Philippa Kaur

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

Nussey, D. H., Froy, H., Lemaitre, J. F., Gaillard, J. M. & Austad, S. N. Senescence in natural populations of animals: Widespread evidence and its implications for bio-gerontology. Ageing Res. Rev. 12, 214–225 (2013).Article 

Google Scholar 
Bouwhuis, S., Choquet, R., Sheldon, B. C. & Verhulst, S. The forms and fitness cost of senescence: Age-specific recapture, survival, reproduction, and reproductive value in a wild bird population. Am. Nat. 179, E15–E27 (2011).Article 

Google Scholar 
Lemaître, J.-F. et al. Early-late life trade-offs and the evolution of ageing in the wild. Proc. Biol. Sci. 282, 20150209 (2015).PubMed 
PubMed Central 

Google Scholar 
Millon, A., Petty, S. J. & Lambin, X. Pulsed resources affect the timing of first breeding and lifetime reproductive success of tawny owls. J. Anim. Ecol. 79, 426–435 (2010).CAS 
Article 

Google Scholar 
Newton, I. & Rothery, P. Senescence and reproductive value in Sparrowhawks. Ecology 78, 1000–1008 (1997).Article 

Google Scholar 
Boonekamp, J. J., Salomons, M., Bouwhuis, S., Dijkstra, C. & Verhulst, S. Reproductive effort accelerates actuarial senescence in wild birds: An experimental study. Ecol. Lett. 17, 599–605 (2014).Article 

Google Scholar 
Péron, G., Gimenez, O., Charmantier, A., Gaillard, J.-M. & Crochet, P.-A. Age at the onset of senescence in birds and mammals is predicted by early-life performance. Proc. R. Soc. B Biol. Sci. 277, 2849–2856 (2010).Article 

Google Scholar 
Pyle, P., Nur, N., Sydeman, W. J. & Emslie, S. D. Cost of reproduction and the evolution of deferred breeding in the western gull. Behav. Ecol. 8, 140–147 (1997).Article 

Google Scholar 
Reid, J. M., Bignal, E. M., Bignal, S., McCracken, D. I. & Monaghan, P. Age specific reproductive performance in red-billed chough Pyrrhocorax pyrrhocorax: Patterns and processes in a natural population. J. Anim. Ecol. 72, 765–776 (2003).Article 

Google Scholar 
Kim, S. Y., Velando, A., Torres, R. & Drummond, H. Effects of recruiting age on senescence, lifespan and lifetime reproductive success in a long-lived seabird. Oecologia 166, 615–626 (2011).ADS 
Article 

Google Scholar 
Nussey, D. H. et al. Environmental conditions in early life influence ageing rates in a wild population of red deer. Curr. Biol. 17, 1–18 (2007).Article 

Google Scholar 
Cam, E. & Monnat, J. Y. Apparent inferiority of first-time breeders in the kittiwake: The role of heterogeneity among age classes. J. Anim. Ecol. 69, 380–394 (2000).Article 

Google Scholar 
Newton, I., McGrady, M. J. & Oli, M. K. A review of survival estimates for raptors and owls. Ibis (Lond. 1859). 158, 227–248 (2016).Clutton-Brock, T. H. Reproductive success: Studies of individual variation in contrasting breeding systems. (The university of Chicago Press, 1988).Ringsby, T. H., Sæther, B. & Solberg, E. J. Factors affecting juvenile survival in house sparrow passer domesticus. J. Avian Biol. 29, 241–247 (1998).Article 

Google Scholar 
Verhulst, S. & Nilsson, J.-A. The timing of birds’ breeding seasons: A review of experiments that manipulated timing of breeding. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 363, 399–410 (2008).Article 

Google Scholar 
Crawley, M. J. The R Book. (John Wiley & Sons, Ltd, 2007). https://doi.org/10.1002/9780470515075Zabala, J. & Zuberogoitia, I. Breeding performance and survival in the peregrine falcon Falco peregrinus support an age-related competence improvement hypothesis mediated via an age threshold. J. Avian Biol. 46, 141–150 (2015).Article 

Google Scholar 
Forslund, P. & Pärt, T. Age and reproduction in birds–hypotheses and tests. Trends Ecol. Evol. 10, 374–378 (1995).CAS 
Article 

Google Scholar 
Millon, A., Petty, S. J., Little, B. & Lambin, X. Natal conditions alter age-specific reproduction but not survival or senescence in a long-lived bird of prey. J. Anim. Ecol. 80, 968–975 (2011).Article 

Google Scholar 
Sergio, F. et al. Variation in age-structured vital rates of a long-lived raptor: Implications for population growth. Basic Appl. Ecol. 12, 107–115 (2010).Article 

Google Scholar 
Murgatroyd, M. et al. Sex-specific patterns of reproductive senescence in a long-lived reintroduced raptor. J. Anim. Ecol. 87, 1587–1599 (2018).Article 

Google Scholar 
Sumasgutner, P., Koeslag, A. & Amar, A. Senescence in the city: Exploring ageing patterns of a long-lived raptor across an urban gradient. J. Avian Biol. 50, 1–14 (2019).Article 

Google Scholar 
Nielsen, J. T. & Drachmann, J. Age-dependent reproductive performance in Northern Goshawks Accipiter gentilis. Ibis (Lond. 1859). 145, 1–8 (2003).Zuberogoitia, I. et al. Population trends of Peregrine Falcon in Northern Spain–results of a long-term monitoring project. Ornis Hungarica 26, 51–68 (2018).Article 

Google Scholar 
Macdonald, D. W. The ecology of carnivore social behaviour. Nature 301, 379–384 (1983).ADS 
Article 

Google Scholar 
Sergio, F. & Boto, A. Nest dispersion, diet, and breeding success of Black Kites (Milvus migrans) in the Italian pre-Alps. J. Raptor Res. 33, 207–217 (1999).
Google Scholar 
Sergio, F. & Newton, I. Occupancy as a measure of habitat quality. J. Anim. Ecol. 72, 857–865 (2003).Article 

Google Scholar 
Millon, A. et al. Dampening prey cycle overrides the impact of climate change on predator population dynamics: A long-term demographic study on tawny owls. Glob. Chang. Biol. 20, 1770–1781 (2014).ADS 
Article 

Google Scholar 
Krüger, O. Long-term demographic analysis in goshawk accipiter gentilis: The role of density dependence and stochasticity. Oecologia 152, 459–471 (2007).ADS 
Article 

Google Scholar 
Oro, D., Hernández, N., Jover, L. & Genovart, M. From recruitment to senescence: Food shapes the age-dependent pattern of breeding performance in a long-lived bird. Ecology 95, 446–457 (2014).Article 

Google Scholar 
Froy, H., Phillips, R. A., Wood, A. G., Nussey, D. H. & Lewis, S. Age-related variation in reproductive traits in the wandering albatross: Evidence for terminal improvement following senescence. Ecol. Lett. 16, 642–649 (2013).Article 

Google Scholar 
McCleery, R. H., Perrins, C. M., Sheldon, B. C. & Charmantier, A. Age-specific reproduction in a long-lived species- the combined effects of senescence and individual quality. Proc. R. Soc. B 275, 963–970 (2008).CAS 
Article 

Google Scholar 
Dixon, A. et al. Seasonal variation in gonad physiology indicates juvenile breeding in the Saker Falcon (Falco cherrug). Avian Biol. Res. 14, 39–47 (2021).Article 

Google Scholar 
Newton, I. & Mearns, R. Population ecology of peregrines in South Scotland. in Peregrine falcon populations. Their management and recovery. (eds. Cade, T. J., Enbderson, J. H., Thelander, C. G. & White, C. M.) 651–665 (The Peregrine Fund Inc., 1988).Brommer, J. E., Pietiäinen, H. & Kolunen, H. The effect of age at first breeding on Ural owl lifetime reproductive success and fitness under cyclic food conditions. J. Anim. Ecol. 67, 359–369 (1998).Article 

Google Scholar 
Zuberogoitia, I., Martínez, J. E., González-Oreja, J. A., Calvo, J. F. & Zabala, J. The relationship between brood size and prey selection in a Peregrine Falcon population located in a strategic region on the Western European Flyway. J. Ornithol. 154, 73–82 (2013).Article 

Google Scholar 
Zuberogoitia, I., Martínez, J. E. & Zabala, J. Individual recognition of territorial peregrine falcons Falco peregrinus : A key for long-term monitoring programmes. Munibe Ciencias Nat. 61, 117–127 (2013).
Google Scholar 
Zabala, J. & Zuberogoitia, I. Individual quality explains variation in reproductive success better than territory quality in a long-lived territorial raptor. PLoS ONE 9, e90254 (2014).ADS 
Article 

Google Scholar 
Zuberogoitia, I., Zabala, J. & Martínez, J. E. Moult in birds of prey: A review of current knowledge and future challenges for research. Ardeola 65, 183–207 (2018).Article 

Google Scholar 
McDonald, T. L. & White, G. C. A comparison of regression models for small counts. J. Wildl. Manage. 74, 514–521 (2010).Article 

Google Scholar 
Zabala, J. et al. Accounting for food availability reveals contaminant-induced breeding impairment, food-modulated contaminant effects, and endpoint-specificity of exposure indicators in free ranging avian populations. Sci. Total Environ. 791, 148322 (2021).ADS 
CAS 
Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach. (Springer-Verlag, 2002).Toms, J. D. & Lesperance, M. L. Piecewise regresion: A tool for identifying ecological tresholds. Ecology 84, 2034–2041 (2003).Article 

Google Scholar 
Van De Pol, M. & Verhulst, S. Age–dependent traits: A new statistical model to separate within–and between–individual effects. Am. Nat. 167, 766–773 (2006).Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, 2009).Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. (Springer New York, 2009). https://doi.org/10.1007/978-0-387-87458-6Therneau, T. A Package for Survival Analysis in S. (2014). 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

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

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

Structural characterization of slag samplesThe FTIR spectra of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) are presented in Fig. 1.Figure 1FTIR spectra of slag samples.Full size imageBy analysing the spectrum (detailed figure) in the range of 700–1100 cm−1, it can be found that there are obvious absorption peaks in the spectrum of all the slag samples. The granulated blast furnace slag shows the characteristic absorption bands at 3640, 1418, 980, 944, 861, 753 and 710 cm−1. The band at 3640 cm−1 is assigned to the stretching vibration of the hydroxyl group originated from the weakly absorbed water molecules on the slag surface24. The characteristic absorption bands at 1418, 861 and 710 cm−1 are ascribed to the asymmetric stretching mode and bending mode of carbonate group, respectively and the band at 980 cm−1 are attributable to the stretching vibrations of Si–O25. The band at 944 and 752 cm−1 represent the internal vibration of [SiO4]4− and [AlO4]5− tetrahedral and comes from Si (Al)–O-antisymmetric stretching vibration26.The different vibration modes for the sample of waste slag can be observed in the FTIR spectrum. The absorption bands shown are at 1418, 873, 712, 667 and 419 cm−1. The peak at 1418 cm−1 is assigned to the asymmetric stretching mode and bending mode of carbonate group. Calcite phase is confirmed by characteristic peaks at 712 cm−1 (ʋ2 out of plane bending vibration of the CO3−2 ion) and 873 cm−1 peak (ʋ2 split in-plane bending vibrations of the CO3−2 ion27. Calcium aluminate phase is identified by characteristic peak at 419 cm−128. Peak around 667 cm−1 is described as absorption band for different M–O (metal oxide) such as Al–O, Fe–O, Mg–O etc.29.In the case of combination of both 50% granulated blast furnace slag and 50% waste slag dumped in landfill the intensity of absorption peaks is smaller in comparison with Sample 1 and Sample 2 of slag. The characteristic absorption peaks (978 and 753 cm−1) which correspond with characteristic peaks of Sample 1 are shifted compared to the Sample 1, assigned to the stretching vibrations of Si–O and to the Si (Al)–O-antisymmetric stretching vibration, respectively, can provide important evidence of chemical interaction between Sample 1 and Sample 2. The decrease of the intensity of the bands appearing at 875 and 709 cm−1 cans be attributed to overlapping the vibrations of the CO3−2 ion from calcite phase.Figure 2 presents the SEM micrographs of the slag samples (Sample 1–3). One can see the characteristic morphology- the sizes and the forms of the slag samples.Figure 2SEM images of slag samples.Full size imageAt larger magnifications it can be observed that the surface is rough and uneven, and one can notice rounded grain-like rugged formations. The slag samples display aggregated particles with average diameter of a few microns. Also, in these rounded formations it can be seen different morphologies like spheres, rods, boards specific each compound/phase from metallurgical slags.Figure 3 illustrates the EDX elemental analysis of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3).Figure 3EDX elemental map of slag samples.Full size imageOne can observe that the predominant elements in the examined area are constated in carbon, oxygen, calcium, and iron, confirming the FTIR spectra.Figure 4 shows EDX spectra of slag samples recorded on different selected punctual area, to obtain more information about the elemental composition of specific areas. For all the tested slag samples have similar elements content.Figure 4EDX spectra analysis of slag samples.Full size imageThe selected punctual areas are highlighted thus: the spheric structure are with yellow line and the structure like boards are with green line for all the analysed slag samples. In the case of Sample 1 for both structures the values of chemical elements present are similar and the silicon has a higher value at spheric structure which can be correlated with the presence of silica (SiO2). The higher content of calcium reveals that the Sample 1 is blast furnace slag dominated by calcium and silicon compositions. In the case of slag dumped in landfill (Sample 2) the content of carbon increase for both structures and some chemical elements like titanium, barium, manganese doesn`t appear in EDX spectra and the explanation for this phenomenon is that the slag was dumped in landfill for more than 30 years. One can observe for combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) that the values of all the chemical elements for both spheric and board-like structure are between the first two samples, confirming the FTIR spectra regarding chemical interaction between Sample 1.XRD patterns of the slag samples with the phases identified are shown in Fig. 5. Sample 1 show minor peaks of free CaO and MgO, which may be deleterious and cause reduction in strength. The phases and amorphous contents of the Sample 1 granulated blast furnace slag are broadly consistent with literature30. Sample 3 of slag consists of crystalline phase – Ca2Mg2SiO7, Ca2Fe2AlO5, CaCO3 and CaO as observed by the XRD analysis. In terms of the relations of phase thermal equilibrium, the compounds identified form an isomorphic series of melilites that is specific to basic metallurgical slags.Figure 5X-ray diffraction patterns of slag samples.Full size imageIn Table 1 are presented the values expressed as ppm of chemical element detected in slag samples (Sample 1, 2 and 3).Table 1 XRF analysis of the slag samples.Full size tableThe results show a large quantity of calcium in all three samples of slag. Also, the elements detected such as Fe, Al, Mg and Si are in accordance with XRD spectra.Physical–chemical characterization of soil-slag mixturesThe chemical composition of the major elements that compound the soil, soil- slag and slag samples was determined by XRF. The values expressed as ppm of chemical elements are presented in Table 2. In the case of soil sample the content of the main constituents is iron, titanium, manganese, and potentially toxic elements (PTE) such as arsenic, zinc, copper, and cobalt. For soil-slag 1 with weight ratio soil: slag (1:1) it can be observed the disappearance of the potentially toxic elements (PTE) founded in soil sample and the decrease of concentration value of zinc. When the weight ratio of slag increases at 3 (soil-slag 2 sample) the values of main component increased in accordance with values of slag sample, but in the case of soil-slag 3 sample where the weight ratio of soil is bigger (3) it can be observed the cobalt presence. Based on these XRF results we can say that take place an elimination of potentially toxic elements in contaminated soil by applying slag in a bigger proportion.Table 2 XRF analysis of the soil-slag samples.Full size tableWith the aid of a pH meter, CONSORT C 533 the important parameters of soil and slag solutions were measured as: the pH, conductivity, and the salinity, as shown in Table 3. The data presented in Table 3 suggest that the soil sampled has the pH = 5.2 corresponding to a medium acid soil, which does not sustain a high fertility and is not able to offer proper conditions for crops. Also, the pH of soil has important influence on soil fertility, decreases the availability of essential elements and the activity of soil microorganisms which can determine calcium and magnesium deficiency in plants and decreases phosphorous availability. The pH value of slag solution (12.5) corresponds as strongly basic character which is beneficial in amelioration process of acidic soils and the presence of this type of slag sustain the improving of soil characteristics, too. For the soil-slag samples the pH value increase with the increasing of the weight ratio of slag and the mixtures soil-slag obtained can be framed into the category of weakly alkaline soils.Table 3 The physical–chemical characteristics of soil and slag solutions.Full size tableThe data given in Table 4 show that the humidity of soil is bigger and decreases in soil-slag samples with adding of slag content. The values of total soil-slag porosity are between 40 and 50% and depends on the density and apparent density of the soil being influenced by the mineralogical composition, the content of organic matter and the degree of compaction and loosening of the soil, the crystalline structure of soil minerals.Table 4 The physical–chemical characteristics of soil-slag samples.Full size tableConsidering the structural and morphological characterization of the investigated slag samples we propose a recipe of blast furnace slag and of waste slag dumped in landfill in accordance with the waste directive 2008/98/EC regarding the strategic goal of EU to a complete elimination of the disposal of wastes. The slag dump of Steel Plant of Galati has an enormous quantity of unused waste slag which may be mixed with granulated blast furnace slag, to save the natural resources used as raw materials in the metallurgical technological process.The presence of Ca2+ in the composition of the slag can maintain high alkalinity in the soil for a long time in the natural environment. The alkaline pH of the soil may contribute to a decrease the available concentration of heavy metals by reducing metal mobility and bonding metals into more stable fractions. One of the objectives of this research is improving the quality of the environment by using the mixture between two different slags on agricultural lands and reintroducing them in the agricultural centre, especially in acid soils. Acidic soils are characterized by an acidic pH that has spread in recent years due to excessive fertilizers or far too aggressive work31. The production is significantly influenced, and the treatment of acid soils is usually done using a series of natural materials (lime, dolomite), the consumption being approx. 20 t/hectare depending on the acidity of the soil and the nature of the plants grown on the respective surfaces.Our research consists in improving the characteristics and qualities of the acidic soils and helping to reintroduce it into the agricultural circuit by transforming a waste into a new material friendly-environmental, the mixture of blast furnace slag and waste slag dumped in landfill. 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

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