5% of the number of assemblages per realm to explore whether niche loss is affected by the inherently lower count of hotspots. This analysis confirmed the robustness of our niche loss estimate to differences in sample size, being similar between the small set of 5% random assemblages and the 95% of non-hotspot assemblages per realm (Extended Data Fig. 4 ). Data gaps Despite extensive data mobilization and integration efforts (Supplementary Figs. 1, 2 and Table 1) one third of the described butterfly species lacked sufficient data to be included, particularly for the Afrotropic (42% missing), Indomalayan, and Neotropic realms (34% and 32%; Supplementary Figs. 5, 6). We therefore assessed hotspots within realms to limit the impact of these main differences in data coverage 20 , but also evaluated the robustness of within-realm patterns. We expect regions with particularly large portions of unmapped species to see refinements to their hotspot locations using coarse but near-complete country-level distribution data 6 . We find that among countries, species-coverage is negatively correlated with the count of identified hotspots, contrasting the expected effect of sampling bias (Spearman’s ρ = − 0.43, P = 0.001; Supplementary Fig. 7). Adapting the hotspot definition with a country-level species coverage weight based on these data, our supplementary analysis confirmed only minor changes in the locations of species richness and rarity hotspots (83% and 89% remain unchanged, Supplementary Fig. 8). Hence, although many species are added to certain countries this generally does not impact realm-level priorities. Stronger mismatch (37% globally) is observed for hotspots of phylogenetic diversity, highlighting mountains in New Guinean and southern China. These areas are important targets for additional sampling of phylogenetic data, but note that their confirmation would shift priorities mainly from lowlands to highlands. Declarations Data availability All produced hotspots and diversity maps for butterflies will be made available through map.half-earthproject.org and mol.org upon acceptance. Distribution data for amphibians can be requested from IUCN (https://www.iucnredlist.org/resources/spatial-data-download). Distribution data for birds 80 , mammals 79 and reptiles 21 can be requested from authors of the respective publications. Plant species richness predictions 81 are available from the authors upon request. Ant richness and rarity data 23 is available at DataDryad DOI:10.5061/dryad.wstqjq2pp. The layer of terrestrial ecoregions used to generate ecoregional range maps for butterfly species can be retrieved from OneEarth.org. Digitized expert range maps are available from mol.org through the source links in the Supplementary Information. Global country-level distribution information for butterflies 6 can be retrieved from https://mol.org/datasets/149345d9-eb27-4140-ba60-eaa32ff08ff8. The phylogenetic tree for butterflies is available from the authors 7 . Any additional data not listed can be made available upon reasonable request from the authors of this article. Code availability No custom code or mathematical algorithm was used in the analysis. Code to analyze niche loss and the proportion of hotspots in mountains will be uploaded to figshare upon acceptance. Competing interests The authors declare no competing interests. References Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574 , 671–674 (2019). Seibold, S. et al. The contribution of insects to global forest deadwood decomposition. Nature 597 , 77–81 (2021). Wagner, D. L., Grames, E. 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Supplementary Files SuppInfoGlobalbutterflydiversity.docx Supplementary Information EXTENDEDDATA.docx Cite Share Download PDF Status: Published Journal Publication published 24 Mar, 2025 Read the published version in Nature Ecology & Evolution → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4437399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":306493296,"identity":"c7cc3650-8a78-472a-98df-27fac4fb15e4","order_by":0,"name":"Stefan Pinkert","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie2QMUvDQBiG3yDc9ElWBzV/4SRgEUT/SkMgP6F0cEg4uMnuHcT+BafimOPgprP+AYeI4Ca0FAQHa5PUiggXOjrcs93HPbzf+wEezz8kyoE5sCJQ8xxu58ytcINgDOiNUtrt7y6FbRS0ipK7KJFQ+ce9PuyRNsvF7dNxNBGqwuDc3UWypBjZFZ2NZDpW09eYG5ZyzDJ3jKFetS9L4o8UQ011csfo9CCQukMJF8Vnq4RLqBudTGT4XitfXSmBaFMervegcp3khlitlO76JjsRR41iTQxrmi5ZzPuz1F1f6OfiTZaX3KYvGF7VF6sn1Xxw4V7sD9/79HcWfhSPx+Px/GINsNheWmh6PK0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8348-2337","institution":"Yale University","correspondingAuthor":true,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Pinkert","suffix":""},{"id":306493297,"identity":"53104ace-843a-4ccb-93e9-557fe25d8878","order_by":1,"name":"Nina Farwig","email":"","orcid":"https://orcid.org/0000-0002-0554-5128","institution":".","correspondingAuthor":false,"prefix":"","firstName":"Nina","middleName":"","lastName":"Farwig","suffix":""},{"id":306493298,"identity":"63030c9e-be7b-438b-a324-84be49081ce3","order_by":2,"name":"Akito Kawahara","email":"","orcid":"https://orcid.org/0000-0002-3724-4610","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Akito","middleName":"","lastName":"Kawahara","suffix":""},{"id":306493299,"identity":"996d7aa3-7e2c-4a6d-8c9c-5ddc9377f9e4","order_by":3,"name":"Walter Jetz","email":"","orcid":"https://orcid.org/0000-0002-1971-7277","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Walter","middleName":"","lastName":"Jetz","suffix":""}],"badges":[],"createdAt":"2024-05-17 14:41:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4437399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4437399/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41559-025-02664-0","type":"published","date":"2025-03-24T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57825249,"identity":"8036428a-99e5-43ce-bd37-48a52e56060d","added_by":"auto","created_at":"2024-06-06 06:51:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3975232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal variation in butterfly diversity in space and along elevation. \u003c/strong\u003ePanels (\u003cstrong\u003ea\u003c/strong\u003e), (\u003cstrong\u003eb\u003c/strong\u003e), and (\u003cstrong\u003ec\u003c/strong\u003e) display species richness (SR), range rarity (RR), and phylogenetic diversity (PD) of butterfly assemblages, respectively.\u003cstrong\u003e \u003c/strong\u003eRange rarity is calculated as the average inverse occupancy of co-occurring species. Phylogenetic diversity estimates are standardized based on realm-wide null models. Values in inset boxes represent pairwise correlation coefficients (Spearman’s ρ). Trends surfaces on the left show the relationships of each diversity aspect with latitude and elevation (all \u003cem\u003en\u003c/em\u003e = 12,515; SR: F = 446.22 (lat.) and 35.73 (elev.), adj. R\u003csup\u003e2\u003c/sup\u003e = 0.25; RR: F = 1229.40 (lat.) and 96.67 (elev.), adj. R\u003csup\u003e2\u003c/sup\u003e = 0.52; PD: F = 460.73 (lat.) and 17.48 (elev.), adj. R\u003csup\u003e2\u003c/sup\u003e = 0.27). For realm delineations see Fig. 2.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/668738f62ba1831be17db5f2.png"},{"id":57825230,"identity":"3ba35f98-4679-40f4-8b67-40106b3f4c76","added_by":"auto","created_at":"2024-06-06 06:51:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":878397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHotspot locations and overlap in mountain and non-mountain regions.\u003c/strong\u003e The maps depict hotspots of species richness, range rarity, and phylogenetic diversity (SR, RR, PD) and their congruence across six biogeographical realms. Cells with white outlines represent hotspots in lowlands. Inset bar plots indicate the probability of a hotspot of species richness, range rarity, phylogenetic diversity, and all aspects combined (any) to be located in mountains or lowlands (white outline). Values above the bars indicate odd ratios of these two probabilities. Inset violine plots show the frequency distributions of the percentage of species’ ranges in mountains for species occurring in each hotspot type and realm, along with realm-specific null expectations (i.e. the proportion of mountain cells per realm) represented by dashed horizontal lines. The species coverage varies across the realms: 93% (Nearctic), 75% (Palearctic), 77% (Australasia), 58% (Afrotropic), 66% (Indomalayan), 68% (Neotropic). See Supplementary Figs. 7 and 8 for analysis of the robustness of these hotspots against missing species’ distribution data based on near-complete country-level occurrences.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/3c85004e57f1ea530dfccda9.png"},{"id":57825229,"identity":"50a5e9cf-c933-4e36-8960-6fc7f78426f3","added_by":"auto","created_at":"2024-06-06 06:51:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCongruence in diversity patterns and overlap in hotspots among taxa.\u003c/strong\u003e Pairwise Spearman’s rank correlations of overall diversity patterns (lower left corner) and overlap of hotspots (upper right corner) of species richness and range rarity among butterflies, vertebrates, ants, and plants. Range rarity data was not available for plants. Overlap of hotspots (i.e. the top 5% of assemblages in each taxon) is calculated as the count of hotspot assemblages (identified at realm-level) shared between taxon pairs divided by the total count of hotspot assemblages per taxon. Silhouettes are from https://www.flaticon.com/.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/e8d48eb7877b0258f209e156.png"},{"id":57825247,"identity":"383b3a5c-c815-4126-af32-72eb12df9519","added_by":"auto","created_at":"2024-06-06 06:51:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1160429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimilarity of diversity hotspots between butterflies and vertebrate taxa. \u003c/strong\u003eMaps show the overlap of hotspots of species richness (\u003cstrong\u003ea\u003c/strong\u003e) and range rarity (\u003cstrong\u003eb\u003c/strong\u003e) of butterflies with those of the four terrestrial vertebrate taxa that are typically considered in setting global-scale conservation priorities. Mismatches (shown in light blue and light red) highlight regions that would most effectively complement the protection of insect biodiversity.\u003cstrong\u003e \u003c/strong\u003eValues in the map legend represent the counts of hotspots for each category of overlap, with values in brackets indicating proportions relative to the count of unique hotspots assemblages (Ʃ) for all five taxa combined. For estimates of congruence and overlap between taxa see Fig. 3.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/b1724f7052a8c7a663e716fa.png"},{"id":57825246,"identity":"5aee382c-91d0-4b4e-982d-954bbe92eb7b","added_by":"auto","created_at":"2024-06-06 06:51:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":344334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecies richness and range rarity in mountains and lowlands across taxa.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e, Probability of a hotspot of species richness, range rarity, and their combination to be located in either mountains or lowlands (white outline). Values above the bars indicate odd ratios of these two probabilities. In contrast to the proportion of hotspots inside and outside mountains, these probabilities account for the fact that mountains cover only 38% of the Earth’s surface at our grain. \u003cstrong\u003eb\u003c/strong\u003e, Elevational gradients of standardized (z-scaled) species richness and range rarity of butterfly, vertebrate, ant, and plant assemblages, employing spline-based smoothed regression with 95% confidence intervals. Values for vertebrates were individually standardized and subsequently summed up for each assemblage. Further details on elevation gradients are provided in Supplementary Fig. 10. For estimates of congruence and overlap between taxa see Fig. 3.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/e66900186a9f9da8fa5a8f4b.png"},{"id":57825233,"identity":"1ff794be-88d5-44d4-a5e7-9e31ccdb9370","added_by":"auto","created_at":"2024-06-06 06:51:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":235187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjected future temperature niche loss and temperature difference in non-hotspots and hotspots. a\u003c/strong\u003e, Temperature niche loss for non-hotspots and hotspots (for \u003cem\u003en\u003c/em\u003e and location see Fig. 2) across realms and aspects of butterfly diversity under global warming (RCP 8.5) until the year 2070. \u003cstrong\u003eb\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eDensity plot exemplifying the calculation of niche loss (blue area) and median temperature difference (ΔT) between current and projected future mean annual temperatures for hotspots of species richness in the Afrotropic (non-hotspots: left, hotspots: right of 0-line). Corresponding plots for all realms and diversity aspects can be found in Extended Data Fig. 2. \u003cstrong\u003ec\u003c/strong\u003e, Relationship of temperature niche loss and temperature difference among realm-level hotspots of species richness, range rarity, and phylogenetic diversity under an ensemble model for the scenario RCP 8.5 (see Supplementary Fig. 4 for RCP 4.5 results). Plots in (\u003cstrong\u003ea\u003c/strong\u003e) show median values and inner interquartile range/ its 1.5-fold (dots, thick/thin bars). For corresponding plots of temperature difference see Extended Data Fig. 5. Note that projected future hotspot temperature niche loss and temperature difference are negatively related across realms and diversity aspects (Spearman’s ρ = –0.53; \u003cem\u003eP\u003c/em\u003e = 0.023; \u003cem\u003en\u003c/em\u003e = 18). Further analyses comparing projected temperature niche loss and temperature difference between non-hotspots and hotspots can be found in Extended Data Fig. 1 and 3.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/111f3ee9f1a2532e01618d60.png"},{"id":79159269,"identity":"dcc5ced5-080c-4700-b7a8-49fec8b9b29c","added_by":"auto","created_at":"2025-03-25 07:07:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8839715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/6fd9295a-0383-4835-9c9b-9b009db03b4e.pdf"},{"id":57825228,"identity":"1be7ec4c-3d0e-454c-a600-4e9947e51ed9","added_by":"auto","created_at":"2024-06-06 06:51:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7372962,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SuppInfoGlobalbutterflydiversity.docx","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/d054a669ccd4d2086c10d232.docx"},{"id":57825232,"identity":"98af706b-0829-46fc-95bd-01bea6c15453","added_by":"auto","created_at":"2024-06-06 06:51:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1376374,"visible":true,"origin":"","legend":"","description":"","filename":"EXTENDEDDATA.docx","url":"https://assets-eu.researchsquare.com/files/rs-4437399/v1/645f576739fbd9fdfb86c53e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Global hotspots of butterfly diversity in a warming world","fulltext":[{"header":"Main","content":"\u003cp\u003eUnderstanding the distribution of biodiversity on Earth is the prerequisite for effective conservation and for mitigating the loss of species and their functions under rapid environmental change\u003csup\u003e9–13\u003c/sup\u003e. Recent work has highlighted the range of implications arising from an uneven geographic distribution of biodiversity, such as vast differences in countries’ conservation responsibilities\u003csup\u003e14\u003c/sup\u003e and heterogenous representation in protected areas\u003csup\u003e15,16\u003c/sup\u003e. These geographic differences in cause and consequence extend to, and are often exacerbated for, the functional and phylogenetic aspects of biodiversity\u003csup\u003e17\u003c/sup\u003e, which are recognized as central for supporting ecosystem resilience and preserving critical evolutionary heritage\u003csup\u003e18–20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe same motivation for a more comprehensive understanding extends to different taxa. For terrestrial vertebrates, the thus far dominant model system for global ecology and conservation\u003csup\u003e10,13,21,22\u003c/sup\u003e, prior work has documented marked differences in diversity patterns of endothermic and ectothermic taxa. While birds and mammals (endotherms) share 75% of their richness and rarity hotspots, their respective overlap with amphibians and reptiles (ectotherms) is not strong\u003csup\u003e20,21\u003c/sup\u003e. The extent to which these differences apply to plants and invertebrates remains poorly understood. This is especially true for insects, despite their essential ecosystem functions\u003csup\u003e2,5\u003c/sup\u003e, outstanding diversity, and alarming decline\u003csup\u003e3\u003c/sup\u003e. For ants, species richness and rarity hotspots are uniquely concentrated in regions with dry conditions that reflect the xeric preference and advantages of the groups’ social organization\u003csup\u003e23\u003c/sup\u003e. These recognized differences exemplify the precarious foundation of the current knowledgebase for global biodiversity at large scales and leave insect biodiversity poorly represented.\u003c/p\u003e\n\u003cp\u003eThe uneven distribution of biodiversity\u0026nbsp;takes on an additional weight\u0026nbsp;when hotspots\u0026nbsp;cluster into\u0026nbsp;distinct portions of the climate space that are particularly strongly impacted by anthropogenic change\u003csup\u003e22,24\u003c/sup\u003e. One prominent case are mountains. Owing to their historical role in supporting species’ survival and speciation through long-term climate dynamics and their own isolation, many mountain systems are centers of biodiversity\u003csup\u003e25–27\u003c/sup\u003e. Over 40% of terrestrial vertebrate species are mountain endemics\u003csup\u003e28\u003c/sup\u003e, but despite the strong implication for conservation whether and how this extends to insects remains unknown. At the same time, temperature-induced up-slope shifts and losses in habitat combined with strong geographic isolation put mountain biodiversity in great peril\u003csup\u003e29–32\u003c/sup\u003e. Impacts from warming might be particularly severe for insects due to their known strong temperature sensitivity\u003csup\u003e33–36\u003c/sup\u003e, but to date a global assessment of the geographic coincidence of diversity, rarity, and climate change threats for an insect system does not exist.\u003c/p\u003e\n\u003cp\u003eHere we address these issues using butterflies as global insect system. Given their ecological importance\u003csup\u003e5\u003c/sup\u003e, high surrogacy for insect diversity\u003csup\u003e4\u003c/sup\u003e, and their uniquely comprehensive distributional and phylogenetic information\u003csup\u003e6,7\u003c/sup\u003e, butterflies offer pivotal insights into complementary priorities for insect conservation. To achieve global coverage, we\u0026nbsp;mobilized, modelled, and validated species distribution information for 12,119 species based on over 8 million occurrence records and newly digitized expert range maps (Supplementary Figs. 1, 2 and Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRichness, rarity and phylogenetic diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross our 110 km-resolution analysis grid, butterfly species richness (SR) increases toward lower latitudes and higher elevation (Fig. 1a), peaking near the equator and at approximately 2500 m mean elevation (see methods and supplement for additional results addressing data gap species). In contrast, average range rarity (RR), defined as the average inverse of species’ global range sizes per assemblage – a continuous measure of endemism, bimodally peaks at around 20° and 40° latitude and 3500 m elevation (Fig. 1b). Notably in most realms, both metrics show limited congruence, particularly for peaks in narrow-ranged species, which are usually of greatest conservation concern (Spearman’s rank SR-RR: ρ = 0.40, \u003cem\u003en\u003c/em\u003e = 12,515). These differences become more evident in an assessment of global hotspots, defined as the top 5% of assemblages of the six major biogeographical realms (Fig. 2). Only 10% of richness and range rarity hotspots are shared globally, with no overlap in the Indomalayan and Afrotropic realms and as few as 2% in the Afrotropic and 4% in the Palearctic realms. This suggests that the hotspot disparity previously documented for vertebrates\u003csup\u003e10,11,13\u003c/sup\u003e, and hence the limited value of richness hotspots for identifying places of greatest global conservation concern, also extends to butterflies.\u003c/p\u003e\n\u003cp\u003eA recently completed comprehensive phylogenetic framework\u003csup\u003e7\u003c/sup\u003e enables us to consider the geography of butterfly phylogenetic diversity. Assessments of the phylogenetic aspect of insect biodiversity at large scale are still sparse, but offer important opportunities to determine the degree of congruence with traditional diversity measures\u003csup\u003e20\u003c/sup\u003e. We find that phylogenetic diversity (PD, defined as deviation from a realm-level null expectation) peaks at about 20° latitude and at elevations of 3000 m and 4000 m (Fig 1c).\u0026nbsp;This PD only weakly correlates with species richness and range rarity (SR-PD: ρ = 0.17, RR-PD: ρ = 0.29; \u003cem\u003en\u003c/em\u003e = 12,515), and respective global hotspots show only limited congruence (SR-PD: 29%; RR-PD: 15%). The mismatch is particularly strong in the central Neotropics, south-east Palearctic and central Indomalaya (Fig. 2), all representing global centers of PD. These centers of PD (Fig. 1c; see also\u0026nbsp;Supplementary Fig. 3 for an alternative PD measure)\u0026nbsp;match the previously argued hotspots of\u0026nbsp;deep lineage diversification and potential evolutionary origin of butterflies based on phylogeographical reconstructions\u003csup\u003e7\u003c/sup\u003e. The limited congruence between PD and other measures of diversity we uncover, highlights the importance of geographically targeted efforts to protect the evolutionary heritage of butterflies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-taxon congruence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch on global priorities for biodiversity conservation has offered important insights, but remains mostly limited to terrestrial vertebrates\u003csup\u003e10,11,13,20\u003c/sup\u003e. Because of their ectothermic physiology and thus direct dependence on ambient temperature\u003csup\u003e34,37\u003c/sup\u003e, we expected butterfly richness patterns to be more similar with those of amphibians, reptiles, and ants than with birds and mammals. Within ectotherm taxa, we expect the diversity of butterflies and ants to be more congruent with one another than with vertebrates, given that insects are orders of magnitude smaller and differ in both scale of habitat selection and physiological rates from vertebrates\u003csup\u003e34,35\u003c/sup\u003e. Due to the known co-evolutionary association\u003csup\u003e7\u003c/sup\u003e, we also predict a particularly high association between the richness patterns of butterflies and plants. Given a much lesser dominance of a minority of wide-ranging species in driving range rarity\u003csup\u003e38\u003c/sup\u003e and a stronger signature of idiosyncratic biogeographic histories, we expect overall weaker associations for this measure than for species richness patterns\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe find that across the assessed 110 km-resolution assemblages, the species richness of butterflies is moderately positively associated with those of plants, mammals, birds, and amphibians (all\u0026nbsp;ρ \u0026gt; 0.70, \u003cem\u003en\u003c/em\u003e = 12,515), but less so with that of ants\u0026nbsp;(0.67) and reptiles (ρ = 0.55; Fig. 3). This suggests that differences in thermal strategies or ecophysiology play a limited role for driving richness differences at this scale. Instead, the environmental drivers of immigration and survival that ultimately underpin richness patterns appear shared among taxa. While the exact contributions of evolutionary, abiotic, and biotic factors shaping cross-taxon richness similarities remain largely unknown\u003csup\u003e33\u003c/sup\u003e, our results for insects, vertebrates, and plants emphasize the potential to derive generalities of broad relevance for biodiversity\u003csup\u003e28,39,40\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRange rarity hotspots harbor many species not occurring anywhere else, and their recognition is thus of extraordinary conservation concern. Compared to geographical congruence in species richness, cross-taxon similarity in range rarity patterns is much weaker\u0026nbsp;(all\u0026nbsp;ρ \u0026gt; 0.50, \u003cem\u003en\u003c/em\u003e = 12,515), and particularly poor with amphibians (ρ = 0.39). Even stronger cross-taxon mismatches emerge when comparing the top 5% diversity centers per realm and taxon (Fig. 3, 4). For these hotspots, overlap among taxa is generally poor, varying from 19% to 36% for species richness and 14% to 33% for range rarity. Notably, over 41% of the butterfly range rarity hotspots – approximately 4% of the land surface – are not shared with a single terrestrial vertebrate group (Fig. 4). Only 16% of butterfly range rarity hotspots overlap with a currently recognized rarity hotspot of ants. The hotspots areas distinct to butterflies include parts of the SW US, W Madagascar, S Asian highlands and select other locations. These results indicate that priority areas identified based on vertebrates miss critical places needed to safeguard the diversity of insects. Advancing the information foundation and conservation actions for these newly recognized butterfly hotspots should be of highest urgency\u003csup\u003e17,20\u003c/sup\u003e (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe unique role of mountains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOwing to their geographically isolated nature, strong topographic heterogeneity, and rapid environmental turnover, mountains are well-recognized for their role as catalysts for speciation and for offering refugia for species survival\u003csup\u003e27,28,41,42\u003c/sup\u003e.\u0026nbsp;All of these factors are expected to contribute to the large number of butterfly species found to be concentrated in, or endemic to, mountain regions. In addition, a broad range of insects possesses physiological adaptations to cold conditions\u003csup\u003e43–46\u003c/sup\u003e.\u0026nbsp;We thus expected mountains to play an important role in supporting exceptionally high levels of species richness and range rarity of butterflies compared to vertebrates. Our results confirm several of these expectations. Although mountains represent only 38% of the world’s terrestrial surface outside the polar regions at our study grain, they harbor 72% and 76% of global hotspots of species richness and range rarity, respectively (Figs. 2, 4; odds ratio of 4.29 and 5.24, respectively). This proportion is lower for phylogenetic diversity (59%), but still more than twice as high than expected by chance alone (odd ratio of 2.35). On the species level, these differences are reflected in the much greater portion of butterflies’ geographic ranges in mountains than elsewhere (violin plots in Fig. 2). The importance of mountains differs among realms, with generally lower mountain association in tropical compared to temperate realms (all hotspots combined: 1.43 to 7.09; bar plots in Fig. 2).\u003c/p\u003e\n\u003cp\u003eOur findings corroborate existing work recognizing the evolutionary and conservation significance of mountains for biodiversity\u003csup\u003e26–28,42\u003c/sup\u003e, but also highlight that their role might be even greater than previously thought. Greater concentrations of insect compared to vertebrate diversity toward higher elevations have been documented at local scales\u003csup\u003e47\u003c/sup\u003e, but at broad scales remain untested. We find that the concentration of butterfly diversity in mountains substantially exceeds that of almost all globally studied taxa, especially so for range rarity (Fig. 5a). Toward higher average elevations, butterfly richness decreases weakly, and butterfly rarity increases much more strongly than found in ants and terrestrial vertebrates. Above 2000 m, plant and butterfly richness remain relatively high, and butterfly range rarity markedly exceeds that of other taxa. We attribute this to several factors, including physiological adaptations such as color- and size-based thermoregulation of butterflies\u003csup\u003e43,46\u003c/sup\u003e supporting short windows of activity\u003csup\u003e48\u003c/sup\u003e even in cold, high-elevation settings. The remarkably strong association of butterflies with mountains seems to be further driven by their strong co-evolution with plants\u003csup\u003e7\u003c/sup\u003e and its impact on resource availability in colder climates\u003csup\u003e49\u003c/sup\u003e. Specifically, grasslands and open habitats above the tree line are known to support mountain-top endemics, including \u003cem\u003eErebia\u003c/em\u003e and \u003cem\u003eParnassius\u003c/em\u003e species highly specialized to grasses (mostly Poaceae) and other alpine plants (e.g. \u003cem\u003eSedum\u003c/em\u003e)\u003csup\u003e50,51\u003c/sup\u003e. For instance, in the European Alpes alone at least 30% (1,489) of all European Lepidoptera (butterflies and moths) species occur and 15% (220) are endemic to the alpine region\u003csup\u003e51\u003c/sup\u003e. This extraordinary concentration in narrow parts of geographical and environmental space\u003csup\u003e39\u003c/sup\u003e highlights a precarious situation of butterflies in a rapidly changing world.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProjected erosion of butterfly niches\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough their role as buffers for past climatic change, their diversity of microclimatic conditions, and their isolation, mountains have repeatedly served as fundamental refugium for terrestrial global biodiversity\u003csup\u003e26–28,31,33,42\u003c/sup\u003e.\u0026nbsp;During the projected upcoming period of rapid global warming, the same attributes have been hypothesized to convert mountain habitats from\u0026nbsp;safe havens\u003csup\u003e26\u003c/sup\u003e to graves, especially in combination with growing land-use pressures\u003csup\u003e52–54\u003c/sup\u003e. To gauge this threat, we assess the relative temperature exposure and niche erosion of butterfly hotspots in a future\u0026nbsp;warmer world using ensemble predictions of change in mean annual temperature between now and 2070 (see methods).\u003c/p\u003e\n\u003cp\u003eAssessing the availability of temperatures within realms while accounting for the inherently small portion of hotspot assemblages\u003csup\u003e55\u003c/sup\u003e reveals that even minor warming can have severe impacts on hotspots of species richness, range rarity, and phylogenetic diversity (Fig. 6). For example, the rare cold temperatures of Afrotropical richness hotspots are predicted to erode by 60% despite a comparatively minor temperature increase of 2.6°C (RCP 8.5). This temperature niche loss is five times greater than for Afrotropical non-hotspots, which are mainly encompassing warmer conditions. Similar trends underpin other realms, especially in the tropics. Butterfly hotspot temperature niche loss ranges from 13% to 64% (mean: 31%) for species richness, from 6% to 14% (mean: 11%) for range rarity, and from 9% to 60% (mean: 33%) for PD, respectively (Fig. 6a, Extended Data Fig. 4).\u003c/p\u003e\n\u003cp\u003eIn almost all cases, the niche loss is greater for hotspots compared to non-hotspots (Fig. 6a) even though non-hotspots usually experience greater projected increase in absolute temperature (Extended Data Fig. 5). However, because temperature regimes of biodiversity hotspots are distinct and within a realm geographically much rarer compared to non-hotspots, they are more susceptible to niche loss (Fig. 6 and Extended Data Fig. 2, 3). For butterfly hotspots, we found a negative relationship between projected warming and resulting niche loss (RCP 4.5\u0026nbsp;and\u0026nbsp;8.5: Spearman’s ρ =\u0026nbsp;–0.43 and\u0026nbsp;–0.53; \u003cem\u003eP\u003c/em\u003e = 0.072 and 0.023; Fig. 6c and Supplementary Fig. 4). Together, these findings reveal that under the anticipated rapid global warming, mountains do not function as safe havens, but instead might be traps for butterfly biodiversity.\u003c/p\u003e\n\u003cp\u003eAssessments of climate change impacts often neglect niche availability, for instance because they are based on absolute climate changes in focal areas\u003csup\u003e56–58\u003c/sup\u003e, or rely on simple, binary scenarios of whether or not species might track their niches\u003csup\u003e59,60\u003c/sup\u003e. Our results suggest that quantifying geographic niche availability is crucial for understanding threats to mountain biodiversity, due to the nuanced and gradual decline of specific temperature regimes at upslope locations following warming. These trends are mirrored by local population declines at highest elevations despite concurrent up-slope range shifts in the last decades\u003csup\u003e29,61\u003c/sup\u003e. However, we note that additional dispersal constraints not accounted for in our analysis might cause yet greater niche losses in island endemics (e.g. range rarity hotspots in Australasia and Indomalaya) and in regions where the latitudinal orientation of mountain ranges hinders northward shifts (Fig. 6 and Extended Data Fig. 4).\u003c/p\u003e\n\u003cp\u003eDespite a growing recognition of their worldwide populations declines\u003csup\u003e3\u003c/sup\u003e, less than 1% of all insects and under 8% of butterflies have so far been assessed for their global threat status). Our study uncovered that the global conservation hotspots for butterflies vary strongly across different aspects of diversity, show limited overlap with other taxa, and are unusually strongly exposed to global warming. This threat arises from butterflies’ strong concentration at higher elevations that differs markedly from other species groups assessed globally to date. Mountains play a pivotal role for this species group yet due to their geographically rare and isolated environmental conditions are now bound to become ecological dead ends. There is an urgent need for targeted conservation strategies that address the connectivity, protection, and restoration of mountain areas where identified rarity and threat most strongly coincide. Our findings sound a clarion call for a more comprehensive global biogeographic knowledgebase to ensure the recognition of imminent threats to biodiversity and guide effective biodiversity conservation and management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAll analyses were conducted in R v.3.5.1.\u003c/p\u003e\n\u003ch3\u003eDistributional data\u003c/h3\u003e\n\u003cp\u003eDespite the rich natural history records for butterflies, the limited availability of expert range maps thus far hampered global-scale assessments of their diversity and conservation prioritization. We therefore used a broad spectrum of approaches and types of distributional data to fill this knowledge gap. Specifically, globally comprehensive country-level distribution information was used in cleaning all available occurrence records and digitized records from publications to model species\u0026rsquo; distributions and generate ecoregional ranges of species (for sources see Supplementary Table\u0026nbsp;1). This information was complemented with digitized regional expert range maps. All data were taxonomically harmonized using the most up-to-date taxonomic reference\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. We considered Hesperiidae, Lycaenidae, Nymphalidae, Papilionidae, Pieridae, Riodinidae, but excluded Hedylidae (American moth-butterflies) due to their limited species diversity, unique ecology among butterflies, and poor representation in literature\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. We preferred species distribution models due to their high accuracy and resolution and used expert range maps only for species with fewer than five records or those with SDMs of poor accuracy. In the absence of expert range maps, records for species with fewer than five records or those with SDMs of poor quality were intersected with terrestrial ecoregions. The final analyses were based on 6,650 species distribution models, expert range maps for additional 881 species and ecoregional range maps for additional 5,137 species, covering a total of 65% of all butterfly species\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. All distribution data were resampled to the same grid of assemblages with an approximate size 110 km \u0026times; 110 km as a compromise between the high accuracy of species distribution models (here 1 km-resolution) and the rather low accuracy of range maps\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData integration\u003c/h2\u003e \u003cp\u003e \u003cb\u003eOccurrence records.\u003c/b\u003e For generating SDMs and ecoregional ranges, we used 8,160,747 taxonomically harmonized and spatially cleaned records of 10,565 species (original data retrieved from gbif.org February 9, 2021; data and download filters at DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15468/dl.92t83z\u003c/span\u003e\u003cspan address=\"10.15468/dl.92t83z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e6\u003c/sup\u003e. Cleaning steps for these data, included the removal of duplicated records, of non-terrestrial records and of spatial outliers. To improve the accuracy of occurrence records based on expert knowledge, we employed a validation step based on country-level occurrences from a near-complete checklist\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, we included 11,839 digitized records of 593 species from literature for the Iran and Cambodia for which only ecoregional range maps were generated due to their lower spatial accuracy (Supplementary Fig.\u0026nbsp;2 and Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecies distribution models.\u003c/b\u003e Spatially explicit and cleaned occurrences recorded after the year 1970 were thinned at a 10 km-resolution\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. These records and twelve biologically relevant environmental variables\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, were used to build Maximum Entropy-based species distribution models\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e with functions of the R-package \u003cem\u003edismo\u003c/em\u003e. Five variables that describe annual and seasonality trends in temperature and precipitation were retrieved from chelsa.org (Bio1, Bio4, Bio10, Bio12, Bio15; CHELSA v2 current condition records\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e). Species distribution models also included the coefficient of variation in elevation and average elevation\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, mean annual EVI (Enhanced Vegetation Index), Winter EVI and Summer EVI\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, and the standard deviation of interannual variation in MODIS-based cloud cover\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e downloaded from EarthEnv.org. In addition, as a proxy for paleoclimatic stability we used the standard deviation of mean annual temperatures across the Pleistocene calculated based on climate simulations in 10-thousand-year intervals\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. All variables were cropped to the same extent and if necessary resampled to a 1 km-resolution. MaxEnt models were fitted using 10,000 randomly sampled background points as suggested by ref.\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and default settings. Note that the inclusion of more variables than occurrence records is unproblematic for MaxEnt SDMs as they evaluate predictive gain and incorporate an overfitting penalty for each predictor\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Models were evaluated on a held-out test set consisting of a fifth of the original presences and sampled pseudo-absences. The raw MaxEnt output of species\u0026rsquo; habitat suitability at 1 km-resolution was then converted to binary data, by using the 95% quantile of the suitability values extracted from the underlying occurrences records as presence threshold. Finally, these binary distribution data were masked with terrestrial ecoregions\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e (retrieved from OneEarth.org) that intersected with occurrence records of the species\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. 336 species distribution models with poor accuracy as determined by a low area under the curve (i.e. an AUC lower than 0.5) were visually checked by experts. 51 of them were discarded because the predictions were evaluated as unrealistic. The median AUC of the remaining 6,650 species distribution models was 0.93 (mean: 0.87). The final binary species distribution maps were reassigned to our equal-area grid\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e and only range fragments overlapping more than 50% with an assemblage were considered presences.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpert and ecoregional range maps.\u003c/b\u003e 10,314 vector distribution maps for 5,303 butterfly species from 35 field guides were georeferenced, quality controlled, taxonomically harmonized\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and spatially merged (Supplementary Table\u0026nbsp;1). However, for many species both sufficient records for SDMs as well as expert range maps were lacking, and these data shortfall was most severe in tropical regions and taxa (Supplementary Fig.\u0026nbsp;6). We therefore adopted a regionalization scheme commonly used in global-scale conservation for these species, if at least one record was available. This scheme, the ecoregions of the world, represents a downscaling of biogeographical realms based on expert synthesis and species turnover\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. The 5,137 generated ecoregional range maps\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e were highly spatially and taxonomically complementary to SDMs and expert range maps, particularly for tropical regions and taxa (Supplementary Fig.\u0026nbsp;2). We preferred ecoregional range maps over alternative approaches, such as alpha hulls and geographical buffers, as they have been shown to be more concordant with expert range maps and SDMs, particularly for data-poor species and because they span ecologically meaningful extents rather than being based on spatial proximity alone\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Both expert and ecoregional range maps were reassigned to our equal-area grid and only range fragments overlapping more than 50% with an assemblage were considered presences.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiversity aspects.\u003c/b\u003e Species richness (SR) was calculated as the count of co-occurring species per assemblage. As a measure of endemism, we calculated the range rarity (\u0026lsquo;RR\u0026rsquo;) as the mean of the inverse occupancy (total count of assemblages globally/ count of assemblages occupied by a species) of co-occurring species. Our choice of this measure, in contrast to the one employed in pioneering studies on vertebrates and plants\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, was driven by the absence of established thresholds for endemic species and regions crucial for butterfly conservation. Additionally, range rarity proved to be less depended on species richness\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, making it a more effective complement for the conservation of biogeographical unique and highly threatened species\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo also incorporate concentrations of distantly related species into our prioritization, we calculated the phylogenetic distinctiveness of co-occurring butterfly species (hereafter \u0026lsquo;phylogenetic diversity\u0026rsquo; or \u0026lsquo;PD\u0026rsquo; for simplicity). We used a recently published genus-level phylogeny\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and the taxonomic names of all accepted butterfly species\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, to add all butterfly species for which distribution data was available at their respective genus and randomly resolved the intra-genus relationships with functions of the R-package \u003cem\u003ephytools\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Species were not added if they were represented by only one taxon in the phylogeny or if the genus to which they belong was paraphyletic. Note that this phylogenetic tree included only 11,143 species, while species richness and range rarity were calculated based on the distribution data for 12,119 species. For the calculation of phylogenetic diversity, the tree was pruned to include only the regional (realm-specific) species pool. Phylogenetic diversity was calculated as the standardized effect sizes of mean pairwise distances (MPD) across co-occurring species, i.e., the observed MPD minus the random MPD divided by the standard deviation of the random MPD values, to account for the effect of species richness\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Random MPD represents a null model expectation of the phylogenetic composition of an assemblage based on the average of 10,000 calculations of the MPD for the same number, but a randomly drawn sample, of species per assemblage. As for range rarity the choice of this measure was driven by its empirical independence of species richness and range rarity, compared to, for instance, Faith\u0026rsquo;s phylogenetic diversity (Supplementary Fig.\u0026nbsp;3) or phylogenetic endemism\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and consequently its greater emphasis on unique phylogenetic aspects. We restricted the species pool to the respective biogeographical realms to accommodate species turnover resulting from past and present geographical dispersal barriers. Using Chao\u0026rsquo;s dissimilarity index with species\u0026rsquo; range size as weight, we confirmed that the selected realm definition\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e effectively delimited highly dissimilar species pools (Supplementary Table\u0026nbsp;2). Only Oceania was included in the Australasian realm due to its small extent and a similar species composition of the two realms.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHotspots of diversity.\u003c/b\u003e Hotspots of diversity patterns for all taxa were arbitrarily defined as the 5% assemblages with the highest SR, RR, and PD per realm, respectively. PD was transformed to positive values for this purpose [PD+(min(PD*-1))]. We identified hotspots at the realm scale, instead of the global scale, to ensure accurate representation of the three complementary aspects of diversity within and between regions\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, while minimizing the impact of potential sampling biases. Leveraging coarse, but near-complete country-level distribution data, we document the species coverage of our high-resolution data (Supplementary Figs.\u0026nbsp;5, 6). With this information on data shortfalls, we also assessed the robustness of our realm-level hotspot definition. Specifically, we multiplied the respective diversity estimate per assemblage by the inverse of the country-specific species coverage and compared our core hotspots with those derived based on the weighted diversity estimates (Supplementary Fig.\u0026nbsp;8). For instance, while the species richness for a given assemblage that falls into a country with 100% species coverage remains the same that of an assemblage with a lower coverage in a data-poor country is upweighted by the factor 1.67 (1/0.60 in the most extreme case) before hotspots are defined. This allowed an evaluation of the robustness of hotspot locations for all diversity aspects, based on the assumption that the magnitude, but not the relative within-country variation of diversity, will change with the addition of species. Thus, greater emphasis is placed on data-poor countries across each realm. We thereby confirm that only 19% of the combined hotspots of SR, RR and PD do not overlap with any of the coverage weighted ones (17% for SR, 11% for RR, and 37% for PD, separately; Supplementary Fig.\u0026nbsp;8). Strongest mismatches highlight potential shifts in PD hotspot locations from lowland Japan to highlands in southern China and from lowlands of Borneo to highlands of New Guinea. However, note that our measure of PD is richness-corrected and should be hence robust to the sampling of additional species that by chance alone add phylogenetic diversity to an assemblage. Following ref.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, we also evaluated the influence of different hotspot definitions in percent top-ranking assemblages by recalculating overlap of diversity aspects for the top 1\u0026ndash;100% assemblages in 1%-steps. This analysis showed that the increase in congruence among the three diversity aspects is proportional to the increase in area until approximately 20% of all assemblages are considered (Supplementary Fig.\u0026nbsp;9). For butterflies both congruence and overlap were compared between SR, RR, and PD. To assess to congruence and hotspot overlap of butterfly diversity with terrestrial vertebrate diversity, expert range maps for 32,851 amphibians (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/resources/spatial-data-download\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org/resources/spatial-data-download\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), reptiles\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, mammals\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e, and birds\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e were retrieved, and then resampled to our grid. We also leveraged ant richness and rarity data based on 14,324 (sub-)species\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e as well as a prediction of plant richness based on more than 10,000 species (maximum regional estimate)\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Layers of plant and ant diversity data had a two times finer resolution and were therefore averaged per intersecting cell of our grid. Although thus far, this ant and plant data have not been explicitly factored into the establishment of conservation priorities, they serve as valuable benchmarks for comparing elevational gradients and assessing the prevalence of hotspots in mountainous regions. This is particularly important due to the co-evolution of butterflies with plants\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and the potential ecological parallels among insects\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Hotspots of all taxa were calculated in the same way as for butterflies, yielding in total 626 hotspot assemblage for each vertebrate taxon-diversity aspect combination, but fewer for ants and plants (622 and 592, respectively) due a slightly lower spatial coverage of the underlying data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMountain diversity\u003c/h2\u003e \u003cp\u003eTo evaluate the likelihood that a hotspot was located in mountains, we took into account that mountain regions cover a smaller proportion (38% at our grain) of the terrestrial area of the Earth and that hotspot regions represent only 5% of all assemblages within each realm, as per our definition. We intersected our grid with the most up-to-date classification of mountains from a global inventory (GMBA)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, considering cells as mountainous if they were covered by more than 5% of the area. We chose this threshold to exclude smaller mountain fragments, but also accommodate the rather coarse resolution of the distribution data. For each realm and diversity aspect the proportion of hotspot areas in mountains was divided by the proportion of hotspot areas in lowlands. For instance, the likelihood of range rarity hotspots being located in mountains is 5.24 times greater (472 of 4,696 grid cells\u0026thinsp;\u0026asymp;\u0026thinsp;10.0%) than that for lowlands (150 of 7,819 grid cells\u0026thinsp;\u0026asymp;\u0026thinsp;1.9%). For species-level analyses, the proportion of species\u0026rsquo; ranges in mountains was calculated based on those species that occur in each respective type of hotspot and realm. As the regional baseline for this measure, we calculated the proportion of grid cells that are mountains on the total number of grid cells per realm. Species whose ranges cover a higher proportion of mountains than the baseline (disproportionately more mountain) were considered primarily mountain-dwelling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTemperature difference and niche loss under global change scenarios\u003c/h2\u003e \u003cp\u003eFor analysis of the impact of temperature changes on species assemblages in hotspots and non-hotspots, we generated ensembles of mean annual temperature across predictions from complementary climate models for the period of 2061\u0026ndash;2080. These models provided predictions for two representative carbon pathways, one with net carbon emissions of zero until 2050 and one with continued emissions until the end of this century (RCP 4.5 and 8.5 scenario), as defined by the Intergovernmental Panel on Climate Change\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. We focused on mean annual temperature as it is the most important and general determinant of species\u0026rsquo; distributions and diversity patterns\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The selection of eight models (CESM1-BGC, CESM1-CAM5, CMCC-CM, FIO-ESM, INMCM4, IPSL-CM5A, MIROC5, MPI-ESM-MR) from climatologies available at chelsa.org (v1.2 CMIP5) followed ref.\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e in using predictions with low interdependence. Temperature values were averaged across predictions for each of the two RCP\u0026rsquo;s and aggregated to mean values per grid cell. The temperature difference was calculated from these ensembles by subtracting the current mean annual temperature from that of future temperature ensembles (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Absolute temperature differences are a common measure to identify regions with high potential threat to biodiversity under future climate change (i.e. climate change exposure). However, here we argue that even small difference in temperature can have drastic impacts on biodiversity as certain climates and particularly hotspot climates might be rarer than those of non-hotspots. To address this issue, we calculated the loss of the current temperature niche of hotspots and their corresponding non-hotspots for each realm and diversity aspect based on density overlap in a Bayesian framework\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Frequency distributions were converted to densities with a sample size of 1,000 samples and the default probability region size of 95% was used for 1,000 repeated calculations (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Overlap was calculated as the probability that a given current temperature of an assemblage is found in the probability region of future temperatures, and the non-overlapping part of the current conditions we denominate the \u0026lsquo;temperature niche loss\u0026rsquo; (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Unlike other niche overlap metrics, this probabilistic measure explicitly accounted for uncertainty in the niche regions definition and for an uneven number of compared entities (i.e. in our case grid cells). Note that the direction of comparison matters for this calculation, and we did not discuss the gain in temperature niche (compared future with current temperature) because novel very warm climates will not maintain current biodiversity. In addition to that of hotspots and non-hotspots, we also calculated the niche loss for a random subset of 5% of the number of assemblages per realm to explore whether niche loss is affected by the inherently lower count of hotspots. This analysis confirmed the robustness of our niche loss estimate to differences in sample size, being similar between the small set of 5% random assemblages and the 95% of non-hotspot assemblages per realm (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eData gaps\u003c/h2\u003e \u003cp\u003eDespite extensive data mobilization and integration efforts (Supplementary Figs.\u0026nbsp;1, 2 and Table\u0026nbsp;1) one third of the described butterfly species lacked sufficient data to be included, particularly for the Afrotropic (42% missing), Indomalayan, and Neotropic realms (34% and 32%; Supplementary Figs.\u0026nbsp;5, 6). We therefore assessed hotspots within realms to limit the impact of these main differences in data coverage\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, but also evaluated the robustness of within-realm patterns. We expect regions with particularly large portions of unmapped species to see refinements to their hotspot locations using coarse but near-complete country-level distribution data\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. We find that among countries, species-coverage is negatively correlated with the count of identified hotspots, contrasting the expected effect of sampling bias (Spearman\u0026rsquo;s ρ = \u0026minus;\u0026thinsp;0.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Supplementary Fig.\u0026nbsp;7). Adapting the hotspot definition with a country-level species coverage weight based on these data, our supplementary analysis confirmed only minor changes in the locations of species richness and rarity hotspots (83% and 89% remain unchanged, Supplementary Fig.\u0026nbsp;8). Hence, although many species are added to certain countries this generally does not impact realm-level priorities. Stronger mismatch (37% globally) is observed for hotspots of phylogenetic diversity, highlighting mountains in New Guinean and southern China. These areas are important targets for additional sampling of phylogenetic data, but note that their confirmation would shift priorities mainly from lowlands to highlands.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll produced hotspots and diversity maps for butterflies will be made available through map.half-earthproject.org and mol.org upon acceptance. Distribution data for amphibians can be requested from IUCN (https://www.iucnredlist.org/resources/spatial-data-download). Distribution data for birds\u003csup\u003e80\u003c/sup\u003e, mammals\u003csup\u003e79\u003c/sup\u003e and reptiles\u003csup\u003e21\u003c/sup\u003e can be requested from authors of the respective publications. Plant species richness predictions\u003csup\u003e81\u003c/sup\u003e are available from the authors upon request. Ant richness and rarity data\u003csup\u003e23\u003c/sup\u003e is available at DataDryad DOI:10.5061/dryad.wstqjq2pp. The layer of terrestrial ecoregions used to generate ecoregional range maps for butterfly species can be retrieved from OneEarth.org. Digitized expert range maps are available from mol.org through the source links in the Supplementary Information. Global country-level distribution information for butterflies\u003csup\u003e6\u003c/sup\u003e can be retrieved from https://mol.org/datasets/149345d9-eb27-4140-ba60-eaa32ff08ff8. The phylogenetic tree for butterflies is available from the authors\u003csup\u003e7\u003c/sup\u003e. Any additional data not listed can be made available upon reasonable request from the authors of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo custom code or mathematical algorithm was used in the analysis. Code to analyze niche loss and the proportion of hotspots in mountains will be uploaded to figshare upon acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSeibold, S. \u003cem\u003eet al.\u003c/em\u003e Arthropod decline in grasslands and forests is associated with landscape-level drivers. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e574\u003c/strong\u003e, 671\u0026ndash;674 (2019).\u003c/li\u003e\n \u003cli\u003eSeibold, S. \u003cem\u003eet al.\u003c/em\u003e The contribution of insects to global forest deadwood decomposition. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e597\u003c/strong\u003e, 77\u0026ndash;81 (2021).\u003c/li\u003e\n \u003cli\u003eWagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. \u0026amp; Stopak, D. 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F. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eIPCC - Intergovernmental Panel on Climate Change Report 2013 - The Physical Science Basis\u003c/em\u003e. 1535 pp https://www.ipcc.ch/report/ar5/wg3/ (2013).\u003c/li\u003e\n \u003cli\u003eSanderson, B. M., Knutti, R. \u0026amp; Caldwell, P. A representative democracy to reduce interdependency in a multimodel ensemble. \u003cem\u003eJ. Clim.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 5171\u0026ndash;5194 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4437399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4437399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInsects and their many ecosystem functions are in decline and threatened by climate change\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, yet lack of globally comprehensive information limits the understanding and management of this crisis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Here we use butterflies as a global model insect system\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and uncover a strong coincidence of their diversity and threat. Integrating comprehensive phylogenetic and geographic range data for 12,119 species, we find that global centers of butterfly richness, rarity, and phylogenetic diversity are unusually concentrated in tropical and sub-tropical mountain systems. Mountains\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e hold 3.5 times more butterfly hotspots (top 5%) than lowlands and two thirds of the species are primarily mountain-dwelling. Only a small portion (14%-54%) of these diversity centers overlap with those of ants, terrestrial vertebrates and vascular plants, and this spatial coincidence rapidly decreases above 2,000 m elevation where butterflies are uniquely concentrated. The geographically restricted temperature conditions of these mountain locations now put butterflies at extreme risk from global warming. We project that 64% of butterflies\u0026rsquo; temperature niche space in tropical realms will erode by 2070. Our study identifies critical conservation needs for butterflies and illustrates how the consideration of global insect systems is key for assessing and managing biodiversity loss in a rapidly warming world.\u003c/p\u003e","manuscriptTitle":"Global hotspots of butterfly diversity in a warming world","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 06:51:13","doi":"10.21203/rs.3.rs-4437399/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-ecology-and-evolution","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natecolevol","sideBox":"Learn more about [Nature Ecology \u0026 Evolution](http://www.nature.com/natecolevol/)","snPcode":"","submissionUrl":"","title":"Nature Ecology \u0026 Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"621a8159-8415-4532-9315-1610f239236b","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32377024,"name":"Biological sciences/Ecology/Conservation biology"},{"id":32377025,"name":"Biological sciences/Ecology/Biodiversity"},{"id":32377026,"name":"Biological sciences/Zoology/Entomology"},{"id":32377027,"name":"Biological sciences/Ecology/Climate-change ecology"},{"id":32377028,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts"}],"tags":[],"updatedAt":"2025-03-25T07:07:14+00:00","versionOfRecord":{"articleIdentity":"rs-4437399","link":"https://doi.org/10.1038/s41559-025-02664-0","journal":{"identity":"nature-ecology-and-evolution","isVorOnly":false,"title":"Nature Ecology \u0026 Evolution"},"publishedOn":"2025-03-24 04:00:00","publishedOnDateReadable":"March 24th, 2025"},"versionCreatedAt":"2024-06-06 06:51:13","video":"","vorDoi":"10.1038/s41559-025-02664-0","vorDoiUrl":"https://doi.org/10.1038/s41559-025-02664-0","workflowStages":[]},"version":"v1","identity":"rs-4437399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4437399","identity":"rs-4437399","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}