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Honey bee colony loss and parasites across space and timeHoney bee colony loss strongly depends on spatio-temporal factors33,42, which in turn have to be jointly modeled with other stressors. Focusing on CONUS climatic regions, defined by the National Centers for Environmental Information40 (see Fig. 1), this is supported by the box plots in Fig. 2 which depict appropriately normalized honey bee colony loss (upper panel) and presence of V. destructor (lower panel) quarterly between 2015 and 2021. Specifically, Fig. 2a highlights that the first quarter generally accounts for a higher and more variable proportion of losses. Average losses are typically lower and less dispersed during the second quarter, and then tend to increase again during the third and fourth quarters. The Central region, which reports the highest median losses during the first quarter (larger than 20%) exemplifies this pattern, which is in line with existing studies that link overwintering with honey bee colony loss6,29,30,31,32,33,43. On the other hand, the West North Central region follows a different pattern, where losses are typically lower during the first quarter and peak during the third. This holds, albeit less markedly, also for Northwest and Southwest regions. These differing patterns are also depicted in Fig. 3, which shows the time series of normalized colony loss for each state belonging to Central and West North Central regions – with the smoothed conditional means highlighted in black and red, respectively. Figure 2b shows that also the presence of V. destructor tends to follow a specific pattern; in most regions it increases from the first to the third quarter, and then it decreases in the fourth – with the exception of the Southwest region, where it keeps increasing. This is most likely because most beekeepers try to get V. destructor levels low by fall, so that colonies are as healthy as possible going into winter, and also because of the population dynamics of V. destructor alongside honey bee colonies – i.e., their presence typically increases as the colony grows and has more brood cycles, since this parasite develops inside honey bee brood cells44,45. The West region (which encompasses only California since Nevada was missing in the honey bee dataset; see Data) reports high levels of V. destructor throughout the year, with very small variability. A comparison of Fig. 2a and b shows that honey bee colony loss and the presence of V. destructor tend to be higher than the corresponding medians during the third quarter, suggesting a positive association. This is further confirmed in Fig. 4, which shows a scatter plot of normalized colony loss against V. destructor presence, documenting a positive association in all quarters. Although with the data at hand we are not able to capture honey bee movement across states, as well as intra-quarter losses and honey production, these preliminary findings can be useful to support commercial beekeeper strategies and require further investigation.Figure 2Empirical distribution of honey bee (Apis mellifera) colony loss (a) and Varroa destructor presence (b) across quarters (the first one being January-March) and climatic regions; red dashed lines indicate the overall medians. (a) Box plots of normalized colony loss (number of lost colonies over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. At the contiguous United States level, this follows a stable pattern across the years, with higher and more variable losses during the first quarter (see Supplementary Figs. S2-S6), but some regions do depart from this pattern (e.g., West North Central). (b) Box plots of normalized V. destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. The maximum number of colonies is defined as the number of colonies at the beginning of a quarter, plus all colonies moved into that region during the same quarter.Full size imageFigure 3Comparison of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) between Central and West North Central climatic regions for each quarter of 2015–2021 (the first quarter being January-March). (a) Trajectory of each state belonging to Central (yellow) and West North Central (blue) climatic regions. (b) Smoothed conditional means for each of the two sets of curves based on a locally weighted running line smoother where the width of the sliding window is equal to 0.2 and corresponding standard error bands are based on a 0.95 confidence level46.Full size imageFigure 4Scatter plot of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) against normalized Varroa destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each state and each quarter of 2015–2021 (the first quarter being January-March). Points are color-coded by quarter, and ordinary least squares fits (with corresponding standard error bands based on a 0.95 confidence level) computed by quarter are superimposed to visualize the positive association.Full size imageUp-scaling weather dataThe data sets available to us for weather related variables had a much finer spatio-temporal resolution (daily and on a (4 times 4) kilometer grid) than the colony loss data (quarterly and at the state level). Therefore, we aggregated the former to match the latter. For similar data up-scaling tasks, sums or means are commonly employed to summarize the variables available at finer resolution47. The problem with aggregating data in such a manner is that one only preserves information on the “center” of the distributions – thus losing a potentially considerable amount of information. To retain richer weather related information in our study, we considered additional summaries capturing more complex characteristics, e.g., the tails of the distributions or their entropy, to ascertain whether they may help in predicting honey bee colony loss. Within each state and quarter we therefore computed, in addition to means, indexes such as standard deviation, skewness, kurtosis, (L_2)-norm (or energy), entropy and tail indexes48. This was done for minimum and maximum temperatures, as well as precipitation data (see Data processing for details).Next, as a first way to validate the proposed weather data up-scaling approach, we performed a likelihood ratio test between nested models. Specifically, we considered a linear regression for colony loss (see Statistical model) and compared an ordinary least squares fit comprising all the computed indexes as predictors (the full model) against one comprising only means and standard deviations (the reduced model). The test showed that the use of additional indexes provides a statistically significant improvement in the fit (p-(text {value}=0.03)). This test, which can be replicated for other choices of models and estimation methods (see Supplementary Table S5), supports the use of our up-scaling approach.Figure 5 provides a spatial representation of (normalized) honey bee colony losses and of three indexes relative to the minimum temperature distribution; namely, mean, kurtosis and skewness (these all turn out to be relevant predictors based on subsequent analyses; see Table 1). For each of the four quantities, the maps are color-coded by state based on the median of first quarter values over the period 2015-2021 (first quarters typically have the highest losses, but similar patterns can be observed for other quarters; see Supplementary Figs. S12-S14). Notably, the indexes capture characteristics of the within-state distributions of minimum temperatures that do vary geographically. For example, considering minimum temperature, skewness is an index that (broadly speaking) provides information on whether the data tends to accumulate at one end or the other of the observed range of minimum temperatures (i.e., a positive/negative skewness indicates that the data accumulates towards the lower/upper range, respectively). On the other hand, kurtosis is an index that captures the presence of “extreme” values in the tails of the data (i.e., a low/high value of kurtosis indicates that the tail minimum temperatures are relatively close/very far from the typical minimum temperatures). With this in mind, going back to Fig. 5, we can see that minimum temperatures in states in the north-west present large kurtosis (a prevalence of extreme values in the tails) and negative skewness (a tendency to accumulate towards the upper values of the minimum temperature range), while the opposite is true for states in the south-east. More generally, the mean minimum temperature separates northern vs southern states, kurtosis is higher for states located in the central band of the CONUS, and skewness separates western vs eastern states.We further note that the states with lower losses during the first quarter (e.g., Montana and Wyoming) do not report extreme values in any of the considered indexes. Although these states are generally characterized by low minimum temperatures, these are somewhat “stable” (they do not show marked kurtosis or skewness in their distributions) – perhaps allowing honey bees and beekeepers to adapt to more predictable conditions. On the other hand, states with higher losses during the first quarter such as New Mexico have higher minimum temperatures as well as marked kurtosis, and thus higher chances of extreme minimum temperatures – which may indeed affect honey bee behavior and colony loss. Overall, across all quarters of the years 2015-2021, we found that normalized colony losses and mean minimum temperatures are negatively associated (the Pearson correlation is -0.17 with a p-(text {value} More

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Suborder Glossiphoniiformes Tessler & de Carle, 2018Family Glossiphoniidae Vaillant, 1890Comments. Our two-locus phylogeny reveals the presence of two large clades, corresponding to the subfamilies Glossiphoniinae and Haementeriinae (Fig. 1). The subfamily Theromyzinae Sawyer, 1986, delineated by some authors2,10,22, was not supported as a distant phylogenetic clade and their representatives are clustered within the monophyletic Glossiphoniinae. The same pattern was recovered by earlier phylogenetic reconstructions3,30,33,34. These data indicate that Theromyzinae may represent a synonym of the latter subfamily. However, a subfamily-level revision of the Glossiphoniidae is beyond the framework of the present study.Subfamily Glossiphoniinae Vaillant, 1890Genus Alboglossiphonia Lukin, 1976Type species: Alboglossiphonia heteroclita (Linnaeus, 1761) (= Hirudo heteroclita Linnaeus, 1761; by original designation).Arctic occurrences. Our results reveal that members of this genus are not common inhabitants of the Arctic but two species, A. heteroclita (Linnaeus, 1761) and A. sibirica sp. nov., cross the Arctic Circle on the Yamal Peninsula through the Ob and Taz rivers (Table 1). Previously, it was shown that A. heteroclita occurs in the lower Ob Basin, northern edge of Western Siberia23.Comments. This genus contains inconspicuous minute leeches and is characterized by a nearly global distribution1. It definitely requires an integrative taxonomic revision. Available genetic evidence (Fig. 1 and Supplementary Fig. S1) reveals that the North American populations of what was traditionally assigned to A. heteroclita should be considered a separate species, A. pallida (Verrill, 1872) (type locality: West River near New Haven, Connecticut, USA)35,36. Other species, which occurs in Siberia and the Far East, was tentatively assigned to Alboglossiphonia cf. papillosa (Braun, 1805) based on a darker pigmentation of its dorsum37,38 but it represents a separate North Asian species, which is described here.
Alboglossiphonia sibirica Bolotov, Eliseeva, Klass & Kondakov sp. nov = Alboglossiphonia heteroclita Lukin (1957): 27339 (identification error). = Alboglossiphonia heteroclita papillosa Kaygorodova et al. (2014): 337; Kaygorodova (2015): 4140 (identification error). = Alboglossiphonia cf. papillosa Klass et al. (2018): 2638 (identification error).Figures 4a, 5a, 7a, Supplementary Figs. S2a, S3a, S4, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:19B581C3-E912-487C-B9EC-8E50DDEFD380.Holotype. RMBH Hir_0542_2-H (non-sequenced), RUSSIA: Lake Torfyanka, 43.0761° N, 131.9620° E, Vladivostok, Primorye, August 12, 2021, Y. E. Chapurina leg.Paratypes (N = 13). RUSSIA: 1 specimen RMBH Hir_0542_2 (sequenced: COI sequence acc. No. ON873332), the type locality, the same date, and collector; 1 specimen RMBH Hir_0396 (non-sequenced), an oxbow lake of Taz River, near Tazovsky settlement, 67.5063° N, 78.6751° E, Yamal-Nenets Region, August 22, 2019, E. S. Babushkin leg.; 1 specimen RMBH Hir_0394 (DNA voucher; sequenced: COI sequence acc. No. ON548508), Vitim River, 57.2010° N, 116.4300° E, Lena River basin, Vitimsky Nature Reserve, Irkutsk Region, July 12, 2019, E. S. Babushkin leg.; 4 specimens RMBH Hir_0013 (3 sequenced with DNA vouchers and one placed on 36 permanent slides as a series of slices; COI sequence acc. No. MH286267, MH286268, and MH286269; 18S rRNA sequence acc. No. MH286273), between zooids of a bryozoan colony, small floodplain lake of the Lena River near Yakutsk, 62.3076° N, 129.8999° E, Yakutia Republic, August 20, 2017, I. N. Bolotov leg.; 1 specimen RMBH Hir_0417_2 (DNA voucher; sequenced: COI sequence acc. No. ON548511), Oron Lake, Gnilaya Kurya Bay, 57.1750° N, 116.4031° E, Lena River basin, Vitimsky Nature Reserve, Irkutsk Region, July 1, 2019, E. S. Babushkin leg.; 1 specimen RMBH Hir_0409_1 (sequenced: COI sequence acc. No. ON548509), a roadside ditch in Knevichi settlement, 43.3886° N, 132.1880° E, Primorye, September 10, 2020, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0413 (sequenced: COI sequence acc. No. ON548510), a puddle near railway at Artem city, 43.3794° N, 132.2188° E, Primorye, September 10, 2020, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0003_3 (DNA voucher; sequenced: COI sequence acc. No. MN393256), Tumnin River, 49.9451° N, 139.9181° E, Khabarovsk Region, July 14, 2014, I. N. Bolotov & I. V. Vikhrev leg.; 1 specimen RMBH Hir_0509_1 (sequenced: COI sequence acc. No. ON548516), a reservoir on the Bolshoy Alim River, near Tolstovka settlement, 50.1981° N, 127.9431° E, Amur Region, July 3, 2021, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0510_1 (DNA voucher; sequenced: COI sequence acc. No. ON548517), an oxbow lake of Bureya River, near Novospassk, 49.6756° N, 129.7343° E, Amur Region, July 3, 2021, O. V. Aksenova et al. leg.Etymology. The name of this species reflects its broad distribution in Siberia.Differential diagnosis. Small leech, which could be distinguished from other congeners by a combination of the following characters: dorsum covered by numerous small, shallow, and indistinct papillae, light yellow, with multiple dark spots and short dashes arranged to 18–24 longitudinal rows; these spots and dashes merged into longitudinal lines in the anterior half of the body (the dark markings pattern often lost in ethanol-preserved animals). Externally, the new species is similar to A. heteroclita, A. hyalina (O. F. Müller, 1773), and A. striata (Apáthy, 1888). However, all these species do not have numerous dark spots and short dashes arranged to multiple longitudinal rows. Additionally, A. heteroclita differs from the new species by having a median row of segmentally arranged dark spots and a smooth dorsum without papillae. A. hyalina differs from A. sibirica sp. nov. by the general lack of dark pigmentation. A. striata differs from the new species by having a median row of segmentally arranged dark transverse stripes and a smooth dorsum without papillae.Molecular diagnosis. The new species represents a separate genetic lineage but is more closely related to A. heteroclita (mean pairwise COI p-distance = 5.1%; range 4.9–5.4%). The intraspecific pairwise COI p-distance ranges from 0.0 to 2.1% (mean ± s.e.m. = 1.31 ± 0.10%; N = 14 sequences and 91 pairwise distance values). The GenBank acc. numbers of reference DNA sequences (COI and 18S rRNA) are given in Supplementary Table S2 and Supplementary Datasets S1–S2.Description. Small leech (body length up to 11.9 mm). Measurements of the type series are given in Supplementary Table S2. Body broad, leaf-like, ovate. Dorsum with numerous small, shallow, and indistinct papillae. Posterior sucker small, circular (maximum diameter of 2.25 mm), ventrally directed. Proboscis pore in the center of anterior sucker. Coloration of living animals: body dirty yellow with multiple brown spots and dashes arranged to longitudinal rows; in the anterior half of the body, these spots and dashes merged into longitudinal lines. Coloration of ethanol-preserved animals: body light yellow; dorsum with multiple dark spots and short dashes arranged to 18–24 longitudinal rows; these spots and dashes merged into longitudinal lines in the anterior half of the body but the dark markings pattern often lost due to preservation. Three pairs of eyespots; the eyespots of the distal pair joined into a single spot; the eyespots of the next two pairs are spaced apart and fused together. Venter light yellow or whitish. Total number of annuli: 70. Somites I–IV joined to form a head region, somites V–XXIV triannulate, somites XXV–XXVII uniannulate. Gonopores joined and open in the furrow XIIa1/a2. Reproductive system: 6 pairs of large, bag-like testisacs inter-segmentally from XIII/XIV to XIX/XX; atrium small, spherical, the atrial cornua twisted anteriorly; paired ejaculatory ducts twisted, short; paired ovisacs narrow, very short. Digestive system: proboscis sheath massive, long, thick; salivary glands diffuse; crop with 6 pairs of crop caeca: 1st-5th uniform, bag-like, 6th pair (posterior caeca) with 3 blind processes; intestine with 4 pairs of rather short processes and an ovate extention after the last pair of processes.Distribution. North Asia: Western Siberia, Eastern Siberia, the Russian Far East, and Mongolia39.Habitats and ecology. This species is known to occur in a broad range of freshwater environments such as rivers, oxbow lakes, large to small natural lakes, reservoirs, road ditches, and even puddles (Supplementary Dataset S2). An unusual example of its association with a bryozoan species was described from Eastern Siberia38. Probably, the record of an Alboglossiphonia leech in the mantle cavity of an unidentified lymnaeid snail from the Altai Mountains, Russia41 could also be attributed to this species. The life cycle of the new species is unknown.Genus Glossiphonia Johnson, 1816Type species: Glossiphonia complanata (Linnaeus, 1758) (= Hirudo complanata Linnaeus, 1758; by subsequent designation).Arctic occurrences. Representatives of this genus are the most remarkable component of the Arctic Glossiphoniidae fauna. Altogether seven species were recorded north of the Arctic Circle, two of which are new to science and described here (Table 1).Comments. In general, sequenced representatives of the genus Glossiphonia could phylogenetically be delineated to three species groups (or subgenera): (1) the complanata-group (= subgenus Glossiphonia s. str.); (2) the verrucata-group (= subgenus Boreobdella Johansson, 1929; type species: Clepsine verrucata Müller, 1844); and (3) the concolor-group (= subgenus Paratorix Lukin & Epstein, 1960; type species: Torix baicalensis Stschegolew, 1922) (Table 1, Fig. 1 and Supplementary Fig. S1).
Glossiphonia arctica Bolotov, Eliseeva, Klass & Kondakov sp. novFigures 4B, 5b,c, 7c, Supplementary Figs. S2b, S3b, S5, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:FADF0993-A946-413A-9680-25BA0F9BE90D.Holotype. RMBH Hir_0457_2_1-H (sequenced: COI sequence acc. No. ON810735; 18S rRNA sequence acc. No. ON819028), RUSSIA: a large lake near Sob’ railway station, 67.0480°N, 65.6316°E, Polar Urals, June 23, 2021, A. V. Kondakov et al. leg.Paratypes (N = 18). 18 specimens RMBH Hir_0457 (two specimens sequenced: COI sequence acc. No. ON810736 and ON810737; 18S rRNA sequence acc. No. ON819029; one specimen placed on 18 permanent slides as a series of slices), the type locality, the same date, and collectors.Etymology. The name of the new species indicates that its type locality is situated in the Arctic Region.Differential diagnosis. Medium-sized leech, which could be distinguished from other congeners by a combination of the following characters: dorsum with four rows of ovate, broad but very shallow and indistinct papillae on annulus a2 (outer paramedian and inner paramarginal series); each papilla bears ovate light yellow or white spot; dorsal black markings pattern absent. Externally, the new species is similar to G. mollissima. However, the latter species differs from G. arctica sp. nov. by having larger papillae and a well-developed black markings pattern dorsally.Molecular diagnosis. The new species represents a separate genetic lineage belonging to the verrucata-group (Fig. 1). The pairwise COI p-distance of the new species from other congeners varies from 7.0 to 12.4%. The intraspecific pairwise COI p-distance ranges from 0.0 to 0.2% (mean ± s.e.m. = 0.10 ± 0.05%; N = 3 sequences and 3 pairwise distance values). The GenBank acc. numbers of reference DNA sequences (COI and 18S rRNA) are given in Supplementary Table S2 and Supplementary Datasets S1–S2.Description. Medium-sized leech (body length up to 13.3 mm). Measurements of the type series are given in Supplementary Table S2. Body broad, leaf-like, ovate. Dorsum with four rows of ovate, broad but very shallow and indistinct papillae on annulus a2 (outer paramedian and inner paramarginal series). Posterior sucker small, circular (maximum diameter of 1.9 mm), ventrally directed. Proboscis pore in the center of anterior sucker. Coloration of living animals: body almost transparent, light brown, with multiple yellowish pigment cells. Coloration of ethanol-preserved animals: dorsum beige to light brown, with darker broad inner paramedian lines and light yellowish areas laterally and anteriorly; ovate light yellow or white spots at each papillae on annulus a2 arranged into four longitudinal rows (outer paramedian and inner paramarginal), sometimes with a few white spots between them. Three pairs of ovate eyespots arranged to two parallel rows; in some specimens eyes on each side are joined to a single large spot. Venter whitish to light brown, sometimes with irregular brownish shading. Total number of annuli: 70. Somites I–III uniannulate, IV biannulate, V–XXIV triannulate, XXV biannulate, XXVII uniannulate. The male and female genital pores are separated by two annuli and are located in the furrows XIa3/XIIa1 and XIIa2/a3, respectively. Reproductive system: 6 pairs of spherical testisacs inter-segmentally from XIII/XIV to XVIII/XIX; atrium spherical, the atrial cornua large, twisted anteriorly; paired ejaculatory ducts very long, extending to XVIII; paired ovisacs massive, long, with multiple lobes, arranged as loops, extending to XVIII (pregnant specimen with eggs). Digestive system: proboscis sheath massive, thick, elongated; esophagus narrow; salivary glands diffuse; crop with 7 pairs of crop caeca: 1st-6th uniform, bag-like, 7th pair (posterior caeca) with 4 blind processes and several smaller lobes; intestine enlarged, with 4 pairs of large, long, bag-like processes, expanding distally, each with several short lobes; a large circular extension after the last pair of processes.Distribution. Polar Urals (not known beyond the type locality).Habitats and ecology. The type series of this species was collected from a natural mountain lake with stony bottom. The leeches were recorded beneath flat stones (Fig. 3b); their feeding behavior and life cycle remain unknown.
Glossiphonia taymyrensis Bolotov, Eliseeva, Klass & Kondakov sp. novFigures 4E, 5d, 7b, Supplementary Figs. S2h, S3c, S6, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:40269BF4-FE1C-4269-A7CC-41020789DC44.Holotype. RMBH Hir_0258_1-H (sequenced: COI sequence acc. No. ON810695), RUSSIA: small lake near Dudinka on Taymyr Peninsula, 69.4008°N, 86.3384°E, July 16, 2018, O. V. Aksenova et al. leg.Paratypes (N = 8). RUSSIA: 2 specimens RMBH Hir_0263_1 and RMBH Hir_0264_3 (sequenced: COI sequence acc. No. ON810701 and ON810705; 18S rRNA sequence acc. No. ON819017), the type locality, the same date, and collectors; 2 specimens RMBH Hir_0256_1 (one sequenced and one placed on 20 permanent slides as a series of slices; COI sequence acc. No. ON810693), small lake near Dudinka on Taymyr Peninsula, 69.3987° N, 86.3505° E, July 16, 2018, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0261_2 (sequenced: COI sequence acc. No. ON810699; 18S rRNA sequence acc. No. ON819016), small lake near Dudinka on Taymyr Peninsula, 69.4014° N, 86.3250° E, July 16, 2018, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0265_2 (sequenced: COI sequence acc. No. ON810706; 18S rRNA sequence acc. No. ON819021), Bolgokhtokh River near Dudinka, Taymyr Peninsula, 69.3780° N, 87.2215° E, July 21, 2018, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0488 (sequenced: COI sequence acc. No. ON810755), a lake on Putorana Plateau, 68.7607° N, 91.9014° E, July, 2021, E. S. Chertoprud leg.; 1 specimen RMBH Hir_0449 (sequenced: COI sequence acc. No. ON810731), Pyzas River near Ust-Kabyrza settlement, 52.8277° N, 88.3973° E, Tashtagolsky District, Kemerovo Region, July 23, 2020, E. S. Babushkin & M. V. Vinarski leg.Etymology. The new species is named after the Taymyr Peninsula, where the majority of the type specimens were collected.Differential diagnosis. Small leech with broad, leaf-like, ovate body; three pairs of eyespots (distal pair joined; next two pairs separate); dorsal papillae absent; dorsal coloration with two inner paramedian rows of black spots, sometimes joining into unclear dashed lines; two annuli between the male (XIa3/XIIa1) and female (XIIa2/a3) genital pores. The new species largely resembles G. complanata but could be distinguished from it by having a smooth dorsum, without clear papillae. These taxa seem to have non-overlapping, allopatric ranges and, hence, could be separated on the basis of geographic criteria. However, the DNA approach seems to be the most appropriate way to distinguish these two species.Molecular diagnosis. The new species represents a separate genetic lineage belonging to the complanata-group (Fig. 1). The pairwise COI p-distance of the new species from other congeners varies from 6.0 to 12.2%. The intraspecific pairwise COI p-distance ranges from 0.0 to 1.1% (mean ± s.e.m. = 0.52 ± 0.07%; N = 8 sequences and 28 pairwise distance values). The GenBank acc. numbers of reference DNA sequences (COI and 18S rRNA) are given in Supplementary Table S2 and Supplementary Datasets S1–S2.Description. Small leech (body length up to 11.3 mm). Measurements of the type series are given in Supplementary Table S2. Body broad, leaf-like, ovate. Dorsum smooth, without clear papillae. Posterior sucker ovate (maximum diameter of 3.0 mm), ventrally directed. Proboscis pore in the center of anterior sucker. Coloration of living animals: not examined. Coloration of ethanol-preserved animals: (1) typical form having beige to light brown ground color without light spots but with darker brown coloration between inner paramedian lines; (2) melanic forms having dark brown ground color with four rows of large yellow spots (outer paramedian and marginal series) and yellow median stripe anteriorly (f. ‘maculosa’) or with strongly reduced yellow markings pattern. In all forms, there are two inner paramedian rows of black spots, sometimes joining into unclear dashed lines. Three pairs of ovate eyespots; the eyespots of the distal pair joined into a single spot; the eyespots of the next two pairs separate and are spaced apart. In the typical form, venter light yellow, with paired brown median and outer paramedian lines, which may be reduced to series of narrow brown longitudinal stripes. In melanic forms, ventral markings is more developed, with a series of brown longitudinal lines from median to inner paramarginal position and outer paramarginal brown spots. Posterior sucker with dense brown spots in melanic forms and with scarce brown spots in typical form. Total number of annuli: 68. Somites I–IV uniannulate, V–XXIV triannulate, XXV biannulate, XXVI–XXVII uniannulate. The male and female genital pores are separated by two annuli and are located in the furrows XIa3/XIIa1 and XIIa2/a3, respectively. Reproductive system: 6 pairs of spherical testisacs inter-segmentally from XII/XIII to XVIII/XIX; atrium ovate, the atrial cornua directed laterally; paired ejaculatory ducts twisted, short; paired ovisacs short, thick (undeveloped). Digestive system: salivary glands diffuse; proboscis sheath moderately thick; esophagus ovate; crop with 6 pairs of massive, bag-like, uniform crop caeca; intestine with 4 pairs of processes.Distribution. Western and Eastern Siberia.Habitats and ecology. The new species was recorded from natural lakes and rivers (Supplementary Dataset S2); its feeding behavior and life cycle are unknown.Genus Hyperboreomyzon Bolotov, Eliseeva, Klass & Kondakov gen. novLSID: https://zoobank.org/urn:lsid:zoobank.org:act:298FF41E-AF0D-4442-9F82-3022B8094A67.Type species: Hyperboreomyzon polaris gen. & sp. nov.Etymology. This name is compiled using two Greek words: ‘Hyperborea’ (meaning a mythical far northern land) and ‘myzon’ (meaning sucking).Diagnosis. Medium-sized, elongate, sub-fusiform glossiphoniid leeches; body and posterior sucker densely covered by shallow, ‘fish-scale’-like papillae; somite V biannulate; somites XII–XXIII secondarily sexannulate dorsally and ventrally due to the presence of very deep, prominent furrows separating each annulus to two semi-annuli; six rows of prominent dorsal tubercle-like papillae at a2 (inner paramedian, inner paramarginal, and marginal series) from V to XXVI; two pairs of circular eyespots on II and Va1 at inner paramedian position; gonopores at the furrows XIa3/XIIa1 (male) and XIIa2/a3 (female) and separated by two annuli; male atrium spherical; proboscis pore opens in a thick velar fold in the anterior half of oral sucker; one pair of compact, massive, elongated, incurved salivary glands, each gland with a bunch of a few short processes apically; 9 crop caeca pairs. Comparison of the new genus with other genera in the family based on morphological and anatomical features is presented in
Supplementary Table S3. Sexannulate condition was also recorded in the genus Actinobdella Moore, 1901 from North America36, but it differs from Hyperboreomyzon gen. nov. by having one pair of eyespots, diffuse salivary glands, and an apical position of proboscis pore (Supplementary Table S3).Comments. This genus is established for a single species, which is described below.
Hyperboreomyzon polaris Bolotov, Eliseeva, Klass & Kondakov gen. & sp. nov.Figures 4J, 5e, 6a-j, 7c, Supplementary Figs. S21, S8, S9, S10, S11, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:503A9A26-CEDE-4747-952D-8416AE4EF4EB.Holotype. RMBH Hir_0486-H (sequenced: COI sequence acc. No. ON810753; 18S rRNA sequence acc. No. ON819030), RUSSIA: small alpine lake on Putorana Plateau, 68.9008°N, 94.1599°E, July, 2021, E. S. Chertoprud leg.Paratypes (N = 2). RUSSIA: 1 specimen RMBH Hir_0689 (dissected and placed on 60 permanent slides as a series of slices), small alpine lake on Putorana Plateau, 68.6659° N, 93.1365° E, August 11, 2021, E. S. Chertoprud leg.; 1 specimen RMBH Hir_0216 (sequenced and dissected; COI sequence acc. No. ON810677; 18S rRNA sequence acc. No. ON819005), water puddle on Kolguev Island, 68.9300° N, 49.0303° E, August 12, 2018, O. V. Travina & V. M. Spitsyn leg.Etymology. The name of the new species reflects its occurrences in polar (Arctic) areas of Eurasia.Differential diagnosis. As for the genus.Molecular diagnosis. None of congeneric species is known. Based on uncorrected pairwise COI p-distances between a haplotype of the new taxon and selected species-level haplotypes in each genus (Supplementary Table S1), Hyperboreomyzon seems to be more closely related to members of Hemiclepsis (mean distance ± s.e.m. = 11.62 ± 0.15%, range = 9.75–14.08%, N = 9) and Theromyzon (mean distance ± s.e.m. = 11.37 ± 0.07%, range = 10.47–12.64%, N = 9) without significant differences between distances from these two genera (Mann–Whitney test: p = 0.72). Other Glossiphoniidae genera are more distantly related, with a mean pairwise uncorrected COI p-distance of  > 13.0% (Mann–Whitney test: p  More

Study siteThe Cariaco Basin, located on the continental shelf off Venezuela, is a large (about 160 km long and about 65 km wide) depression, composed of two approximately 1,400-m-deep sub-basins. It is partially isolated from the Caribbean Sea by a series of sills with depths of less than 150 m (ref. 47). This limits renewal of deep water in the basin and, paired with the high oxygen demand resulting from intense surface primary productivity, leads to anoxic waters below a depth of about 275 m at present47,48.The marked seasonality in the Cariaco Basin, combined with anoxic bottom waters that effectively prevent bioturbation, results in the accumulation of annually laminated (varved) sediments. As sediments are varved for the last deglaciation and the Holocene, and because of the sensitivity of the area to climate change, they are considered to be one of the most valuable high-resolution marine climate archives and have been successfully used to study climate variability in the tropics3,11,16,17,18. Varve thickness is about 1 mm or more during the YD–Holocene transition18.Core and age modelCore MD03-2621 was retrieved during IMAGES cruise XI (PICASSO) aboard R/V Marion Dufresne in 2003 (Laj and Shipboard Party 2004). Cariaco cores have been collected under the regulations of the Ocean Drilling Program and the IMAGES coring programme. In this study, data from depths between 480 and 540 cm below the seafloor are presented, encompassing the YD–Holocene transition. A detailed age model for core MD03-2621 was established by Deplazes et al.11 and is based on the cross-correlation of total reflectance to dated colour records from the Cariaco Basin49,50. For the studied interval, the original age model is based on a floating varve chronology anchored to tree ring data by matching 14C data49. The age model for core MD03-2621 was further fine-tuned by correlation of reflectance data to the NGRIP ice core δ18O record on the GICC05 age scale11. The transition from the YD to the Holocene is characterized by a decrease in the sedimentation rate from 1.4 to 0.5 mm year−1.To account for possible depth offsets during storage and subsampling, we matched sediment colour data expressed as greyscale (GS) to the reflectance data from Deplazes et al.11 with the software QAnalySeries51. To enable comparison with our record, ages in Lea et al.3 were corrected for the age difference between the sediment-colour-based midpoint of the YD–Holocene transition in their record (11.56 kyr b2k) and in data from Deplazes et al.11 (11.673 kyr b2k). The start and end of the change in reflectance were determined by the RAMPFIT software52.Sample preparationSamples for MSI of molecular proxies were prepared as described in Alfken et al.53: the original core was subsampled by LL channels, from which X-ray pictures (Hewlett-Packard Faxitron 43855A X-ray cabinet) and high-resolution digital images (smart-CIS 1600 Line Scanner) were obtained. The LL channels were then cut into 5-cm pieces, which were subsequently freeze-dried, embedded in a gelatin:carboxymethyl cellulose (4%:1%) mixture and thin-sectioned on a Microm HM 505 E cryomicrotome. From each piece, one 60-µm-thick and one 100-µm-thick, longitudinal slice (spanning the whole 5 cm piece) were prepared and affixed to indium-tin-oxide-coated glass slides (Bruker Daltonik, Bremen, Germany) for MSI and elemental mapping, respectively. Slices for MSI were further amended with a fullerite matrix54.For all slices, a high-resolution picture was taken on a M4 Tornado micro-X-ray fluorescence spectroscopy system (Bruker Nano Analytics). This picture was used as a reference to set up elemental mapping and MSI analysis, and also for the 2D comparison of elemental and proxy data to sediment colour. Sediment colour is expressed as GS value. To account for differences between single slices, ΔGS was calculated as the difference between a value and the median GS of each individual slice. Very low GS values corresponding to areas devoid of sediment, identified by a black background, were excluded from analysis.Elemental mappingElemental mapping of 100-µm-thick slices was performed on a M4 Tornado micro-X-ray fluorescence spectroscopy system (Bruker Nano Analytics) equipped with a micro-focused Rh source (50 kV, 600 µA) with a polycapillary optic. Measurements were conducted under vacuum, with a resolution of 50 µm, two scans per spot and a scan time of 5 ms per scan. Data were initially processed and visualized with M4 Tornado Software version 1.3. XY matrices of relevant elements and sediment colour were imported into MATLAB (R2016b) for further processing. To assess the correspondence between sediment colour and elemental composition, for each 5-cm piece, signal intensities of Ca, Fe, Ti and Si in single spots were binned according to ΔGS and average intensities were calculated for each bin (Extended Data Fig. 5). The bin size was 5 GS units.Molecular proxy analysis by MSIMSI was carried out on a 7T solariX XR Fourier transform ion cyclotron resonance mass spectrometer coupled to a matrix-assisted laser desorption/ionization source equipped with a Smartbeam II laser (Bruker Daltonik, Bremen, Germany). Analyses were performed in positive ionization mode selecting for a continuous accumulation of selective ions window of m/z 554 ± 12. Spectra were acquired with 25% data reduction to limit data size. Spatial resolution was obtained by rastering the ionizing laser across the sample in a defined rectangular area at a 100-µm spot distance. Considering laminae thickness in the millimetre range18, such raster resolution is suited for seasonally resolved SST reconstruction. Settings for laser power, frequency and number of shots were adjusted for optimal signal intensities before each measurement; typical values were 250 shots with 200 Hz frequency and 60% laser power. External mass calibration was performed in electrospray ionization mode with sodium trifluoroacetate (Sigma-Aldrich). Each spectrum was also calibrated after data acquisition by an internal lock mass calibration using the Na+ adduct of pyropheophorbide a (m/z 557.2523), a chlorophyll a derivative generally present in relatively young marine sediments. Around 20,000 individual spots were thereby obtained for every 5-cm slice, each spot containing information on the abundance of diunsaturated and triunsaturated C37 alkenones needed to calculate the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) SST proxy.We provide a two-pronged approach to decode SST proxy information: (1) a downcore ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) profile is obtained by pooling alkenone data from coeval horizons, and results in SST reconstructions with annual resolution, and (2) 2D images of alkenone distribution are examined in conjunction with maps of sediment colour and elemental distribution to filter single-spot alkenone data for season of deposition.SST reconstruction with yearly resolutionFor the downcore profile, MSI data were referenced to the X-ray image by the identification of three teaching points per 5-cm piece. Afterwards, the X-ray image was corrected for tilting of laminae in the LL channels. This was done by identification of single laminae in the X-ray image and selection of a minimum of four tie points per lamina. A detailed description can be found in Alfken et al.9. After applying the corresponding age model, downcore profiles were established with 1-year resolution: the intensity of the two alkenone species relevant to the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) proxy (C37:2 and C37:3) were recorded for each individual laser spot and filtered for a signal-to-noise threshold of 3. Only spots in which both compounds were detected were further considered. Intensity values were then summed over the depth corresponding to 1 year. By pooling proxy data into 1-year horizons, the effect of changing sedimentation rate and, thereby, changing downcore resolution is minimized. If at least ten spots presenting both compounds were available for a single horizon, data quality criteria were satisfied54 and a ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) value was calculated as defined by Prahl and Wakeham22:$${{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}=frac{{text{C}}_{37:2}}{{text{C}}_{37:2}+{text{C}}_{37:3}}$$
(1)
To apply the gas chromatography (GC)-based calibrations for the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) proxy, MSI-based data were converted to GC equivalents. Therefore, after MSI, sediment slices were extracted for conventional proxy analysis. Sediment was scraped off the slide and extracted following a modified Bligh and Dyer procedure55,56. Extracts were evaporated under a stream of nitrogen, re-dissolved in n-hexane and analysed on a Thermo Finnigan Trace GC-FID equipped with a Restek Rxi-5ms capillary column (30 m × 0.25 mm ID). For each 5-cm piece, a ratio between the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) values obtained by GC flame ionization detector analysis and MSI of the whole piece was calculated. The average ratio of all pieces for which GC-based values could be obtained was 1.194, with a standard deviation of 0.021.$${{rm{U}}}_{37,{rm{G}}{rm{C}}-{rm{F}}{rm{I}}{rm{D}}}^{{{rm{K}}}^{{prime} }}=1.194times {{rm{U}}}_{37,{rm{M}}{rm{S}}{rm{I}}}^{{{rm{K}}}^{{prime} }}$$
(2)
This ratio was used to calculate GC-equivalent ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) values, which were then translated into SST using the BAYSPLINE calibration57. The average standard error of the BAYSPLINE model is 0.049 ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) units (corresponding to 1.4 °C) for samples with SST below 23.4 °C, but increases at higher values (to up to 4.4 °C)57. This is explained by the fact that sensitivity of the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) to SST (that is, the slope of the regression) declines at higher values. In the current dataset, the 95% confidence interval is, on average, ±3.6 °C. The analytical precision of MSI-based SST reconstructions for the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}), using at least ten data points, according to Alfken et al.9, is about 0.3 °C. Sources of uncertainty are summarized in Extended Data Fig. 10a.For frequency analysis, a continuous, annually spaced record was constructed by linearly interpolating 49 missing values. The record was subsequently detrended. Spectral analysis was performed with the REDFIT module58 using a Hanning window (oversample 2, segments 2). Continuous wavelet transforms were applied to investigate changes in cyclicity over time, using the Morlet wavelet with code provided by Torrence and Compo59 for MATLAB. All steps, except for the wavelet analysis, were performed with the PAST software60.For the assessment of the interannual variability, the SST record was band-pass-filtered for periods between 2 and 8 years. The record is based on 1-year binned data; seasonality is thereby nullified and the highest frequency to be evaluated (Nyquist frequency) corresponds to a period of 2 years. Variability of this time series was quantified by calculating the standard deviation of the band-pass-filtered ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) signal in 25-year intervals. To account for the potential impact of analytical precision on the observed signal (Methods, section titled ‘The effect of changing sedimentation rate on reconstructed interannual SST variability during the YD–Holocene transition’), the variability experiment from Alfken et al.9 was revisited. A sediment extract had been sprayed on an ITO slide and analysed by MSI. We then randomly selected n spots and obtained a ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) value for the summed intensities of these spots. Precision was calculated as the standard deviation of five replicate experiments for n = 1, 5, 10, 20, 30, 40, 50 and 60. Decreasing analytical variability with increasing number of observations was fitted to a curve (R2 = 0.838) described by the equation$${rm{Analytical}},{rm{variability}}=0.0741times {rm{number}},{rm{of}},{{rm{spots}}}^{-0.558}$$
(3)
On the basis of this equation, analytical variability for each horizon could be calculated on the basis of the number of values included (Extended Data Fig. 10b). The mean variability for each 25-year window was then subtracted from the observed variability in the band-pass-filtered signal and the resulting proxy values were translated to SST following the equation by Müller et al.61. Statistical significance of the change in corrected SST variability after 11.66 kyr b2k was assessed with a t-test.Assessment of SST seasonalityFor the assessment of SST seasonality, alkenone intensities from individual spots were binned according to ΔGS, with a bin size of 1 unit. Spots were then separated into the categories upwelling season and non-upwelling season by identifying the threshold ΔGS value that maximized the difference between average SST in the bins above and below it. Furthermore, this value had to fulfill three conditions: (1) be higher (lighter) than the bins with the highest relative abundance of Ca, Ti and Fe, which is indicative of the dark sediments associated to non-upwelling season, (2) be lower (darker) than the bin with highest relative abundance for Si indicative of light sediment associated to the upwelling season and (3) the number of spots categorized as upwelling and non-upwelling had to account for at least 25% of total spots. If criteria 1 and 2 prevented criteria 3 from being fulfilled, a limit of 15% was set. After separating data into these two categories, data were processed separately as described above for the unfiltered dataset and a downcore temporal resolution of 5 years was applied. Seasonality was calculated as the difference between both records and thus represents the difference between 5-year average SST in the non-upwelling and upwelling seasons.Shift in seasonality was fitted to two different ramps with the RAMPFIT software52. An unconstrained approach and a constrained approach (in which the start and end points of the ramp were restricted to the intervals 11.725–11.8 kyr b2k and 11.6–11.675 kyr b2k) were applied. Negative values were excluded from this fitting. The resulting groups of data were compared by a Mann–Whitney rank test.SST seasonality in the modern Cariaco Basin was calculated for the years 1980 to 2020 based on the HadISST dataset62 by dividing monthly data from each year into two groups and searching for the largest difference between the average temperatures of both groups. Each group had to include at least three consecutive months. In 36 out of 41 years, the warm season was defined from May to November or from July to November.Decadal-scale to centennial-scale SST changes during the YD–Holocene transition and in the early HoloceneAnnually reconstructed SST (average SST = 24.3 °C) remains relatively stable during the YD–Holocene transition. At around 11.4 kyr b2k, a warming trend is observed. Averaging all data before 11.39 kyr and after 11.37 kyr results in a warming from 23.9 ± 1.6 °C to 25.5 ± 1.4 °C. Trends identified by MSI are consistent with conventional ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) analyses performed in the present study and those previously reported by Herbert and Schuffert23 on Ocean Drilling Program core 165-1002C (Extended Data Fig. 1). These authors observed a slight warming several hundred years after the transition into the Holocene, between about 11.53 and 11.32 kyr b2k.Three prominent SST maxima are observed between about 11.50 and 11.45 kyr b2k. The average SST in these 50 years is 1.3 °C higher than in the 50 years before and after. These maxima are synchronous with the 11.4-ka cold event or PBO characterized by a negative excursion in δ18O and reduced snow accumulation rates in Greenland ice cores63 (Extended Data Fig. 2). The PBO coincides with the oldest of the Bond events, that is, pulses of ice rafting in the Northern Atlantic indicative of climatic deterioration64.A warm tropical response to the PBO would be supported by the lower-resolution foraminiferal SST record of Lea et al.3, which shows two data points of increased SST shortly after the end of the YD–Holocene transition. To enable direct comparison, ages in Lea et al.3 were corrected for the age difference between the sediment-colour-based YD termination midpoint in their record and in data from Deplazes et al.11. After this correction, these maxima correspond to 11.43 and 11.50 kyr b2k (Extended Data Fig. 2). Further, the SST maxima coincide with a short-lived change to lighter-coloured sediments. Hughen et al.19 described a correlation between brief North Atlantic cold events, such as the PBO, and changes in tropical primary productivity mediated by stronger upwelling that result in lighter sediments in the Cariaco Basin. Far-reaching effects of the PBO have previously been described in West Asia, with increased dust plumes being related to a southward shift of the westerlies65.The identification of the mechanisms behind a potential TNA response to the PBO is beyond the scope of this study. However, we wish to point out that high-resolution records are crucial to identify such events and to differentiate between underlying changes coinciding in time and, as in the present case, sharp signals that act on the same multidecadal timescales and can potentially be triggered by the same processes66.The effect of changing sedimentation rate on reconstructed interannual SST variability during the YD–Holocene transitionPooling proxy data into 1-year horizons establishes a constant sampling rate and thereby prevents potential effects of changing sedimentation rates. The onset of the Holocene in the Cariaco Basin sediments is characterized by a sharp decrease in sedimentation rates from 1.4 to 0.5 mm year−1 (refs. 11,19). Consequently, in the yearly pooled data, we observe a reduction in the number of values summed for each horizon (Extended Data Fig. 10b), as fewer laser spots fit into the thinner Holocene annual layers. At the same time, the mean intensity in each of these spots slightly increases, consistent with a relative increase of the contribution of haptophytes to primary production20.We have previously shown that the precision of MSI-based molecular proxy analysis is dependent on both the number of spots pooled per data point and the signal intensity in these spots54. All horizons used in the downcore record are above the established threshold of ten spots and proxy variability was shown to stabilize above this threshold9,54. However, as a decrease in the number of values per horizon might still result in lower analytical precision and contribute to higher signal variability, we corrected variability in the 2–8-year window with the estimated analytical variability (see equation (3)). With this correction, the magnitude of the described variability decreases across the record, but the trend towards higher interannual variability in the Holocene persists (Fig. 2c).Varve formation and alkenone deposition in the sediments of the Cariaco Basin during the YD–Holocene transitionComparison of elemental maps and sediment colour (Extended Data Fig. 5) shows a consistent pattern of lamination across the YD–Holocene transition that results from the seasonal interplay of precipitation, upwelling and dominant phytoplankton community composition. Darker laminae represent the rainy, non-upwelling (summer/fall) season and are enriched in Fe and Ti from terrigenous material and Ca sourced from biogenic CaCO3 produced by foraminifera or coccolithophores. Lighter laminae are characterized by high abundance of Si and correspond to the increased production of biogenic opal by diatoms during the upwelling (winter/spring) season67. This is in agreement with observations by Hughen et al.18, who described the laminae couplets in the Cariaco Basin as representing annual cycles, whereby light laminae are an indicator of high productivity associated with the winter/spring upwelling season and dark laminae are an indicator of summer/fall runoff and accumulation of terrigenous material. Deplazes et al.68 described a divergent origin of lamination for a deeper section of the YD, with light laminae being rich in calcareous and terrigenous elements characteristic for the summer season, whereas dark layers were enriched in Si and Br, indicative of diatoms and organic-walled primary producers characteristic for the more productive winter season. Such an alteration of the characteristic pattern of lamination is not observed in the late YD investigated here.This blueprint of seasonality was used to assess the seasonal behaviour of alkenones. Alkenones were deposited throughout the year, as evidenced by the fact that the number of spots containing detectable amounts of both alkenone species are not restricted to the upwelling or non-upwelling seasons but distributed across a relatively wide range of GS values to both sides of the median (Extended Data Fig. 6). Average alkenone signal intensity is higher in the non-upwelling season, pointing to a preference of alkenone producers for this season and/or to a stronger dilution of the signal in the upwelling season. In regards to the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) SST proxy distribution in light versus dark layers, our observations are in agreement with the ability to capture the seasonal SST cycle with alkenones in sinking particles in the modern Cariaco Basin69.Effect of changing seasonality on YD and early Holocene SST records from the western TNAChanging seasonality can contribute to explaining contrasting lower-resolution SST records in the western TNA during the YD and the early Holocene. The strong warming during the YD–Holocene transition recorded in the foraminiferal Mg/Ca record of the Cariaco Basin (Lea et al.3; Extended Data Fig. 1) might be reflecting the more robust thermohaline stratification and increasingly warmer non-upwelling seasons, given the preference of Globigerinoides ruber for this season.Globigerinoides ruber (white), as used by Lea et al.3, is considered to be a dominant species in the tropics, with a relatively uniform annual distribution. However, in the modern Cariaco Basin, upwelling leads to a distinct foraminiferal community composition and seasonal turnover70, consistent with the notion of warm-water foraminifera narrowing their occurrence to the warmest season71. The relative abundance of G. ruber increases in the non-upwelling (warm) season but rarely exceeds 15%, whereas the upwelling season is clearly dominated by Globigerina bulloides72,73. Globigerinoides ruber fluxes are consistently lowest when upwelling is most vigorous, as expressed in annual minima in SST (Extended Data Fig. 9b). As upwelling during the YD and early Holocene was more intense than in the present70, the preference of G. ruber for the summer (non-upwelling) season might have been even more pronounced.The development of a stronger seasonality in the early Holocene would thus have led to a narrower temporal occurrence of G. ruber in the non-upwelling season, during which it would also be exposed to higher SST. The average SST difference between seasons obtained in our analysis can be converted into annual SST amplitude by assuming a sinusoidal curve. By doing so, we observe an increase in the seasonal amplitude of 1.5 to 1.9 °C (depending on the ramp fitted), which is similar to the warming described by Lea et al.3.This interpretation is in agreement with Bova et al.46, who observed that most Holocene climate reconstructions are biased towards the boreal summer/fall and reflect the evolution of seasonal rather than annual temperatures. As discussed above, this is probably not true for the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) index in the Cariaco Basin, as alkenones are deposited throughout the year. The suggested weakening of summer stratification during the YD (as compared with the Holocene) might, however, explain why the lower-resolution ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) records from the semi-enclosed Cariaco Basin show no or weaker warming23 than other, open-ocean, tropical YD records4, where the interplay of upwelling, freshwater input and stratification are less relevant to the SST signal. More