Ecology

The Chaihuín stratigraphyCore transects (Fig. 2b) reveal three sand layers, intercalated between herbaceous peats, that are laterally extensive over 600 m across the marsh (Fig. 3a). In all cases, the sand layers have sharp lower contacts and transitional upper contacts. Ten accelerator mass spectrometric (AMS) radiocarbon dates modelled using a Bayesian phased sequence model provide the chronology (Fig. 3c and Supplementary Table 1). The age of plant macrofossils immediately beneath the upper layer, sand A, are consistent with burial by the 1960 tsunami. The age model places the deposition of the middle sand B at 1600–1820 and lower layer, sand C, at 1486–1616 CE. The calibrated age ranges for sands B and C are reasonably broad due to plateaux in the radiocarbon calibration curve, which affect dates from the seventeenth to twentieth centuries21.Fig. 3: Geological evidence from Chaihuín.a Stratigraphy of selected coring transects showing three laterally extensive sand sheets. Transect locations X–X’ and Y–Y’ shown on Fig. 2; b sedimentology of sand sheets, including grain size, sorting and clastic composition (%) classified relative to six modern environments established by discriminant analysis (see Supplementary Discussion), with images of sands A and B in CN17/8. Box-and-whisker plots show the statistical parameters measured in sand samples with the horizontal line inside the box representing the median, the box representing the upper and lower quartiles, the whiskers representing the minimum and maximum values excluding any outliers and the crosses the extreme outlier values. The number within each box indicates the number of samples in each group; c probability density functions (95.4%) of radiocarbon dates and modelled ages for the three earthquakes. Full radiocarbon results in Supplementary Table 1.Full size imageThe sedimentology and mineralogical signatures of the sand sheets are described in detail elsewhere based on over 100 hand-driven cores22 and summarised in Supplementary Discussion; here we analyse diatoms in three representative cores and present reconstructions of marsh surface elevation change over time from a diatom-based transfer function (Fig. 4 and Supplementary Data 1). From diatom analysis of the three cores, we identified 170 species indicative of differing tolerances to tidal inundation. Only 14 species were absent from a previously published modern training set that includes 29 samples from Chaihuín20, and 9 of these species constituted 2% of any sample (comprising 4–5% in 2 non-sand samples).Fig. 4: Diatom assemblages and estimates of land-level change derived from a regional south-central Chile transfer function for three cores from Chaihuín.a–c Diatom assemblage summaries and dominant taxa in cores CN14/5 (a), CN17/8 (b) and CN18/11 (c) at elevations of 0.88, 0.89 and 1.10 m above mean sea level (MSL), respectively. Elevation optima of diatom species are classified based on weighted averaging of the modern training set and reported relative to mean higher high water (MHHW). The modern analogue technique was used to calculate the squared chord distance to the closest modern analogue, and the threshold for a fossil sample having a close modern analogue is defined as the 20th percentile of the dissimilarity values (MinDC) for the modern training set44. Reconstructed palaeomarsh surface elevations (PMSE) and coseismic subsidence are shown from the weighted averaging partial least squares (WA-PLS) model only. d Estimates of coseismic subsidence in 1737 from three cores and three different diatom-based transfer function approaches, showing 95.4% uncertainties.Full size imageThe laterally extensive uppermost coarse to medium-grained sand sheet (A) is mid grey, varies in thickness between 1 and 19 cm, has a median grain size of 0.49 mm and is upwards fining (0.27–0.71 mm) in 61 cores (80% of those in which A is preserved, massive in the others). The marsh grades steeply into freshwater scrub, and there is no sand unit in cores just above the high marsh limit. There is an abrupt contact between the sand and dark brown silty herbaceous peat below, which contains plant material including below-ground stems (rhizomes) of Scirpus americanus. In many cores, there are rip-up clasts (~2 cm) of peat encased in the sand sheet, as well as vegetation rooted in the peat below. The peat below the sand sheet contains a diatom assemblage that is almost entirely composed of species found on the contemporary high marsh above mean higher high water (MHHW) (e.g. Eunotia praerupta, Nitzschia acidoclinata), with higher elevation optima than the diatoms found in the herbaceous peat above the sand unit (e.g. Rhopalodia constricta) (Fig. 4a). The overlying peat also contains low, albeit important, percentages (5–24%) of taxa with elevation optima below MHHW. By contrast to the peats, sand A is dominated by species with lower elevation optima (59–72% of the total assemblage have optima below MHHW), including Achnanthes reversa and Planothidium delicatulum.The middle brown-grey to dark grey mica-rich coarse to medium-grained sand sheet (B) is similarly laterally extensive across the entire marsh, varying in thickness between 2 and 32 cm. It has a median grain size of 0.47 mm and is upwards-fining (0.38–0.68 mm) in 31 cores (50% of those in which B is preserved, massive in others), but rip-up clasts of peat were only occasionally observed. In some cases, we observe a 2–4-cm-thick cap of horizontally bedded detrital plant fragments and wood at the top of the sand layer. The sand sheet abruptly overlays a red-brown to dark brown silty herbaceous peat with variable silt content and humification. Humidophila contenta dominates the diatom assemblage in the peat below sand B (up to 37% of the assemblage) and is also present in the peat overlying the sand sheet, which remains dominated by species with elevation optima above MHHW. In the core from the lowest contemporary marsh elevation (CN14/5, Fig. 4a), there is an increase in low marsh diatom species (elevation optima below MHHW) above the sand compared to below (e.g. A. reversa, P. delicatulum). Diatom assemblages are relatively consistent across the five samples from the sand unit, with 54–76% of the assemblages being species with elevation optima below MHHW, including A. reversa, Fallacia tenera and P. delicatulum.A third sand deposit (C) is found in 16 cores at the southern end of the marsh, although still traceable over 200 m and across most cores that penetrated deep enough to potentially sample sand C. The deposit is a dark grey fine to medium-grained massive sand (median grain size 0.25 mm, range 0.22-0.29 mm), with a maximum thickness of 51 cm and contains occasional rip-up clasts from the buried organic unit below encased in the sand. The basal contact is abrupt, with the sand overlying a brown clayey silt with occasional herbaceous plant remains, humified organic matter and woody plant material. The organic horizon below sand C contains more diatom species typically found at lower elevations in the tidal frame than the peats below A and B (Fig. 4a). There is also a change in species composition approaching the top of the peat, with abundances of Opephora pacifica and Pseudostaurosira perminuta decreasing and H. contenta and E. perpusilla increasing from the base to top of the peat below sand C. Also in contrast to the other two buried organic deposits, there is a change in species composition approaching the top of the peat and samples immediately above and below sand unit C have very similar diatom assemblages, dominated by H. contenta and E. perpusilla. Diatom preservation in the sand unit was very poor, and it was not possible to obtain representative counts from this unit.Brown silty herbaceous peats separate the three sand sheets, deposited intertidally on the basis of their diatom composition. In addition to the relative variations in freshwater and brackish diatom composition of peats described above, the peat units also vary in their degree of humification. While peats below sands A and C contain humified organic matter, the peat below sand B is unhumified. Additionally, two layers of highly humified black peat were observed immediately above and below sand A in low marsh cores from the southwest of the marsh, varying in thickness between 1 and 15 cm.Evidence for a locally sourced tsunamiWe interpret all three sand sheets as being deposited by locally sourced tsunamis, rather than far-field tsunamis or non-seismic processes (e.g. storms, river floods or aeolian processes). This is based primarily on coincident land deformation, and also upon their lateral extent, diatom composition, and sedimentological signatures. Dealing first with the latter lines of reasoning, sands A and B are not only dominated by marine sublittoral and epipsammic diatom species but also contain substantial numbers of benthic silty intertidal mudflat and freshwater taxa, which also dominate the underlying peats. This is consistent with mixed diatom assemblages in tsunami deposits worldwide and indicative of tsunamis eroding, transporting and redepositing diatoms from diverse environments as they cross coastal and inland areas23,24,25,26. The presence of marine and tidal flat diatoms excludes deposition of sand by river flooding25,27, and statistical comparison of the sedimentological and mineralogical signatures of the sands with modern depositional environments, reported by Aedo et al.22 and summarised in Supplementary Discussion, further supports a seaward rather fluvial sediment source. We observe a maximum sedimentary contribution of 12% from upstream fluvial sources (Fig. 3b) and do not observe erosional or depositional features characteristic of fluvial flood deposits, such as a high basal mud content reflective of suspended loads during the initial stages of flooding or inverse grading as energy increases28.Meteorologically driven deposition of the sands, either during storm surges or other transient sea-level fluctuation events (e.g. El Niño), is discounted as the diatoms in the overlying organic units demonstrate lasting ecological change27,29. While a non-tsunamigenic earthquake followed closely in time by a large storm surge may impact diatom assemblages in the same way, there are several further characteristics of the three sand sheets which are consistent with a tsunami origin, even though these, in themselves, are not diagnostic. These include the lateral extent (traceable across 230 m), upwards-fining grain size of sand sheets A and B, and clasts of underlying peats observed within sands A and C and occasionally within B. The absence of extreme climatic phenomena, such as hurricanes and tropical storms, in the Chaihuín area during the historic period also minimises the possibility of finding storm deposits. However, while it is recognised that the above criteria cannot be used individually to confirm tsunami deposition, it is the combination of all sedimentological and diatom evidence that we use here in support of the most compelling evidence for tsunami deposition, which comes from the accompanying abrupt land-level change. The latter rules out deposition by tsunamis sourced in the far-field, storms or aeolian processes.Evidence for coseismic land-level changeFollowing established criteria30,31, we use the sedimentary and diatom evidence to propose that the Chaihuín sequence records three earthquake events, associated with vertical coseismic deformation and tsunami deposition. Diatom assemblages from immediately below sand layers A and B are characterised by species with higher elevation preferences than those found immediately above the sands, suggesting decreases in marsh surface elevation consistent with coseismic subsidence (Fig. 4). Diatom assemblages show minimal change across sand layer C; instead a transition occurs prior to event C whereby species with lower elevation preferences are replaced by those with higher elevation preferences, indicating net emergence prior to event C followed by minimal coseismic subsidence.The transfer function reconstructs 0.35 ± 0.42 m of subsidence occurred in event A, which local testimony and radiocarbon dating confirm to be the 1960 earthquake. Compared to our previous estimate for this event20, refining the transfer function method and expanding the modern training set here, reduces the uncertainty by 0.26 m. Reconstructed subsidence agrees with observations of 0.7 ± 0.4 m19. By contrast, the transfer function reconstructs very minor subsidence of 0.10 ± 0.36 m occurred in event C, but this needs confirmation from analyses of additional cores.The transfer function predicts that coseismic subsidence occurred in event B, with reconstructions varying between 0.10 ± 0.33 and 0.52 ± 0.39 m, and averaging 0.22 ± 0.38 m (Fig. 4d). While this is close to the detection limit of coseismic land-level change30 and the error term is large compared to the amount of deformation, we interpret event B as being associated with net submergence for two reasons. First, changes in diatom-inferred marsh elevations between pre- and post-earthquake samples are greater than other sample-to-sample changes. Second, all nine reconstructions, regardless of core location or transfer function approach, indicate submergence rather than a mixture of submergence and emergence (Fig. 4d).Linking the geologic and historical recordsDespite the broad modelled age ranges for events B and C of 1600–1820 and 1486–1616 CE, respectively, each range only includes one historically reported earthquake. If the historical catalogue is complete, sands B and C represent tsunamis accompanying the 1737 and 1575 earthquakes, respectively. Although other great tsunamigenic earthquakes occurred in the time range of event B (1657, 1730, 1751), their rupture areas have been placed much further north8,32 and therefore are very unlikely sources for the observed deformation. Age ranges do not include 1837; therefore, absence of evidence for this earthquake at Chaihuín supports the chronicle-based interpretation that the 1837 rupture area lies further south11,16. The preservation of turbidites from 1837 at sites to the north of Chaihuín14 is consistent with observations of earthquake-triggered turbidites some distance outside the rupture zone, as observed for the Mw 8.8 2010 Maule earthquake14.Implications for the rupture depth in 1737The Chaihuín record provides the first evidence for crustal deformation during the 1737 earthquake and the first evidence for the earthquake being tsunamigenic. While the nearshore bathymetry and orientation of the coastline may amplify tsunami inundation and the abundant sediment source may enhance the potential for evidence creation during even moderate tsunamis, the direction of land-level change at Chaihuín (subsidence) calls for reconsideration of the associated rupture depth. While correlation with evidence of shaking-induced turbidites from Calafquén and Riñihue lakes14, along with the absence of a 1737 event in sedimentary records from Río Maullín and Chucalén to the south9,11, supports the hypothesis that a smaller section of the plate interface ruptured in 1737 (between 39 and 41°S) than in 1960 and 157514, the Chaihuín record also forms an important constraint on the depth of local slip in 1737.By combining deformation and tsunami modelling, we show that our evidence of coastal subsidence and tsunami inundation at Chaihuín is better explained by offshore, shallow megathrust slip rather than by deeper slip below land as previously suggested16 (Fig. 5 and Supplementary Fig. 1). This is demonstrated by a simple numerical experiment designed to find the most likely depth range of the causative earthquake rupture that can explain the coastal subsidence inferred at Chaihuín and also the tsunami inundation.Fig. 5: Results of model tests to show that the 1737 rupture must have been confined to the offshore region at shallower fault depths than previously proposed.a The lower panel shows the trench-normal section of the megathrust and seafloor geometry at the latitude of Chaihuín used in the modelling experiment. The upper panel shows the bell-shaped slip distributions for a suite of eight earthquake ruptures and the middle panel shows the modelled vertical surface deformations using an elastic dislocation model (see “Methods”). The red and blue curves are the deep and shallow ruptures used as illustrative examples in the text. In this suite of models, the rupture width and peak slip are fixed at 100 km and 1 m, respectively, and the rupture location is systematically shifted horizontally in the trench-normal direction to represent ruptures at different depths. b Summary plot showing the modelled coastal uplift (left vertical axis) and tsunami runup (right vertical axis) predicted by the suite of models. Note that coastal subsidence can only be produced by offshore ruptures, with slip shallower than ~20 km. Ruptures deeper than this produce uplift at the coast. This opposing pattern of coastal deformation between shallow versus deeper ruptures is insensitive to how much slip is prescribed at the fault. Supplementary Fig. 1 shows the results for two different suite of models, in which the rupture width varies by fixing the updip (Supplementary Fig. 1a) and downdip (Supplementary Fig. 1b) limits.Full size imageOur numerical approach (see also “Methods”) leverages the sensitivity of the deformation sign (uplift or subsidence) and tsunami size at the Chaihuín coast to the depth of megathrust slip33 (Fig. 5). An earthquake rupture with maximum slip at 33 km fault depth (Fig. 5a, red model), as previously inferred from historical records16, will result in coastal uplift and a relatively small tsunami. Instead, if the rupture occurs offshore (Fig. 5a, blue model), the deformation will result in coastal subsidence and a much larger tsunami. From a systematic analysis in which the hypothetical rupture models are shifted horizontally in the trench-normal direction or vertically in the depth direction (Fig. 5a, upper panel), we conclude that subsidence at the Chaihuín coast could only be produced by ruptures placed mainly offshore, at average megathrust depths shallower than 20 km (Fig. 5b, downward triangles). Deeper ruptures will produce coastal uplift and consequent smaller tsunamis (Fig. 5b). The same conclusion is reached by varying the rupture width with fixed updip and downdip limits (Supplementary Fig. 1).Our conclusions are independent of the use of a normalised unit displacement in all models (i.e. 1 m at the centre of its corresponding bell-shaped rupture) because the opposing effects of deep versus shallow ruptures at Chaihuín are insensitive to the magnitude of slip involved and depend on its locus. The amount of slip determines the magnitude of deformation but not its sign due to the elastic response of the crust during earthquakes34. However, with evidence at only one location we only feel confident to constrain the depth range but not the magnitude nor along-strike extent of the causative slip. Therefore, from our numerical experiment we conclude that to produce subsidence at the Chaihuín coast, an offshore rupture likely shallower than 20 km is required as a deeper source would result in coastal uplift. This is also consistent with the inferred tsunami heights (Fig. 5b), which are larger for a shallower rupture and therefore more likely to produce inundation on land independent of the local topography. This geologically-based inference of an offshore rupture (blue curve in Fig. 5b) contrasts with the deeper rupture below land (red curve in Fig. 5b) previously inferred from historical observations alone16.Implications for tsunami recurrence intervalsThe average interval between the three events preserved at Chaihuín, 193 years, is shorter than the interval proposed for full segment 1960-style ruptures of 270-280 years9,11,14. This supports the notion that the Chilean subduction zone displays a variable rupture mode, in which the size, depth, tsunamigenic potential and recurrence interval vary between earthquakes10. Of greatest importance, however, is the shorter average recurrence interval of tsunami inundation than previously reported. With the addition of the 1737 tsunami alongside previously known events in 1960, 1837 and 1575, the historical recurrence interval for tsunamis generated anywhere along the Valdivia segment of the Chilean subduction zone is reduced to 130 years. This holds even if the inferred tsunami inundation is not associated with the 1737 earthquake, but with another earthquake of similar age missed in the historical catalogue. More

1.Stirton, C. H. Polhillia, a new genus of papilionoid legumes endemic to South Africa. South African J. Bot. 52, 167–180 (1986).
Google Scholar 
2.Boatwright, J. S., Tilney, P. M. & Van Wyk, B.-E. Taxonomy of Wiborgiella (Crotalarieae, Fabaceae), a genus endemic to the greater Cape Region of South Africa. Syst. Bot. 35, 325–340 (2010).
Google Scholar 
3.Moiloa, N. A., Chimphango, S. B. M. & Muasya, A. M. A phylogenetic study of the genus Wiborgia (Crotalarieae, Fabaceae). South African J. Bot. 115, 179–193 (2018).
Google Scholar 
4.Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
CAS 
PubMed 

Google Scholar 
5.Goldblatt, P. & Manning, J. C. Plant diversity of the Cape region of southern Africa. Ann. Missouri Bot. Gard. 281–302 (2002).6.Forest, F., Colville, J. F. & Cowling, R. M. Evolutionary diversity patterns in the Cape flora of South Africa. in Phylogenetic Diversity 167–187 (Springer, 2018).7.Boatwright, J. S. & Cupido, C. N. Aspalathus crewiana sp. Nov. (Crotalarieae, Fabaceae) from the Western Cape Province, South Africa. Nord. J. Bot. 29, 513–517 (2011).
Google Scholar 
8.Mpai, T., Jaiswal, S. K. & Dakora, F. D. Accumulation of phosphorus and carbon and the dependency on biological N-2 fixation for nitrogen nutrition in Polhillia, Wiborgia and Wiborgiella species growing in natural stands in cape fynbos, South Africa. SYMBIOSIS (2020).9.Van Zwieten, L. et al. Enhanced biological N 2 fixation and yield of faba bean (Vicia faba L.) in an acid soil following biochar addition: Dissection of causal mechanisms. Plant Soil 395, 7–20 (2015).
Google Scholar 
10.Jaiswal, S. K., Naamala, J. & Dakora, F. D. Nature and mechanisms of aluminium toxicity, tolerance and amelioration in symbiotic legumes and rhizobia. Biol. Fertil. Soils https://doi.org/10.1007/s00374-018-1262-0 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Araújo, S. S. et al. Abiotic stress responses in legumes: Strategies used to cope with environmental challenges. CRC. Crit. Rev. Plant Sci. 34, 237–280 (2015).
Google Scholar 
12.Etesami, H., Alikhani, H. & Akbari, A. Evaluation of plant growth hormones production (IAA) ability by Iranian soils rhizobial strains and effects of superior strains application on wheat growth. World Appl. Sci. J. 6, 1576–1584 (2009).CAS 

Google Scholar 
13.Ibny, F. Y. I., Jaiswal, S. K., Mohammed, M. & Dakora, F. D. Symbiotic effectiveness and ecologically adaptive traits of native rhizobial symbionts of Bambara groundnut (Vigna subterranea L. Verdc.) in Africa and their relationship with phylogeny. Sci. Rep. 9, 1–17 (2019).CAS 

Google Scholar 
14.Kanu, S. A. & Dakora, F. D. Symbiotic nitrogen contribution and biodiversity of root-nodule bacteria nodulating Psoralea species in the Cape Fynbos, South Africa. Soil Biol. Biochem. 54, 68–76 (2012).CAS 

Google Scholar 
15.Lemaire, B. et al. Symbiotic diversity, specificity and distribution of rhizobia in native legumes of the Core Cape Subregion (South Africa). FEMS Microbiol. Ecol. 91, 2–17 (2015).
Google Scholar 
16.Brink, C., Postma, A. & Jacobs, K. Rhizobial diversity and function in rooibos (Aspalathus linearis) and honeybush (Cyclopia spp.) plants: A review. South African J. Bot. 110, 80–86 (2017).
Google Scholar 
17.Dludlu, M. N., Chimphango, S. B. M., Walker, G., Stirton, C. H. & Muasya, A. M. Horizontal gene transfer among rhizobia of the Core Cape Subregion of southern Africa. South African J. Bot. 118, 342–352 (2018).CAS 

Google Scholar 
18.Aliero, B. L. Effects of sulphuric acid, mechanical scarification and wet heat treatments on germination of seeds of African locust bean tree, Parkia biglobosa. African J. Biotechnol. 3, 179–181 (2004).CAS 

Google Scholar 
19.Hematifar, M., Tehranifar, A. & Abedi, B. Facilitating Seed Germination of Eight Species of Hawthorn (Crataegus spp.) Native of Iran, Using Chemical Scarification and Cold Stratification. Iran. J. Seed Res. 4, 13–22 (2018).
Google Scholar 
20.Vincent, J. M. A Manual for the Practical Study of Root-Nodule Bacteria: A Manual for the Practical Study of Root-Nodule Bacteria Vol. 15 (Blackwell Scientific, 1970).
Google Scholar 
21.Unkovich, M. & Baldock, J. Measurement of asymbiotic N2 fixation in Australian agriculture. Soil Biol. Biochem. 40, 2915–2921 (2008).CAS 

Google Scholar 
22.Somasegaran, P. & Hoben, H. J. Handbook for Rhizobia: Methods in Legume-Rhizobium Technology (Springer, 2012).
Google Scholar 
23.Sneath, P. H. A., Sokal, R. R. Numerical taxonomy. The principles and practice of numerical classification. (1973).24.Rohlf, F. J., Applied Biostatistics, I. & Exeter Software (Firm). NTSYS-pc : Numerical taxonomy and multivariate analysis system. (Applied Biostatistics, Inc., 2009).25.Hall, T. BioEdit version 7.0. 0. Distributed by the author, website: www.mbio.ncsu.edu/BioEdit/bioedit.html. (2004).26.Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Nei, M. & Kumar, S. Molecular Evolution and Phylogenetics (Oxford University Press, 2000).
Google Scholar 
29.Saitou, N. & Nei, M. The neighbor-joining method : A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).CAS 
PubMed 

Google Scholar 
30.Felsenstein, J. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 

Google Scholar 
31.Morón, B. et al. Low pH changes the profile of nodulation factors produced by Rhizobium tropici CIAT899. Chem. Biol. 12, 1029–1040 (2005).PubMed 

Google Scholar 
32.Moroenyane, I., Chimphango, S. B. M., Wang, J., Kim, H. K. & Adams, J. M. Deterministic assembly processes govern bacterial community structure in the Fynbos, South Africa. Microb. Ecol. 72, 313–323 (2016).CAS 
PubMed 

Google Scholar 
33.Dabo, M., Jaiswal, S. K. & Dakora, F. D. Phylogenetic evidence of allopatric speciation of bradyrhizobia nodulating cowpea ( Vigna unguiculata L. walp ) in South African and Mozambican soils Department of Crop Sciences, Tshwane University of Technology, Private Bag Chemistry Department. Tshw. FEMS Microbiol. Ecol. 19, 1–14 (2019).
Google Scholar 
34.Singh, S. K., Jaiswal, S. K., Vaishampayan, A. & Dhar, B. Physiological behavior and antibiotic response of soybean (Glycine max L.) nodulating rhizobia isolated from Indian soils. African J. Microbiol. Res. 7, 2093–2102 (2013).
Google Scholar 
35.Hayat, R., Ali, S., Amara, U., Khalid, R. & Ahmed, I. Soil beneficial bacteria and their role in plant growth promotion: A review. Ann. Microbiol. 60, 579–598 (2010).
Google Scholar 
36.Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 

Google Scholar 
37.Maseko, S. T. & Dakora, F. D. Rhizosphere acid and alkaline phosphatase activity as a marker of P nutrition in nodulated Cyclopia and Aspalathus species in the Cape fynbos of South Africa. South African J. Bot. 89, 289–295 (2013).CAS 

Google Scholar 
38.Dludlu, M. N., Chimphango, S., Stirton, C. H. & Muasya, A. M. Differential preference of burkholderia and mesorhizobium to pH and soil types in the core cape subregion, South Africa. Genes 9, 2 (2017).PubMed Central 

Google Scholar 
39.Graham, P. H. et al. Acid pH tolerance in strains of Rhizobium and Bradyrhizobium, and initial studies on the basis for acid tolerance of Rhizobium tropici UMR1899. Can. J. Microbiol. 40, 198–207 (1994).CAS 

Google Scholar 
40.Fikri-Benbrahim, K., Chraibi, M., Lebrazi, S., Moumni, M. & Ismaili, M. Phenotypic and Genotypic Diversity and Symbiotic Effectiveness of Rhizobia Isolated from Acacia sp. Grown in Morocco. J. Agric. Sci. Technol. 19, (2017).41.Moumni, M., Fikri-Benbrahim, K., Ismaili, M., Lebrazi, S. & Chraibi, M. Phenotypic and G enotypic D iversity and S ymbiotic E ffectiveness of R hizobia I solated from Acacia sp. G rown in Morocco. JKUAT (2018). http://hdl.handle.net/123456789/373842.Farissi, M. et al. Growth, nutrients concentrations, and enzymes involved in plants nutrition of alfalfa populations under saline conditions. (2014).43.Lebrazi, S. & Benbrahim, K. F. Environmental stress conditions affecting the N2 fixing Rhizobium-legume symbiosis and adaptation mechanisms. African J. Microbiol. Res. 8, 4053–4061 (2014).
Google Scholar 
44.Bhargava, Y., Murthy, J. S. R., Kumar, T. V. R. & Rao, M. N. Phenotypic, stress tolerance and plant growth promoting characteristics of rhizobial isolates from selected wild legumes of semiarid region, Tirupati, India. Adv. Microbiol. 6, 1 (2016).CAS 

Google Scholar 
45.Sankhla, I. S. et al. Molecular characterization of nitrogen fixing microsymbionts from root nodules of Vachellia (Acacia) jacquemontii, a native legume from the Thar Desert of India. Plant Soil 410, 21–40 (2017).CAS 

Google Scholar 
46.Rathi, S. et al. Selection of Bradyrhizobium or Ensifer symbionts by the native Indian caesalpinioid legume Chamaecrista pumila depends on soil pH and other edaphic and climatic factors. FEMS Microbiol. Ecol. 94, 1–17 (2018).
Google Scholar 
47.Choudhary, D., Rai, M. K., Shekhawat, N. S. & Kataria, V. In vitro propagation of Farsetia macrantha Blatt. & Hallb.: An endemic and threatened plant of Indian Thar Desert. Plant Cell, Tissue Organ Cult. 142, 519–526 (2020).CAS 

Google Scholar 
48.de Castro Pires, R. et al. Soil characteristics determine the rhizobia in association with different species of Mimosa in central Brazil. Plant Soil 423, 411–428 (2018).
Google Scholar 
49.Verma, J. P., Yadav, J., Tiwari, K. N. & Kumar, A. Effect of indigenous Mesorhizobium spp. and plant growth promoting rhizobacteria on yields and nutrients uptake of chickpea (Cicer arietinum L.) under sustainable agriculture. Ecol. Eng. 51, 282–286 (2013).
Google Scholar 
50.Datta, C. & Basu, P. S. Indole acetic acid production by a Rhizobium species from root nodules of a leguminous shrub, Cajanus cajan. Microbiol. Res. 155, 123–127 (2000).CAS 
PubMed 

Google Scholar 
51.Brink, C. J. Plant Growth-Promoting Properties of Fynbos Rhizobia and Their Diversity (Stellenbosch University, 2018).
Google Scholar 
52.Naamala, J., Jaiswal, S. K. & Dakora, F. D. Antibiotics resistance in Rhizobium: Type, process, mechanism and benefit for agriculture. Curr. Microbiol. 72, 804–816 (2016).CAS 
PubMed 

Google Scholar 
53.Baba, T. & Schneewind, O. Instruments of microbial warfare: Bacteriocin synthesis, toxicity and immunity. Trends Microbiol. 6, 66–71 (1998).CAS 
PubMed 

Google Scholar 
54.Menezes, K. A. S., Nunes, G. F. O. & Sampaio, A. A. Diversity of new root nodule bacteria from Erythrina velutina Willd., a native legume from the Caatinga dry forest (Northeastern Brazil). Rev Cienc Agrárias 39, 222–233 (2016).
Google Scholar 
55.Pagano, M. C. Rhizobia associated with neotropical tree Centrolobium tomentosum used in riparian restoration. Plant, Soil Environ. 54, 498–508 (2008).CAS 

Google Scholar 
56.Hong, W., Zeng, J. & Xie, J. Antibiotic drugs targeting bacterial RNAs. Acta Pharm. Sin. B 4, 258–265 (2014).PubMed 
PubMed Central 

Google Scholar 
57.Elliott, G. N. et al. Nodulation of Cyclopia spp. (Leguminosae, Papilionoideae) by Burkholderia tuberum. Ann. Bot. 100, 1403–1411 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Hassen, A. I., Bopape, F. L., Habig, J. & Lamprecht, S. C. Nodulation of rooibos (Aspalathus linearis Burm. f.), an indigenous South African legume, by members of both the α-proteobacteria and β-proteobacteria. Biol. Fertil. Soils 48, 295–303 (2012).CAS 

Google Scholar 
59.Gerding, M., O’Hara, G. W., Bräu, L., Nandasena, K. & Howieson, J. G. Diverse Mesorhizobium spp. with unique nodA nodulating the South African legume species of the genus Lessertia. Plant Soil 358, 385–401 (2012).CAS 

Google Scholar 
60.Lemaire, B. et al. Recombination and horizontal transfer of nodulation and ACC deaminase (acdS) genes within Alpha-and Beta-proteobacteria nodulating legumes of the Cape Fynbos biome. FEMS Microbiol. Ecol. 91, (2015).61.Gogarten, J. P., Doolittle, W. F. & Lawrence, J. G. Prokaryotic evolution in light of gene transfer. Mol. Biol. Evol. 19, 2226–2238 (2002).CAS 
PubMed 

Google Scholar 
62.Andrews, M. et al. Horizontal transfer of symbiosis genes within and between rhizobial genera: Occurrence and importance. Genes 9, 321 (2018).PubMed Central 

Google Scholar 
63.Turner, S. L. & Young, J. P. W. The glutamine synthetases of rhizobia : Phylogenetics and evolutionary implications. 17, 309–319 (2000).64.Gevers, D. et al. Re-evaluating prokaryotic species. Nat. Rev. Microbiol. 3, 733 (2005).CAS 
PubMed 

Google Scholar 
65.Ormeño-Orrillo, E. et al. Phylogenetic evidence of the transfer of nodZ and nolL genes from Bradyrhizobium to other rhizobia. Mol. Phylogenet. Evol. 67, 626–630 (2013).PubMed 

Google Scholar 
66.Parker, M. A., Lafay, B., Burdon, J. J. & Van Berkum, P. Conflicting phylogeographic patterns in rRNA and nifD indicate regionally restricted gene transfer in Bradyrhizobiumaa. Microbiology 148, 2557–2565 (2002).CAS 
PubMed 

Google Scholar 
67.Duran, D. et al. Bradyrhizobium paxllaeri sp. Nov. and Bradyrhizobium icense sp. Nov., nitrogen-fixing rhizobial symbionts of Lima bean (Phaseolus lunatus L.) in Peru. Int. J. Syst. Evol. Microbiol. 64, 2072–2078 (2014).PubMed 

Google Scholar 
68.Grönemeyer, J. L., Kulkarni, A., Berkelmann, D., Hurek, T. & Reinhold-Hurek, B. Identification and characterization of rhizobia indigenous to the Okavango region in Sub-Saharan Africa. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02417-14 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Rogel, M. A., Ormeno-Orrillo, E. & Romero, E. M. Symbiovars in rhizobia reflect bacterial adaptation to legumes. Syst. Appl. Microbiol. 34, 96–104 (2011).PubMed 

Google Scholar 
70.Lindstrom, K., Murwira, M., Willems, A. & Altier, N. The biodiversity of beneficial microbe-host mutualism : The case of rhizobia. Res. Microbiol. 161, 453–463 (2010).PubMed 

Google Scholar 
71.Barcellos, F. G., Menna, P., da Silva Batista, J. S. & Hungria, M. Evidence of horizontal transfer of symbiotic genes from a Bradyrhizobium japonicum inoculant strain to indigenous diazotrophs Sinorhizobium (Ensifer) fredii and Bradyrhizobium elkanii in a Brazilian Savannah soil. Appl. Environ. Microbiol. 73, 2635–2643 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Jourand, P., Mateille, T., Fargette, M. & Rapior, S. Nematostatic activity of aqueous extracts of West African Crotalaria species. Nematology 6, 765–771 (2004).
Google Scholar 
73.Chen, W.-M. et al. Legume symbiotic nitrogen fixation by β-proteobacteria is widespread in nature. J. Bacteriol. 185, 7266–7272 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Aoki, S., Ito, M. & Iwasaki, W. From β-to α-proteobacteria: The origin and evolution of rhizobial nodulation genes nodIJ. Mol. Biol. Evol. 30, 2494–2508 (2013).CAS 
PubMed 

Google Scholar 
75.Moulin, L., Béna, G., Boivin-Masson, C. & Stkepkowski, T. Phylogenetic analyses of symbiotic nodulation genes support vertical and lateral gene co-transfer within the Bradyrhizobium genus. Mol. Phylogenet. Evol. 30, 720–732 (2004).CAS 
PubMed 

Google Scholar 
76.Lu, Y. L. et al. Genetic diversity and biogeography of rhizobia associated with Caragana species in three ecological regions of China. Syst. Appl. Microbiol. 32, 351–361 (2009).CAS 
PubMed 

Google Scholar 
77.Ochman, H., Lawrence, J. G. & Groisman, E. A. Lateral gene transfer and the nature of bacterial innovation. Nature 405, 299–304 (2000).ADS 
CAS 
PubMed 

Google Scholar  More

ModelAt a given temperature i there should be a maximum body mass, Mmaxi, for which the maximum temperature-dependent surface-specific flux of oxygen, fmaxi (with unit mass O2 area−1 time−1) allows for oxygen uptake to match consumption, and where a further increase in size would lead to an oxygen deficit. This can be expressed as:$$fma{x}_{i}cdot Ama{x}_{i}={k}_{i}Mma{{x}_{i}}^{beta },$$
(1)
where the left side of the equation gives oxygen uptake and the right side represents oxygen demand. Amaxi is the maximum surface area used for oxygen uptake. Thus, the exact area of the organism that should be considered here will depend on the type of organism (i.e. gill surface area [e.g. fish] or other specific areas of the body surface where oxygen uptake occurs [e.g. ventral body region of Daphnia]). β is the allometric scaling exponent describing the relationship between body mass and oxygen consumption, and ki is the parameter describing temperature-dependent oxygen consumption (with unit mass O2 body mass−1 time−1). The relationship between A and M can be expressed as A = α∙Mc, where the constant α gives the mass specific surface area used for oxygen uptake (with units area mass−1) when M = 1. The constant c is the allometric scaling exponent describing the relationship between body mass and area over which oxygen can diffuse. Thus, since maximum body size will only be limited by oxygen availability when oxygen demand increases faster than supply with increasing body size, the model is only valid for c  More

1.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 
Article 
ADS 

Google Scholar 
2.Fischer, J. & Lindenmayer, D. Landscape modification and habitat fragmentation: a synthesis. Global Ecol. Biogepogr. 16, 265–280 (2005).Article 

Google Scholar 
3.Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 
ADS 

Google Scholar 
4.Boyles, J., Cryan, P., McCracken, G. F. & Kunz, T. H. Economic importance of bats in agriculture. Science 332, 41–42 (2011).Article 
ADS 

Google Scholar 
5.Puig-Montserrat, X. et al. Pest control service provided by bats in Mediterranean rice paddies: linking agroecosystems structure to ecological functions. Mamm. Biol. 80, 237–245 (2015).Article 

Google Scholar 
6.Maas, B. et al. Bird and bat predation services in tropical forests and agroforestry landscapes. Biol. Rev. 91, 1081–1101 (2015).Article 

Google Scholar 
7.Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).Article 

Google Scholar 
8.Alkemade, R., Burkhard, B., Crossman, N. D., Nedkov, S. & Petz, K. Quantifying ecosystem services and indicators for science, policy and practice. Ecol. Indic. 37, 161–162 (2014).Article 

Google Scholar 
9.Mandle, L. et al. Assessing ecosystem service provision under climate change to support conservation and development planning in Myanmar. PLoS ONE 12(9), 23 (2017).Article 

Google Scholar 
10.Dang, A. N., Jackson, B. M., Benavidez, R. & Tomscha, S. A. Review of ecosystem service assessments: Pathways for policy integration in Southeast Asia. Ecosyst. Serv. 49, 101266 (2021).Article 

Google Scholar 
11.Eurostats. Agriculture, Forestry and Fisheries. European Statistics. https://ec.europa.eu/eurostat (2021).12.Eurostats. Pests and diseases in viticulture. EIP-AGRI Focus Group. https://ec.europa.eu/eip/agriculture/ (2019).13.Eurostats. Pests and diseases of the olive tree. EIP-AGRI Focus Group. https://ec.europa.eu/eip/agriculture/ (2019).14.EPPO. EPPO Global Database. https://gd.eppo.int (2018).15.Chaplin-Kramer, R., O’Rourke, M. E., Blitzer, L. J. & Kremen, C. A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).Article 

Google Scholar 
16.Equipa Atlas. Atlas das Aves Nidificantes em Portugal (1999–2005). Instituto da Conservação da Natureza e da Biodiversidade, Sociedade Portuguesa para o Estudo das Aves, Parque Natural da Madeira e Secretaria Regional do Ambiente e do Mar. Assírio & Alvim, Lisboa (2008).17.Rainho, A., Alves, P., Amorim, F. & Marques, J. T. Atlas dos morcegos: de Portugal continental. Instituto da Conservação da Natureza e das Florestas (2013).18.Herrera, J. M., Ploquin, E., Rodriguez-Pérez, J. & Obeso, J. R. Determining habitat suitability of a mountain bumblebee fauna: a baseline approach for testing the impact of climate change on species distribution and abundance. J. Biogeogr. 41, 700–712 (2014).Article 

Google Scholar 
19.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
20.Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).Article 
ADS 

Google Scholar 
21.Jiménez-Valverde, A., Lobo, J. M. & Hortal, J. Not as good as they seem: the importance of concepts in species distribution modelling. Divers. Distrib. 14, 885–890 (2008).Article 

Google Scholar 
22.Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).Article 

Google Scholar 
23.Valiente-Banuet, A. et al. Beyond species loss: the extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

Google Scholar 
24.Karp, D. S. et al. Forest bolsters bird abundance, pest control, and coffee yield. Ecol. Lett. 16, 1339–1347 (2013).Article 

Google Scholar 
25.Maas, B., Clough, Y. & Tscharntke, T. Bats and birds increase crop yield in tropical agroforestry landscapes. Ecol. Lett. 16, 1480–1487 (2013).Article 

Google Scholar 
26.Barbaro, L. et al. Avian pest control in vineyards is driven by interactions between bird functional diversity and landscape heterogeneity. J. App. Ecol. 54, 500–508 (2016).Article 

Google Scholar 
27.Paiola, A. et al. Exploring the potential of vineyards for biodiversity conservation and delivery of biodiversity-mediated ecosystem services: a global-scale systematic review. Sci. Total Environ. 706, 135839 (2020).CAS 
Article 
ADS 

Google Scholar 
28.Charbonnier, Y. et al. Pest control services provided by bats in vineyard landscapes. Agric. Ecosyst. Environ. 306, 107207 (2021).CAS 
Article 

Google Scholar 
29.Rey, P. J. et al. Landscape-moderated biodiversity effects of ground herb cover in olive groves: implications for regional biodiversity conservation. Agr. Ecosyst. Environ. 277, 61–73 (2020).Article 

Google Scholar 
30.Morgado, R. et al. A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities. Agric. Ecosyst. Environ. 288, 106694 (2020).Article 

Google Scholar 
31.Martínez-Núñez, C. et al. Direct and indirect effects of agricultural practices, landscape complexity and climate on insectivorous birds, pest abundance and damage in olive groves. Agric. Ecosyst. Environ. 304, 107145 (2020).Article 

Google Scholar 
32.Herrera, J. M., Costa, P., Medinas, D., Marques, J. T. & Mira, A. Community composition and activity of insectivorous bats in Mediterranean olive farms. Anim. Conserv. 18, 557–566 (2015).Article 

Google Scholar 
33.Costa, A. et al. Structural simplification compromises the potential of common insectivorous bats to provide biocontrol services against the major olive pest Prays oleae. Agric. Ecosyst. Environ. 287, 106708 (2020).Article 

Google Scholar 
34.Puig-Montserrat, X., Mas, M., Flaquer, C., Tuneu-Corrala, C. & López-Baucells, A. Benefits of organic olive farming for the conservation of gleaning bats. Agric. Ecosyst. Environ. 313, 107361 (2021).Article 

Google Scholar 
35.Rey, P. J. Preserving frugivorous birds in agro-ecosystems: lessons from Spanish olive orchards. J. Appl. Ecol. 48, 228–237 (2011).Article 

Google Scholar 
36.Rodríguez-San Pedro, A. et al. Influence of agricultural management on bat activity and species richness in vineyards of central Chile. J. Mamm. 99, 1495–1502 (2018).
Google Scholar 
37.Pithon, J. A., Beaujouan, V., Daniel, H., Pain, G. & Vallet, J. Are vineyards important habitats for birds at local or landscape scales?. Basic Appl. Ecol. 17, 240–251 (2016).Article 

Google Scholar 
38.Froidevaux, J. S. P., Louboutin, B. & Jones, G. Does organic farming enhance biodiversity in Mediterranean vineyards? A case study with bats and arachnids. Agr. Ecosyst. Environ. 249, 112–122 (2017).Article 

Google Scholar 
39.Van der Biest, K. et al. Aligning biodiversity conservation and ecosystem services in spatial planning: focus on ecosystem processes. Sci. Total Environ. 712, 136350 (2020).Article 
ADS 

Google Scholar 
40.Janzen, D. H. Latent extinction-the living dead. Encycl. Biodivers. 3, 689–699 (2001).Article 

Google Scholar 
41.Herrera, J. M. et al. Generalities of vertebrate responses to landscape composition and configuration gradients in a highly heterogeneous Mediterranean region. J. Biogeogr. 43, 1203–1214 (2016).Article 

Google Scholar 
42.Ponti, L., Gutierrez, A. P., Rutid, P. M. & Dell’Aquila, A. Fine-scale ecological and economic assessment of climate change on olive in the Mediterranean Basin reveals winners and losers. Proc. Nat. Acad. Sci. 111, 5598–5603 (2014).CAS 
Article 
ADS 

Google Scholar 
43.Silva, L. P. et al. Advancing the integration of multi-marker metabarcoding data in dietary analysis of trophic generalists. Mol. Ecol. Resour. 19, 1420–1432 (2019).Article 

Google Scholar 
44.Pejchar, L. et al. Net effects of birds in agroecosystems. Bioscience 68, 896–904 (2018).
Google Scholar 
45.Alberdi, A. et al. DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats. Nat. Comm. 11, 1154 (2020).CAS 
Article 
ADS 

Google Scholar  More

Weight and dimensions of fruit and stoneThe mean weight of a fruit from a particular tree ranged from 21.25 ± 4.26 to 58.26 ± 10.44 g, with the weight of the heaviest mean fruit weight being 2.74 times that of the lightest. Similarly, the mean stone weight ranged from 8.99 ± 2.35 to 20.32 ± 3.14 g, with a 2.26 times difference between the heaviest and lightest stones (Table 2). There were significant differences (p  More

1.Clapham, P. J. & Mead, J. G. Sharing the space: Review of humpback whale occurrence in the Amazonian equatorial coast. In: Mammalian Species: Megaptera novaeangliae. American Society of Mammalogists Issue, vol 604, 5 (1999). https://doi.org/10.1016/j.gecco.2019.e00854.2.Rasmussen, K. et al. Southern Hemisphere humpback whales wintering off Central America: Insights from water temperature into the longest mammalian migration. Biol. Lett. 3, 302–305. https://doi.org/10.1098/rsbl.2007.0067 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
3.De Weerdt, J., Ramos, E. A. & Cheeseman, T. Northernmost records of Southern Hemisphere humpback whales (Megaptera novaeangliae) migrating from the Antarctic Peninsula to the Pacific coast of Nicaragua. Mar. Mamm. Sci. 36, 1015–1021. https://doi.org/10.1111/mms.12677 (2020).Article 

Google Scholar 
4.Mikhalev, Y. A. Humpback whales Megaptera novaeangliae in the Arabian Sea. Mar. Ecol. Prog. Ser. 149, 13–21. https://doi.org/10.3354/meps149013 (1997).ADS 
Article 

Google Scholar 
5.Ristau, N. G. et al. Sharing the space: Review of humpback whale occurrence in the Amazonian Equatorial Coast. Glob. Ecol. Conserv. 22, e00854. https://doi.org/10.1016/j.gecco.2019.e00854 (2020).Article 

Google Scholar 
6.Kellogg, R. What is known of the migration of some of the whalebone whales U.S.G.P.O. In Publication Smithsonian Institution, 2997 Rex Nan Kivell Collection, NK5765, 467e494, 2997 (2) leaves of plates (Smithsonian Publication, 1929).7.Clapham, P. J. Humpback whale. In Megaptera novaeangliae. Encyclopedia of Marine Mammals, 3rd edn, 489–492. (Academic Press, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00154-0.8.Chereskin, E. et al. Song structure and singing activity of two separate humpback whales populations wintering off the coast of Caño Island in Costa Rica. J. Acoust. Soc. Am. 146, EL509–EL515 (2020).Article 

Google Scholar 
9.Jackson, J. et al. Global diversity and oceanic divergence of humpback whales (Megaptera novaeangliae). Proc. R. Soc. B 281, 20133222. https://doi.org/10.1098/rspb.2013.3222 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Baker, C. S. et al. Abundant mitochondrial DNA variation and world-wide population structure in humpback whales. Proc. Natl. Acad. Sci. 90, 8239–8243 (1993).ADS 
CAS 
Article 

Google Scholar 
11.Palsbøll, P. J. et al. Distribution of mtDNA haplotypes in North Atlantic humpback whales: The influence of behaviour on population structure. Mar. Ecol. Progr. Ser. 116, 1–10 (1995).ADS 
Article 

Google Scholar 
12.Rosenbaum, H. C. et al. First circumglobal assessment of Southern Hemisphere humpback whale mitochondrial genetic variation and implications for management. Endang. Species Res. 32, 551–567. https://doi.org/10.3354/esr00822 (2017).Article 

Google Scholar 
13.Kershaw, F. et al. Multiple processes drive genetic structure of humpback whale (Megaptera novaeangliae) populations across spatial scales. Mol. Ecol. 26, 977–994. https://doi.org/10.1111/mec.13943 (2017).Article 
PubMed 

Google Scholar 
14.Baker, C. S. et al. Strong maternal fidelity and natal philopatry shape genetic structure in North Pacific humpback whales. Mar. Ecol. Progr. Ser. 494, 291–306 (2013).ADS 
Article 

Google Scholar 
15.Garland, E. C. et al. Dynamic horizontal cultural transmission of humpback whale song at the ocean basin scale. Curr. Biol. 21, 687–691. https://doi.org/10.1016/j.cub.2011.03.019 (2011).CAS 
Article 
PubMed 

Google Scholar 
16.Garland, E. C. et al. Humpback whale song on the Southern Ocean feeding grounds: Implications for cultural transmission. PLoS ONE 8, 11. https://doi.org/10.1371/journal.pone.0079422 (2013).CAS 
Article 

Google Scholar 
17.Donovan, G. A. Review of IWC stock boundaries. In Report of the International Whaling Commission (Special Issue), vol. 13, 39–68 (1991).18.IWC. JCRM (Supplement), vol. 15, 287–288 (2014).19.Félix, F. & Guzmán, H. M. Satellite tracking and sighting data analyses of Southeast Pacific humpback whales (Megaptera novaeangliae): Is the migratory route coastal or oceanic?. Aquat. Mamm. 40, 329–340. https://doi.org/10.1578/AM.40.4.2014.329 (2014).Article 

Google Scholar 
20.Albertson, G. R. et al. Temporal stability and mixed-stock analyses of humpback whales (Megaptera novaeangliae) in the nearshore waters of the Western Antarctic Peninsula. Polar Biol. 41, 323–340. https://doi.org/10.1007/s00300-017-2193-1 (2018).Article 

Google Scholar 
21.Acevedo, J. et al. First evidence of interchange of humpback whales (Megaptera novaeangliae) between the Magellan Strait and Antarctic Peninsula feeding grounds. Polar Biol. 44, 613–619. https://doi.org/10.1007/s00300-021-02827-2 (2021).Article 

Google Scholar 
22.Andriolo, A., Kinas, P. G., Engel, M. H., Martins, C. C. A. & Rufino, A. M. Humpback whales within the Brazilian breeding ground: Distribution and population size estimate. Endanger. Species Res. 11, 233–243. https://doi.org/10.3354/esr00282 (2010).Article 

Google Scholar 
23.Martins, C. C. A., Andriolo, A., Engel, M. H., Kinas, P. G. & Saito, C. H. Identifying priority areas for humpback whale conservation at Eastern Brazilian Coast. Ocean Coast. Manag. 75, 63–71. https://doi.org/10.1016/j.ocecoaman.2013.02.006 (2013).Article 

Google Scholar 
24.Dalla Rosa, L. et al. Feeding ground of the eastern South Pacific humpback whale population include the south Orkney island. Polar Res. 31, 17324. https://doi.org/10.3402/polar.v31i0.17324 (2012).Article 

Google Scholar 
25.Zerbini, A. N. et al. Satellite-monitored movements of humpback whales Megaptera novaeangliae in the southwest Atlantic Ocean. Mar. Ecol. Prog. Ser. 313, 295e304. https://doi.org/10.3354/meps313295 (2006).Article 

Google Scholar 
26.Zerbini, A. et al. Migration and summer destinations of humpback whales (Megaptera novaeangliae) in the western South Atlantic Ocean. J. Cetacean Res. Manag. 3, 113–118. https://doi.org/10.47536/jcrm.vi.315 (2011).Article 

Google Scholar 
27.Engel, M. H. et al. Mitochondrial DNA diversity of the Southwestern Atlantic humpback whale (Megaptera novaeangliae) breeding area off Brazil, and the potential connections to Antarctic feeding areas. Conserv. Genet. 9, 1253e1262. https://doi.org/10.1007/s10592-007-9453-5 (2008).CAS 
Article 

Google Scholar 
28.Engel, M. H. & Martin, A. R. Feeding grounds of the western South Atlantic humpback whale population. Mar. Mamm. Sci. 25, 964e969. https://doi.org/10.1111/j.1748-7692.2009.00301.x (2009).Article 

Google Scholar 
29.IWC. Report of the workshop on the comprehensive assessment of Southern hemisphere humpback whales. J. Cetacean Res. Manag. 1, 1–50 (2011).30.Horton, T., Zerbini, A., Andriolo, A., Danilewicz, D. & Sucunza, F. Multi-decadal humpback whale migratory route fidelity despite oceanographic and geomagnetic change. Front. Mar. Sci. 7, 414. https://doi.org/10.3389/fmars.2020.00414 (2020).Article 

Google Scholar 
31.Stevick, P. T. et al. Population spatial structuring on the feeding grounds in North Atlantic humpback whales (Megaptera novaeangliae). J. Zool. 270, 244e255. https://doi.org/10.1111/j.1469-7998.2006.00128.x (2006).Article 

Google Scholar 
32.IWC. Report of the scientific committee. Rep. Int. Whal. Commun. 48, 53–118 (1998).33.Cypriano-Souza, A. L. et al. Genetic differentiation between humpback whales (Megaptera novaeangliae) from Atlantic and Pacific breeding grounds of South America. Mar. Mamm. Sci. 33, 457–479. https://doi.org/10.1111/mms.12378 (2017).CAS 
Article 

Google Scholar 
34.IWC. J. Cetacean Res. Manag. (Supplement) 7, 235–246 (2005).35.Dalla Rosa, L., Secchi, E. R., Maia, Y. G., Zerbini, A. N. & Heide-Jørgensen, M. P. Movements of satellite-monitored humpback whales on their feeding ground along the Antarctic Peninsula. Polar Biol. 31, 771–781 (2008).Article 

Google Scholar 
36.Bombosch, A. et al. Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys. Deep Sea Res. (I Oceanogr. Res. Pap.) 91, 101–114. https://doi.org/10.1016/j.dsr.2014.05.017 (2014).ADS 
Article 

Google Scholar 
37.Stevick, P. et al. Migrations of individually identified humpback whales between the Antarctic Peninsula and South America. J. Cetacean Res. Manag. 6, 109–113 (2004).
Google Scholar 
38.Pomilla, C. & Rosenbaum, H. C. Against the current: An inter-oceanic whale migration event. Biol. Lett. 1, 476–479. https://doi.org/10.1098/rsbl.2005.0351 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Stevick, P. T. et al. A quarter of a world away: Female humpback whale moves 10 000 km between breeding areas. Biol. Lett. 7, 299–302. https://doi.org/10.1098/rsbl.2010.0717 (2011).Article 
PubMed 

Google Scholar 
40.Stevick, P. T. et al. Inter-oceanic movement of an adult female humpback whale between Pacific and Atlantic breeding grounds off South America. J. Cetacean Res. Manag. 13, 159–162 (2013).
Google Scholar 
41.Félix, F. et al. A new case of interoceanic movement of a humpback whale in the Southern hemisphere: The El Niño link. Aquat. Mamm. 46, 578–583. https://doi.org/10.1578/AM.46.6.2020.578 (2020).Article 

Google Scholar 
42.Castro, C. Engel, M., Martin, A. & Kaufman, G. Comparison of humpback whale catalogues between Ecuador, and South Georgia and Sandwich Island: Evidence of increased feeding area I boundary or overlap between feeding areas I and II? Report of the scientific committee. Rep. Int. Whal. Comm. SC/66/SH (2015).43.Cheeseman, T. et al. Advanced image recognition: A fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. https://doi.org/10.1007/s42991-021-00180-9 (in press).44.Gura, T. Citizen science: Amateur experts. Nature 496, 259–261. https://doi.org/10.1038/nj7444-259a (2013).CAS 
Article 
PubMed 

Google Scholar 
45.Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294. https://doi.org/10.1016/j.biocon.2016.09.004 (2017).Article 

Google Scholar 
46.de Sherbinin, A. et al. The critical importance of citizen science data. Front. Clim. 3, 650760. https://doi.org/10.3389/fclim.2021.650760 (2021).Article 

Google Scholar 
47.Pallin, L. J., Robbins, J., Kellar, N., Bérubé, M. & Friedlaender, A. Validation of a blubber-based endocrine pregnancy test for humpback whales. Conserv. Physiol. https://doi.org/10.1093/conphys/coy031 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Gabriele, C. M., Straley, J. M. & Neilson, J. L. Age at first calving of female humpback whales in Southeastern Alaska. In Proceedings of the Fourth Glacier Bay Science Symposium, October 26–28, 2004: U.S. Geological Survey Scientific Investigations Report (eds. Piatt, J. F. & Gende, S. M.) vol. 2007–5047, 159–162 (2007).49.Baker, C. S. & Medrano-González, L. Worldwide distribution and diversity of humpback whale mitochondrial DNA lineages. In Molecular and Cell Biology of Marine Mammals (ed. Pfeiffer, C. J.) 84–99 (Krieger Publishing Company, 2002).
Google Scholar 
50.Bettridge, S. et al. Status Review of the Humpback Whale (Megaptera novaeangliae) under the Endangered Species Act. NOAA-TM-NMFS-SWFSC-540, ID#4883, 241. https://repository.library.noaa.gov/view/noaa/4883 (2015).51.IWC. Annex H: Report of the sub-committee on other Southern hemisphere whale stocks. J. Cetacean Res. Manag.(Supplement) 17, 250–282 (2016).52.Zerbini, A. et al. Assessing the recovery of an Antarctic predator from historical exploitation. R. Soc. Open Sci. 6, 190368. https://doi.org/10.1098/rsos.190368 (2019).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Zerbini, A. N., Clapham, P. J. & Wade, P. R. Assessing plausible rates of population growth in humpback whales from life-history data. Mar. Biol. 157, 1432e1793. https://doi.org/10.1007/s00227-010-1403-y (2010).Article 

Google Scholar 
54.Gonçalves, M. I. C. et al. Low latitude habitat use patterns of a recovering population of humpback whales. J. Mar. Biol. Assoc. U. K. 98, 1087–1096. https://doi.org/10.1017/S0025315418000255 (2018).Article 

Google Scholar 
55.Riekkola, L. et al. Longer migration not necessarily the costliest strategy for migrating humpback whales. Aquat. Conserv. Mar. Freshw. Ecosyst. 1, 12. https://doi.org/10.1002/aqc.3295 (2020).Article 

Google Scholar 
56.Pallin, L. J. et al. High pregnancy rates in humpback whales (Megaptera novaeangliae) around the Western Antarctic Peninsula, evidence of a rapidly growing population. R. Soc. Open Sci. 5, 180017. https://doi.org/10.1098/rsos.180017 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Avila, I. C. et al. Whales extend their stay in a breeding ground in the Tropical Eastern Pacific. ICES J. Mar. Sci. 77, 109–118. https://doi.org/10.1093/icesjms/fsz251 (2020).Article 

Google Scholar 
58.Fritsen, C. H., Memmott, J. C. & Stewart, F. J. Inter-annual sea-ice dynamics and micro-algal biomass in winter pack ice of Marguerite Bay, Antarctica. Deep Sea Res II Top. Stud. Oceanogr. 55, 2059–2067. https://doi.org/10.1016/j.dsr2.2008.04.034 (2008).ADS 
Article 

Google Scholar 
59.Meyer, B. The overwintering of Antarctic krill, Euphausia superba, from an ecophysiological perspective. Polar Biol. 35, 15–37. https://doi.org/10.1007/s00300-011-1120-0 (2012).Article 

Google Scholar 
60.Seyboth, E. et al. Influence of krill (Euphausia superba) availability on humpback whale (Megaptera novaeangliae) reproductive rate. Mar. Mamm. Sci. https://doi.org/10.1111/mms.12805 (2021).Article 

Google Scholar 
61.Atkinson, A. A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103. https://doi.org/10.1038/nature02996 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
62.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147. https://doi.org/10.1038/s41558-018-0370-z (2019).ADS 
Article 

Google Scholar 
63.Loeb, V. J. & Santora, J. A. Climate variability and spatiotemporal dynamics of five Southern Ocean krill species. Prog. Ocean. 134, 93–122. https://doi.org/10.1016/j.pocean.2015.01.002 (2015).Article 

Google Scholar 
64.Forcada, J., Trathan, P. & Murphy, E. J. Life history buffering in Antarctic mammals and birds against changing patterns of climate and environmental variation. Glob. Change Biol. 14, 2473–2488 (2008).ADS 
Article 

Google Scholar 
65.Fielding, S. et al. Interannual variability in Antarctic krill (Euphausia superba) density at South Georgia, Southern Ocean: 1997–2013. ICES J. Mar. Sci. 71, 2578–2588. https://doi.org/10.1093/icesjms/fsu104 (2014).MathSciNet 
Article 

Google Scholar 
66.Wedekin, L. L. et al. Running fast in the slow lane: Rapid population growth of humpback whales after exploitation. Mar. Ecol. Prog. Ser. 575, 195–206. https://doi.org/10.3354/meps12211 (2017).ADS 
Article 

Google Scholar 
67.Rogers, A. D. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the Southern Ocean. Ann. Rev. Mar. Sci. 12, 87–120. https://doi.org/10.1146/annurev-marine-010419-011028 (2020).CAS 
Article 
PubMed 

Google Scholar 
68.Glockner, D. A. & Venus, S. Determining the sex of humpback whales (Megaptera novaeangliae) in their natural environment. In Behavior and Communication of Whales. (Westview Press, 1983).69.Darling, J. D. & Berubé, M. Interactions of singing humpback whales with other males. Mar. Mamm. Sci. 17, 570–584. https://doi.org/10.1111/j.1748-7692.2001.tb01005.x (2001).Article 

Google Scholar 
70.Noad, M. J., Cato, D. H., Bryden, M. M., Jenner, M. N. & Jenner, K. C. S. Cultural revolution in whale songs. Nature 408, 537–537 (2000).ADS 
CAS 
Article 

Google Scholar 
71.Darling, D. J. & Sousa-Lima, R. S. Songs indicate interaction between humpback whale (Megaptera novaeangliae) populations in western and eastern South Atlantic Ocean. Mar. Mamm. Sci. 21, 557–566. https://doi.org/10.1111/j.1748-7692.2005.tb01249.x (2006).Article 

Google Scholar 
72.McKnight, A., Allyn, A. J., Duffy, D. C. & Irons, D. B. ‘Stepping stone’ pattern in Pacific Arctic tern migration reveals the importance of upwelling areas. Mar. Ecol. Prog. Ser. 491, 253–264. https://doi.org/10.3354/meps10469 (2013).ADS 
Article 

Google Scholar 
73.Groch, K. R. et al. Cetacean morbilivirus in humpback whale’s exhaled breath. Transbound. Emerg. Dis. https://doi.org/10.1111/tbed.13883 (2020).Article 
PubMed 

Google Scholar 
74.Ballance, L. T. Contributions of photographs to cetacean science. Aquat. Mamm. 44, 668–682 (2018).Article 

Google Scholar 
75.Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560. https://doi.org/10.1002/fee.1436 (2016).Article 

Google Scholar 
76.Vieira, E. A., Souza, L. R. & Longo, G. O. Diving into science and conservation: Recreational divers can monitor reef assemblages. Perspect. Ecol. Conserv. 18, 51–59. https://doi.org/10.1016/j.pecon.2019.12.001 (2020).Article 

Google Scholar  More

Bed bugsFour bed bug populations (one laboratory strain and three collected from infested homes) were used in this study (Table 1). All populations were reared in the laboratory as described by DeVries et al.28. Briefly, bed bugs were maintained in 168 cm3 plastic containers on paper substrate at 25 °C, 50% relative humidity, and a photoperiod of 12 h:12 h (Light:Dark). Bed bugs were fed defibrinated rabbit blood (Hemostat Laboratories, Dixon, CA, USA) weekly using an artificial feeding system. This system maintained blood at 35 °C by circulating water through custom-made water-jacketed glass feeders. An artificial membrane (plant budding tape, A.M. Leonared, Piqua, OH, USA) was stretched over the bottom of each glass feeder, containing the blood while simultaneously allowing bed bugs to feed through it. In all experiments, adult males starved for 7–10 days were used. All populations were used for documenting responses to human skin swabs. The WS population was used for bioassays with various human volunteers and hexane extracted swabs, and the JC population was used for testing various lipids.Table 1 Bed bug populations used in this study.Full size tableSkin swab collectionThe North Carolina State University Institutional Review Board approved this study (IRB #14173). Informed consent was obtained from all human participants, and all the methods were performed according to the relevant guidelines and regulations. Six human volunteers (3 males, 3 females) ranging from 25 to 50 years old representing several ethnicities (white/Caucasian, Hispanic, Asian) provided samples for this project. Skin swabs were collected following the exact methods outlined by DeVries et al.16. In our 2019 study, these swabs were reported to attract bed bugs independent of other cues in Y-tube olfactometer assays. Briefly, participants were asked to follow a standard operating procedure, which was reviewed with them prior to sample collection. Before collecting skin swabs, participants were asked to not to eat ‘spicy’ food for at least 24 h, take a morning shower, avoid the use of deodorant and cosmetics after showering, and avoid strenuous physical activity. Skin swabs were collected 4–8 h after showering. Hands were washed with water only before lifting filter paper. Swabs were collected using 4.5 cm diameter filter paper discs (#1; Whatman plc, Madistone, United Kingdom). Both sides of a single filter paper disc were rubbed over the left arm from hand to armpit for 12 s, left leg from lower thigh to ankle for 12 s, and left armpit for 6 s. This procedure was repeated on the right side using a new filter paper disc, so that two samples were collected during each swabbing session. The skin swab samples were then stored in glass vials at − 20 °C, and used within one month of collection. The swabs from all human volunteers were used to compare participants and establish that bed bugs responded similarly to all, and participant A’s skin swabs were used for all subsequent bioassays.Two-choice arrestment bioassaysTwo-choice bioassays were conducted in plastic Petri dishes of 6 cm diameter (Corning Life Sciences, Durham, NC, USA) (Fig. 1). The bottom surface of each Petri dish was roughened so that bed bugs could freely move about the arena. Two tents (3 × 1.5 cm) were created using filter paper (Whatman #1). One tent served as the control tent, and the other served as the treatment tent. Control tents were either untreated (nothing added) or treated with hexane only. Treatment tents were either made directly from human odor swabs, treated with human odor extract (in hexane), or treated with a specific compound (in hexane). Tents were allowed 60 min to acclimate to room conditions and allow for the solvent to evaporate prior to initiating bioassays. The positions of tents (treatment and control) were alternated to account for any side-bias.Figure 1Two-choice behavioral assay (top-view) consisting of two equal size paper shelter tents. A clean filter paper (control) was always paired with a treated filter paper that either represented a human skin swab, hexane extract of swabbed paper, SPE fraction of human skin swab extract, or authentic TAGs. A single male bed bug was introduced into the center of each arena and allowed to select a tent to arrest under.Full size imageAdult male bed bugs were housed in individual vials for 24 h prior to each experiment. A single adult male bed bug was released in the middle of the arena 5 h into the scotophase, by transferring it on its harborage. The harborage material was removed immediately after the bed bug moved off of it (the harborage). Bed bugs were allowed the remaining 7 h of the scotophase to freely move around the arena, with their final position reported 3 h into the photophase. Bed bugs that were in contact with the filter paper with any part of their body were recorded as making a choice (i.e. arrestment state); others not in contact with either filter paper tent were recorded as non-responders, reported in the figures, but not used in data analysis. It should be noted that momentary pauses in movement (feeding or other behaviors) are not referred to as arrestment in this study. In total, 15–39 replicates were performed for each experiment (reported for each bioassay).Bioassays with human skin swabsBioassays with human skin swabs were performed to understand if bed bug arrestment behavior (1) differed among different bed bug populations, and (2) influenced by different host odors. Skin swabs were removed from the freezer, equilibrated to room temperature, divided into three equal parts and trimmed to a rectangular shape corresponding to the size of a shelter tent (Fig. 1). Skin swabs from participant A were used to evaluate the responses of four bed bug populations (Table 1). Skin swabs from all participants A–F were used to evaluate the robustness of our findings across multiple human hosts.Skin swab extraction and fractionationSkin swabs collected from volunteer A were pooled and extracted in hexane. Extraction procedures were carried out sequentially by placing a single skin swab into a 20 ml glass vial containing 5 ml of hexane, vortexing for 30 s, then moving the filter paper to a new 20 ml vial containing 5 ml of hexane and repeating the process. Three sequential extractions were performed for each skin swab, and a minimum of 10 skin swabs (collected over several days) were used for each extraction. After all skin swabs were extracted, all sequential hexane extracts were combined and concentrated to a final concentration of one skin swab equivalent per 300 µl, or one bioassay equivalent (BE) per 100 µl (since each swab was used for 3 bioassays; see “Bioassays with human skin swabs” for more information on the size used for each bioassay). Control swabs were also extracted. These swabs were treated identically to the skin swabs, except they did not contact human skin.To determine what compound classes were responsible for the observed behavior, hexane extracts were fractionated using solid phase extraction (SPE). Extracted samples were concentrated to 1 BE/10 µl hexane, then loaded onto a 1 g silica SPE column (6 ml total volume; J.T. Baker, Phillipsburg, NJ, USA). The column was eluted with the following solvents (4 ml of each, each repeated twice sequentially): hexane, 2% ether (in hexane), 5% ether (in hexane), 10% ether (in hexane), 20% ether (in hexane), 50% ether (in hexane), 100% ether, ethyl acetate, and methanol (all solvents acquired from Sigma Aldrich, St. Louis, MO, USA). Each solvent fraction was then concentrated to a final concentration of 1 BE/100 µl and stored at − 20 °C.Bioassays with extracted and fractionated human skin swabsFor all extraction and fractionation bioassays, filter paper tents were cut to a size of 3 cm × 1.5 cm (Fig. 1) and treated with 100 µl (1 BE) of extracted or fractionated human skin swabs (50 µl on each side). A dose–response bioassay was run first to determine if the compounds responsible for bed bug arrestment responses could be extracted and at what concentration (BE) they were behaviorally active. Dilutions were made in hexane, with control tents receiving extracts of control filter paper. At least 20 replicates were conducted for each concentration. After validating an appropriate BE that could be used in future experiments, SPE fractions were diluted in hexane to 0.1 BE and applied to filter paper tents as previously described (50 µl per side). A minimum of 15 replicates were conducted for each fraction to identify behaviorally active fractions.Compound identificationTo better understand what classes of compounds were present in behaviorally active fractions, we conducted thin layer chromatography (TLC) with known standards. A flexible, silica (250 µm) TLC plate (Whatman) was placed into a glass chamber containing a solvent layer of 1.5 cm. The plate was cleaned twice with acetone, then standards (triglyceride [TAG], wax ester, squalene) and samples (fractions) were each loaded into separate lanes. The plate was developed twice in 10% ether (in hexane), then visualized non-destructively with iodine.In addition, behaviorally active fractions were further evaluated for their composition with GC–MS and LC–MS. GC–MS was employed to analyze free fatty acids, squalene, and cholesterol29, whereas LC–MS was employed to characterize the intact skin lipids as previously described30. Samples were analyzed with a GC 7890A coupled to the MS 5975 VL analyzer (Agilent Technologies, CA, USA) following derivatization. Briefly, 50 µL of the extract dissolved in isopropanol were dried under nitrogen and derivatized with 100 µL BSTFA containing 1% trimethylchlorosilane (TCMS) in pyridine to generate the trimethylsilyl (TMS) derivatives at 60 °C for 60 min. GC separation was performed with a 30 m × 0.250 mm (i.d.) × 0.25 µm film thickness DB-5MS fused silica column (Agilent). Helium was used as the carrier gas. Samples were acquired in scan mode by means of electron impact (EI) MS.Liquid-chromatography coupled to the MS analyzer by means of an electrospray interface (ESI) was used to determine abundance and ESI tandem MS of non-volatile lipids as previously described29,30. LC separation was performed with a reverse phase Zorbax SB-C8 column (2.1 × 100 mm, 1.8 μm particle size, Agilent). Data were acquired in the positive ion mode at unit mass resolving power by scanning ions between m/z 100 and 1000 with G6410A series triple quadrupole (QqQ) (Agilent). LC runs and MS spectra were processed with the Mass Hunter software (B.09.00 version).Bioassays with triglyceridesAfter determining that TAGs were prominent compounds in bioactive skin swab fractions, commercially available TAGs were evaluated for behavioral activity. Filter paper tents were treated with 100 µl of hexane (50 µl to each side) containing TAG standards. First, tripalmitin (16:0/16:0/16:0) (Sigma-Aldrich) was evaluated in a dose–response fashion (60 µg to 0.6 µg) to determine what level of TAG was appropriate for bioassays. The upper level of testing was set at 60 µg as a conservative estimate of the amount of TAGs bed bugs may be exposed to, based on calculations of our arena size and previous reports of TAGs on human skin and sebum. Specifically, previous reports documented that 1.5 mg of sebum could be passively collected using Sebutape from an area of 4.7 cm230,31. Because TAGs typically constitute 60% of human sebum32, it is reasonable to assume that passive collection of sebum can result in  > 190 µg/cm2 of TAGs in a short amount of time (30 min). Our sampling methods involved swabbing rather than passive collection, but our use of 60 µg over a 9 cm2 (two sides of 4.5 cm2) shelter tent (6.67 µg/cm2) is a low-estimate of the amount of TAGs collected (although this was not directly measured in the current study). Other TAGs that we tested at a concentration of 60 µg per 9 cm2 included the saturated TAGs trimyristin (14:0/14:0/14:0) and tristearin (18:0/18:0/18:0) and the unsaturated TAGs triolein (18:1/18:1/18:1), trilinolein (18:2/18:2/18:2), and trilinolenin (18:3/18:3/18:3) (all from Sigma-Aldrich). A minimum of 30 replicates were conducted with each TAG.Statistical analysisA Chi-square goodness of fit test was used to compare the responses of bed bugs to control versus treated tents in all two-choice bioassays, with the null hypothesis that if bed bugs do not respond differentially to treated tents they should display a 1:1 preference ratio for both sides of the assay. All tests were conducted in SPSS Version 26 (IBM Corp., Armonk, NY). More

1.Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 
PubMed 

Google Scholar 
2.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
PubMed 

Google Scholar 
3.Boivin, N. L. et al. Ecological consequences of human niche construction: examining long-term anthropogenic shaping of global species distributions. Proc. Natl Acad. Sci. USA 113, 6388–6396 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Asner, G. P., Vaughn, N., Smit, I. P. J. & Levick, S. Ecosystem-scale effects of megafauna in African savannas. Ecography (Cop.). 39, 240–252 (2016).
Google Scholar 
5.Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).ADS 
CAS 
PubMed 

Google Scholar 
6.Bakker, E. S., Pagès, J. F., Arthur, R. & Alcoverro, T. Assessing the role of large herbivores in the structuring and functioning of freshwater and marine angiosperm ecosystems. Ecography (Cop.). 39, 162–179 (2016).
Google Scholar 
7.Brault, M. O., Mysak, L. A., Matthews, H. D. & Simmons, C. T. Assessing the impact of late Pleistocene megafaunal extinctions on global vegetation and climate. Clim 9, 1761–1771 (2013).ADS 

Google Scholar 
8.Doughty, C. E., Faurby, S. & Svenning, J. C. The impact of the megafauna extinctions on savanna woody cover in South America. Ecography (Cop.). 39, 213–222 (2016).
Google Scholar 
9.Doughty, C. E., Wolf, A. & Malhi, Y. The legacy of the Pleistocene megafauna extinctions on nutrient availability in Amazonia. Nat. Geosci. 6, 761–764 (2013).ADS 
CAS 

Google Scholar 
10.Doughty, C. E. et al. Global nutrient transport in a world of giants. Proc. Natl Acad. Sci. USA 113, 1–6 (2015).
Google Scholar 
11.Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proc. Natl Acad. Sci. USA 113, 838–846 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Smith, F. A. et al. Exploring the influence of ancient and historic megaherbivore extirpations on the global methane budget. Proc. Natl Acad. Sci. USA 113, 201502547 (2015).
Google Scholar 
13.le Roux, E., Kerley, G. I. H. & Cromsigt, J. P. G. M. Megaherbivores modify trophic cascades triggered by fear of predation in an African Savanna Ecosystem. Curr. Biol. 28, 2493–2499.e3 (2018).PubMed 

Google Scholar 
14.Boulanger, M. T. & Lyman, R. L. Northeastern North American Pleistocene megafauna chronologically overlapped minimally with Paleoindians. Quat. Sci. Rev. 85, 35–46 (2013).ADS 

Google Scholar 
15.Rozas-Dávila, A., Valencia, B. G. & Bush, M. B. The functional extinction of Andean megafauna. Ecology 97, 2533–2539 (2016).PubMed 

Google Scholar 
16.Guthrie, R. D. New Carbon Dates Link Climatic Change with Human Colonization and Pleistocene Extinctions. Nature 441, 207–209 (2006).ADS 
CAS 
PubMed 

Google Scholar 
17.Meltzer, D. J. Overkill, glacial history, and the extinction of North America’s Ice Age megafauna. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2015032117 (2020).18.Sandom, C., Faurby, S., Sandel, B. & Svenning, J.-C. Global late Quaternary megafauna extinctions linked to humans, not climate change. Proc. R. Soc. Lond. B Biol. Sci. 281, 20133254 (2014).
Google Scholar 
19.Martin, P. S. in Quaternary Extinctions: A Prehistoric Revolution (eds. Martin, P. S. & Klein, R. G.) 354–403 (University of Arizona Press, 1984).20.Braje, T. J. & Erlandson, J. M. Human acceleration of animal and plant extinctions: a late Pleistocene, Holocene, and Anthropocene continuum. Anthropocene 4, 14–23 (2013).
Google Scholar 
21.Smith, F. A., Smith, R. E. E. E., Lyons, S. K. & Payne, J. L. Body size downgrading of mammals over the late Quaternary. Science. 360, 310–313 (2018).ADS 
CAS 
PubMed 

Google Scholar 
22.Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late pleistocene extinctions on the continents. Science 306, 70–75 (2004).ADS 
CAS 
PubMed 

Google Scholar 
23.Zimov, S. A. et al. Steppe-Tundra Transition: A Herbivore-Driven Biome Shift at the End of the Pleistocene. Am. Nat. 146, 765–794 (1995).
Google Scholar 
24.Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359–364 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Mann, D. H., Groves, P., Gaglioti, B. V. & Shapiro, B. A. Climate-driven ecological stability as a globally shared cause of Late Quaternary megafaunal extinctions: the Plaids and Stripes Hypothesis. Biol. Rev. 94, 328–352 (2019).
Google Scholar 
26.Zazula, G. D. et al. American mastodon extirpation in the Arctic and Subarctic predates human colonization and terminal Pleistocene climate change. Proc. Natl Acad. Sci. USA 111, 18460–18465 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Stuart, A. J. Late Quaternary megafaunal extinctions on the continents: a short review. Geol. J. 50, 414–433 (2015).
Google Scholar 
28.Mann, D. H., Groves, P., Kunz, M. L., Reanier, R. E. & Gaglioti, B. V. Ice-age megafauna in Arctic Alaska: extinction, invasion, survival. Quat. Sci. Rev. 70, 91–108 (2013).ADS 

Google Scholar 
29.Mann, D. H. et al. Life and extinction of megafauna in the ice-age Arctic. Proc. Natl Acad. Sci. USA 112, 14301–14306 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Rabanus-Wallace, M. T. et al. Megafaunal isotopes reveal role of increased moisture on rangeland during late Pleistocene extinctions. Nat. Ecol. Evol. 1, 1–5 (2017).
Google Scholar 
31.Zimov, S. A., Zimov, N. S., Tikhonov, A. N. & Chapin, I. S. Mammoth steppe: a high-productivity phenomenon. Quat. Sci. Rev. 57, 26–45 (2012).ADS 

Google Scholar 
32.Owen-Smith, N. Pleistocene extinctions: the pivotal role of megaherbivores. Paleobiology 13, 351–362 (1987).
Google Scholar 
33.Willerslev, E. et al. Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506, 47–51 (2014).ADS 
CAS 
PubMed 

Google Scholar 
34.Jackson, S. T. Representation of flora and vegetation in Quaternary fossil assemblages: known and unknown knowns and unknowns. Quat. Sci. Rev. 49, 1–15 (2012).ADS 

Google Scholar 
35.Froese, D. G. et al. The Klondike goldfields and Pleistocene environments of Beringia. GSA Today 19, 4–10 (2009).
Google Scholar 
36.Murchie, T. J. et al. Optimizing extraction and targeted capture of ancient environmental DNA for reconstructing past environments using the PalaeoChip Arctic-1.0 bait-set. Quat. Res. 99, 305–328 (2021).CAS 

Google Scholar 
37.Haile, J. et al. Ancient DNA reveals late survival of mammoth and horse in interior Alaska. Proc. Natl Acad. Sci. USA 106, 22352–22357 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Clark, P. U. The last glacial maximum. Science 325, 710–714 (2009).ADS 
CAS 
PubMed 

Google Scholar 
39.Zazula, G. D. et al. A middle Holocene steppe bison and paleoenvironments from the versleuce meadows, Whitehorse, Yukon, Canada. Can. J. Earth Sci. 54, 1138–1152 (2017).ADS 

Google Scholar 
40.Heintzman, P. D. et al. Bison phylogeography constrains dispersal and viability of the Ice Free Corridor in western Canada. Proc. Natl Acad. Sci. USA 113, 8057–8063 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Graham, R. W. et al. Timing and causes of mid-Holocene mammoth extinction on St. Paul Island, Alaska. Proc. Natl Acad. Sci. USA 113, 9310–9314 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Vartanyan, S. L., Arslanov, K. A., Karhu, J. A., Possnert, G. & Sulerzhitsky, L. D. Collection of radiocarbon dates on the mammoths (Mammuthus primigenius) and other genera of Wrangel Island, northeast Siberia, Russia. Quat. Res. 70, 51–59 (2008).CAS 

Google Scholar 
43.Faith, J. T. & Surovell, T. A. Synchronous extinction of North America’s Pleistocene mammals. Proc. Natl Acad. Sci. USA 106, 20641–20645 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Signor, P. W. & Lipps, J. H. Sampling bias, gradual extinction patterns and catastrophes in the fossil record. GSA Spec. Pap. 190, 291–296 (1982).
Google Scholar 
45.Fiedel, S. in American Megafaunal Extinctions at the End of the Pleistocene (ed. Haynes, G.) 21–37 (Springer Netherlands, 2009).46.Graf, K. E. Uncharted Territory: Late Pleistocene Hunter-Gatherer Dispersals in the Siberian Mammoth-Steppe (University of Nevada, 2008).47.Kuzmina, S. A. et al. The late Pleistocene environment of the Eastern West Beringia based on the principal section at the Main River, Chukotka. Quat. Sci. Rev. 30, 2091–2106 (2011).ADS 

Google Scholar 
48.Hoffecker, J. F., Elias, S. A. & Rourke, D. H. O. Out of Beringia? Science 343, 979–980 (2014).ADS 
CAS 
PubMed 

Google Scholar 
49.Zazula, G. D. et al. Ice-age steppe vegetation in East Beringia. Nature 423, 603 (2003).ADS 
CAS 
PubMed 

Google Scholar 
50.Guthrie, R. D. Origin and causes of the mammoth steppe: a story of cloud cover, woolly mammal tooth pits, buckles, and inside-out Beringia. Quat. Sci. Rev. 20, 549–574 (2001).ADS 

Google Scholar 
51.Pavelková Řičánková, V., Robovský, J. & Riegert, J. Ecological structure of recent and last glacial mammalian faunas in northern Eurasia: the case of Altai-Sayan refugium. PLoS ONE 9, e85056 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
52.Bocherens, H. Isotopic tracking of large carnivore palaeoecology in the mammoth steppe. Quat. Sci. Rev. 117, 42–71 (2015).ADS 

Google Scholar 
53.Ritchie, J. C. & Cwynar, L. C. in Paleoecology of Beringia (eds. Hopkins, D. M. et al.) 113–126 (Academic Press, 1982).54.Zhu, D. et al. The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0481-y (2018).55.Hopkins, D. M., Matthews, J. V., and Schweger, C. E. eds. Paleoecology of Beringia. (Academic Press, 1982).56.Stivrins, N. et al. Biotic turnover rates during the Pleistocene-Holocene transition. Quat. Sci. Rev. 151, 100–110 (2016).ADS 

Google Scholar 
57.Bakker, E. S., Ritchie, M. E., Olff, H., Milchunas, D. G. & Knops, J. M. H. Herbivore impact on grassland plant diversity depends on habitat productivity and herbivore size. Ecol. Lett. 9, 780–788 (2006).PubMed 

Google Scholar 
58.Bradshaw, R. H. W., Hannon, G. E. & Lister, A. M. A long-term perspective on ungulate-vegetation interactions. Ecol. Manag. 181, 267–280 (2003).
Google Scholar 
59.Gill, J. L. Ecological impacts of the late Quaternary megaherbivore extinctions. N. Phytologist 201, 1163–1169 (2014).
Google Scholar 
60.Gill, J. L., Williams, J. W., Jackson, S. T., Donnelly, J. P. & Schellinger, G. C. Climatic and megaherbivory controls on late-glacial vegetation dynamics: a new, high-resolution, multi-proxy record from Silver Lake, Ohio. Quat. Sci. Rev. 34, 66–80 (2012).ADS 

Google Scholar 
61.Gill, J. L., Williams, J. W., Jackson, S. T., Lininger, K. B. & Robinson, G. S. Pleistocene megafaunal collapse, novel plant communities, and enhanced fire regimes in North America. Science 326, 1100–1103 (2009).ADS 
CAS 
PubMed 

Google Scholar 
62.Johnson, C. N. Ecological consequences of Late Quaternary extinctions of megafauna. Proc. Biol. Sci. 276, 2509–2519 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Owen-Smith, N. Megaherbivores: The Influence of Very Large Body Size on Ecology (Cambridge University Press, 1992).64.Wright, J. P. & Jones, C. G. The concept of organisms as ecosystem engineers ten years on: progress, limitations, and challenges. Bioscience 56, 203 (2006).
Google Scholar 
65.Gutierrez, J. L. & Jones, C. G. Physical ecosystem engineers as agents of biogeochemical heterogeneity. Bioscience 56, 227 (2006).
Google Scholar 
66.Berke, S. K. Functional groups of ecosystem engineers: a proposed classification with comments on current issues. Integr. Comp. Biol. 50, 147–157 (2010).PubMed 

Google Scholar 
67.Ries, L., Fletcher, R. J. J., Battin, J. & Sisk, T. D. Ecological responses to habitat edges: Mechanisms, models, and variability explained. Annu. Rev. Ecol., Evolution, Syst. 35, 491–522 (2004).
Google Scholar 
68.Rasmussen, S. O. et al. A new Greenland ice core chronology for the last glacial termination. J. Geophys. Res. Atmos. 111, 1–16 (2006).
Google Scholar 
69.Swift, J. A. et al. Micro methods for Megafauna: novel approaches to late quaternary extinctions and their contributions to faunal conservation in the Anthropocene. Bioscience 69, 877–887 (2019).PubMed 
PubMed Central 

Google Scholar 
70.Andersen, K. et al. Meta-barcoding of ‘dirt’ DNA from soil reflects vertebrate biodiversity. Mol. Ecol. 21, 1966–1979 (2012).CAS 
PubMed 

Google Scholar 
71.Comandini, O. & Rinaldi, A. C. Tracing megafaunal extinctions with dung fungal spores. Mycologist 18, 140–142 (2004).
Google Scholar 
72.Säterberg, T., Sellman, S. & Ebenman, B. High frequency of functional extinctions in ecological networks. Nature 499, 468–470 (2013).ADS 
PubMed 

Google Scholar 
73.Courchamp, F., Berec, L. & Gascoigne, J. Allee Effects in Ecology and Conservation. Allee Effects in Ecology and Conservation (Oxford University Press, 2008).74.Allee, W. C. Animal aggregations. Q. Rev. Biol. 2, 367–398 (1927).
Google Scholar 
75.Allee, W. C. & Bowen, E. S. Studies in animal aggregations: mass protection against colloidal silver among goldfishes. J. Exp. Zool. 61, 185–207 (1932).CAS 

Google Scholar 
76.Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For Biodiversity Research and Monitoring. (Oxford University Press, 2018).77.Edwards, M. E. et al. Metabarcoding of modern soil DNA gives a highly local vegetation signal in Svalbard tundra. Holocene 28, 2006–2016 (2018).ADS 

Google Scholar 
78.Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).ADS 
CAS 
PubMed 

Google Scholar 
79.Anderson-Carpenter, L. L. et al. Ancient DNA from lake sediments: bridging the gap between paleoecology and genetics. BMC Evol. Biol. 11, 1–15 (2011).
Google Scholar 
80.Bellemain, E. et al. Fungal palaeodiversity revealed using high-throughput metabarcoding of ancient DNA from arctic permafrost. Environ. Microbiol. 15, 1176–1189 (2013).CAS 
PubMed 

Google Scholar 
81.Ahmed, E. et al. Archaeal community changes in Lateglacial lake sediments: evidence from ancient DNA. Quat. Sci. Rev. 181, 19–29 (2018).ADS 

Google Scholar 
82.Niemeyer, B., Epp, L. S., Stoof-Leichsenring, K. R., Pestryakova, L. A. & Herzschuh, U. A comparison of sedimentary DNA and pollen from lake sediments in recording vegetation composition at the Siberian treeline. Mol. Ecol. Resour. 17, e46–e62 (2017).CAS 
PubMed 

Google Scholar 
83.Rawlence, N. J. et al. Using palaeoenvironmental DNA to reconstruct past environments: progress and prospects. J. Quat. Sci. 29, 610–626 (2014).
Google Scholar 
84.Blum, S. A. E., Lorenz, M. G. & Wackernagel, W. Mechanism of retarded DNA degradation and prokaryotic origin of DNases in nonsterile soils. Syst. Appl. Microbiol. 20, 513–521 (1997).CAS 

Google Scholar 
85.Greaves, M. P. & Wilson, M. J. The degradation of nucleic acids and montmorillonite-nucleic-acid complexes by soil microorganisms. Soil Biol. Biochem. 2, 257–268 (1970).CAS 

Google Scholar 
86.Gardner, C. M. & Gunsch, C. K. Adsorption capacity of multiple DNA sources to clay minerals and environmental soil matrices less than previously estimated. Chemosphere 175, 45–51 (2017).ADS 
CAS 
PubMed 

Google Scholar 
87.Lorenz, M. G. & Wackernagel, W. Adsorption of DNA to sand and variable degradation rates of adsorbed DNA. Appl. Environ. Microbiol. 53, 2948–2952 (1987).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Ogram, A., Sayler, G., Gustin, D. & Lewis, R. DNA adsorption to soils and sediments. Environ. Sci. Technol. 22, 982–984 (1988).ADS 
CAS 
PubMed 

Google Scholar 
89.Lorenz, M. G. & Wackernagel, W. Adsorption of DNA to sand and variable degradation of adsorbed DNA. Appl. Environ. Microbiol. 53, 2948–2952 (1987).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Morrissey, E. M. et al. Dynamics of extracellular DNA decomposition and bacterial community composition in soil. Soil Biol. Biochem. 86, 42–49 (2015).CAS 

Google Scholar 
91.Arnold, L. J. et al. Paper II – Dirt, dates and DNA: OSL and radiocarbon chronologies of perennially frozen sediments in Siberia, and their implications for sedimentary ancient DNA studies. Boreas 40, 417–445 (2011).
Google Scholar 
92.Allentoft, M. E. et al. The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2012.1745 (2012).93.Kistler, L., Ware, R., Smith, O., Collins, M. & Allaby, R. G. A new model for ancient DNA decay based on paleogenomic meta-analysis. Nucleic Acids Res. 45, 6310–6320 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
94.Cribdon, B., Ware, R., Smith, O., Gaffney, V. & Allaby, R. G. PIA: more accurate taxonomic assignment of metagenomic data demonstrated on sedaDNA from the North Sea. Front. Ecol. Evol. 8, 1–12 (2020).
Google Scholar 
95.Yoccoz, N. G. et al. DNA from soil mirrors plant taxonomic and growth form diversity. Mol. Ecol. 21, 3647–3655 (2012).CAS 
PubMed 

Google Scholar 
96.Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).CAS 

Google Scholar 
97.Burn, C. R., Michel, F. A. & Smith, M. W. Stratigraphic, isotopic, and mineralogical evidence for an early Holocene thaw unconformity at Mayo, Yukon Territory. Can. J. Earth Sci. 23, 794–803 (1986).ADS 
CAS 

Google Scholar 
98.Kotler, E. & Burn, C. R. Cryostratigraphy of the Klondike ‘muck’ deposits, west-central Yukon Territory. Can. J. Earth Sci. 37, 849–861 (2000).ADS 
CAS 

Google Scholar 
99.Fraser, T. A. & Burn, C. R. On the nature and origin of ‘muck’ deposits in the Klondike area, Yukon Territory. Can. J. Earth Sci. 34, 1333–1344 (1997).ADS 

Google Scholar 
100.Mahony, M. E. 50,000 years of paleoenvironmental change recorded in meteoric waters and coeval paleoecological and cryostratigraphic indicators from the Klondike goldfields, Yukon, Canada. (University of Alberta, 2015). https://doi.org/10.7939/R34T6FF58.101.Lydolph, M. C. et al. Beringian paleoecology inferred from permafrost-preserved fungal DNA. Appl. Environ. Microbiol. 71, 1012–1017 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
102.Willerslev, E. et al. Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science 300, 791–795 (2003).ADS 
CAS 
PubMed 

Google Scholar 
103.Haile, J. et al. Ancient DNA chronology within sediment deposits: are paleobiological reconstructions possible and is DNA leaching a factor? Mol. Biol. Evol. 24, 982–989 (2007).CAS 
PubMed 

Google Scholar 
104.Willerslev, E., Hansen, A. J. & Poinar, H. N. Isolation of nucleic acids and cultures from fossil ice and permafrost. Trends Ecol. Evol. 19, 141–147 (2004).PubMed 

Google Scholar 
105.Hansen, A. J. et al. Crosslinks rather than strand breaks determine access to ancient DNA sequences from frozen sediments. Genetics 173, 1175–1179 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
106.D’Costa, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).ADS 
PubMed 

Google Scholar 
107.Johnson, S. S. et al. Ancient bacteria show evidence of DNA repair. Proc. Natl Acad. Sci. USA 104, 14401–14405 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
108.Hebsgaard, M. B. et al. ‘The Farm Beneath the Sand’- an archaeological case study on ancient ‘dirt’ DNA. Antiquity 83, 430–444 (2009).
Google Scholar 
109.Sadoway, T. R. A Metagenomic Analysis of Ancient Sedimentary DNA Across the Pleistocene-Holocene Transition (McMaster University, 2014).110.Bronk Ramsey, C. Deposition models for chronological records. Quat. Sci. Rev. 27, 42–60 (2008).ADS 

Google Scholar 
111.Reimer, P. J. et al. The IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0-55 cal kBP). Radiocarbon 62, 725–757 (2020).CAS 

Google Scholar 
112.Nichols, R. V. et al. Minimizing polymerase biases in metabarcoding. Mol. Ecol. Resour. 18, 927–939 (2018).CAS 

Google Scholar 
113.Wei, N., Nakajima, F. & Tobino, T. A microcosm study of surface sediment environmental DNA: decay observation, abundance estimation, and fragment length comparison. Environ. Sci. Technol. 52, 12428–12435 (2018).ADS 
CAS 
PubMed 

Google Scholar 
114.Matesanz, S. et al. Estimating belowground plant abundance with DNA metabarcoding. Mol. Ecol. Resour. 19, 1265–1277 (2019).CAS 
PubMed 

Google Scholar 
115.Takahara, T., Minamoto, T., Yamanaka, H., Doi, H. & Kawabata, Z. Estimation of fish biomass using environmental DNA. PLoS ONE 7, 3–10 (2012).
Google Scholar 
116.Doi, H. et al. Use of droplet digital PCR for estimation of fish abundance and biomass in environmental DNA surveys. PLoS ONE 10, 1–11 (2015).
Google Scholar 
117.Debruyne, R. et al. Out of America: ancient DNA evidence for a new world origin of late Quaternary Woolly Mammoths. Curr. Biol. 18, 1320–1326 (2008).CAS 
PubMed 

Google Scholar 
118.Metcalfe, J. Z., Longstaffe, F. J. & Zazula, G. D. Nursing, weaning, and tooth development in woolly mammoths from Old Crow, Yukon, Canada: Implications for Pleistocene extinctions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 298, 257–270 (2010).
Google Scholar 
119.Shapiro, B. et al. Rise and fall of the Beringian steppe bison. Science 306, 1561–1565 (2004).ADS 
CAS 
PubMed 

Google Scholar 
120.Sinclair, P. H., Nixon, W. A., Eckert C. D. & Hughes, N. L.Hughes, eds. Birds of the Yukon Territory. (UBC Press, 2003).121.Keesing, F. & Young, T. P. Cascading consequences of the loss of large mammals in an African Savanna. Bioscience 64, 487–495 (2014).
Google Scholar 
122.Taberlet, P. et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 35, e14 (2007).PubMed 

Google Scholar 
123.Chevalier, M. et al. Pollen-based climate reconstruction techniques for late Quaternary studies. Earth-Sci. Rev. 210, 103384 (2020).
Google Scholar 
124.Wang, X.-C. & Geurts, M.-A. Post-glacial vegetation history of the Ittlemit Lake basin, southwest Yukon Territory. Arctic 44, 23–30 (1991).
Google Scholar 
125.Wang, X.-C. & Geurts, M.-A. Late Quaternary pollen records and vegetation history of the southwest Yukon Territory: a review. Geogr. Phys. Quat. 45, 175–193 (1991).
Google Scholar 
126.Rainville, R. A. & Gajewski, K. Holocene environmental history of the Aishihik region, Yukon, Canada. Can. J. Earth Sci. 50, 397–405 (2013).ADS 
CAS 

Google Scholar 
127.Lacourse, T. & Gajewski, K. Late Quaternary vegetation history of Sulphur Lake, southwest Yukon Territory, Canada. Arctic 53, 27–35 (2000).
Google Scholar 
128.Bunbury, J. & Gajewski, K. Postglacial climates inferred from a lake at treeline, southwest Yukon Territory, Canada. Quat. Sci. Rev. 28, 354–369 (2009).ADS 

Google Scholar 
129.Gajewski, K., Bunbury, J., Vetter, M., Kroeker, N. & Khan, A. H. Paleoenvironmental studies in Southwestern Yukon. Arctic 67, 58–70 (2014).
Google Scholar 
130.Schofield, E. J., Edwards, K. J. & McMullen, A. J. Modern Pollen-Vegetation Relationships in Subarctic Southern Greenland and the Interpretation of Fossil Pollen Data from the Norse landnám. J. Biogeogr. 34, 473–488 (2007).
Google Scholar 
131.Pennington, W. & Tutin, T. G. Modern pollen samples from west greenland and the interpretation of pollen data from the british late-glacial (late Devesian). N. Phytol. 84, 171–201 (1980).
Google Scholar 
132.Bradshaw, R. H. W. Modern pollen-representation factors for Woods in South-East England. J. Ecol. 69, 45 (1981).
Google Scholar 
133.Roy, I. et al. Over-representation of some taxa in surface pollen analysis misleads the interpretation of fossil pollen spectra in terms of extant vegetation. Trop. Ecol. 59, 339–350 (2018).
Google Scholar 
134.Bryant, J. P. et al. Biogeographic evidence for the evolution of chemical defense by boreal birch and willow against mammalian browsing. Am. Nat. 134, 20–34 (1979).
Google Scholar 
135.Christie, K. S. et al. The role of vertebrate herbivores in regulating shrub expansion in the Arctic: a synthesis. Bioscience 65, 1123 (2015).
Google Scholar 
136.Bryant, J. P. et al. Can antibrowsing defense regulate the spread of woody vegetation in arctic tundra? Ecography (Cop.). 37, 204–211 (2014).137.Bryant, J. P. & Kuropat, P. J. Selection of winter forage by subarctic browsing vertebrates: the role of plant chemistry. Annu. Rev. Ecol. Syst. 11, 261–285 (1980).CAS 

Google Scholar 
138.Fox-Dobbs, K., Leonard, J. A. & Koch, P. L. Pleistocene megafauna from eastern Beringia: Paleoecological and paleoenvironmental interpretations of stable carbon and nitrogen isotope and radiocarbon records. Palaeogeogr. Palaeoclimatol. Palaeoecol. 261, 30–46 (2008).
Google Scholar 
139.Gardner, C., Berger, M. & Taras, M. Habitat assessment of potential wood bison relocation sites in Alaska. Arctic 1–30 (2007).140.Jiménez-Hidalgo, E. et al. Species diversity and paleoecology of late pleistocene horses from Southern Mexico. Front. Ecol. Evol. 7, 1–18 (2019).
Google Scholar 
141.van Geel, B. et al. The ecological implications of a Yakutian mammoth’s last meal. Quat. Res. 69, 361–376 (2008).
Google Scholar 
142.van Geel, B. et al. Palaeo-environmental and dietary analysis of intestinal contents of a mammoth calf (Yamal Peninsula, northwest Siberia). Quat. Sci. Rev. 30, 3935–3946 (2011).ADS 

Google Scholar 
143.Guthrie, R. D. Rapid body size decline in Alaskan Pleistocene horses before extinction. Nature 426, 169–171 (2003).ADS 
PubMed 

Google Scholar 
144.Bourgeon, L. Bluefish Cave II (Yukon Territory, Canada): Taphonomic Study of a Bone Assemblage. PaleoAmerica 1, 105–108 (2015).
Google Scholar 
145.Bourgeon, L., Burke, A. & Higham, T. Earliest human presence in North America dated to the last glacial maximum: new radiocarbon dates from Bluefish Caves, Canada. PLoS ONE 12, e0169486 (2017).PubMed 
PubMed Central 

Google Scholar 
146.Bourgeon, L. Revisiting the mammoth bone modifications from Bluefish Caves (YT, Canada). J. Archaeol. Sci. Rep. 37, 102969 (2021).147.Bourgeon, L. & Burke, A. Horse exploitation by Beringian hunters during the Last Glacial Maximum. Quat. Sci. Rev. 261, (2021).148.Vachula, R. S., Sae-Lim, J. & Russell, J. M. Sedimentary charcoal proxy records of fire in Alaskan tundra ecosystems. Palaeogeogr. Palaeoclimatol. Palaeoecol. 541, 109564 (2020).149.Vachula, R. S. Alaskan lake sediment records and their implications for the Beringian standstill hypothesis. PaleoAmerica 6, 303–307 (2020).
Google Scholar 
150.Vachula, R. S. et al. Evidence of Ice Age humans in eastern Beringia suggests early migration to North America. Quat. Sci. Rev. 205, 35–44 (2019).ADS 

Google Scholar 
151.Vachula, R. S. et al. Sedimentary biomarkers reaffirm human impacts on northern Beringian ecosystems during the Last Glacial period. Boreas 49, 514–525 (2020).
Google Scholar 
152.Abramova, Z. A. in Paleolit Kavkaza i Severnoi Azii (ed. Boriskovskii, P. I.) 145–243 (Nauka, 1989).153.Abramova, Z. A., Astakhov, S. N., Vasil’ev, S. A., Ermolva, N. M. & Lisitsyn, N. F. Paleolit Eniseya. (Nauka, 1991).154.Goebel, T. in Encyclopedia of prehistory. Vol 2: Arctic and Subarctic (eds. Peregrine, P. N. & Ember, M.) 192–196 (Kluwer Academic Publishers, 2002).155.Ermolova, N. M. Teriofauna doliny Angary v pozdem antropogene. (Nauka, 1978).156.Hoffecker, J. F. & Elias, S. A. Human Ecology of Beringia. (Columbia University Press, 2007).157.Johnson, C. N. Determinants of loss of mammal species during the Late Quaternary ‘megafauna’ extinctions: life history and ecology, but not body size. Proc. Biol. Sci. 269, 2221–2227 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
158.Laland, K. N. & O’Brien, M. J. Niche Construction Theory and Archaeology. J. Archaeol. Method Theory 17, 303–322 (2010).
Google Scholar 
159.Riede, F. Adaptation and niche construction in human prehistory: a case study from the southern Scandinavian Late Glacial. Philos. Trans. R. Soc. Lond. 366, 793–808 (2011).
Google Scholar 
160.Roos, C. I., Zedeño, M. N., Hollenback, K. L. & Erlick, M. M. H. Indigenous impacts on North American Great Plains fire regimes of the past millennium. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1805259115 (2018).161.Pinter, N., Fiedel, S. & Keeley, J. E. Fire and vegetation shifts in the Americas at the vanguard of Paleoindian migration. Quat. Sci. Rev. 30, 269–272 (2011).ADS 

Google Scholar 
162.Haynes, G. Extinctions in North America’s Late Glacial landscapes. Quat. Int. 285, 89–98 (2013).
Google Scholar 
163.Graf, K. E. in Paleoamerican Odyssey (eds. Graf, K. E., Ketron, C. V. & Waters, M. R.) 65–80 (Texas A&M University Press, 2014).164.Pečnerová, P. et al. Mitogenome evolution in the last surviving woolly mammoth population reveals neutral and functional consequences of small population size. Evol. Lett. 1, 292–303 (2017).165.Conroy, K. J. et al. Tracking late-Quaternary extinctions in interior Alaska using megaherbivore bone remains and dung fungal spores. Quat. Res. https://doi.org/10.1017/qua.2020.19 (2020).166.Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).ADS 
CAS 
PubMed 

Google Scholar 
167.Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
168.Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 5, pdb.prot5448 (2010).169.Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, 1–8 (2012).
Google Scholar 
170.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 

Google Scholar 
171.Agarwala, R. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 44, D7–D19 (2016).CAS 

Google Scholar 
172.Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2013).173.Huson, D. H. et al. MEGAN Community Edition – Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 

Google Scholar 
174.Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
175.Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. MapDamage2.0: Fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).PubMed 
PubMed Central 

Google Scholar 
176.Bronk Ramsey, C. & Lee, S. Recent and planned developments of the program OxCal. Radiocarbon 55, 720–730 (2013).
Google Scholar 
177.Bronk Ramsey, C. Dealing with outliers and offsets in radiocarbon dating. Radiocarbon 51, 1023–1045 (2009).
Google Scholar 
178.Davies, L. J., Jensen, B. J. L., Froese, D. G. & Wallace, K. L. Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: key beds and chronologies over the past 30,000 years. Quat. Sci. Rev. 146, 28–53 (2016).ADS 

Google Scholar 
179.Westgate, J. A., Preece, S. J., Kotler, E. & Hall, S. Dawson tephra: a prominent stratigraphic marker of Late Wisconsinan age in west-central Yukon, Canada. Can. J. Earth Sci. 37, 621–627 (2000).ADS 
CAS 

Google Scholar 
180.Froese, D., Westgate, J., Preece, S. & Storer, J. Age and significance of the Late Pleistocene Dawson tephra in eastern Beringia. Quat. Sci. Rev. 21, 2137–2142 (2002).ADS 

Google Scholar 
181.Zazula, G. D. et al. Vegetation buried under Dawson tephra (25,300 14C years BP) and locally diverse late Pleistocene paleoenvironments of Goldbottom Creek, Yukon, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 242, 253–286 (2006).
Google Scholar 
182.Froese, D. G., Zazula, G. D. & Reyes, A. V. Seasonality of the late Pleistocene Dawson tephra and exceptional preservation of a buried riparian surface in central Yukon Territory, Canada. Quat. Sci. Rev. 25, 1542–1551 (2006).ADS 

Google Scholar 
183.Klunk, J. et al. Genetic resiliency and the Black Death: no apparent loss of mitogenomic diversity due to the Black Death in medieval London and Denmark. Am. J. Phys. Anthropol. 169, 240–252 (2019).PubMed 

Google Scholar 
184.Renaud, G., Stenzel, U. & Kelso, J. LeeHom: Adaptor trimming and merging for Illumina sequencing reads. Nucleic Acids Res 42, e141 (2014).PubMed 
PubMed Central 

Google Scholar 
185.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
186.Adobe Inc. Adobe Illustrator. (2020). https://adobe.com/products/illustrator.187.Lebart, L., Morineau, A. & Tabard, N. Techniques De La Description Statistique Méthodes Et Logiciels Pour L’analyse Des Grands Tableaux. (Dunod, 1977).188.Potter, B. A. et al. Current evidence allows multiple models for the peopling of the Americas. Sci. Adv. 4, 1–9 (2018).
Google Scholar 
189.Grootes, P. M. & Stuiver, M. Oxygen 18/16 variability in Greenland snow and ice with 10-3- to 105-year time resolution. J. Geophys. Res. Ocean. 102, 26455–26470 (1997).ADS 
CAS 

Google Scholar 
190.Wolbach, W. S. et al. Extraordinary Biomass-Burning Episode and Impact Winter Triggered by the Younger Dryas Cosmic Impact ∼12,800 Years Ago. 2. Lake, Marine, and Terrestrial Sediments. J. Geol. 126, 185–205 (2018).ADS 
CAS 

Google Scholar  More