Ecology

External datasetsWoody biomass carbon dataThe dataset by ref. 16 maps annual global woody biomass carbon densities for 2000–2019 at a spatial resolution of ~10 km. The annual estimates represent averages for the tropical regions and growing-season (April–October) averages for the extra-tropical regions. Ref. 16 analyse global trends of gains and losses in woody biomass carbon for 2000–2019. Overall, they find that grid cells with (significant) net gains of vegetation carbon are by a factor of 1.4 more abundant than grid cells with net losses of vegetation carbon, indicating that there is a global greening trend when only considering the areal extent of biomass gains and not the magnitude of carbon gains. Their regionally distinct analysis of trends shows that almost all regions, except for the tropical moist forests in South America and parts of Southeast Asia, experienced net gains in biomass carbon. On the country scale, the largest net increase in biomass carbon is shown in China, which is mainly attributed to the large-scale afforestation programs in the southern part of the country and increased carbon uptake of established forests. On the other hand, the largest vegetation carbon losses are shown for Brazil and Indonesia, which is partly attributed to deforestation, degradation, and drought events. All of the mentioned trends have been found to be significant16. The decreasing carbon sink in Brazil is in line with ref. 44, who, considering both natural and anthropogenic fluxes, show that the southeastern Amazon has even turned from a carbon sink to a carbon source, mainly owing to fire emissions from forest clearing. Isolating carbon fluxes in intact, old-growth Amazonian rainforests (i.e., SLAND,B), ref. 45 also find evidence for a significantly decreasing carbon sink due to the negative effects of increasing temperatures and droughts on carbon uptake since the 1990s.The dataset was remapped to the BLUE resolution of 0.25∘ through conservative remapping (i.e., area-weighted averaging).ERA-5 dataThe ERA-5 variables were downloaded from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/home). Monthly air temperature (Ta) at 2 m height was averaged over each year, and annual precipitation was calculated by taking the sum of the monthly total precipitation (P). Both variables were regridded from the original resolution of ~0.1° to 0.25° resp. to the TRENDY resolution of 0.5° through conservative remapping.TRENDY dataWe used the TRENDY model ensemble version 8 (conducted for the 2019 GCB; ref. 8). We used net biome production (NBP) and annual vegetation carbon stocks (cVeg) for 2000–2018 from four different model setups (S2, S3, S5, and S6) and eight resp. 13 DGVMs (depending on the data available). The selection of DGVMs is done as in ref. 19 (Supplementary Tab. 3), but we included one additional model (ISAM) for the S2 simulations. The terrestrial biomass carbon sink (SLAND,B) was calculated for 13 DGVMs following the GCB 2020 approach, i.e., from the S2 simulation, which is the simulation without LULCC (i.e., fixed pre-industrial land cover) under transient environmental conditions (climate, nitrogen deposition, CO2 evolution). SLAND,B is the annual difference of cVeg and makes no statements about the further fate of biomass if cVeg decreases. SLAND,B, therefore, should not be interpreted as equivalent to the flux to/from the atmosphere, since parts of cVeg may be transferred to litter, dead wood, or soil. The same applies to our BLUE estimates of SLAND,B, ensuring comparability between our BLUE estimates and the TRENDY estimates. Increases (decreases) of cVeg between two years are a net uptake (release) of carbon from the terrestrial biosphere. The global sums of biomass carbon stocks under transient climate and CO2 were calculated from the S3 setup (LULCC under historical environmental conditions), whereas the S5 setup provides biomass carbon under constant present-day environmental forcing (closest to the classical bookkeeping approach). In line with the GCB, ELUC was calculated under historical environmental conditions as the difference in NBP between the S2 and S3 simulations (ELUC = NBP_S2 – NBP_S3). ELUC under constant present-day environmental forcing was calculated as the difference in NBP between the S6 (fixed pre-industrial land cover under present-day environmental forcing) and S5 simulations (ELUC = NBP_S6 – NBP_S5)19. All datasets were remapped to a common resolution of 0.5∘ through conservative remapping (area-weighted average) for the data analysis.Assimilation of observed woody biomass carbon in BLUEThe observed woody biomass carbon densities by ref. 16 are assimilated in BLUE in several steps.Carbon transfer in the default setup of BLUEThe BLUE simulation is started in AD 850. Biomass and soil vegetation carbon densities are based on ref. 17, which are converted to exponential time constants. A detailed explanation of the exponential model can be found in ref. 5.While in the default setup, changes are only due to LULCC, our assimilation approach now introduces environmental effects on woody vegetation carbon by assimilating the observed woody biomass carbon densities in BLUE from 2000 onward according to the methodological considerations explained below.Calculation of woody biomass carbon densities for different land cover types and PFTsWithin each 0.25° cell of the global grid, the (remapped) woody biomass carbon density from ref. 16 must be the sum of woody biomass carbon stored in all woody PFTs of all woody land cover types. The distribution of the woody biomass carbon across PFTs and land cover types is achieved by distributing the observed (i.e., actual) woody biomass carbon densities (ρBa) from ref. 16 across the two land cover types (j) and the eight PFTs (l) that can be woody vegetation (primary land, called virgin, “v” in BLUE and secondary, “s”, land) according to the fraction of total woody biomass carbon (fB) contained in each land cover type and each PFT (fB,j,l) as estimated by BLUE. fB,j,l varies for different PFTs and land cover types, depending on their history of LULCC and their potential for carbon uptake (i.e., the potential carbon densities).fB,j,l is extracted from the default simulations for the first year of the time series (i.e., 2000) and calculated for subsequent years from the BLUE simulations using the assimilated woody vegetation carbon densities for that year:$${f}_{B,j,l}(t)=frac{{C}_{B,j,l}(t)}{{C}_{B}(t)}$$
(1)
where CB is the woody biomass carbon stock.Consequently, the assimilated woody biomass carbon stock per cover type and PFT (CB_as,j,l) at each time step can be calculated as:$${C}_{B_as,j,l}(t)={rho }_{Ba}(t);*;A;*;{f}_{B,j,l}(t)$$
(2)
with j{v, s}; l{1. . 8}; t{2000. . 2019}. A is the area per grid cell.Thresholds for excluding inconsistent woody biomass carbon densitiesWe eliminate unrealistically large values for woody biomass carbon densities that our assimilation framework produces. Woody biomass carbon densities in BLUE that exceed the highest value (~374 t ha−1) of the original dataset indicate inconsistencies between the observed woody biomass carbon estimates and the fractional grid cell areas per PFT and land cover types that BLUE simulates. To account for uncertainties related to the criteria for exclusion of grid cells, multiple threshold approaches are applied and the results are compared. To maintain a temporally and spatially consistent time series of woody biomass carbon, grid cells that are excluded according to the chosen threshold approach are interpolated through linear barycentric interpolation. A first approach relies on a uniform upper threshold of More

We focused on the Northern High Latitudes (NHL, latitude > 50°N, excluding Greenland) due to their importance for carbon (CO2-C, the same hereafter)-climate feedbacks in the Earth system. To minimize the potential human influence on the CO2 cycle, we excluded areas under agricultural management (croplands, cropland/natural vegetation mosaic, and urban types), and considered only pixels of natural vegetation defined from the MODIS MCD12Q1 (v006) based IGBP land cover classification. Our main focus was the NHL permafrost region because permafrost plays a critical role in the ecology, environment, and society in the NHL. Permafrost, or permanently frozen ground, is defined as ground (soil, sediment, or rock) that remains at or below 0 °C for at least two consecutive years. The occurrence of permafrost is primarily controlled by temperature and has a strong effect on hydrology, soils, and vegetation composition and structure. Based on the categorical permafrost map from the International Permafrost Association58, the permafrost region (excluding permanent snow/ice and barren land), including sporadic (10–50%), discontinuous (50–90%), and continuous ( >90%) permafrost, encompasses about 15.7 × 106 km2, accounts for 57% of the NHL study dominion, and is dominated by tundra (shrubland and grass) and deciduous needleleaf (i.e., larch) forest that is regionally abundant in Siberia. The NHL non-permafrost region covers about 11.9 × 106 km2 and is dominated by mixed and evergreen needleleaf boreal forests (Fig. S1).Atmospheric CO2 inversions (ACIs)ACIs provide regionally-integrated estimates of surface-to-atmosphere net ecosystem CO2 exchange (NEEACI) fluxes by utilizing atmospheric CO2 concentration measurements and atmospheric transport models59. ACIs differ from each other mainly in their underlying atmospheric observations, transport models, spatial and temporal flux resolutions, land surface models used to predict prior fluxes, observation uncertainty and prior error assignment, and inversion methods. We used an ensemble mean of six different ACI products, each providing monthly gridded NEEACI at 1-degree spatial resolution, including Carbon‐Tracker 2019B (2000-2019, CT2019)60, Carbon‐Tracker Europe 2020 (2000–2019, CTE2020)61, Copernicus Atmosphere Monitoring Service (1979–2019, CAMS)62, Jena CarboScope (versions s76_v4.2 1976–2017, and s85_v4.2 1985-2017)63,64, and JAMSTEC (1996–2017)65. The monthly gridded ensemble mean NEEACI at 1-degree spatial resolution was calculated using the available ACIs from 1980-2017. Monthly ACI ensemble mean NEEACI data were summed to seasonal and annual values, and used to calculate the spatial and temporal trends of net CO2 uptake, and to investigate its relationship to climate and environmental controls.Productivity datasetDirect observations of vegetation productivity do not exist at a circumpolar scale. We therefore used two long-term gridded satellite-based estimates of vegetation productivity, including gross primary production (GPP) derived using a light use efficiency (LUE) approach (LUE GPP, 1982–1985)21,66 and satellite observations of Normalized Difference Vegetation Index (NDVI) from the Global Inventory Modeling and Mapping Studies (GIMMS NDVI, 1982–1985)67. LUE GPP (monthly, 0.5° spatial resolution, 1982–2015) is calculated from satellite observations of NDVI from the Advanced Very High-Resolution Radiometer (AVHRR; 1982 to 2015) combined with meteorological data, using the MOD17 LUE approach. LUE GPP has been extensively validated with a global array of eddy-flux tower sites68,69,70 and tends to provide better estimates in ecosystems with greater seasonal variability at high latitudes. Following66,71, we used the ensemble mean of GPP estimates from three of the most commonly used meteorological data sets: National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis; NASA Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); and European Center for Medium-Range Weather Forecasting (ECMWF). GIMMS NDVI (bimonthly, 1/12 spatial resolution, 1982–2015) provides the longest satellite observations of vegetation “greenness”, and is widely used in studies of phenology, productivity, biomass, and disturbance monitoring as it has proven to be an effective surrogate of vegetation photosynthetic activity72.The gridded GPP data were resampled to 1-degree resolution at monthly time scales, to be consistent with NEEACI, and used to test (H1) whether greater temperature sensitivity of vegetation productivity explains the different trends in net CO2 uptake across the NHL. LUE GPP was also used to calculate monthly total ecosystem respiration (TER) as the difference between GPP and NEEACI (i.e., TERresidual =  GPP– NEEACI) from 1982-2015, as global observations of respiration do not exist. The NEEACI, GPP and TERresidual were used as observation-constrained top-down CO2 fluxes to investigate mechanisms underlying the seasonal CO2 dynamics in the structural equation modeling and additional decision tree-based analysis.Eddy Covariance (EC) measurements of bottom-up CO2 fluxesA total of 48 sites with at least three years of data representing the major NHL ecosystems were obtained from the FLUXNET2015 database (Table S1 and Fig. S1). EC measurements provide direct observations of net ecosystem CO2 exchange (NEE) and estimate the GPP and TER flux components of NEE using other climate variables. Daily GPP and TER were estimated as the mean value from both the nighttime partitioning method73 and the light response curve method74. More details on the flux partitioning and gap-filling methods used are provided by75. Daily fluxes were summed into seasonal and annual values and used to compare with trends from ACIs (Fig. S7), to estimate the climate and environmental controls on the CO2 cycle in the pathway analysis (Fig. 5), and to calculate the net CO2 uptake sensitivity to spring temperature (Fig. S14).Ensemble of dynamic global vegetation models (TRENDY simulations)The TRENDY intercomparison project compiles simulations from state-of-the-art dynamic global vegetation models (DGVMs) to evaluate terrestrial energy, water, and net CO2 exchanges76. The DGVMs provide a bottom-up approach to evaluate terrestrial CO2 fluxes (e.g., net biome production [NBP]) and allow deeper insight into the mechanisms driving changes in carbon stocks and fluxes. We used monthly NBP, GPP, and TER (autotrophic + heterotrophic respiration; Ra + Rh) from ten TRENDY v7 DGVMs76, including CABLE-POP, CLM5.0, OCN, ORCHIDEE, ORCHIDEE-CNP, VISIT, DLEM, LPJ, LPJ-GUESS, and LPX. We analyzed the “S3” simulations that include time-varying atmospheric CO2 concentrations, climate, and land use. All simulations were based on climate forcing from the CRU-NCEPv4 climate variables at 6-hour resolution. CO2 flux outputs were summarized monthly at 1-degree spatial resolution from 1980 to 2017. Monthly ensemble mean NBP, GPP, and TER were summed to seasonal and annual values, and then used to compare with observation-constrained ACI top-down CO2 fluxes (Figs. 4 and 5).Satellite data-driven carbon flux estimates (SMAP L4C)We also used a much finer spatio-temporal simulation of carbon fluxes from the NASA Soil Moisture Active Passive (SMAP) mission Level 4 Carbon product (L4C) to quantify the temperature and moisture sensitivity of NHL CO2 exchange77. The SMAP L4C provides global operational daily estimates of NEE and component CO2 fluxes for GPP and TER at 9 km resolution since 2015; whereas, an offline version of the L4C model provides a similar Nature Run (NR) carbon flux record over a longer period (2000-present), but without the influence of SMAP observational inputs. The L4C model has been calibrated against FLUXNET tower CO2 flux measurements and shows favorable performance and accuracy in high latitude regions4,77. In this analysis, daily gridded CO2 fluxes at 9-km resolution from the L4C NR record were summed to seasonal and annual values, and used to calculate the sensitivity of net C uptake in response to spring temperature (Fig. S14).CO2 fluxes in this analysis are defined with respect to the biosphere so that a positive value indicates the biosphere is a net sink of CO2 absorbed from the atmosphere. The different data products described above use different terminology (e.g., NEE, NBP) with slightly different meanings; however, they all provide estimates of net land-atmosphere CO2 exchange78.Climate, tree cover, permafrost, and soil moisture dataMonthly gridded air temperatures at 0.5-degree spatial resolution from 1980 to 2017 were obtained from the Climate Research Unit (CRU TS v4.02) at the University of East Anglia79. Air temperature was summarized at seasonal and annual scales to calculate temperature sensitivities of net CO2 uptake and to investigate the mechanism underlying the seasonal CO2 dynamics.Percent tree cover (%TC) at 0.05-degree spatial resolution was averaged over a 35-year (1982-2016) period using annual %TC layers derived from the Advanced Very High-Resolution Radiometer (AVHRR) (Fig. 1a)42. %TC was binned using 5% TC intervals to assess its relation to net CO2 uptake, or aggregated at a regional scale (e.g., TC  > 50% or TC  90%), discontinuous permafrost (DisconP, 10% < P  90%), discontinuous (DisconP, 10% < P  0.05 indicate a good fitting model), Bentler’s comparative fit index (CFI, where CFI ≈ 1 indicates a good fitting model), and the root mean square error of approximation (RMSEA; where RMSEA ≤ 0.05 and p  > 0.1 indicate a good fitting model). The standardized regression coefficient can be interpreted as the relative influences of exogenous (independent) variables. The R2 indicates the total variation in an endogenous (dependent) variable explained by all exogenous (independent) variables.Direct and legacy effects of temperature on seasonal net CO2 uptakeBecause landscape thawing and snow conditions regulate the onset of vegetation growth and influence the seasonal and annual CO2 cycles in the NHL24,84, we also analyzed the legacy effects of spring (May–Jun) temperature on seasonal net CO2 uptake. We regressed seasonal and annual net CO2 uptake from the site-level EC observations, regional-level ACI ensemble, and the TRENDY NBP ensemble against spring (May-June) air temperature. For EC observations, net CO2 uptake (i.e., NEE) and air temperature were summarized from site-level measurements. For the ACIs and TRENDY ensemble, net CO2 uptake (i.e., NEEACI and NBP) was summarized as regional means from the ACIs and TRENDY ensemble outputs, and air temperature was summarized as regional means from CRU temperature. The slope of the regression line was interpreted as the spring temperature sensitivity of the CO2 cycle. Simple linear regression was used here mainly due to the strong influence of spring temperature on the seasonal and annual CO2 cycle in NHL ecosystems30. Temperature sensitivity (γ: g C m−2 day−1 K−1) is the change in net CO2 flux (g C m−2 day−1) in response to a 1-degree temperature change. The sensitivity of net CO2 uptake to warm spring anomalies was calculated for different seasons (EGS, LGS, and annual) and regions (i.e., permafrost and non-permafrost), and the T-test was used to test for the difference in γ among different regions, seasons, and datasets. Similarly, direct effects of temperature on net CO2 uptake were calculated using the same season data (Fig. S14).Observationally-constrained estimates (EC and ACIs) showed that the sensitivity of net CO2 uptake in the EGS to spring temperature is positive (γ  > 0) and not statistically different (p  > 0.05) between permafrost and non-permafrost regions (({gamma }_{{ACI}}^{{np}})=0.125 ± 0.020 gC m−2 d−1 K−1; ({gamma }_{{EC}}^{{np}}) = 0.052 ± 0.013 gC m−2 d−1 K−1). In contrast, the sensitivity of net CO2 uptake in LGS to spring temperature is negative (γ  More

Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS 
PubMed 
Article 

Google Scholar 
van der Sande, M. T. et al. Biodiversity in species, traits, and structure determines carbon stocks and uptake in tropical forests. Biotropica 49, 593–603 (2017).Article 

Google Scholar 
Grace, O. M. et al. Plant power: opportunities and challenges for meeting sustainable energy needs from the plant and fungal kingdoms. Plants People Planet 2, 446–462 (2020).Article 

Google Scholar 
Howes, M. J. R. et al. Molecules from nature: reconciling biodiversity conservation and global healthcare imperatives for sustainable use of medicinal plants and fungi. Plants People Planet 2, 463–481 (2020).Article 

Google Scholar 
Ulian, T. et al. Unlocking plant resources to support food security and promote sustainable agriculture. Plants People Planet 2, 421–445 (2020).Article 

Google Scholar 
Brondizio, E., Diaz, S., Settele, J. & Ngo, H. T. (eds) Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on biodiversity and ecosystem services. Zenodo https://doi.org/10.5281/zenodo.3831673 (2019).Bennun, L. et al. The value of the IUCN Red List for business decision-making. Conserv. Lett. 11, e12353 (2018).Betts, J. et al. A framework for evaluating the impact of the IUCN Red List of threatened species. Conserv. Biol. 34, 632–643 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Maira, L. et al. Achieving international species conservation targets: closing the gap between top-down and bottom-up approaches. Conserv. Soc. 19, 25–33 (2021).Article 

Google Scholar 
IUCN Red List version 2022-2: Table 1a (IUCN, 2022); https://www.iucnredlist.org/resources/summary-statistics#Figure2Rivers, M. The global tree assessment—red listing the world’s trees. BGjournal 14, 16–19 (2017).
Google Scholar 
Nic Lughadha, E. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).Article 

Google Scholar 
Silva, S. V. et al. Global estimation and mapping of the conservation status of tree species using artificial intelligence. Front. Plant Sci. 13, 839792 (2022).ThreatSearch Online Database (Botanic Gardens Conservation International, accessed 12 October 2021); https://tools.bgci.org/threat_search.phpBachman, S. P., Nic Lughadha, E. M. & Rivers, M. C. Quantifying progress toward a conservation assessment for all plants. Conserv. Biol. 32, 516–524 (2018).PubMed 
Article 

Google Scholar 
Rondinini, C., Di Marco, M., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN Red List. Conserv. Lett. 7, 126–130 (2014).Article 

Google Scholar 
Cazalis, V. et al. Bridging the research–implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).PubMed 
Article 

Google Scholar 
Dauby, G. et al. ConR: an R package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecol. Evol. 7, 11292–11303 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).PubMed 
Article 

Google Scholar 
Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Using coarse-scale species distribution data to predict extinction risk in plants. Divers. Distrib. 23, 435–447 (2017).Article 

Google Scholar 
Pelletier, T. A., Carstens, B. C., Tank, D. C., Sullivan, J. & Espíndola, A. Predicting plant conservation priorities on a global scale. Proc. Natl Acad. Sci. USA 115, 13027–13032 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zizka, A., Silvestro, D., Vitt, P. & Knight, T. M. Automated conservation assessment of the orchid family with deep learning. Conserv. Biol. 35, 897–908 (2021).PubMed 
Article 

Google Scholar 
Walker, B. E., Leão, T. C. C., Bachman, S. P., Bolam, F. C. & Nic Lughadha, E. Caution needed when predicting species threat status for conservation prioritization on a global scale. Front. Plant Sci. 11, 520 (2020).Lughadha, E. N. et al. The use and misuse of herbarium specimens in evaluating plant extinction risks. Philos. Trans. R. Soc. B 374, 20170402 (2019).Article 

Google Scholar 
Walker, B. E., Leão, T. C. C., Bachman, S. P., Lucas, E. & Nic Lughadha, E. M. Evidence-based guidelines for developing automated assessment methods. Preprint at https://ecoevorxiv.org/zxq6s/ (2021).Isaac, N. J. B., Turvey, S. T., Collen, B., Waterman, C. & Baillie, J. E. M. Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE 2, e296 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Grenié, M., Denelle, P., Tucker, C. M., Munoz, F. & Violle, C. funrar: an R package to characterize functional rarity. Divers. Distrib. 23, 1365–1371 (2017).Article 

Google Scholar 
Lindegren, M., Holt, B. G., MacKenzie, B. R. & Rahbek, C. A global mismatch in the protection of multiple marine biodiversity components and ecosystem services. Sci. Rep. 8, 4099 (2018).Pollock, L. J. et al. Protecting biodiversity (in all its complexity): new models and methods. Trends Ecol. Evol. 35, 1119–1128 (2020).PubMed 
Article 

Google Scholar 
Arnan, X., Cerdá, X. & Retana, J. Relationships among taxonomic, functional, and phylogenetic ant diversity across the biogeographic regions of Europe. Ecography 40, 448–457 (2017).Article 

Google Scholar 
Wong, J. S. Y. et al. Comparing patterns of taxonomic, functional and phylogenetic diversity in reef coral communities. Coral Reefs 37, 737–750 (2018).Article 

Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 

Google Scholar 
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).CAS 
PubMed 
Article 

Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
PubMed 
Article 

Google Scholar 
Cámara-Leret, R. et al. Fundamental species traits explain provisioning services of tropical American palms. Nat. Plants 3, 16220 (2017).Saslis-Lagoudakis, C. H. et al. Phylogenies reveal predictive power of traditional,medicinein bioprospecting. Proc. Natl Acad. Sci. USA 109, 15835–15840 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Molina-Venegas, R., Rodríguez, M., Pardo-de-Santayana, M., Ronquillo, C. & Mabberley, D. J. Maximum levels of global phylogenetic diversity efficiently capture plant services for humankind. Nat. Ecol. Evol. 5, 583–588 (2021).PubMed 
Article 

Google Scholar 
Molina-Venegas, R. Conserving evolutionarily distinct species is critical to safeguard human well-being. Sci. Rep. 11, 24187 (2021).Zaman, W. et al. Predicting potential medicinal plants with phylogenetic topology: inspiration from the research of traditional Chinese medicine. J. Ethnopharmacol. 281, 114515 (2021).CAS 
PubMed 
Article 

Google Scholar 
Cámara-Leret, R. et al. Climate change threatens New Guinea’s biocultural heritage. Sci. Adv. 5, eaaz1455 (2019).Lima, V. P. et al. Climate change threatens native potential agroforestry plant species in Brazil. Sci. Rep. 12, 2267 (2022).Johnson, D. V. Tropical Palms 2010 Revision Non-Wood Forest Products 10 (FAO, 2010).Johnson, D. V. & Sunderland, T. C. H. Rattan Glossary and Compendium Glossary with Emphasis on Africa Non-Wood Forest Products 16 (FAO, 2004).Ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).PubMed 
Article 
CAS 

Google Scholar 
Zona, S. & Henderson, A. A review of animal-mediated seed dispersal of palms. Selbyana 11, 6–21 (1989).
Google Scholar 
Kissling, W. D. et al. PalmTraits 1.0, a species-level functional trait database of palms worldwide. Sci. Data 6, 178 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Tomlinson, P. B. The uniqueness of palms. Bot. J. Linn. Soc. 151, 5–14 (2006).Article 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 
Article 
CAS 

Google Scholar 
Muscarella, R. et al. The global abundance of tree palms. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).Article 

Google Scholar 
Dransfield, J. et al. Genera Palmarum: The Evolution and Classification of Palms (Kew Publishing, 2008).Diazgranados, M. et al. World Checklist of Useful Plant Species (Royal Botanic Gardens, Kew, 2020).Couvreur, T. L. P. & Baker, W. J. Tropical rain forest evolution: palms as a model group. BMC Biol. 11, 2–5 (2013).Article 

Google Scholar 
Faurby, S., Eiserhardt, W. L., Baker, W. J. & Svenning, J. Molecular phylogenetics and evolution: an all-evidence species-level supertree for the palms (Arecaceae). Mol. Phylogenet. Evol. 100, 57–69 (2016).PubMed 
Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2021-2 (IUCN, accessed 12 October 2021); https://www.iucnredlist.orgBaker, W. J. & Dransfield, J. Beyond genera Palmarum: progress and prospects in palm systematics. Bot. J. Linn. Soc. 182, 207–233 (2016).Article 

Google Scholar 
Henderson, A. A revision of Calamus (Arecaceae, Calamoideae, Calameae, Calaminae). Phytotaxa https://doi.org/10.11646/phytotaxa.445.1.1 (2020).Rakotoarinivo, M., Dransfield, J., Bachman, S. P., Moat, J. & Baker, W. J. Comprehensive red list assessment reveals exceptionally high extinction risk to Madagascar palms. PLoS ONE 9, e103684 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cosiaux, A. et al. Low extinction risk for an important plant resource: conservation assessments of continental African palms (Arecaceae/Palmae). Biol. Conserv. 221, 323–333 (2018).Article 

Google Scholar 
Johnson, D. & UICN/SSC Palm Specialist Group (eds) Palms, Their Conservation and Sustained Utilization—Status Survey and Conservation Action Plan (Union Internationale pour la Conservation de la Nature et de ses Ressources, 1996).Bachman, S., Walker, B. E., Barrios, S., Copeland, A. & Moat, J. Rapid least concern: towards automating red list assessments. Biodivers. Data J. 8, e47018 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Enquist, B. J. et al. The commonness of rarity: global and future distribution of rarity across land plants. Sci. Adv. https://doi.org/10.1126/sciadv.aaz0414 (2019).Vieilledent, G. et al. Combining global tree cover loss data with historical national forest cover maps to look at six decades of deforestation and forest fragmentation in Madagascar. Biol. Conserv. 222, 189–197 (2018).Article 

Google Scholar 
Gaveau, D. L. A. et al. Rise and fall of forest loss and industrial plantations in Borneo (2000–2017). Conserv. Lett. 12, e12622 (2019).Gamoga, G., Turia, R., Abe, H., Haraguchi, M. & Iuda, O. The forest extent in 2015 and the drivers of forest change between 2000 and 2015 in Papua New Guinea: deforestation and forest degradation in Papua New Guinea. Case Stud. Environ. 5, 1442018 (2021).Cámara-Leret, R. & Bascompte, J. Language extinction triggers the loss of unique medicinal knowledge. Proc. Natl Acad. Sci. USA 118, e2103683118 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Henderson, A., Fischer, B., Scariot, A., Whitaker Pacheco, M. A. & Pardini, R. Flowering phenology of a palm community in a central Amazon forest. Brittonia 52, 149–159 (2000).Article 

Google Scholar 
Olivares, I. & Galeano, G. Leaf and inflorescence production of the wine palm (Attalea butyracea) in the dry Magdalena river valley, Colombia. Caldasia 35, 37–48 (2013).
Google Scholar 
Voeks, R. A. Disturbance pharmacopoeias: medicine and myth from the humid tropics. Ann. Assoc. Am. Geogr. 94, 868–888 (2004).
Google Scholar 
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change 9, 758–763 (2019).Article 

Google Scholar 
Govaerts, R., Dransfield, J., Zona, S. & Henderson, A. World Checklist of Arecaceae (Royal Botanic Gardens, Kew, accessed 1 March 2018); http://wcsp.science.kew.org/Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.6.0 (2021).Zizka, A. et al. CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10, 744–751 (2019).Article 

Google Scholar 
Plants of the World Online (Royal Botanic Gardens, Kew, accessed 1 March 2018); http://www.plantsoftheworldonline.org/South, A. rworldmap v.1.3-6: Mapping global data (2016).Bivand, R. et al. maptools v.0.9-2: Tools for handling spatial objects (2017).Arel-Bundock, V., Enevoldsen, N. & Yetman, C. countrycode: an R package to convert country names and country codes. J. Open Source Softw. 3, 848 (2018).Article 

Google Scholar 
Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps v.3.3.0: Draw geographical maps (2018).Pebesma, E. et al. sp v.1.2-7: Classes and methods for spatial data (2018).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Wickham, H., Hester, J. & Chang, W. devtools v.1.13.5: Tools to make developing R packages easier (2018).World Geographic Scheme for Recording Plant Distributions Standard (TDWG, 2001); http://www.tdwg.org/standards/109Brummitt, R. K. World Geographical Scheme for Recording Plant Distributions (Hunt Institute for Botanical Documentation, 2001).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Moat, J. & Bachman, S. P. rCAT v.0.1.6: Conservation assessment tools (2017).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Plants of the World Online (Royal Botanic Gardens, Kew, accessed 10 June 2020); http://www.plantsoftheworldonline.org/Csárdi, G. & FitzJohn, R. progress v.1.2.2: Terminal progress bars (2019).Microsoft Corporation & Weston, S. doParallel: Foreach parallel adaptor for the ‘parallel’ package. R package version 1.0.16 (2020).Microsoft Corporation & Weston, S. foreach: Provides foreach looping construct. R package version 1.5.0 (2020).Ooms, J., Lang, D. T. & Hilaiel, L. jsonlite v.1.7.2: A simple and robust JSON parser and generator for R (2020).Wickham, H. httr v.1.4.2: Tools for working with URLs and HTTP (2020).Global Human Footprint (Geographic), v2 (1995 – 2004) (SEDAC, accessed 14 May 2018); https://doi.org/10.7927/H4M61H5FFick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 
Article 

Google Scholar 
Wickham, H. plyr v.1.8.6: Tools for splitting, applying and combining data (2021).Wickham, H. & RStudio. tidyr v.1.1.4: Tidy messy data (2021).Wickham, H., François, R., Henry, L. & Müller, K. dplyr v.1.0.7: A grammar of data manipulation (2021).Bivand, R. et al. rgdal v.1.5-8: Bindings for the ‘geospatial’ data abstraction library (2020).Greenberg, J. A. & Mattiuzzi, M. gdalUtils v.2.0.3.2: Wrappers for the Geospatial data Abstraction Library (GDAL) utilities (2020).Hijmans, R. J. et al. raster v.3.1-5: Geographic data analysis and modeling (2020).The IUCN Red List of Threatened Species (IUCN, accessed 22 March 2018); https://www.iucnredlist.org/ThreatSearch Online Database (Botanic Gardens Conservation International, accessed 1 March 2018); https://tools.bgci.org/threat_search.phpChamberlain, S., ROpenSci & Salmon, M. rredlist: ‘IUCN’ Red List client (2020).Wickham, H. stringr v.1.4.0: Simple, consistent wrappers for common string operations (2019).Gagolewski, M. & Tartanus, B. stringi v.1.7.5: Character string processing facilities (2021).Kuhn, M. caret: Classification and regression training. R package version 6.0-86 (2020).Torgo, L. Data Mining with R, Learning with Case Studies (Chapman and Hall/CRC, 2010).Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2020).Article 

Google Scholar 
Stokely, M. HistogramTools: Utility functions for R histograms. R package version 0.3.2 (2015).Sarkar, D. et al. lattice v.0.20-40: Trellis graphics for R (2020).Wickham, H. ggplot2 Elegant Graphics for Data Analysis (Springer, 2016).Auguie, B. & Antonov, A. gridExtra v.2.3: Miscellaneous functions for ‘grid’ graphics (2017).Pruim, R., Kaplan, D. T. & Horton, N. J. mosaic v.1.6.0: Project MOSAIC statistics and mathematics teaching utilities (2020).Meyer, D. & Buchta, C. proxy v.0.4-23: Distance and similarity measures (2019).Wickham, H. & Seidel, D. scales v.1.1: Scale functions for visualization (2019).Branco, P., Ribeiro, R. & Torgo, L. UBL v.0.0.6: An implementation of re-sampling approaches to utility-based learning for both classification and regression tasks (2017).Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).Article 

Google Scholar 
Ripley, B. & Venables, W. nnet v.7.3-13: Feed-forward neural networks and multinomial log-linear models (2020).Warnes, G. R. et al. gdata v.2.18.0: Various R programming tools for data manipulation (2017).Wright, M. N., Wager, S. & Probst, P. ranger v.0.12.1: A fast implementation of random forests (2020).Arya, S., Mount, D., Kemp, S. E. & Jefferis, G. RANN v.2.6.1: Fast nearest neighbour search (wraps ANN Library) using L2 metric (2019).Meyer, D. et al. e1071 v.1.7-3: Misc Functions of the Department of Statistics, Probability Theory Group (formerly: E1071), TU Wien (2019).Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).
Google Scholar 
Greenwell, B. fastshap v.0.0.7: Fast approximate Shapley values (2021).Greenwell, B. vip v.0.3.2: Variable importance plots (2020).Donoghoe, M. W. glm2 v.1.2.1: Fitting generalized linear models (2018).Wickham, H. reshape2 v.1.4.4: Flexibly reshape data: a reboot of the reshape package (2020).Robin, X. et al. pROC v.1.18.0: Display and analyze ROC curves (2020).Warnes, G. R. et al. gplots v.3.0.3: Various R programming tools for plotting data (2019).Müller, K. & Bryan, J. here v.1.0.1: A simpler way to find your files (2017).Wickham, H., Hester, J., Francois, R., Jylänki, J. & Jørgensen, M. readr v.1.3.1: Read rectangular text data (2018).Wickham, H. et al. readxl v.1.3.1: Read Excel files (2019).Henry, L. & Wickham, H. purrr v.0.3.4: Functional programming tools (2020).Lin Pedersen, T. ggforce v.0.3.1: Accelerating ‘ggplot2’ (2019).Lin Pedersen, T. patchwork v.1.0.0: The composer of plots (2019).Hester, J. glue v.1.3.1: Interpreted string literals (2019).Ooms, J. & McNamara, J. writexl v.1.2: Export data frames to Excel ‘xlsx’ format (2019).Horikoshi, M. et al. ggfortify v.0.4.8: Data visualization tools for statistical analysis results (2019).Liaw, A. randomForest v.4.6-14: Breiman and Cutler’s random forests for classification and regression (2018).Kassambara, A. ggpubr v.0.2.5: ‘ggplot2’ based publication ready plots (2020).Gruca, M., Blach-Overgaard, A. & Balslev, H. African palm ethno-medicine. J. Ethnopharmacol. 165, 227–237 (2015).PubMed 
Article 

Google Scholar 
Cámara–Leret, R. & Dennehy, Z. Indigenous knowledge of New Guinea’s useful plants: a review. Econ. Bot. 73, 405–415 (2019).Article 

Google Scholar 
Macía, M. J. et al. Palm uses in Northwestern South America: a quantitative review. Bot. Rev. 77, 462–570 (2011).Article 

Google Scholar 
Orme, D. et al. caper: Comparative analyses of phylogenetics and evolution in R. R package version 1.0.1 https://cran.r-project.org/package=caper (2018).Kowarik, A. & Templ, M. Imputation with the R package VIM. J. Stat. Softw. 74, 1–16 (2016).Alfons, A. & Templ, M. Estimation of social exclusion indicators from complex surveys: the R package laeken. J. Stat. Softw. 54, 1–25 (2013).Article 

Google Scholar 
Milliken, W., Walker, B. E., Howes, M. J. R., Forest, F. & Nic Lughadha, E. Plants used traditionally as antimalarials in Latin America: mining the tree of life for potential new medicines. J. Ethnopharmacol. 279, 114221 (2021).PubMed 
Article 

Google Scholar 
Fritz, S. A. & Purvis, A. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conserv. Biol. 24, 1042–1051 (2010).PubMed 
Article 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Paradis, E. & Schliep, K. Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 

Google Scholar 
Govaerts, R., Nic Lughadha, E., Black, N., Turner, R. & Paton, A. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci. Data 8, 215 (2021).Yu, G. ggplotify v.0.0.4: Convert plot to ‘grob’ or ‘ggplot’ object (2019).Yu, G. aplot v.0.0.3: Decorate a ‘ggplot’ with associated information (2020).Slowikowski, K. et al. ggrepel v.0.8.1: Automatically position non-overlapping text labels with ‘ggplot2’ (2019).Schloerke, B. et al. GGally v.1.4.0: Extension to ‘ggplot2’ (2018).Rubis, B. et al. hrbrthemes v.0.6.0: Additional themes, theme components and utilities for ‘ggplot2’ (2019).Henry, L., Wickham, H. & Chang, W. ggstance v.0.3.3: Horizontal ‘ggplot2’ components (2019).Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. Y. Ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

Google Scholar 
Brown, C. hash v.2.2.6.1: Full feature implementation of hash/associated arrays/dictionaries (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).RStudio Team. RStudio: Integrated Development for R (RStudio, 2021).Bellot, S. et al. Workflow and code used to perform palm extinction risk and regional palm use resilience analyses. Zenodo https://doi.org/10.5281/zenodo.6678122 (2022). More

Bellot, S. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01858-0 (2022).Article 

Google Scholar 
Eiserhardt, W. L. et al. Ann. Bot. 108, 1391–1416 (2011).Article 

Google Scholar 
Muscarella, R. et al. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).Article 

Google Scholar 
Cámara-Leret, R. et al. Nat. Plants 3, 16220 (2017).Article 

Google Scholar 
The IUCN Red List of Threatened Species (IUCN, 2018).BGCI ThreatSearch Online Database (BGCI, 2018).Carlos-Júnior, L. A. et al. Divers. Distrib. 25, 743–757 (2019).Article 

Google Scholar 
Pollock, L. J., Thuiller, W. & Jetz, W. Nature 546, 141–144 (2017).CAS 
Article 

Google Scholar 
Rice, J. et al. The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas (IPBES, 2018).Coelho de Souza, F. et al. Nat. Ecol. Evol. 3, 1754–1761 (2019).Article 

Google Scholar  More

Literature values of R
To and Q10 of leaf respirationData of RTo were read from texts, tables, and figures in all available literature (18 species; Supplementary Tables 1, 2) when measured more than once within a period of darkness in lab- and field studies where measurement temperature, To, was kept constant. The RTo-initial was defined as the initial measurement of RTo for each study/species, and further values of RTo at later points within the same night of the same study were read as well.Apparent- and inherent temperature sensitivities (Q10, Equation 1; Fig. 2b) were obtained from all available literature (ten species; Supplementary Table 2) where in the same study/species, both nocturnal values of Q10,app and of Q10,inh were obtained in response to long-term natural T-changes in the environment during the night (hours) and nocturnal values were obtained in response to short-term artificial T-changes (max 30 min), respectively.Measurements of R
To and Q10 of leaf respirationIn the field (United Kingdom, Denmark, Panama, Colombia and Brazil), RTo (µmol CO2 m−2 s−1) in 16 species (Supplementary Tables 1, 3) was measured through nocturnal periods at constant To (controlled either by block-T or leaf-T) with infra-red gas analysers (Li-Cor-6400(XT) or Li-Cor-6800, Lincoln, Nebraska, USA). Mature, attached leaves positioned in the sunlight throughout the day were chosen. Target [CO2] in the leaf cuvette was set to ambient, ranging from 390 to 410 ppm, depending on when measurements were made, and target RH = 65 ± 10%, with a flow rate of 300 µmol s–1. The RTo-initial was defined as RTo at first measurement after darkness 30 min after sunset (to conservatively avoid light-enhanced dark respiration, LEDR50,51. Leak tests were conducted prior to measurements52. The temporal resolution of measurements varied between every three minutes to once per hour for the different species. Data were subsequently binned in hourly bins.Measurements to derive Q10,inh and Q10,app were conducted in two species in a T-controlled growth cabinet and in six species in the field (Supplementary Table 2), where Q10,inh was measured in response to 10–30 min of artificial changes in T and Q10,app was calculated from measurements of RT in response to T of the environment (growth cabinet or field) at the beginning of the night and again at the end of the night (hours apart).Tree level measurements in whole-tree chambersThe night-time respiratory efflux of the entire above-ground portion (crown and bole) in large growing trees of Eucalyptus tereticornis was measured in whole-tree chambers (WTCs) in Richmond, New South Wales (Australia, (33°36ʹ40ʺS, 150°44ʹ26.5ʺE). The WTCs are large cylindrical structures topped with a cone that enclose a single tree rooted in soil (3.25 m in diameter, 9 m in height, volume of ~53 m3) and under natural sunlight, air temperature and humidity conditions. An automated system measured the net exchange of CO2 between the canopy and the atmosphere within each chamber at 15-min resolution. During the night, we used the direct measurements of CO2 evolution (measured with an infra-red gas analyser; Licor 7000, Li-Cor, Inc., Lincoln, NE)53,54 as a measure of respiration.Due to the high noise-to-signal ratio in the CO2-exchange measurements from this system when analysing the high-resolution temporal variation through each night, we chose to only analyse temporal variation in tree-RT for the nights when tree-RT-initial were amongst the top 10% of CO2-exchange signals for the entire data set. The resulting data spanned 62 nights and included hourly average measurements from three replicate chambers.Data analysis of R
To
Measurements of nocturnal leaf respiration under constant temperature conditions (RTo) were divided by the initial rate of respiration (RTo-initial) at the onset of each night. Hourly means of RTo/RTo-initial were calculated for each leaf replicate to remove measurement noise and reduce bias due to the measurement of some species at more frequent intervals throughout the night. For species with multiple leaf replicates, these hourly means of RTo/RTo-initial were then combined to create hourly averages of RTo/RTo-initial at the species level. For each species, these values were plotted as a function of time to demonstrate how RTo/RTo-initial decreases with time since the onset of darkness, from sunset until sunrise (Supplementary Fig. 1). For each species, hourly means of RTo/RTo-initial plotted as a function of time were linearised by log-transforming data and the slope of the relationship determined. To test whether the slopes of the lines differed significantly within plant functional groups (woody, non-woody), species originating from the same biome (temperate, tropical) or species measured under the same conditions (lab, field), the slopes of the lines for all species from a given functional group, biome or measurement condition were tested pairwise against each other using the slope, standard error and sample size (number of points on the x-axis) for each line and applying a 0.05 cut-off for p values after Bonferroni correction for multiple testing. 11 out of 701 comparisons came out as being significantly different, which is why within-group slope differences were considered to be overall non-significant for this analysis. t-tests were used to test whether the slopes differed between plant functional groups (tree, non-woody), species originating from different biomes (temperate, tropical) and species measured under different environmental conditions (lab, field). In these tests, the degrees of freedom varied according to the different sample sizes. Since RTo/RTo-initial plotted as a function of time always starts at 1, the intercepts do not differ between species. t-tests were performed on linearised power functions by log-transforming data in order to test potential differences between lab and field, origin of species, between woody and non-woody species and between temperate and tropical biomes. Since these functions were statistically indistinguishable in each pairing, all measurements of nocturnal leaf respiration under constant temperature conditions (n = 967 nights, 31 species) were collated into a single plot. The data were binned hourly since some studies had very few measurements on half-hourly steps. A power function was fitted with a weighting of each hourly binned value using 1/(standard error of the mean). The power function was chosen as it, better than the exponential- or linear function, can capture both sudden steep- as well as slower decrease in RTo/RTo-initial in different species. The 95% confidence interval of the power function, following the new model equation, overlaps with all the 95% confidence intervals of the hourly binned values (Fig. 1a). All data analysis, including statistical analysis and figures were performed using Python version 3.9.4.Evaluation of new equationWe performed four sets of simulations (S1-S4) using different representations of leaf and plant respiration as outlined in Supplementary Table 4. Evaluation of Equation 4 (S2; Equation 3 from Fig. 1a merged with Equation 1) in comparison with Equation 1 (S1) and Equation 5 (S4) in comparison with Equation 2 (S3), respectively, for predictions of nocturnal variation in response to natural variation in temperature, was conducted by use of independent sets of leaf level data and tree scale data. The effect of including variable nocturnal RTo is estimated as the difference between S1 and S2 and between S3 and S4, respectively.The first data set used for the evaluation consists of nine broad-leaf species for which spot measurements of leaf respiration under ambient conditions were taken at sunset and before sunrise in the field (Fig. 1b and Supplementary Fig. 2a). Of these nine species, three species (Fig. 1c) were further measured throughout the night at ambient conditions. Further, whole-tree measurements measured throughout the night at ambient conditions (Supplementary Fig. 3a–d) were also used for evaluation. Finally, comparisons of Q10,inh with Q10,app in another ten species were used to test if RTo appeared constant as assumed in Equation 1 (Supplementary Tables 2, 3 and Fig. 2b).To validate the suitability of Equation 4 and Equation 5 over equations with full temporal control, modelled respiration values were compared against observed measurements for three species at the leaf level (Supplementary Fig. 2b–d) and for Eucalyptus tereticornis at the whole-tree level using three chamber replicates and during 62 nights using hourly measurements (Supplementary Fig. 3a–d). Linear fits were applied, using ordinary least squares regressions, to plots of normalised respiration (({R}_{T}/{R}_{{T}_{0}})) predicted by the four models against the observed values. The first measurements of the night were excluded from the fits, as these were necessarily equal to unity. The standardised residuals (S) in Supplementary Figs. 2c, 3b are calculated using the equation ({S}_{i}=({R}_{{{{{{{rm{modelled}}}}}}}_{i}}/{R}_{{{{{{{rm{Modelled}}}}}}}_{0}}-{R}_{{T}_{i}}/{R}_{{T}_{0}})/sqrt{(mathop{sum }nolimits_{i}^{N}{({R}_{{{{{{{rm{modelled}}}}}}}_{i}}/{R}_{{{{{{{rm{Modelled}}}}}}}_{0}}-{R}_{{T}_{i}}/{R}_{{T}_{0}})}^{2})/{df}}), for the residual of the ith measurement, where the sum is over all measurements, df is the number of degrees of freedom, and Rmodelled are the respiration values modelled by the four equations in Supplementary Table 4.Evaluation is done by comparing observed and simulated RT/RT, initial. We evaluate the nocturnal evolution of RT/RT, initial and use (i) one-to-one line figures that include fitted regression line, R2, p value and RMSE, (ii) Taylor diagrams and (iii) use plots of standardised residuals against temperature and hours since darkness for a qualitative assessment of the simulations, to identify whether there are any model biases at specific times or temperatures. Model evaluation, statistical analysis and figures were done using python version 3.9.4.Global scale modelling of plant R and NPP
We applied the novel formulation derived in this study (Equation 4 and Equation 5) to quantify the impact of incorporating variable RTo on simulated plant R and NPP globally using the JULES land surface model32,33 following simulations outlined in Supplementary Table 4.Plant respiration in JULES and simulations for this study: The original leaf respiration representation in JULES follows either eqn 1 ({{R}_{T}={R}_{{T}_{0}}{Q}}_{10}^{(T-{T}_{0})/10}) with Q10 = 2 and To = 25 oC or Equation 1 with an additional denominator ({{R}_{T}={R}_{{T}_{0}}{Q}}_{10}^{(T-{T}_{0})/10}/leftlfloor left(1+{e}^{0.3(T-{T}_{{upp}})}right)times left(1+{e}^{0.3({T}_{{low}}-T)}right)rightrfloor) (Equation 6). For the purpose of this application, we have used Equation 1 to represent leaf respiration in standard JULES simulations. The remaining components of maintenance respiration in JULES, i.e. fine root and wood are represented as a function of leaf to root and leaf to wood nitrogen ratios and leaf respiration rates following RT (β + (Nr + Ns)/Nl) (Equation 6) with RT as leaf respiration, Nr, Ns and Nl as root, stem and leaf Nitrogen content respectively and β as a soil water factor (Equation 42 in ref. 32). This implies that any variation in leaf respiration is passed to root and wood respiration as well30,31,35. Growth respiration is estimated as a fraction (25%) of the difference between GPP and maintenance respiration (Rm) expressed as Rg = 0.25 (GPP-Rm).JULES version 5.2 was modified to simulate leaf and plant respiration using the various descriptions (Equations 1–5) outlined in the modelling protocol in Supplementary Table 4. JULES uses standard astronomical equations to calculate the times of sunrise and sunset on a given day at each grid point. We used the model leaf temperature and RT at the timestep at or immediately preceding sunset to represent Tsunset, and RT,sunset and at every timestep through the night, the time since sunset (h) was updated. We performed global simulations for the period 2000–2018 with JULES, using the global physical configuration GL8, which is an update from GL755. We used WFDEI meteorological forcing data56 available at 0.5-degree spatial resolution and 3-h temporal resolution, and disaggregated and run in JULES with a 15 min timestep. Simulations were performed using nine plant functional types (PFTs)33. To isolate the effects of the new formulation on simulated Rp and NPP from possible impacts on leaf area index (LAI) or vegetation dynamics, we prescribed vegetation phenology via seasonal LAI fields and vegetation fractional cover based on the European Space Agency’s Land Cover Climate Change Initiative (ESA LC_CCI) global vegetation distribution57, processed to the JULES nine PFTs and re-gridded to the WFDEI grid. Annual variable fields of CO2 concentrations are based on annual mean observations from Mauna Loa58. JULES was spun up using the three cycles of the 2000–2018 meteorological forcing data to equilibrate the soil moisture stores. The mean annual output of Rp and NPP over the study period (2000–2018) is computed for all simulations and the effect of the new formulation is presented as the difference between the temporal mean of simulations with and without nocturnal variation in whole plant RTo for NPP and vice versa for Rp and as percentage respect to simulations without nocturnal variation in RTo. Results are presented for grid cells where grid level NPP is >50 g m−2 yr −1 in the standard simulations to avoid excessively large % effects at very low NPP. Output from JULES was analysed and plotted using python version 2.7.16.PermitsNo permit was required in Denmark as measurements were taken in private land (of author) and public land and measurements were non-destructive. Data were collected under the Panama Department of the Environment (current name MiAmbiente) research permit under the name of Dr Kaoru Kitajima. Permit number: SE/P-16-12. Data in Brazil were collected under the minister of Environment (Ministério do Meio Ambiente—MMA), Instituto Chico Mendes de Conservação da Biodiversidade—ICMBio, Sistema de Autorização e Informação em Biodiversidade—SISBIO permit number 47080-3. No permit was required in Colombia as measurements were taken on private land, no plant samples were collected, and trees were part of an existing experiment for which one of the co-authors is the lead. No access permits were required in the UK as they were conducted on the campus of own university plus in their own private garden.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

White, T. D. et al. Pleistocene Homo sapiens from Middle Awash, Ethiopia. Nature 423, 742–747 (2003).Article 

Google Scholar 
McDougall, I., Brown, F. H. & Fleagle, J. G. Stratigraphic placement and age of modern humans from Kibish, Ethiopia. Nature 433, 733–736 (2005).Article 

Google Scholar 
Potts et al. Increased ecological resource variability during a critical transition in hominin evolution. Sci. Adv. 6, eabc8975 (2020).Article 

Google Scholar 
Faith, J. T. et al. Rethinking the ecological drivers of hominin evolution. Trends Ecol. Evol. 36, 797–807 (2021).Article 

Google Scholar 
Mounier, A. & Lahr, M. M. Deciphering African late Middle Pleistocene hominin diversity and the origin of our species. Nat. Commun. 10, 10–13 (2019).Article 

Google Scholar 
Cohen, A. et al. The Hominin Sites and Paleolakes Drilling Project: inferring the environmental context of human evolution from eastern African rift lake deposits. Sci. Drill. 21, 1–16 (2016).Article 

Google Scholar 
Foerster, V. et al. Climatic change recorded in the sediments of the Chew Bahir basin, southern Ethiopia, during the last 45,000 years. Quat. Int. 274, 25–37 (2012).Article 

Google Scholar 
Fischer, M. L. et al. Determining the pace and magnitude of lake level changes in southern Ethiopia over the last 20,000 years using lake balance modeling and SEBAL. Front. Earth Sci. https://doi.org/10.3389/feart.2020.00197 (2020).Roberts, H. M. et al. Using multiple chronometers to establish a long, directly-dated lacustrine record: constraining >600,000 years of environmental change at Chew Bahir, Ethiopia. Quat. Sci. Rev. 266, 107025 (2021).Article 

Google Scholar 
Galway-Witham, J., Cole, J. & Stringer, C. Aspects of human physical and behavioural evolution during the last 1 million years. J. Quat. Sci. https://doi.org/10.1002/jqs.3137 (2019).Bergström, A., Stringer, C., Hajdinjak, M., Scerri, E. M. L. & Skoglund, P. Origins of modern human ancestry. Nature 590, 229–237 (2021).Article 

Google Scholar 
Scerri, E. M. L. et al. Did our species evolve in subdivided populations across Africa, and why does it matter? Trends Ecol. Evol. 33, 582–594 (2018).Article 

Google Scholar 
Schaebitz, F. et al. Hydroclimate changes in eastern Africa over the past 200,000 years may have influenced early human dispersal. Commun. Earth Environ. 2, 1–10 (2021).Article 

Google Scholar 
Lahr, M. M. & Foley, R. A. Towards a theory of modern human origins: geography, demography, and diversity in recent human evolution. Yearb. Phys. Anthropol. 41, 137–176 (1998).Article 

Google Scholar 
Duesing, W. et al. Multiband wavelet age modeling for a 293 m (600 kyr) sediment core from Chew Bahir basin, southern Ethiopian Rift. Front. Earth Sci. 9, 1–15 (2021).Article 

Google Scholar 
Foerster, V. et al. Towards an understanding of climate proxy formation in the Chew Bahir basin, Southern Ethiopian Rift. Palaeogeogr. Palaeoclimatol. Palaeoecol. 501, 111–123 (2018).Article 

Google Scholar 
Kaboth-Bahr, S. et al. Paleo-ENSO influence on African environments and early modern humans. Proc. Natl Acad. Sci. USA 118, e2018277118 (2021).Article 

Google Scholar 
Duesing, W. et al. Changes in the cyclicity and variability of the eastern African paleoclimate over the last 620 kyrs. Quat. Sci. Rev. 273, 107219 (2021).Article 

Google Scholar 
Herbert, T., Cleaveland Peterson, L., Lawrence, K. T. & Liu, Z. Tropical ocean temperatures over the past 3.5 million years. Science 328, 1530–1534 (2010).Article 

Google Scholar 
Trauth, M. H. et al. High- and low-latitude forcing of Plio–Pleistocene East African climate and human evolution. J. Hum. Evol. 53, 475–486 (2007).Article 

Google Scholar 
Bereiter, B. et al. Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present. Geophys. Res. Lett. 42, 542–549 (2015).Article 

Google Scholar 
Clemens, S., Prell, W., Murray, D., Shimmield, G. & Weedon, G. Forcing mechanisms of the Indian Ocean monsoon. Nature 353, 720–725 (1991).Article 

Google Scholar 
Owen, R. B. et al. Progressive aridification in East Africa over the last half million years and implications for human evolution. Proc. Natl Acad. Sci. USA 115, 11174–11179 (2018).Article 

Google Scholar 
Grant, K. M. et al. A 3 million year index for North African humidity/aridity and the implication of potential pan-African humid periods. Quat. Sci. Rev. 171, 100–118 (2017).Article 

Google Scholar 
Trauth, M. H. et al. Recurring types of variability and transitions in the ~620 kyr record of climate change from the Chew Bahir basin, southern Ethiopia. Quat. Sci. Rev. 266, 106777 (2021).Article 

Google Scholar 
Wagner, B. et al. Mediterranean winter rainfall in phase with African monsoons during the past 1.36 million years. Nature 573, 256–260 (2019).Article 

Google Scholar 
Gunz, P. et al. Early modern human diversity suggests subdivided population structure and a complex out-of-Africa scenario. Proc. Natl Acad. Sci. USA 106, 6094–6098 (2009).Article 

Google Scholar 
Pearson, O. M., Royer, D. F., Grine, F. E. & Fleagle, J. G. A description of the Omo I postcranial skeleton, including newly discovered fossils. J. Hum. Evol. 55, 421–437 (2008).Article 

Google Scholar 
Sahle, Y., Morgan, L. E., Braun, D. R., Atnafu, B. & Hutchings, W. K. Chronological and behavioural contexts of the earliest Middle Stone Age in the Gademotta Formation, Main Ethiopian Rift. Quat. Int. 331, 6–19 (2014).Article 

Google Scholar 
McBrearty, S. & Tryon, C. in Transitions Before the Transition: Evolution and Stability in the Middle Paleolithic and Middle Stone Age (eds Hovers, E. & Kuhn, S. L.) Ch. 14 (Springer, 2006).Clark, J. D., Beyene, Y. & WoldeGabriel, G. Stratigraphic, chronological and behavioural contexts of Pleistocene Homo sapiens from Middle Awash, Ethiopia. Nature 423, 747–752 (2003).Article 

Google Scholar 
de la Torre, I., Mora, R., Arroyo, A. & Benito-Calvo, A. Acheulean technological behaviour in the Middle Pleistocene landscape of Mieso (East-Central Ethiopia). J. Hum. Evol. 76, 1–25 (2014).Article 

Google Scholar 
Vidal, C. et al. Age of the oldest known Homo sapiens from eastern Africa. Nature 601, 579–583 (2022).Article 

Google Scholar 
Sahle, Y. et al. in Modern Human Origins and Dispersal (eds. Sahle, Y. et al.) 73–104 (Kerns Verlag, 2019).Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. Phil. Trans. R. Soc. B 359, 183–195 (2004).Article 

Google Scholar 
Brandt, S., Hildebrand, E., Vogelsang, R., Wolfhagen, J. & Wong, H. A new MIS 3 radiocarbon chronology for Mochena Borago Rockshelter, SW Ethiopia: implications for the interpretation of Late Pleistocene chronostratigraphy and human behavior. J. Archaeol. Sci. Rep. 11, 352–369 (2017).
Google Scholar 
Creanza, N., Kolodny, O. & Feldman, M. W. Greater than the sum of its parts? Modelling population contact and interaction of cultural repertoires. J. R. Soc. Interface 14, 20170171 (2017).Article 

Google Scholar 
Derex, M., Perreault, C. & Boyd, R. Divide and conquer: intermediate levels of population fragmentation maximize cultural accumulation. Phil. Trans. R. Soc. B 373, 20170062 (2018).Article 

Google Scholar 
Derex, M. & Mesoudi, A. Cumulative cultural evolution within evolving population structures. Trends Cogn. Sci. 24, 654–667 (2020).Article 

Google Scholar 
Grove, M. Environmental complexity, life history, and encephalisation in human evolution. Biol. Phil. 32, 395–420 (2017).Article 

Google Scholar 
Haidle, M. N. et al. The Nature of Culture: an eight-grade model for the evolution and expansion of cultural capacities in hominins and other animals. J. Anthropol. Sci. 93, 43–70 (2015).
Google Scholar 
Brooks, A. S. et al. Long-distance stone transport and pigment use in the earliest Middle Stone Age. Science 360, 90–94 (2018).Article 

Google Scholar 
Murren, C. J., Julliard, R., Schichting, C. D., & Clobert, J. in Dispersal (eds Clobert, J. et al.) 261–273 (Oxford Univ. Press, 2001).Grove, M. et al. Climatic variability, plasticity, and dispersal: a case study from Lake Tana, Ethiopia. J. Hum. Evol. 87, 32–47 (2015).Article 

Google Scholar 
Hershkovitz, I. et al. The earliest modern humans outside Africa. Science 359, 456–459 (2018).Article 

Google Scholar 
Tryon, C. A. The Middle/Later Stone Age transition and cultural dynamics of Late Pleistocene East Africa. Evol. Anthropol. 28, 267–282 (2019).Article 

Google Scholar 
Grove, M. & Blinkhorn, J. Neural networks differentiate between Middle and Later Stone Age lithic assemblages in eastern Africa. PLoS ONE 15, e0237528–27 (2020).Article 

Google Scholar 
Grove, M. & Blinkhorn, J. Testing the integrity of the Middle and Later Stone Age cultural taxonomic division in eastern Africa. J. Paleolit. Archaeol. 4, 14 (2021).Article 

Google Scholar 
Assefa, Z., Lam, Y. M. & Mienis, H. K. Symbolic use of terrestrial gastropod opercula during the Middle Stone Age at Porc-Epic Cave, Ethiopia. Curr. Anthropol. 49, 746–756 (2008).Article 

Google Scholar 
Assefa, Z. et al. Engraved ostrich eggshell from the Middle Stone Age contexts of Goda Buticha, Ethiopia. J. Archaeol. Sci. Rep. 17, 723–729 (2018).
Google Scholar 
Laskar, J. et al. A long term numerical solution for the insolation quantities of Earth. Astron. Astrophys. 428, 261–285 (2004).Article 

Google Scholar 
Campisano, C. et al. The Hominin Sites and Paleolakes Drilling Project: high-resolution paleoclimate records from the East African Rift System and their implications for understanding the environmental context of hominin evolution. PaleoAnthropology https://doi.org/10.1130/abs/2017am-295426 (2017).Davidson, A. The Omo River Project: Reconnaissance Geology and Geochemistry of Parts of Ilubabor, Kefa, Gemu Gofa and Sidamo (Ethiopian Institute of Geological Surverys, 1983).Noren, A. HSPDP-CHB_public. OSF https://doi.org/10.17605/OSF.IO/M8QU5 (2022).Gebregiorgis, D. et al. Modern sedimentation and authigenic mineral formation in the Chew Bahir basin, southern Ethiopia: implications for interpretation of late Quaternary paleoclimate records. Front. Earth Sci. 9, 244 (2021).Article 

Google Scholar 
Folk, R. L. & Ward, W. C. Brazos River bar [Texas]: a study in the significance of grain size parameters. J. Sediment. Res. 27, 3–26 (1957).Article 

Google Scholar 
Blott, S. J. & Pye, K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf. Process. Landf. 26, 1237–1248 (2001).Article 

Google Scholar 
Opitz, S. et al. Holocene lake stages and thermokarst dynamics in a discontinuous permafrost affected region, north-eastern Tibetan Plateau. J. Asian Earth Sci. 76, 85–94 (2013).Article 

Google Scholar 
Opitz, S. et al. Spatio-temporal pattern of detrital clay-mineral supply to a lake system on the north-eastern Tibetan Plateau, and its relationship to late Quaternary paleoenvironmental changes. Catena 137, 203–218 (2016).Article 

Google Scholar 
Viehberg, F. A. et al. Environmental change during MIS 4 and MIS 3 opened corridors in the Horn of Africa for Homo sapiens expansion. Quat. Sci. Rev. 202, 139–153 (2018).Article 

Google Scholar 
Koerting, F. et al. Drill core mineral analysis by means of the hyper spectral imaging spectrometer HySpex, XRD and ASD in proximity of the Mytina Maar, Czech Republic. Int. Arch. Photogram. Remote Sens. Spatial Inf. XL-1/W5, 417–424 (2015).Giosan, L., Flood, R. D. & Aller, R. C. Paleoceanographic significance of sediment color on western North Atlantic drifts: I. Origin of color. Mar. Geol. 189, 25–41 (2002).Article 

Google Scholar 
Weltje, G. J. et al. in Micro-XRF Studies of Sediment Cores (eds Croudace, I. W. & Rothwell, R. G.) 507–534 (Springer, 2015).Löwemark, L. et al. Normalizing XRF-scanner data: a cautionary note on the interpretation of high resolution records from organic-rich lakes. J. Asian Earth Sci. 40, 1250–1256 (2011).Article 

Google Scholar 
Lyle, M. et al. Data report: raw and normalized elemental data along the Site U1338 splice from X-ray fluorescence scanning. Proc. Integr. Ocean Drill. Program 320/321, 1–19 (2012).
Google Scholar 
Schlolaut, G. et al. An extended and revised Lake Suigetsu varve chronology from ∼50 to ∼10 ka bp based on detailed sediment micro-facies analyses. Quat. Sci. Rev. 200, 351–366 (2018).Article 

Google Scholar 
Surdam, R. C. & Eugster, H. P. Mineral reactions in the sedimentary deposits of the Lake Magadi region, Kenya. Geol. Soc. Am. Bull. 87, 1739–1752 (1976).Article 

Google Scholar 
Davies, S. J., Lamb, H. F. & Roberts, S. J. in Micro-XRF Studies of Sediment Cores (eds Croudace, I. W. & Rothwell, R. G.) 189–226 (Springer, 2015).Elbert, E. et al. Late Holocene air temperature variability reconstructed from sediments of Laguna Escondida, Patagonia, Chile (45° 30’ S). Palaeogeogr. Palaeoclimatol. Palaeoecol. 369, 482–492 (2013).Article 

Google Scholar 
Trauth, M. H., Larrasoaña, J. C. & Mudelsee, M. Trends, rhythms and events in Plio–Pleistocene African climate. Quat. Sci. Rev. 28, 399–411 (2009).Article 

Google Scholar 
Trauth, M. H. et al. Abrupt or gradual? Change point analysis of the Late Pleistocene–Holocene climate record from Chew Bahir, southern Ethiopia. Quat. Res. 90, 321–330 (2018).Article 

Google Scholar  More

Jiang, D., Klaus, S., Zhang, Y.-P., Hillis, D. M. & Li, J.-T. Asymmetric biotic interchange across the Bering land bridge between Eurasia and North America. Natl Sci. Rev. 6, 739–745 (2019).Article 

Google Scholar 
Bailey, G. et al. in The Archaeology of Europe’s Drowned Landscapes (eds Bailey, G. et al.) 189–219 (Springer, 2020).Erlandson, J., Braje, T., Gill, K. & Graham, M. Ecology of the kelp highway: did marine resources facilitate human dispersal from northeast Asia to the Americas? J. Isl. Coast. Archaeol. 10, 392–411 (2015).Article 

Google Scholar 
McLaren, D. et al. Terminal Pleistocene epoch human footprints from the Pacific coast of Canada. PLoS ONE 13, e0193522 (2018).Article 

Google Scholar 
Woodward, J. C. The Ice Age: A Very Short Introduction (Oxford Univ. Press, 2014).Pettitt, P. & White, M. The British Palaeolithic: Human Societies at the Edge of the Pleistocene World (Routledge, 2012).Bell, M. Prehistoric Coastal Communities: The Mesolithic in Western Britain (Council for British Archaeology, 2007).Gaffney, V., Fitch, S. & Smith, D. Europe’s Lost World: The Rediscovery of Doggerland (Council for British Archaeology, 2009).Gutiérrez–Zugasti, I. et al. Shell midden research in Atlantic Europe: state of the art, research problems and perspectives for the future. Quat. Int. 239, 70–85 (2011).Article 

Google Scholar 
Milner, N., Craig, O.E. & Bailey, G. N. Shell Middens in Atlantic Europe (Oxbow Books, 2007).Neto de Carvalho, C. et al. First tracks of newborn straight-tusked elephants (Palaeoloxodon antiquus). Sci. Rep. 11, 17311 (2021).CAS 
Article 

Google Scholar 
Ashton, N. et al. Hominin footprints from Early Pleistocene deposits at Happisburgh, UK. PLoS ONE 9, e88329 (2014).Article 

Google Scholar 
Duveau, J., Berillon, G. & Verna, C. in Reading Prehistoric Human Tracks: Methods and Material (eds Pastoors, A. & Tilman, L.) 183–200 (Springer, 2021).Allen, J. R. L. Subfossil mammalian tracks (Flandrian) in the Severn Estuary, S.W. Britain: mechanics of formation, preservation and distribution. Philos. Trans. R. Soc. B 352, 481–518 (1997).Article 

Google Scholar 
Aldhouse-Green, S. H. R. et al. Prehistoric footprints from the Severn Estuary at Uskmouth and Magor Pill, Gwent, Wales. Archaeol. Cambrensis 141, 14–55 (1992).
Google Scholar 
Barr, K. & Bell, M. Neolithic and Bronze Age ungulate footprint-tracks of the Severn Estuary: species, age, identification and the interpretation of husbandry practices. Environ. Archaeol. 22, 1–14 (2016).Article 

Google Scholar 
Bennett, M. R. & Morse, S. A. Human Footprints: Fossilised Locomotion? (Springer, 2014).Polton, J. A., Palmer, M. R. & Howarth, M. J. Physical and dynamical oceanography of Liverpool Bay. Ocean Dynam. 91, 1421–1439 (2011).Article 

Google Scholar 
Roberts, G. Ephemeral, subfossil mammalian, avian and hominid footprints within Flandrian sediment exposures at Formby Point, Sefton Coast, North West England. Ichnos 16, 33–48 (2009).Article 

Google Scholar 
Burns, A. An 8000-Year Record of Prehistoric Footprints in a Dynamic Coastal Landscape, Formby Point, UK. PhD thesis, Univ. Manchester (2019).Tooley, M. J. Sea level changes in Northern England. Proc. Geol. Assoc. 93, 43–51 (1982).Article 

Google Scholar 
Pye, K. & Neal, A. in The Dynamics and Environmental context of Aeolian Sedimentary Systems (ed. Pye, K.) 201–217 (Geological Society Special Publication, 1993).Pye, K., Stokes, S. & Neal, A. Optical dating of aeolian sediments from the Sefton Coast, Northwest England. Proc. Geol. Assoc. 106, 281–292 (1995).Article 

Google Scholar 
Reimer, P. J. et al. The IntCal20 Northern Hemisphere radiocarbon calibration curve (0–55 cal kBP). Radiocarbon 62, 725–757 (2020).CAS 
Article 

Google Scholar 
Walker, M. et al. Subdividing the Holocene Series/Epoch: formalisation of stages/ages and subseries/subepochs, and designation of GSSPs and auxiliary stratotypes. J. Quat. Sci. 34, 173–186 (2019).Article 

Google Scholar 
Tooley, M. J. The peat beds of the southwest Lancashire coast. Nat. Lancs. 1, 19–21 (1970).
Google Scholar 
Gonzalez, S., Huddart, D. & Roberts, G. Holocene development of the Sefton coast: a multidisciplinary approach to understanding the archaeology. In Archaeological Sciences 1995 Proc. Conference on the Application of Scientific Techniques to the Study of Archaeology (eds Sinclair, A. et al.) 289–299 (Oxbow Books, 1997).Huddart, D., Roberts, G. & Gonzalez, S. Holocene human and animal footprints and their relationships with coastal environmental change, Formby Point, NW England. Quat. Int. 55, 29–41 (1999).Article 

Google Scholar 
Galbraith, H. et al. Global climate change and sea level rise: potential losses of intertidal habitat for shorebirds. Waterbirds 25, 173–183 (2002).Article 

Google Scholar 
Bellard, C., Leclerc, C. & Courchamp, F. Sea level rise and insular hotspots. Glob. Ecol. Biogeogr. 23, 203–212 (2014).Article 

Google Scholar 
Editorial Why biodiversity matters. Nat. Ecol. Evol. 1, 0042 (2017).Article 

Google Scholar 
Hall, J. G. A comparative analysis of the habitat of the extinct aurochs and other prehistoric mammals in Britain. Ecography 31, 187–190 (2008).Article 

Google Scholar 
Milner, N., Conneller, C. & Taylor, B. Star Carr: A Persistent Place in a Changing World Vol. 1 (White Rose Univ. Press, 2018).Conneller, C. The Mesolithic in Britain: Landscape and Society in Times of Change (Routledge, 2022).Hernandez, L. & Laundre, J. W. Foraging in the ‘landscape of fear’ and its implications for habitat use and diet quality of elk (Cervus elaphus) and bison (Bison bison). Wildl. Biol. 11, 215–220 (2005).Article 

Google Scholar 
Overton, N. J. in Multispecies Archaeology (ed. Pilaar-Birch, S.) 295–309 (Routledge, 2018).Moore, E. K., Britton, A. J., Iason, G., Pemberton, J. & Pakeman, R. J. Landscape-scale vegetation patterns influence small-scale grazing impacts. Biol. Conserv. 192, 218–225 (2015).Article 

Google Scholar 
Gonzalez, S. & Huddart, D. in The Quaternary of Northern England (eds Huddart, D. & Glasser, N. F.) 582–588 (Joint Nature Conservation Committee, 2002).Cowell, R. W. & Innes, J. The Wetlands of Merseyside (Lancaster Univ. Archaeological Unit, 1994).Leonard, P. B. et al. Landscape connectivity losses due to sea level rise and land use change. Anim. Conserv. 20, 80–90 (2017).Article 

Google Scholar 
Myers, N. et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
Article 

Google Scholar 
Stuart, A. J. in Island Britain: a Quaternary Perspective (ed. Preece, R. C.) 111–125 (Geological Society Special Publication, 1995).Maroo, S. & Yalden, D. W. The Mesolithic mammal fauna of Great Britain. Mammal. Rev. 30, 243–248 (2000).Article 

Google Scholar 
Yalden, D. W. The History of British Mammals (T. & A.D. Poyser, 1999).Barnett, R. The Missing Lynx: The Past and Future of Britain’s Lost Mammals (Bloomsbury, 2019).Crees, J. J., Carbone, C., Sommer, R. S., Benecke, N. & Turvey, S. T. Millennial-scale faunal record reveals differential resilience of European large mammals to human impacts across the Holocene. Proc. R. Soc. B 283, 20152152 (2016).Article 

Google Scholar 
Burns, A. in Reading Prehistoric Human Tracks (eds Pastoors, A. & Lenssen-Erz, T.) 295–315 (Springer, 2021).Brown, R., Lawrence, M. & Pope, J. Animals: Tracks, Trails and Signs (Octopus Publishing, 2004).Donovan, S. K. Animal and bird tracks. Ichnos 16, 238–238 (2009).Article 

Google Scholar 
Roberts, G., Gonzalez, S. & Huddart, D. Intertidal Holocene footprints and their archaeological significance. Antiquity 70, 647–651 (1996).Article 

Google Scholar 
Scales, R. in Prehistoric Coastal Communities: The Mesolithic in Western Britain (ed. Bell, M.) 139–159 (Council for British Archaeology, 2007).Robbins, L. M. Estimating height and weight from size of footprints. J. Forensic Sci. 31, 143–152 (1986).CAS 
Article 

Google Scholar 
Stuiver, M. & Polach, H. A. Reporting of C-14 data—discussion. Radiocarbon 19, 355–363 (1977).Article 

Google Scholar  More

Nansen, C., Phillips, T. W., Parajuleeb, M. N. & Franqui, R. A. Comparison of direct and indirect sampling procedures for Plodia interpunctella in a maize storage facility. J. Stored Prod. Res. 40, 151–168 (2004).Article 

Google Scholar 
Gerken, A. R. & Campbell, J. F. Using long-term capture data to predict Trogoderma variabile Ballion and Plodia interpunctella (Hübner) population patterns. Insects 10, 93. https://doi.org/10.3390/insects10040093 (2019).Article 
PubMed Central 

Google Scholar 
Athanassiou, C. G. & Buchelos, C. T. Grain properties and insect distribution trends in silos of wheat. J. Stored Prod Res. 88, 101632 (2020).Article 

Google Scholar 
Campbell, J., Mullen, M. & Dowdy, A. Monitoring stored-product pests in food processing plants with pheromone trapping, contour mapping, and mark-recapture. J. Econ. Entomol. 95, 1089–1101 (2002).CAS 
PubMed 
Article 

Google Scholar 
Arbogast, R. T., Weaver, D. K., Kendra, P. E. & Brenner, R. J. Implications of spatial distribution of insect populations in storage ecosystems. Environ. Entomol. 27, 202–216 (1998).Article 

Google Scholar 
Brenner, R. J., Focks, D. A., Arbogast, R. T., Weaver, D. K. & Shuman, D. Practical use of spatial analysis in precision targeting for integrated pest management. Am. Entomol. 44, 79–102 (1998).Article 

Google Scholar 
Arbogast, R. T., Kendra, P. E., Mankin, R. W. & McGovern, J. E. Monitoring insect pests in retail stores by trapping and spatial analysis. J. Econ. Entomol. 93, 1531–1542 (2000).CAS 
PubMed 
Article 

Google Scholar 
Arthur, F. & Phillips, T.W. Stored-product insect pest management and control. In Food Plant Sanitation; Hui, Y.H., Bruinsma, B.L., Gorham, J.R., Nip, W.-K., Tong, P.S., Ventresca, P., Eds.; Marcel Dekker, Inc, pp. 341–348(2003).Campbell, J. F., Toews, M. D., Arthur, F. H. & Arbogast, R. T. Long-term monitoring of Tribolium castaneum in two flour mills: Seasonal patterns and impact of fumigation. J. Econ. Entomol. 103, 991–1001 (2010).PubMed 
Article 

Google Scholar 
Doud, C. W. & Phillips, T. W. Activity of Plodia interpunctella (Lepidoptera: Pyralidae) in and around flour mills. J. Econ. Entomol. 93, 1842–1847 (2000).CAS 
PubMed 
Article 

Google Scholar 
Campbell, J. & Mullen, M. Distribution and dispersal behavior of Trogoderma variabile and Plodia interpunctella outside a food processing plant. J. Econ. Entomol. 97, 1455–1464 (2004).CAS 
PubMed 
Article 

Google Scholar 
Larson, Z., Subramanyam, B. & Herrman, T. Stored-product insects associated with eight feed mills in the Midwestern United States. J. Econ. Entomol. 101, 998–1005 (2008).PubMed 
Article 

Google Scholar 
Trematerra, P., Paula, M. C., Sciarretta, A. & Lazzari, S. Spatio-temporal analysis of insect pests infesting a paddy rice storage facility. Neotrop. Entomol. 33, 469–479 (2004).Article 

Google Scholar 
Arthur, F. H., Campbell, J. F. & Toews, M. D. Distribution, abundance, and seasonal patterns of Plodia interpunctella (Hübner) in a commercial food storage facility. J. Stored Prod. Res. 53, 7–14 (2013).Article 

Google Scholar 
McKay, T., White, A. L., Starkus, L. A., Arthur, F. H. & Campbell, J. F. Seasonal patterns of stored-product insects at a rice mill. J. Econ. Entomol. 110, 1366–1376 (2017).PubMed 
Article 

Google Scholar 
Roesli, R., Subramanyam, B., Fairchild, F. J. & Behnke, K. C. Trap catches of stored-product insects before and after heat treatment in a pilot feed mill. J. Stored Prod. Res. 39, 521–540 (2003).Article 

Google Scholar 
Campbell, J., Ching’oma, G.M., Toews, M.D. & Ramaswamy, S. Spatial distribution and movement patterns of stored-product insects. In Proceedings of the 9th International Working Conference on Stored Product Protection, Campinas, Sao Paulo, Brazil, 15–18 October 2006; Lorini, I., Bacaltchuk, B., Beckel, H., Deckers, D., Sundfeld, E., Santos, J.P.D., Biagi, J.D., Celaro, J.C., Faroni, L.R.D., Bortolini, L.D.F., Eds.; Brazilian Post-harvest Association—ABRAPOS: Passo Fundo, RS, Brazil, p. 18 (2006).Trematerra, P., Gentile, P., Brunetti, A., Collins, L. & Chambers, J. Spatio-temporal analysis of trap catches of Tribolium confusum du Val in a semolina-mill, with a comparison of female and male distributions. J. Stored Prod. Res. 43, 315–322 (2007).Article 

Google Scholar 
Semeao, A. A., Campbell, J. F., Whitworth, R. J. & Sloderbeck, P. E. Influence of environmental and physical factors on capture of Tribolium castaneum (Coleoptera: Tenebrionidae) in a flour mill. J. Econ. Entomol. 105, 686–702 (2012).PubMed 
Article 

Google Scholar 
Campbell, J.F., Perez-Mendoza, J. &Weier, J. Insect Pest Management Decisions in Food Processing Facilities. In Stored Product Protection; Hagstrum, D.W., Phillips, T.W., Cuperus, G., Eds.; Kansas State University, pp. 219–232 (2012).Mohandass, S., Arthur, F. H., Zhu, K. & Throne, J. E. Biology and management of Plodia interpunctella (Lepidoptera:Pyralidae) in stored products. J. Stored Prod. Res. 43, 302–311 (2007).Article 

Google Scholar 
Hamlin, J.C., Reed, W.D. & Phillips, M.E. Biology of the Indianmeal Moth on Dried Fruits in California. USDA Technical Bulletin No. 242, (1931)Hagstrum, D.W. & Subramanyam, B. Review of Stored-Product Insect Resource. AACC International (2009).Soderstrom, T., Stoica, P. & Trulsson, E. Instrumental variable methods for closed loop systems. IFAC 10th Triennial World Congress, Munich, FRG. pp. 363–368(1987).Johnson, J. A., Valero, K. A., Hannel, M. M. & Gill, R. F. Seasonal occurrence of post harvest dried fruit insects and their parasitoids in a culled fig warehouse. J. Econ. Entomol. 93, 1380–1390 (2000).CAS 
PubMed 
Article 

Google Scholar 
Nansen, C., Subramanyam, B. & Roesli, R. Characterizing spatial distribution of trap captures of beetles in retail pet stores using SADIE® software. J. Stored Prod. Res. 40, 471–483 (2004).Article 

Google Scholar 
Phillips, T.W., Berbert, R.C. &Cuperus, G.W. Post-harvest integrated pest management. In: Francis, F.J. (Ed.), Encyclopedia of Food Science and Technology. 2nd ed. Wiley Inc., pp. 2690–2701(2000).Phillips,T.W., Cogan, P.M. & Fadamiro, H.Y. Pheromones. In: Subramanyam, B., Hagstrum, D.W. (Eds.), Alternatives to Pesticides in Stored-product IPM. Kluwer Academic Publishers, pp. 273–302 (2000).Mullen, M. A. & Dowdy, A. K. A pheromone-baited trap for monitoring the Indian meal moth, Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae). J. Stored Prod. Res. 37, 231–235 (2001).PubMed 
Article 

Google Scholar 
Nansen, C. & Phillips, T. W. Ovipositional responses of the Indian meal moth, Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) to oils. Ann. Entomol. Soc. Am. 96, 524–531 (2003).Article 

Google Scholar 
Hagstrum, D. W. Using five sampling methods to measure insect distribution and abundance in bins storing wheat. J. Stored Prod. Res. 36, 253–262 (2000).CAS 
PubMed 
Article 

Google Scholar 
Athanassiou, C. G., Kavallieratos, N. G., Sciarretta, A. & Trematerra, P. Mating disruption of Ephestia kuehniella (Zeller) (Lepidoptera: Pyralidae) in a storage facility: spatio-temporal distribution changed after long-term application. J. Stored Prod. Res. 67, 1–12 (2016).Article 

Google Scholar 
Lee, W. H., Jung, J. M., Kim, J., Lee, H. & Jung, S. Analysis of the spatial distribution and dispersion of Plodia interpunctella (Lepidoptera: Pyralidae) in South Korea. J. Stored Prod. Res. 86, 101577 (2020).Article 

Google Scholar 
Gerken, A.R. & Campbell, J.F. Spatial and temporal variation in stored-product insect pest distributions and implications for pest management in processing and storage facilities. Ann. Entomol. Soc. Am. saab049(2021).Athanassiou, C. G. & Buchelos, CTh. Detection of stored-wheat beetle species and estimation of population density using unbaited probe traps and grain trier samples. Ent. Exp. et Applic. 98, 67–78 (2001).Article 

Google Scholar 
Subramanyam, B. & Hagstrum, D.W. Sampling. In: Subramanyam B. & Hagstrum D.W. (eds), Integrated Management of Insects in Stored Products. Marcel Dekker Inc., pp. 135–193 (1995).Morrison, W. R. et al. Aeration to manage insects in wheat stored in the Balkan peninsula: Computer simulations using historical weather data. Agronomy 10, 1927 (2020).Article 

Google Scholar 
Toews, M. D., Campbell, J. F. & Arthur, F. H. Temporal dynamics and response to fogging or fumigation of stored-product Coleoptera in a grain processing facility. J. Stored Prod. Res. 42, 480–498 (2006).Article 

Google Scholar 
Buckman, K. A., Campbell, J. F. & Subramanyam, B. Tribolium castaneum (Coleoptera: Tenebrionidae) associated with rice mills: Fumigation efficacy and population rebound. J. Econ. Entomol. 106, 499–512 (2013).PubMed 
Article 

Google Scholar 
Campbell, J. F., Buckman, K. A., Fields, P. G. & Subramanyam, Bh. Evaluation of structural treatment efficacy against Tribolium castaneum and Tribolium confusum (Coleoptera: Tenebrionidae) using meta-analysis of multiple studies conducted in food facilities. J. Econ. Entomol. 108, 2125–2140 (2015).CAS 
PubMed 
Article 

Google Scholar 
Levene, H. Robust tests for equality of variances. In Ingram Olkin; Harold Hotelling; et al. (eds.). Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Stanford University Press. pp. 278–292(1960).SAS Institute. SAS/STAT 9.2 User’s guide. SAS Institute (2008).Taylor, L. R. Aggregation, variance and mean. Nature 189, 732–735 (1961).ADS 
Article 

Google Scholar 
Iwao, S. A new method of sequential sampling to classify populations according to a critical density. Res. Popln. Ecol. 16, 281–288 (1975).
Google Scholar 
Green, R. H. Measurement of non-randomness in spatial distribution. Res. Popln. Ecol. 8, 1–17 (1966).
Google Scholar 
Hillhouse, T. L. & Pitre, H. N. Comparison of sampling techniques to obtain measurements of insect populations on soybeans. J. Econ. Entomol. 67, 411–414 (1974).Article 

Google Scholar 
Cassie, R. M. Frequency distribution models in the ecology of plankton and other organisms. J. Anim. Ecol. 31, 65–92 (1962).Article 

Google Scholar 
Southwood, T. R. E. Ecological Methods, with Particular Reference to the Study of Insect Population (Chapman and Hall, 1995).
Google Scholar 
Costa, M. G., Barbosa, J. C., Yamamoto, P. T. & Leal, R. M. Spatial distribution of Diaphorina citri Kuwayama (Hemiptera: Psyllidae) in citrus orchards. Scientia Agric 67, 546–554 (2010).Article 

Google Scholar 
Patil, G. P. & Stiteler, W. M. Concepts of aggregation and their quantification: A critical review with some new result and applications. Pop. Ecol. 15, 238–254 (1974).Article 

Google Scholar 
David, F. N. & Moor, P. G. Notes on contagious distribution in plant populations. Ann. Bot. 18, 47–53 (1954).Article 

Google Scholar 
Lloyd, M. Mean crowding. J. Anim. Ecol. 36, 1–30 (1967).Article 

Google Scholar 
Southwood, T. R. E. & Henderson, P. A. Ecological Methods 3rd edn. (Blackwell Sciences, 2000).
Google Scholar 
Feng, M. G. & Nowierski, R. M. Spatial distribution and sampling plans for four species of cereal aphids (Homoptera: Aphididae) infesting spring wheat in southwestern Idaho. J. Econ. Entomol. 85, 830–837 (1992).Article 

Google Scholar  More