Philippa Kaur

Environmental and competitive filtering is most important for future tree survivalWe find that individual functional traits of each tree were most important for individual tree survival (40–87%) for all study sites, followed by forest dynamics (16–28%) and functional diversity (10–26%) (Fig. 2). Nevertheless, importance proportions substantially varied in each study site. While individual functional traits were least important in the mixed mountainous forest under reference climate (no climate warming), individual functional traits showed highest influence in the mixed temperate forest under future climate change (Fig. 2).Figure 2Relative importance of functional diversity, forest dynamics and individual traits for individual tree survival under reference climate and future climate conditions (RCP 4.5). Panels correspond to alpine needle-leaved (A), mixed mountainous (B), mixed temperate (C) and temperate broad-leaved (D) forests. Left bars in each panel illustrate reference climate (no warming) and right bars future climate (RCP4.5). Colours indicate forest dynamics (grey), functional diversity (yellow) and individual functional traits (blue). Forest dynamics include the number of locally competing trees ( > 5 m in height) and local biomass as a proxy for the successional status.Full size imageTree survival depends on a mixture of environmental (e.g. climate) and natural competitive filtering, which excludes trees with trait combinations that underperform under local conditions16. Therefore, the high importance of individual functional traits across all study sites suggests a strong environmental and competitive filtering. Under future climate, the importance of individual functional traits generally increases or remains at high levels (Fig. 2). This shows that environmental and competitive filtering through functional traits are important processes to select best performing trees for the future, although being different for each forest type.Changing forest composition and trait shifts require large functional portfolio to secure forest resistanceUnder future climate, we observe trait shifts within plant functional types and strong changes of the forest composition (Figs. 3, 4). Especially in the alpine and mixed forests, the proportion of broad-leaved trees increases to at least ~ 70% towards the end of the twenty-first century (Fig. 3). The changing climate alters environmental and competitive filtering simultaneously, whereby broad-leaved trees become more productive, survive better and increasingly outcompete needle-leaved trees. For instance, in the two mixed forests survival probabilities of broad-leaved trees (high SLA) increase by about ~ 10%, whereas the survival of needle-leaved (low SLA) trees is reduced by 10% to 30% in a warmer climate (see Supplementary Figs. S7, S8, Panel A). Locally better adapted and competitive broad-leaved trees can replace needle-leaved trees if they die and secure the forest’s overall biomass in the future. Nevertheless, our simulated forests still contain significant amounts of needle-leaved trees in the year 2099 in the two coldest study areas (Fig. 3, red and blue lines). Therefore, mixed tree communities with high functional diversity, where broad- and needle-leaved trees coexist, contain a broad range of functional niches out of which the best suitable plant strategies emerge and result in better resistance to climate change.Figure 3Forest compositions and changes in the proportion of broad-leaved trees (summergreen and evergreen plant functional type combined) under climate change (RCP 4.5) from 2000 to 2099 for each study site. The fraction of broad-leaved trees, as simulated by LPJmL-FIT for each site, increases gradually in almost every forest type reaching at least about 70% by the end of the century. Pictures depict snapshots from visualization of model output in the years 2000 and 2099, respectively. For a full animation of all sites from 2000 to 2099 please see Supplementary Video 1.Full size imageFigure 4Trait distributions of specific leaf area in year 2000 and 2099, respectively, under future climate change (RCP 4.5). Arrows indicate trait shifts within plant functional types: BL-S Broad-leaved summergreen, BL-E Broad-leaved evergreen, T-NL Temperate needle-leaved, B-NL Boreal needle-leaved. For more detailed distributions see Supplementary Figs. S3 and S4.Full size imageSimultaneously, we observe strong trait shifts in SLA within plant functional types across all study sites under climate change (Fig. 4). In general, the community of broad-leaved trees shift to lower SLA, while boreal needle-leaved trees are strongly reduced or slowly replaced by their temperate equivalent with higher SLA (Supplementary Fig. S3A, dark blue colours). In contrast, wood density distributions remain relatively broad and do not shift strongly under climate change (Supplementary Fig. S4). Throughout the century, the increasingly warmer climate filters new trait combinations leading to changes in the community composition within and across PFTs (see Supplementary Discussion B). Those trait shifts emerge from changes in the composition within PFTs and newly establishing PFTs, and could be less drastic if trait adaptation of tree individuals was considered (see “Limitations and outlook” section). The points raised above show, that trait ranges within and between PFTs should be wide to cover potential future trait shifts that secure future forest resistance.All this suggests that functionally diverse forests are more resistant to future climate changes, due to their rich portfolio of traits. Broad trait distributions both within and between PFTs form the fundament for environmental and competitive filtering to select the most productive trees, securing the forest’s overall biomass under changing conditions. But can functional diversity further strengthen forest resistance beyond portfolio effects?Functional complementarity helps young trees to surviveWe find that, in addition to port-folio effects, functional diversity increases forest resistance by supporting the survival of young trees to changing climate conditions via trait complementarity. Our results indicate, that trees benefit from functional diversity if they grow in tree communities with high FR, high FDv and low FE (Supplementary Figs. S6–S9, Panel D–F in each figure). Here, functional traits lay highly separated (FDv and FE) and span a broad range in the functional trait space (FR), enabling functional complementarity. Under these conditions the survival of trees increases up to + 16.8% (± 1.6%) depending on the study site and climate (Table 1). This effect is highest in the alpine and mountainous forests (14–17%), whereas it is less prominent or has an opposite effect in the two temperate forests (− 7% to 6%). That suggests, that complementarity effects are stronger in cold-limited and mixed forests where a marked cold winter season fosters a co-existence between broadleaved and needle-leaved trees. Both PFTs are specialized in fixing carbon during different times of the annual cycle: Due to their leaf phenology, needle-leaved trees can already be productive when broad-leaved trees are still in progress of unfolding or shedding their leaves. On the other hand, broad-leaved trees are more productive than needle-leaved trees during warmer months. If coexistence is given, these phenological differences enable complementarity and reduce competition among PFTs. That overall increases tree survival, because trees can invest more carbon in their stems and defensive structures if competition is lower. Therefore, we argue that phenological complementary can enhance tree survival and thus forest resistance. An in-depth discussion of those mechanisms is further found in Supplementary Discussion A.Table 1 Additional survival probabilities for trees in each forest site under reference climate (central column) and future climate (RCP4.5, right column) in case FR and FDv are high, while FE is low.Full size tableSurprisingly, our results show that those complementary effects are much more important for small trees ( 10 m) in right panel, respectively. Functional diversity and forest dynamics are more important for small trees compared to large trees, whereas individual functional traits matter most for large trees. This pattern was found to be consistent across all sites (see Supplementary Table S6).Full size imageFunctionally diverse understoreys unlock the synergy of filtering and complementary effectsOur findings underline the role of functionally diverse trees in the understoreys for forest resistance. On the one hand, functional diversity supports the survival of understorey trees via functional trait complementarity. On the other hand, they form the fundament for competitive and environmental filtering. Only diverse tree communities have trait pools large enough to ensure that their tree portfolio holds trait combinations best suited for changing climate conditions. Therefore, we argue that functional diversity does not only support tree survival through complementarity, but is a prerequisite for filtering resistant trees in the first place.To profit constantly from functional diversity of the understory and ensure constant adaptation, a diverse age structure is a prerequisite. Depending on the forest type, trees are distributed in a broad range of different height and age classes in our study (Supplementary Fig. S5, Supplementary Video 1). This multi-aged structure is preserved under climate change (Supplementary Fig. S5) and allows gradual changes through constant environmental and competitive filtering in the future.In this study, we simulate forests without any human interference or management. Our results are therefore to be interpreted in the context of environmental and competitive filtering as observed in natural forests. Most managed forests lack this natural filtering effect as they are less dense and diverse in their age-structure. Functionally diverse trees in the understorey could provide the fundament for climate adapted multi-aged forests, as they constantly form new better adapted tree generations with natural competition and succession allowed. Therefore, we fully underline the importance of functionally diverse understorey trees and natural competition as the fundament for future forest resistance.Management implicationsThe results of this study highlight the importance of functionally diverse understorey trees. However, browsing by game might damage new tree saplings and limit tree diversity in the understorey. In addition, invasive species like Prunus serotina or herbaceous competition might hinder forest succession and the establishment of woody native species in European forests20,21. Therefore, regulating game, limiting the spread of invasive species and controlling herbaceous vegetation should be considered in future management practices where tree diversity in the understorey seeks to be increased or maintained. Moreover, insufficient dispersal of functionally different tree species might limit the establishment of functionally diverse trees in the understorey. Future forest management may consider to artificially plant functionally different tree species if dispersal from surrounding forests cannot be guaranteed. On the other hand, forests that already contain functionally diverse trees in the understorey should be preserved.In this study, a clear trend from needle-leaved to broad-leaved trees is captured at all sites, whereas within broad-leaved PFTs a shift to lower specific leaf area and higher leaf longevities indicates that future forests might especially benefit from longer vegetation periods (earlier leaf onset, later senescence). Therefore, forests containing broad-leaved tree individuals with high phenological plasticity could be more resistant. The broad simulated wood density ranges, which persist under climate change, imply beneficial effects for forest communities entailing a range of different growing strategies, i.e. early to late successional species. Therefore, we argue that forest fragmentation should be reduced or reversed to foster some natural dispersal of early and late successional species.This study intended to explore the potentials of functionally diverse forests as a possibility to stabilize forests under climate change over a large climatic gradient. The model used in this study operates on the more general level of functional traits and their diversity rather than on species level (see “Methods” section and Supplementary Methods A). Consequently, management implications regarding suitable specific tree species are beyond the scope of this study. However, we think that our results will stimulate the discussion on the importance of functional tree traits and their diversity for species selection.Limitations and outlookThis study focussed on identifying the importance of functional diversity for future tree survival to advance our understanding on the role of biodiversity for future forest resistance using the flexible-trait Dynamic Global Vegetation Model LPJmL-FIT. The general approach of LPJmL-FIT is to simulate biogeographic dynamics purely based on environmental and competitive filtering (see “Methods” section, Supplementary Methods A). Due to missing processes in the model and the ambiguity of former human influence, drawing site-specific implications on future forest dynamics must be taken with caution (see Supplementary Discussion D).Moreover, processes not yet captured in the LPJmL-FIT model, might play a role and could lower forest resistance in the future, which is why we recommend relying on a trait space as broad as possible.Including more climate scenarios would widen the envelope of possible future pathways by considering climate model uncertainties. Insect outbreaks and pathogens might put pressure to the already drought- and temperature-stressed trees and heavily accelerate mortality especially of needle-leaved trees, although functionally diverse forests are less vulnerable to bark beetle outbreaks22,23. Multi-layered forests showed higher growth resilience to structural disturbances such as wind-throw24, likely enhancing the importance of individual tree height and reduce the survival probability of large trees25. Belowground competition and trait plasticity could favour complementarity effects further. Variable rooting strategies could further reduce competition for soil water and thereby increase individual drought resistance of trees26. Trait plasticity can contribute to tree survival by widening niche, further increasing complementarity effects. However, trait plasticity remains one of the most challenging objectives in vegetation modelling as observational data and modelling approaches are scarce27, leaving it open how far trait relationships would hold under climate change. Considering more functional traits in our analysis might increase the overall predictive power of the random forest models. Even though the explained variance increased with the number of analysed traits explaining ecosystem properties in long-term grassland experiments, such improvement is limited as abiotic factors and their interactions with plant traits might be more important for prediction28. We conclude that simulating future forest dynamics dominated by environmental and natural competitive filtering requires to integrate both, abiotic and biotic drivers on forest dynamics. Machine learning techniques are increasingly used in forest ecological research—but mainly applied in the processing of field and forest inventory data29,30. Machine learning can help to understand the complexity of interactions and provide deeper insights into the underlying ecological process in a modelling study as we have shown here using LPJmL-FIT simulation results. Random forest analyses are suitable for a variety of data and applications because they are relatively robust to different data structures. Importance analysis can help to identify the role of underlying processes in complex models and to visualize their changes in a simple way. In doing so, model development is advanced by making use of large data sets, opening the door to further theory building and deeper understanding of plant trait ecology. More

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

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Author notesMikayla M. ClarkPresent address: University of Tennessee, Knoxville, TN, USAMichael W. SneddonPresent address: Predicine, Inc., Hayward, CA, USARoman SutorminPresent address: Google, Inc., San Francisco, CA, USAAuthors and AffiliationsLawrence Berkeley National Laboratory, Berkeley, CA, USADylan Chivian, Sean P. Jungbluth, Paramvir S. Dehal, Elisha M. Wood-Charlson, Richard S. Canon, Gavin A. Price, William J. Riehl, Michael W. Sneddon, Roman Sutormin & Adam P. ArkinOak Ridge National Laboratory, Oak Ridge, TN, USABenjamin H. Allen, Mikayla M. Clark, Miriam L. Land & Robert W. CottinghamArgonne National Laboratory, Lemont, IL, USATianhao Gu, Qizhi Zhang & Chris S. HenryAuthorsDylan ChivianSean P. JungbluthParamvir S. DehalElisha M. Wood-CharlsonRichard S. CanonBenjamin H. AllenMikayla M. ClarkTianhao GuMiriam L. LandGavin A. PriceWilliam J. RiehlMichael W. SneddonRoman SutorminQizhi ZhangRobert W. CottinghamChris S. HenryAdam P. ArkinCorresponding authorsCorrespondence to
Dylan Chivian or Adam P. Arkin. More

Department of Zoology, University of Cambridge, Cambridge, UKYucheng Wang, Bianca De Sanctis, Ruairidh Macleod, Daniel Money & Eske WillerslevLundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, DenmarkYucheng Wang, Ana Prohaska, Jialu Cao, Antonio Fernandez-Guerra, James Haile, Kurt H. Kjær, Thorfinn Sand Korneliussen, Nicolaj Krog Larsen, Ruairidh Macleod, Hugh McColl, Mikkel Winther Pedersen, Fernando Racimo, Alexandra Rouillard, Anthony H. Ruter, Lasse Vinner, David J. Meltzer & Eske WillerslevALPHA, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research (ITPCAS), Chinese Academy of Sciences (CAS), Beijing, ChinaYucheng WangKey Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Science, Lanzhou University, Lanzhou, ChinaHaoran DongGénomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, FranceAdriana Alberti, France Denoeud & Patrick WinckerInstitute for Integrative Biology of the Cell (I2BC), Université Paris-Saclay, CEA, CNRS, Gif-sur-Yvette, FranceAdriana AlbertiThe Arctic University Museum of Norway, UiT—The Arctic University of Norway, Tromsø, NorwayInger Greve Alsos, Eric Coissac, Galina Gusarova, Youri Lammers & Marie Kristine Føreid MerkelDepartment of Geography and Environment, University of Hawaii, Honolulu, HI, USADavid W. BeilmanDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, DenmarkAnders A. BjørkInstitute of Earth Sciences, St Petersburg State University, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovArctic and Antarctic Research Institute, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovUniversité Grenoble-Alpes, Université Savoie Mont Blanc, CNRS, LECA, Grenoble, FranceEric CoissacDepartment of Genetics, University of Cambridge, Cambridge, UKBianca De Sanctis & Richard DurbinCarlsberg Research Laboratory, Copenhagen V, DenmarkChristoph Dockter & Birgitte SkadhaugeSchool of Geography and Environmental Science, University of Southampton, Southampton, UKMary E. EdwardsAlaska Quaternary Center, University of Alaska Fairbanks, Fairbanks, AK, USAMary E. EdwardsSchool of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UKNeil R. Edwards & Philip B. HoldenCenter for the Environmental Management of Military Lands, Colorado State University, Fort Collins, CO, USAJulie EsdaleDepartment of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, CanadaDuane G. FroeseFaculty of Biology, St Petersburg State University, St Petersburg, RussiaGalina GusarovaDepartment of Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen K, DenmarkKristian K. KjeldsenDepartment of Earth Science, University of Bergen, Bergen, NorwayJan Mangerud & John Inge SvendsenBjerknes Centre for Climate Research, Bergen, NorwayJan Mangerud & John Inge SvendsenDepartment of Geology, Quaternary Sciences, Lund University, Lund, SwedenPer MöllerCenter for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen Ø, DenmarkDavid Nogués-Bravo, Hannah Lois Owens & Carsten RahbekCentre d’Anthropobiologie et de Génomique de Toulouse, Faculté de Médecine Purpane, Université Paul Sabatier, Toulouse, FranceLudovic OrlandoCenter for Global Mountain Biodiversity, Globe Institute, University of Copenhagen, Copenhagen, DenmarkHannah Lois Owens & Carsten RahbekGates of the Arctic National Park and Preserve, US National Park Service, Fairbanks, AK, USAJeffrey T. RasicDepartment of Geosciences, UiT—The Arctic University of Norway, Tromsø, NorwayAlexandra RouillardZoological Institute, Russian academy of sciences, St Petersburg, RussiaAlexei TikhonovResource and Environmental Research Center, Chinese Academy of Fishery Sciences, Beijing, ChinaYingchun XingCollege of Plant Science, Jilin University, Changchun, Jilin, ChinaYubin ZhangDepartment of Anthropology, Southern Methodist University, Dallas, TX, USADavid J. MeltzerWellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UKEske WillerslevMARUM, University of Bremen, Bremen, GermanyEske WillerslevAll authors contributed to the conception of the presented ideas. Y.W. and H.D. analysed the data. Y.W., D.J.M., A.P. and E.W. wrote the paper with inputs from all authors. More

arising from Y. Wang et al. Nature https://doi.org/10.1038/s41586-021-04016-x (2021)A unique challenge for environmental DNA (eDNA)-based palaeoecological reconstructions and extinction estimates is that organisms can contribute DNA to sediments long after their death. Recently, Wang et al.1 discovered mammoth eDNA in sediments that are between approximately 4.6 and 7 thousand years (kyr) younger than the most recent mammoth fossils in North America and Eurasia, which they interpreted as mammoths surviving on both continents into the Middle Holocene epoch. Here we present an alternative explanation for these offsets: the slow decomposition of mammoth tissues on cold Arctic landscapes is responsible for the release of DNA into sediments for thousands of years after mammoths went extinct. eDNA records are important palaeobiological archives, but the mixing of undatable DNA from long-dead organisms into younger sediments complicates the interpretation of eDNA, particularly from cold and high-latitude systems.All animal tissues, including faeces, contribute DNA to eDNA records2, but the durations across which tissues can contribute genetic information must vary depending on tissue type and local rates of destruction and decomposition. On high-latitude landscapes, soft tissues and skeletal remains of large mammals may persist, unburied, for millennia3,4,5. For example, unburied antlers of caribou (Rangifer tarandus) from Svalbard (Norway) and Ellesmere Island (Canada) have been dated3,4 to between 1 and 2 cal kyr bp (calibrated kyr before present). Elephant seal (Mirounga leonina) remains near the Antarctic coastline5,6 can persist for more than 5,000 years. This is in contrast to bones in warmer settings, which persist for only centuries or decades7,8. Because bones are particularly resistant to decay, quantifying how their persistence changes across environments enables us to constrain the durations that dead individuals generally contribute to eDNA archives. To do this, we consolidated data on the oldest radiocarbon-dated surface-collected bones from different ecosystems. We included bones that we are reasonably confident persisted without being completely buried (‘never buried’), and bones for which exhumation cannot be confidently excluded (‘potentially never buried’). Pairing bone persistence with mean annual temperatures (MAT) from their sample localities, we find a strong link between the local temperature and the logged duration of bone persistence (Fig. 1, never buried bones: R2 = 0.94, P  More

Rearing O. biroi pupae in social isolation and collecting pupal fluidIn O. biroi colonies, larvae and pupae develop in discrete and synchronized cohorts26. Ten days after the first larvae had entered pupation in a large stock colony, the entire colony was anaesthetized using a CO2 pad, and white pupae were separated using a paintbrush. Pupae were individually placed in 0.2 ml PCR tubes with open lid. These tubes were then placed inside 1.5 ml Eppendorf tubes with 5 µl sterile water at the bottom to provide 100% relative humidity. The outer tubes were closed and kept in a climate room at 25 °C. The inner tube in this design prevents the pupa from drowning in the water reservoir. The outer tubes were kept closed throughout the experiment, except for once a day when the tubes were opened to remove pupal social fluid. Pulled glass capillaries were prepared as described elsewhere29, and used to remove and/or collect secretion droplets. We were careful to leave no remains of the secretion behind on the pupae or the inside of the tubes. To ensure that all secretion had been removed, pupae were taken out of the tube after fluid collection and briefly placed on a tissue paper to absorb any excess liquid. The inner tubes were replaced if needed—for example, if fluid traces were visible on the old tube after collection. Each pupa was checked daily for secretion (absent or present), onset of melanization and eclosion, and whether the pupa was alive (responding to touch). Control groups of 30 pupae and 30 adult ants from the same stock colony and cohort as the isolated pupae were placed in Petri dishes with a plaster of Paris floor, and the same parameters as for the isolated pupae were scored daily. Experiments ended when all pupae had either eclosed or died. Newly eclosed (callow) workers moved freely inside the tube and showed no abnormalities when put in a colony. A pupa was declared dead if it did not shed its pupal skin and did not respond to touch three days after all pupae in the control group had eclosed.To calculate the average secretion volume per secreting pupa (Fig. 1d), the total volume collected daily from a group of isolated pupae (142–166 pupae) was divided by the number of pupae from which fluid had been collected that day. The total volume was determined by multiplying the height of the fluid’s meniscus in the capillary by πr², where r is the inner radius of the capillary (0.29 mm). While pupae were secreting, pupal whole-body wash samples were collected daily. The pupae were removed from colonies with adults and washed promptly with 1500 µl LC–MS grade water. Whole-body wash samples were lyophilized and reconstituted in 15 µl LC–MS grade water.Collecting additional ant species and honeybees, rearing pupae in social isolation, and collecting pupal fluidsColonies of the ants N. flavipes, T. sessile, P. pennsylvanica and Lasius neoniger were collected in NY state, USA (Central Park, Manhattan; Pelham Bay Park, Bronx; Prospect Park, Brooklyn; and Woodstock). Solenopsis invicta colonies were collected in Athens, GA, USA. M. mexicanus colonies were collected in Piñon Hills, CA, USA. Colonies comprised of queens, workers and brood were maintained in the laboratory in airtight acrylic boxes with plaster of Paris floors. Colonies were fed a diet of insects (flies, crickets and mealworms). White pupae were socially isolated, cocoons were removed in the case of P. pennsylvanica, and secretion droplets were collected from melanized pupae as described for O. biroi. A. mellifera pupae of unknown age were socially isolated from hive fragments (A&Z Apiaries, USA) and reared as described for O biroi, except that the rearing temperature was set to 32 °C. Relative humidity was set to either 100% to replicate conditions used for the different ant species, or to 75% as recommended in the literature30.Injecting dye and tracking pupal fluidInjection needles were prepared as in previous studies31. Injections were performed using an Eppendorf Femtojet with a Narishige micromanipulator. The Femtojet was set to Pi 1000 hPa and Pc 60 hPa. Needles were broken by gently touching the capillary tip to the side of a glass slide. To inject, melanized pupae were placed on ‘Sticky note’ tape (Post-it), with the abdomen tip forward and the ventral side upward. Pupae were injected with blue food colouring (McCormick) into the exuvium for 1–2 s by gently piercing the pupal case at the abdominal tip with the needle. During successful injections, no fluid was discharged from the pupa when the needle was removed, and the moulting fluid inside the exuvium was immediately stained. Pupae were washed in water three times to remove any excess dye. Following injections, 10 pupae were reared in social isolation to confirm the secretion of dyed droplets. For experiments, injected pupae were transferred to colonies with adult ants (Figs. 1f and  4c) or to colonies with adult ants and larvae (Figs. 3b and  4c) to track the distribution of the pupal social fluid.After spending 24 h with dye-injected pupae, adults were taken out of the colony, briefly immersed in 95% ethanol, and transferred to PBS. Digestive systems were dissected in cold PBS and mounted in DAKO mounting medium. Crop and stomach images (Fig. 1f, inset and Fig. 4c, inset) were acquired with a Revolve microscope (Echo). Larvae are translucent, and the presence of dye in the digestive system can be assayed without dissection. Whole-body images of larvae were acquired with a Leica Z16 APO microscope equipped with a Leica DFC450 camera and Leica Application Suite version 4.12.0 (Leica Microsystems). In the experiment on larval growth (Fig. 3c), larval length was measured from images using ImageJ32.Occluding pupaeTen pupae were placed on double-sided tape on a glass coverslip with the ventral side up. The area between the pupae was covered with laser-cut filter paper to prevent adults from sticking to the tape. The pupae were then placed in a 5 cm diameter Petri dish with a moist plaster of Paris floor. To block pupal secretion, the tip of the gaster was occluded with a drop of oil-paint (Uni Paint Markers PX-20), which has no discernible toxic effect7. Secreting pupae received a drop of the same paint on their head to control for putative differences resulting from the paint. Pupae were left in isolation for one day before adults were added to the assay chamber.Behavioural tracking of adult preference assayVideos were recorded using BFS-U3-50S5C-C: 5.0 MP, 35 FPS, Sony IMX264, Colour cameras (FLIR) and the Motif Video Recording System (Loopbio). To assess adult preference (Fig. 1g), physical contact of adults with pupae was manually annotated for the first 10 min after the first adult had encountered (physically contacted) a pupa.Protein profilingWe extracted 30 µl of pupal social fluid and whole-body wash samples with 75:25:0.2 acetonitrile: methanol: formic acid. Extracts were vortexed for 10 min, centrifuged at 16,000g and 4 °C for 10 min, dried in a SpeedVac, and stored at −80 °C until they were analysed by LC–MS/MS.Protein pellets were dissolved in 8 M urea, 50 mM ammonium bicarbonate, and 10 mM dithiothreitol, and disulfide bonds were reduced for 1 h at room temperature. Alkylation was performed by adding iodoacetamide to a final concentration of 20 mM and incubating for 1 h at room temperature in the dark. Samples were diluted using 50 mM ammonium bicarbonate until the concentration of urea had reached 3.5 M, and proteins were digested with endopeptidase LysC overnight at room temperature. Samples were further diluted to bring the urea concentration to 1.5 M before sequencing-grade modified trypsin was added. Digestion proceeded for 6 h at room temperature before being halted by acidification with TFA and samples were purified using in-house constructed C18 micropurification tips.LC–MS/MS analysis was performed using a Dionex3000 nanoflow HPLC and a Q-Exactive HF mass spectrometer (both Thermo Scientific). Solvent A was 0.1% formic acid in water and solvent B was 80% acetonitrile, 0.1% formic acid in water. Peptides were separated on a 90-minute linear gradient at 300 nl min−1 across a 75 µm × 100 mm fused-silica column packed with 3 µm Reprosil C18 material (Dr. Maisch). The mass spectrometer operated in positive ion Top20 DDA mode at resolution 60 k/30 k (MS1/MS2) and AGC targets were 3 × 106/2 × 105 (MS1/MS2).Raw files were searched through Proteome Discoverer v.1.4 (Thermo Scientific) and spectra were queried against the O. biroi proteome using MASCOT with a 1% FDR applied. Oxidation of M and acetylation of protein N termini were applied as a variable modification and carbamidomethylation of C was applied as a static modification. The average area of the three most abundant peptides for a matched protein33 was used to gauge protein amounts within and between samples.Functional annotation and gene ontology enrichmentTo supplement the current functional annotation of the O. biroi genome34, the full proteome for canonical transcripts was retrieved from UniProtKB (UniProt release 2020_04) in FASTA format. We then applied the EggNog-Mapper tool35,36 (http://eggnog-mapper.embl.de, emapper version 1.0.3-35-g63c274b, EggNogDB version 2) using standard parameters (m diamond -d none –tax_scope auto –go_evidence non-electronic –target_orthologs all –seed_ortholog_evalue 0.001 –seed_ortholog_score 60 –query-cover 20 –subject-cover 0) to produce an expanded annotation for all GO trees (Molecular Function, Biological Process, Cellular Components). The list of proteins identified in the pupal fluid was evaluated for functional enrichment in these GO terms, P-values were adjusted with an FDR cut-off of 0.05, and the network plots were visualized using the clusterProfiler package37.Metabolite profilingFor bulk polar metabolite profiling, we used 10 µl aliquots of pupal social fluid and whole-body wash (pooled samples). For the time-series metabolite profiling, 1 µl of pupal social fluid and whole-body wash was used. Samples were extracted in 180 µl cold LC–MS grade methanol containing 1 μM of uniformly labelled 15N- and 13C-amino acid internal standards (MSK-A2-1.2, Cambridge Isotope Laboratories) and consecutive addition of 390 µl LC–MS grade chloroform followed by 120 µl of LC–MS grade water.The samples were vortexed vigorously for 10 min followed by centrifugation (10 min at 16,000g and 4 °C). The upper polar metabolite-containing layer was collected, flash frozen and SpeedVac-dried. Dried extracts were stored at −80 °C until LC–MS analysis.LC–MS was conducted on a Q-Exactive benchtop Orbitrap mass spectrometer equipped with an Ion Max source and a HESI II probe, which was coupled to a Vanquish UPLC system (Thermo Fisher Scientific). External mass calibration was performed using the standard calibration mixture every three days.Dried polar samples were resuspended in 60 µl 50% acetonitrile, and 5 µl were injected into a ZIC-pHILIC 150 × 2.1 mm (5 µm particle size) column (EMD Millipore). Chromatographic separation was achieved using the following conditions: buffer A was 20 mM ammonium carbonate, 0.1% (v/v) ammonium hydroxide (adjusted to pH 9.3); buffer B was acetonitrile. The column oven and autosampler tray were held at 40 °C and 4 °C, respectively. The chromatographic gradient was run at a flow rate of 0.150 ml min−1 as follows: 0–22 min: linear gradient from 90% to 40% B; 22–24 min: held at 40% B; 24–24.1 min: returned to 90% B; 24.1 −30 min: held at 90% B. The mass spectrometer was operated in full-scan, polarity switching mode with the spray voltage set to 3.0 kV, the heated capillary held at 275 °C, and the HESI probe held at 250 °C. The sheath gas flow was set to 40 units, the auxiliary gas flow was set to 15 units. The MS data acquisition was performed in a range of 55–825 m/z, with the resolution set at 70,000, the AGC target at 10 × 106, and the maximum injection time at 80 ms. Relative quantification of metabolite abundances was performed using Skyline Daily v 20.1 (MacCoss Lab) with a 2 ppm mass tolerance and a pooled library of metabolite standards to confirm metabolite identity (via data-dependent acquisition). Metabolite levels were normalized by the mean signal of 8 heavy 13C,15N-labelled amino acid internal standards (technical normalization).The raw data were searched for a targeted list of ~230 polar metabolites and the corresponding peaks were integrated manually using Skyline Daily software. We were able to assign peaks to 107 compounds based on high mass accuracy ( More

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