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All procedures accorded to administrative provision of animal welfare of the Fisheries Research Education Agency Japan. All statistical tests used in this study are two-sided.Otolith samplesFrom the western North Pacific, age-0 JP sardine were collected from samples taken during acoustic and sub-surface trawl surveys in the offshore Oyashio region conducted during 2006–2010 and 2014–2015. The surveys were conducted by Japan Fisheries Research and Education Agency every autumn since 2005 which aim to estimate the abundance of small pelagic species. The abundance of young-of-the-year sardine in the region in the season, approximately 10–15 cm in standard length (SL), is considered a proxy for the abundance of recruits of the Pacific stock and used to tune the cohort analysis in stock assessment4. As representatives of the young-of-the-year population in the region, 2–6 trawl stations each year that had relatively larger catch-per-unit-effort were selected (Supplementary Fig. 1), and 9–20 individuals were randomly selected from each station for otolith analyses (Supplementary Table 1). Age of fish was initially judged by SL (10–15 cm) and later confirmed by the counts of otolith daily increments.From the eastern North Pacific, archived otoliths of CA sardine captured in cruise surveys and in the pelagic fishery of the Southern California Bight during 1987, 1991–1998, and 2005–2007 were collected. Fish in the size range of 10–16 cm SL were regarded as age-1 individuals born in the previous year, following Takahashi and Checkley56. The number of individuals varied between year classes in the range of 4–20 (Supplementary Table 2).Otolith processing, microstructure and somatic growth analysisSagittal otoliths were cleaned to remove the attached tissue in freshwater and then air-dried. Otoliths of JP sardine were embedded in epoxy resin (Petropoxy 154, Burnham Petrographics LLC) on slide-glass, while those of CA were glued to slide-glass using enamel resin and then ground and polished with sandpaper to expose the core. For some otoliths of CA sardine, the polished surface was coated with additional resin to facilitate identification of the daily increment width. Using an otolith measurement system (RATOC System Engineering Co. Ltd.), the number and location of daily increments were examined along the axis in the postrostrum from the core. Although daily increments were clearly observed until the otolith edge for JP sardine, it was difficult to do this for CA sardine probably because they had experienced winter when otolith growth slowed down. Therefore, the rings were counted as far as possible for CA sardine, which typically resulted in more than 150 counts. The first daily increment was assumed to form after 3 days post hatch (dph) for JP and 8 dph for CA sardine following Takahashi et al.26 and Takahashi and Checkley56. The otolith radius at each age was calculated by adding all the increment widths up to that age. Standard lengths at each age were back-calculated assuming a linear relationship between otolith radius and standard length using the biological intercept method34 as follows:$${SL}_{n}=left({{SL}}_{{catch}}-{{SL}}_{{first}}right)times left({{OR}}_{n}-{{OR}}_{{first}}right)/left({OR}_{catch}-{{OR}}_{{first}}right)+{{SL}}_{{first}}$$
(1)
where SLn is the standard length at age n, SLcatch is the standard length at catch, SLfirst is the standard length at the age of first daily increment deposition fixed at 5.9 mm for JP sardine and 5.5 mm for CA sardine following the previous studies26,56, ORn is the otolith radius at age n, ORfirst is the otolith radius at the age of first daily increment deposition, and ORcatch is the otolith radius at catch. Based on rearing experiments of field collected eggs, Lasker57 showed the SL of CA sardine at 6–8 dph ranged between 3.8 to 6.5 mm, and Matsuoka and Mitani58 showed the total length at 2–4 dph ranged between 4.8 to 6.2 mm, corresponding to 4.7 to 6.1 mm in SL. To deal with these uncertainties regarding the size at the age of first daily increment deposition, we conducted Monte Carlo simulations (10,000 times) to estimate the uncertainties of back-calculated SL, assuming that the initial SLs fall between 3.8 to 6.5 mm for both sardines. Standard deviations of the temporal back-calculated SL at each age were presented as the uncertainty of each SLn estimation, which varied between 0.51 and 0.73 at the end of larval stage (JP: 45 dph, CA: 60 dph), between 0.34 and 0.64 at the end of early juvenile stage (JP: 75 dph, CA: 90 dph) and between 0.20 and 0.53 at the end of late juvenile stage (JP: 105 dph, CA: 120 dph). These values were significantly smaller than the variability of estimated SL among individuals assuming initial sizes of 5.9 and 5.5 mm for JP and CA sardine, respectively (standard deviations: 4.2, 8.1 and 8.3 in JP sardine and 5.5, 9.1 and 10.3 in CA sardine for the end of larval, early juvenile and late juvenile stages, respectively), suggesting that the back-calculated SL is robust to variations of initial size. Nevertheless, the biological intercept method assumes a constant linear relationship between fish and otolith size within individual59, which can vary depending on physiological or environmental conditions60,61. Therefore, to examine the relationships between temperature and growth, we used both otolith growth, which contains fewer assumptions, and back-calculated somatic growth as growth proxies. Since the use of the two proxies did not show remarkable differences in the relationships between temperature and growth (Supplementary Figs. 11, 12), we mainly used the back-calculated SL in the discussion, which has a more direct ecological implication.To more generally test whether growth trajectories are different between the western and eastern boundary current systems, otolith growth data of JP and CA sardines were compared with those of sardines in the east to south and west coasts of South Africa. The biological intercept method to back-calculate standard length could not be used in sardine from South Africa because the size at catch was large, some over 20 cm, and otolith radius and standard length were not linearly correlated for fish of this size. Therefore, the otolith radius and increment width were directly used as proxy for size and growth in this comparison, respectively. For visualisation (Fig. 2a), the means of year class mean otolith radii were estimated for JP and CA sardines. For CA sardine, otolith radii at ages were simply averaged within each year class. For JP sardine, to account for the variation in the number of individuals captured at the same station, otolith radii were first averaged within each station, and the station means were averaged within each year, weighted by catch-per-unit-effort. For South African sardine, data of otolith daily increment widths from hatch to 100 dph of 67 adults captured at six stations on the east to south coast ( >22oE), and 51 individuals captured at six stations on the west coast ( 0.05). Theoretically, the relationship between metabolism and temperature tends to show a linear trend after the metabolic rate is log-transformed79. Thus, we applied “identity (data without transformed)” and “log (data transformed)” links to evaluate if model shows a better linearity with data transformation. Based on AIC, however, the result showed Moto have a better linearity without data transformation (Supplementary Table 7). We, therefore, used “identity” links for the further model selection. Model selection base on AIC was performed for models including temperature, region (JP and CA sardines), life history stages (larvae, early juvenile and late juvenile) and interactions of these factors. The full model including all the interactions had the lowest AIC (Supplementary Table 7). As the diagnostic for the full model showed normality and homogeneity of residuals (Supplementary Fig. 9), we selected this model for interpretation. The CA sardine at the larval stage as the baseline, we found only JP sardine at early and late juvenile stages has relatively higher Moto values, and the temperature-dependent slope is significantly gentler in JP sardine at early and late juvenile stages (Supplementary Table 8).Next, the diversity of Moto across temperature range was assessed to estimate the optimal temperature in each stage. The relationship between the maximum metabolic rate and temperature is known to be parabolic, while that between the standard metabolic rate and temperature is logarithmic28,79. As the highest field metabolic rate would be constrained by maximum metabolic rate and the lowest field metabolic rate would be close to resting metabolic rate43, fish would have the most diverse metabolic performance at the optimal temperature with the widest aerobic scope. Thus, we modelled the highest and lowest Moto values in each 1 °C bin using a polynomial regression and a generalised linear model with Gaussian distribution and a log link for the 95th and 5th percentile values of each bin, respectively (Supplementary Fig. 10). The values of the bin that included less than four values were excluded from the regression analyses to reduce the uncertainty caused by under-sampled temperature bins. The gap between the two regression lines was considered as a proxy for the aerobic scope, and the temperature at which the gap reached the maximum was regarded as the optimal temperature.Statistical analyses for the relationships between temperature and growthTo understand how variation in ambient water temperature affects early life growth of sardines, we compared back-calculated standard length at around the end of the larval stage (hatch–35 mm; JP: 45 dph, CA: 60 dph), the end of the early juvenile stage (35–60 mm; JP: 75 dph, CA: 90 dph), and the end of the late juvenile stage (60–85 mm; JP: 105 dph, CA: 120 dph) and the mean seawater temperature from hatch to the ages. Median of each sampling batch were used as minimal data unit. Pearson’s r and p-values were first calculated for each comparison (Supplementary Table 9). As the relationship between mean temperature and standard length of JP at 75 dph seemed to be dome-shaped rather than linear, we introduced quadratic term of temperature and tested whether the term increased explanatory power using a linear model and stepwise model selection based on AIC. The model selection showed that the full model (Standard length ∼ Temperature2 + Temperature) was the best model, and the coefficients of the quadratic and linear terms were both significant (Supplementary Table 10). To account for these multiple tests, we corrected the p-values of the coefficients of the quadratic term in the linear model for JP sardine at 75 dph and of the Pearson’s r for the rest using the Benjamini-Hochberg procedure with α = 0.05, and selected the null hypotheses that could be rejected (Supplementary Table 9). To compare the temperature that allow maximisation of growth rate and optimal temperature derived from the analysis of Moto for each stage, median somatic growth rate and otolith increment width in each 1 °C bin was calculated together with its 3-window running mean (Supplementary Figs. 11, 12).Statistical analyses for the relationships between sea surface temperature and survival indexTo test whether habitat temperatures during the first 4 months after hatch affect the survival of sardines in the first year of life on a multidecadal scale, satellite-derived sea surface temperature (SST) since 1982 and survival of JP and CA sardines were compared. The log recruitment residuals from Ricker recruitment models (LNRR)13, representing early life survivals taking into account the effect of population density, were calculated based on the stock assessment data for JP and CA sardines as follows:$${LNR}{R}_{t}={ln}({R}_{t}/{S}_{t}) , – , (a+btimes {S}_{t})$$
(6)
where LNRRt is the LNRR at year t, Rt is the recruitment of year-class t, St is the spawning stock biomass in year t, and a and b are the coefficients of linear regression of ln(Rt/St) on St. Pearson’s r between the LNRR and the mean SST values from March to June for JP and from April to July for CA sardine was calculated for each grid points in the western and eastern boundaries of the North Pacific basin, derived from a SST product based on satellite and in situ observations80 (Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed (https://resources.marine.copernicus.eu/product-detail/SST_GLO_SST_L4_REP_OBSERVATIONS_010_011/INFORMATION), accessed on 11th August and 28th October 2021). The correlations were generally negative and positive in the western and eastern regions, respectively (Supplementary Fig 13a, b). In particular, mean SST values in the area where eggs, larvae and juveniles of JP or CA sardines are mainly found in the months26,39,49,56,78,81,82 (dotted areas in Supplementary Fig 13a, b) were compared with LNRR values to test the relationship between habitat temperature and survival in the early life stages (Supplementary Fig 13c). It should be noted that the mean SST values were not significantly correlated with otolith-derived year-class mean temperatures of JP and CA sardines during the larval to late juvenile stages (JP: r = 0.01, p = 0.98, n = 7, CA: r = 0.29, p = 0.38, n = 11), likely due to the short periods analysed, patchy distribution and inter annual variation in larval and juvenile dispersal and migration patterns. Nevertheless, the regions included areas where SST showed weak to significant (p  More

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Model substancesWe used the sulfoximine insecticide sulfoxaflor, the methoxy-acrylate fungicide Amistar (azoxystrobin 250 g/l, Suspension Concentrate, see supplementary methods, S1) and the glycine herbicide glyphosate (as active substance, RoundUp ProActive or RoundUp FL, see supplementary methods, S1) as model substances. Our choice was justified by their widespread use, regulatory status and systemic uptake in plants. Because of these characteristics, the likelihood of bees being exposed in the field was considered similarly plausible across model substances. Additionally, we are not aware of published evidence of the acute toxicity of these substances across castes and sexes of B. terrestris and O. bicornis.Sulfoxaflor is a relatively novel insecticide55,56,57, developed to replace or complement the use of older chemical classes against which insect pest populations had developed resistance57. However, because of its risks to bees58, its uses have been recently restricted in the EU to indoor growing conditions. As a nicotinic acetylcholine receptor (nAChR) competitive modulator, sulfoxaflor targets the same neural receptor as the bee-harming neonicotinoid insecticides55,56,57. Despite evidence that sulfoxaflor may target the nAChR in a distinct way compared to recently banned neonicotinoids55,56,57, these substances were shown to be similarly lethal in acute exposure laboratory settings for individuals of Apis mellifera, B. terrestris and O. bicornis38. Additionally, sulfoxaflor was shown to reduce reproduction59,60,61 (but not learning62,63) in bumble bees under field-realistic laboratory settings. When applied pre-flowering in a semi-field study design, sulfoxaflor impacted colony growth, colony size and foraging in bumble bees64 but not honey bees65. Azoxystrobin is a broad-spectrum, systemic fungicide, which has been widely used in agriculture since its first marketing authorisation in 199666. Azoxystrobin shows low acute toxicity to honey bees67. Azoxystrobin residues were found in nectar and pollen from treated crops68,69 and subsequently in the bodies of wild bees70. In a semi-field experimental setting, foraging, but not colony growth, was significantly impaired in B. terrestris exposed to Amistar (azoxystrobin 250 g/L SC)64, while no lethal or sublethal effects could be observed in honey bees65 or in O. bicornis71. However, a recent study showed that, when formulated as Amistar this pesticide induced acute mortality in bumble bees at high doses, which was attributed to the dietary toxicity of the co-formulant C16-18 alcohol ethoxylates50.Glyphosate is a broad-spectrum systemic herbicide and the most widely used pesticide in the world72. Products containing glyphosate may be applied to flowering weeds73 and contaminate their pollen and nectar54, thus driving bee contact and oral exposure. Glyphosate showed low lethal hazards in regulatory-ready laboratory74 and semi-field designs when dosed as pure active substance or as MON 52276 (SL formulation containing 360 g glyphosate/L)75. A recent study found ready-to-use consumer products containing glyphosate to be lethally hazardous to bumble bees73. However, this toxicity was attributed to co-formulants, rather than the active substance itself.We characterised the acute oral and contact toxicity to B. terrestris and O. bicornis of sulfoxaflor, azoxystrobin and glyphosate as either pure active substances or formulation (see supplementary material S2 Table S1). Each test was repeated across castes and sexes of these two species. For bumble bees we used workers, males and gynes (i.e., unmated queens), hereby referred to as queens, whereas for O. bicornis we used males and females. Bumble bee experiments were designed following OECD protocols30,31, while O. bicornis was tested following published76 and ring-tested protocols32, as an OECD protocol for this latter species is not yet available.We used a dose response design whenever the test item was found to drive significant mortality in the tested species. In all other cases, a limit test design using a single, high pesticide dose was used. Details on the methods and results of the limit tests are reported in the supplementary materials (S2 and S4).Pesticide treatmentsAll dose response tests were performed with pure sulfoxaflor, while azoxystrobin was tested as a plant protection product (Amistar 250 g a.s./l, SC, Syngenta, UK) in all oral tests, as its solubility in water was insufficient (6.7 mg a.s./L, see EFSA, 2010) to achieve the desired concentrations. Amistar contains co-formulants with hazard classification (54 C16-18 alcohols, ethoxylated  More

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Defining alpine habitat and mountain regionsWe defined alpine habitat as the area above climatic treeline, including the nival belt, where temperature, wind, drought, snow, or nightly frost limit vegetation growth to shrubs, krummholz, or fragmented tree patches less than 3 m in height3,23,24. Realized treeline can be markedly lower than the climatic treeline due to the absence of continuous forest at lower elevations, or human activities such as logging, burning, and livestock grazing25. While anthropogenically influenced treeline produces habitat reminiscent of alpine meadows, these habitats are not climatically representative of alpine ecosystems and thus they were not included when assembling this dataset. Climatic treeline elevation varies globally based on latitude, topography, aspect, and proximity to the coast (i.e., oceanic influence)11. Therefore, we defined alpine habitat separately for each mountain region based on local climate and published accounts of alpine vegetation. While alpine habitats usually occur above at least 1,500 m elevation globally, at high latitudes ( >55°N or 41°S) this elevation can be as low as ~400 m26 (Fig. 2).Fig. 2The median (triangular points) and range (error bars) of treeline elevation for each of the main mountain regions covered in the dataset (Fig. 1). The mountain regions are arranged from north to south (left to right) and the grey dashed line represents the relative position of the equator. Treeline elevation was derived from different sources depending on the region (see ‘Data sources’ in the dataset). The abbreviation ‘NA’, such as in ‘Northwestern NA’, refers to North America.Full size imageThe alpine habitats we identified broadly align with the ‘lower alpine’, ‘upper alpine’, and ‘nival’ belts mapped by Korner et al.9 and made available by the Global Mountain Biodiversity Assessment project (http://www.mountainbiodiversity.org/explore)27,28. However, certain areas, such as the Sierras de Córdoba, Argentina or the Isthmian Páramo on volcanoes in Central America were classified as ‘upper montane’ by Korner et al.9 based on thermal belts alone. For the purposes of this dataset, we considered these regions alpine habitat based on published measurements of treeline and distinct alpine plant communities facilitated by a mixture of temperature, precipitation, nightly frost, and wind constraints. For example, the Drakensberg range in South Africa was identified as ‘upper montane’ only, but botanical studies have characterized the region as Themeda-Festuca grassland from 1,900–2,800 m and alpine heathlands above 2,800 m13, representing extensive habitat above treeline. As a result, our definition of alpine habitat expands on the thermal belts mapped by Korner et al.9. In this way, the avian communities we identified retain species lineages that are confined to cooler high elevation habitats, representing remnants of more extensive alpine ecosystems from the last glaciation event.We grouped mountain ranges into 12 global regions and 38 subregions based on similar climates and alpine vegetation stemming from shared geographic position (Tables 2, 3; Fig. 1). The ‘Islands’ category represents very limited alpine habitat on four disparate islands that do not easily fit within any other major region, but nevertheless occur in subtropical or tropical realms: Hawaii, Sumatra, Borneo, and the Canary Islands. Alpine breeding birds and life-history traits were identified for each individual region so that future analyses can either include or remove mountain ranges depending on their definition of alpine habitat. This approach also promotes comparisons of avian communities at a finer scale across the full diversity of alpine habitats.Table 2 Description of the major regions and specific mountain ranges in the Americas that are included in the dataset.Full size tableTable 3 Description of the major regions and specific mountain ranges from Eurasia, Africa, and Oceania, plus the miscellaneous mountain ‘Islands’ region.Full size tableAlpine breeding bird speciesFor each region described in Tables 2 and 3, we assembled a list of alpine breeding species from published literature, environmental assessment reports, regional monitoring schemes, bird atlases, and expert knowledge following the most recent International Ornithology Committee taxonomy, version 12.129. An alpine breeding bird is any species that nests above treeline, regardless of how frequently, such that all or a certain proportion of a species is dependent on alpine habitat during the breeding season. Due to certain data-deficiencies underlying existing species range estimates above treeline, using knowledge from regional experts was the most accurate method to assemble a global list of alpine breeding birds for most mountain regions. See the Technical Validation section for specifics on how we validated the use of expert knowledge when assembling species and their traits for the Global Alpine Breeding Bird list.Species traitsWe included species traits that fall under three general topics: 1) alpine breeding propensity, 2) ecological traits, and 3) conservation value. Alpine breeding propensity includes breeding habitat specialization and alpine breeding status, ecological traits include migration behaviour and nest traits, while conservation value encompasses mountain endemism and conservation status. Together, these variables broadly reflect alpine habitat use during the breeding season globally, as well as provide the basis for evaluating the conservation potential and risks for alpine bird communities. We recorded general trait specifications for each species using available resources such as Birds of the World30, the IUCN Red List31, and AVONET21. We then solicited region-specific traits from regional experts and the same review process was conducted for these traits as for alpine breeding evidence (see Technical Validation). All traits were specific to alpine breeding birds whenever possible. The global distribution of each species trait can be visualized in Fig. 3.Fig. 3The global distribution for each trait included in the dataset, including (a–c) alpine breeding propensity, (d–f) ecological traits, and (g–i) traits relevant to conservation status and data uncertainty. In all cases except panel c the y-axis is the proportion of all 1,310 alpine breeding species identified in the dataset. Panel c depicts the elevational breeding distribution expected from the different combinations of breeding specialization and alpine breeding status to visualize the probability of breeding above treeline. In Panel e, ‘BP’ refers to brood parasite. See Table 4 or the metadata for full descriptions of each trait.Full size imageSpecialization for breeding in alpine habitats (hereafter ‘breeding specialization’) and the propensity to breed in alpine habitats (hereafter ‘breeding status’) form a tiered estimate of alpine breeding behaviour. First, we classified each species into one of three breeding specialization categories to differentiate among species that predominantly breed above treeline (alpine specialists), breed both above and below treeline (elevational generalists) or are limited to high latitude tundra habitats (tundra specialists). The latter includes alpine-Arctic or alpine-Antarctic transition zones, where species nest in higher, drier tundra (approximately >400 m elevation) but may also breed in wet tundra at lower or coastal elevations. In this way, we clearly identified species that breed in alpine tundra habitat, but where tundra is the primary driver of breeding presence, not necessarily selection for high elevation. Under breeding status, we quantified the likelihood of breeding above treeline relative to below treeline as common, uncommon, or rare. Alpine specialists are always common alpine breeders (regardless of their density and distribution), but generalists or tundra specialists can be common, uncommon, or rare breeders in alpine habitats depending on whether they are found breeding consistently above treeline, more often breeding below treeline, or only incidentally breeding in the alpine, respectively. Together, these variables identify a species’ relative probability of breeding along the elevational gradient and with respect to the treeline (Fig. 3).We used two nest traits to identify the general breeding niche of each species: nest type and nest site. Nest type included three primary category levels (open cup, cavity, domed nest), while nest site was subdivided into seven levels (ground, bank, shrub, tree, rock, cliff, and glacier). Brood parasite species, which will use a range of nest types and sites depending on the host species, were placed in an additional ‘brood parasite’ category for each nest trait. A species with an open cup or domed nest is limited to placing the nest on the ground, in vegetation (e.g., a shrub or stunted tree), or on a cliff, while cavity nesters may be in a bank (i.e., burrow/tunnel), in a rock (e.g., crevice), or in a tree (e.g., natural or excavated cavity). If nest traits were undescribed for a certain species, we inferred nest traits from the most closely related species in similar high elevation habitats (see Data uncertainty).Species were assigned to three migration categories based on their predominant behaviour: resident, short-distance, and long-distance migrants. Resident species remain near their breeding habitat year-round, allowing for occasional, short-term movements in response to extreme weather events. Short-distance migrants conduct seasonal altitudinal migrations, short latitudinal migrations, or nomadic movements where the species remains within the general breeding region (e.g., within the temperate zone). Long-distance migrants travel extensive distances to winter in an entirely different region than their breeding habitat (e.g., temperate breeders to tropical habitats). A general threshold of 3,000 km was used to distinguish between short- and long-distance migrants because it approximates the distance traveled from the Himalayas to the southern coast of India, Northern Europe to the Mediterranean coast, or Alaska to California. In other words, this distance represents a relatively consistent reference across global regions. While there are finer-scale migration designations that could be made, such as partial or altitudinal migration, we lack detailed movement data for most species and regions. Although a global list of potential altitudinal migrants exists that can be incorporated with this alpine breeding bird dataset if desired32, altitudinal migration often co-occurs with short-distance latitudinal movements and there are considerable differences in migration behaviour among subspecies, populations, and even individuals33. We therefore chose to use established migration categories that align with other global trait databases. In fact, our migration designations were largely congruent with those in AVONET21, with the primary difference being between resident and short-distance migrants. We identified ~200 short-distance migrants that were considered sedentary (resident) under the AVONET classification. This difference is to be expected given that we defined migration behaviour for alpine breeding populations compared to global trait values for all populations. For many species, alpine breeding birds will depart higher elevations during winter to avoid severe weather conditions, even though low elevation populations of the same species may be predominantly resident34. Therefore, the three broad categories chosen here are intended to balance available information with sufficient accuracy to provide data useful for large-scale life-history and biogeographic analyses of alpine breeding birds.Mountain endemism refers to a species whose breeding range is restricted by physical, environmental, or biological barriers to a general mountain region and the surrounding low elevation habitat. For example, a species breeding only on the Tibetan Plateau was classified as an endemic species, but a species that breeds across the Tibetan Plateau, the Himalayas, and the Altai Mountains was classified as non-endemic. When possible, we also classified endemism for defined subspecies. Species endemism is a more conservative metric, while subspecies endemism attempts to estimate additional cryptic endemism given that species differentiation is not well-defined for many high elevation birds. For example, the Caucasus Mountains support several distinct subspecies isolated from their primary distributions, including the Great rosefinch (Carpodacus rubicilla rubicilla), Dunnock (Prunella modularis obscura), and Güldenstädt’s redstart (Phoenicurus erythrogastrus erythrogastrus).Finally, conservation status refers to the IUCN Red List designations, version 2022-131. In addition to the traditional IUCN categories (e.g., Least Concern, Near Threatened, Vulnerable, etc.), we also included a Not Assessed (NA) category that generally occurred when a species was recently split. See Table 4 for complete definitions of all traits.Table 4 Definitions of species traits included in the Global Alpine Breeding Bird dataset.Full size tableData uncertaintyGlobally, there is significant variation in accessibility to alpine habitats and funding for alpine research. As a result, uncertainty in alpine breeding status may differ among regions and species. For example, in New Guinea, mist-net surveys and point counts across elevation have identified species that frequently use alpine habitat, but a dearth of breeding biology studies means that there are few nest records above treeline. It is thus necessary to codify this level of uncertainty for each species.To this effect, we included a variable termed ‘Data reliability’, which is a four-level categorical variable from 0 to 3 that is based on the number of reported nests that have been found and described for each species. We used the presence of nest descriptions to evaluate uncertainty because active nests are the must fundamental form of evidence for breeding above treeline, and therefore it is reasonable that a species with less existing knowledge about nest traits or nesting behaviours will have considerably more uncertainty around its designation as an alpine breeding species. For this variable, 0 indicates that nest traits are undescribed for a given species, 1 means less than five nests have been described, 2 indicates more than five nests have been described, but all from a single population, and therefore there is limited understanding of geographic variation, while 3 occurs when nests have been described from multiple populations or regions. If nest traits were undescribed for a species (data reliability = 0), then nest type and site were inferred from the most closely related species with available data, and whenever possible, a congener was selected that also breeds at high elevations or in alpine habitats. While the nest traits of most species have been sufficiently described, there is a significant proportion of alpine breeding birds with less available data (27.0%; Fig. 3i). The relative number of described nests was derived from Birds of the World30. We recognize that these data may not reflect true knowledge of nest traits given that not all species accounts have been recently updated. However, it does represent a consistent data source that allowed us to approximate data reliability sufficiently for our purposes.In combination, data reliability and alpine breeding status fully characterize alpine breeding uncertainty. For example, a species considered a rare alpine breeder with a data reliability of 3, means that there is strong evidence for breeding above treeline, but only incidentally under very specific circumstances. However, a rare alpine breeder with a data reliability of zero (i.e., nest undescribed), means that the likelihood of breeding above treeline may be probable based on behavioural observations, but further confirmation is required. When using this dataset for analyses, one must decide whether to use a conservative approach or consider all potential alpine breeding species with the appropriate caveats (see Usage Notes). More

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