Philippa Kaur

Liu, W. et al. African origin of the malaria parasite Plasmodium vivax. Nat. Commun. 5, 3346 (2014).PubMed 

Google Scholar 
Liu, W. et al. Multigenomic delineation of Plasmodium species of the Laverania subgenus infecting wild-living chimpanzees and gorillas. Genome Biol. Evolution 8, 1929–1939 (2016).CAS 

Google Scholar 
Liu, W. et al. Single genome amplification and direct amplicon sequencing of Plasmodium spp. DNA from ape fecal specimens. Protocol Exchange 1–14 (2010).Liu, W. et al. Wild bonobos host geographically restricted malaria parasites including a putative new Laverania species. Nat. Commun. 8, 1635 (2017).PubMed 
PubMed Central 

Google Scholar 
Prugnolle, F. et al. African great apes are natural hosts of multiple related malaria species, including Plasmodium falciparum. Proc. Natl Acad. Sci. USA 107, 1458–1463 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharp, P. M., Plenderleith, L. J. & Hahn, B. H. Ape origins of human malaria. Annu. Rev. Microbiol. 74, 39–63 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, W. et al. Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature 467, 420–425 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Otto, T. D. et al. Genomes of all known members of a Plasmodium subgenus reveal paths to virulent human malaria. Nat. Microbiol. 3, 687–697 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boundenga, L. et al. Diversity of malaria parasites in great apes in Gabon. Malar. J. 14, 1–8 (2015).CAS 

Google Scholar 
Délicat-Loembet, L. et al. No evidence for ape Plasmodium infections in humans in gabon. Plos One 10, e0126933 (2015).PubMed 
PubMed Central 

Google Scholar 
Sundararaman, S. A. et al. Plasmodium falciparum-like parasites infecting wild apes in southern Cameroon do not represent a recurrent source of human malaria. Proc. Natl Acad. Sci. USA 110, 7020–7025 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Junker, J. et al. Recent decline in suitable environmental conditions for African great apes. Diversity Distrib. 18, 1077–1091 (2012).
Google Scholar 
de Nys, H. M. et al. Age-related effects on malaria parasite infection in wild chimpanzees. Biol. Lett. 9, 20121160 (2013).PubMed 
PubMed Central 

Google Scholar 
de Nys, H. M. et al. Malaria parasite detection increases during pregnancy in wild chimpanzees. Malar. J. 13, 413 (2014).PubMed 
PubMed Central 

Google Scholar 
Kaiser, M. et al. Wild chimpanzees infected with 5 Plasmodium species. Emerg. Infect. Dis. 16, 1956–1959 (2010).PubMed 
PubMed Central 

Google Scholar 
Paupy, C. et al. Anopheles moucheti and Anopheles vinckei are candidate vectors of ape Plasmodium parasites, including Plasmodium praefalciparum in Gabon. PLoS ONE 8, e57294 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl Acad. Sci. USA 113, 5329–5334 (2016).Loy, D. E. et al. Investigating zoonotic infection barriers to ape Plasmodium parasites using faecal DNA analysis. Int. J. Parasitol. 48, 531–542 (2018).Martin, M., Rayner, J., Gagneux, P., Barnwell, J. & Varki, A. Evolution of human–chimpanzee differences in malaria susceptibility: Relationship to human genetic loss of N-glycolylneuraminic acid. Proc. Natl Acad. Sci. USA 102, 12819–12824 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Scully, E. J., Kanjee, U. & Duraisingh, M. T. Molecular interactions governing host-specificity of blood stage malaria parasites. Curr. Opin. Microbiol. 40, 21–31 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sundararaman, S. A. et al. Genomes of cryptic chimpanzee Plasmodium species reveal key evolutionary events leading to human malaria. Nat. Commun. 7, 11078 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wanaguru, M., Liu, W., Hahn, B. H., Rayner, J. C. & Wright, G. J. RH5-Basigin interaction plays a major role in the host tropism of Plasmodium falciparum. Proc. Natl Acad. Sci. USA 110, 20735–20740 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ngoubangoye, B. et al. The host specificity of ape malaria parasites can be broken in confined environments. Int. J. Parasitol. 46, 737–744 (2016).PubMed 

Google Scholar 
Mapua, M. I. et al. A comparative molecular survey of malaria prevalence among Eastern chimpanzee populations in Issa Valley (Tanzania) and Kalinzu (Uganda). Malar. J. 15, 423 (2016).PubMed 
PubMed Central 

Google Scholar 
Wu, D. F. et al. Seasonal and inter-annual variation of malaria parasite detection in wild chimpanzees. Malar. J. 17, 1–5 (2018).CAS 

Google Scholar 
Craig, M., le Sueur, D. & Snow, B. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol. Today 15, 105–111 (1999).CAS 
PubMed 

Google Scholar 
Mordecai, E. A. et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol. Lett. 16, 22–30 (2013).PubMed 

Google Scholar 
Paaijmans, K. P. et al. Influence of climate on malaria transmission depends on daily temperature variation. Proc. Natl Acad. Sci. USA 107, 15135–15139 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Parham, P. E. & Michael, E. Modeling the effects of weather and climate change on malaria transmission. Environ. Health Perspect. 118, 620–626 (2010).PubMed 

Google Scholar 
LaPointe, D. A., Goff, M. L. & Atkinson, C. T. Thermal constraints to the sporogonic development and altitudinal distribution of avian malaria Plasmodium relictum in Hawai’i. J. Parasitol. 96, 318–324 (2010).PubMed 

Google Scholar 
Vanderberg, J. P. & Yoeli, M. Effects of temperature on sporogonic development of Plasmodium berghei. J. Parasitol. 52, 559–564 (1966).Macdonald, G. The Epidemiology and Control of Malaria (Oxford University Press, 1957).Ryan, S. J. et al. Mapping physiological suitability limits for malaria in Africa under climate change. Vector-Borne Zoonotic Dis. 15, 718–725 (2015).PubMed 
PubMed Central 

Google Scholar 
Gemperli, A. et al. Mapping malaria transmission in West and Central Africa. Tropical Med. Int. Health 11, 1032–1046 (2006).
Google Scholar 
Gething, P. W. et al. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasites Vectors 4, 92 (2011).PubMed 
PubMed Central 

Google Scholar 
Weiss, D. J. et al. Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000–2012: a high-resolution spatiotemporal prediction. Malar. J. 13, 171 (2014).PubMed 
PubMed Central 

Google Scholar 
Lyons, C. L., Coetzee, M. & Chown, S. L. Stable and fluctuating temperature effects on the development rate and survival of two malaria vectors, Anopheles arabiensis and Anopheles funestus. Parasites Vectors 6, 104 (2013).PubMed 
PubMed Central 

Google Scholar 
Paaijmans, K. P., Wandago, M. O., Githeko, A. K. & Takken, W. Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS One 2, e1146 (2007).PubMed 
PubMed Central 

Google Scholar 
Faust, C. & Dobson, A. P. Primate malarias: diversity, distribution and insights for zoonotic Plasmodium. One Health 1, 66–75 (2015).PubMed 
PubMed Central 

Google Scholar 
Tucker Lima, J. M., Vittor, A., Rifai, S. & Valle, D. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 372, 20160125 (2017).
Google Scholar 
Borner, J. et al. Phylogeny of haemosporidian blood parasites revealed by a multi-gene approach. Mol. Phylogenetics Evolution 94, 221–231 (2016).CAS 

Google Scholar 
Emery Thompson, M., Muller, M. N., Machanda, Z. P., Otali, E. & Wrangham, R. W. The Kibale Chimpanzee Project: over thirty years of research, conservation, and change. Biol. Conserv. 252, 108857 (2020).
Google Scholar 
Langergraber, K. E., Mitani, J. C. & Vigilant, L. The limited impact of kinship on cooperation in wild chimpanzees. Proc. Natl Acad. Sci. USA 104, 7786–7790 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arandjelovic, M. et al. Two-step multiplex polymerase chain reaction improves the speed and accuracy of genotyping using DNA from noninvasive and museum samples. Mol. Ecol. Resour. 9, 28–36 (2009).CAS 
PubMed 

Google Scholar 
Herbert, A. et al. Malaria-like symptoms associated with a natural Plasmodium reichenowi infection in a chimpanzee. Malar. J. 14, 220 (2015).PubMed 
PubMed Central 

Google Scholar 
Torres, J. R. Therapy of Infectious Diseases 597–613 (2003).Trampuz, A., Jereb, M., Muzlovic, I. & Prabhu, R. M. Clinical review: severe malaria. Crit. Care 7, 315 (2003).PubMed 
PubMed Central 

Google Scholar 
Akim, N. I. et al. Dynamics of P. falciparum gametocytemia in symptomatic patients in an area of intense perennial transmission in Tanzania. Am. J. Tropical Med. Hyg. 63, 199–203 (2000).CAS 

Google Scholar 
Mackinnon, M. J. & Read, A. F. Genetic relationships between parasite virulence and transmission in the rodent malaria Plasmodium chabaudi. Evolution 53, 689–703 (1999).PubMed 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
PubMed 

Google Scholar 
Prugnolle, F. et al. African monkeys are infected by Plasmodium falciparum nonhuman primate-specific strains. Proc. Natl Acad. Sci. USA 108, 11948–11953 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ayouba, A. et al. Ubiquitous Hepatocystis infections, but no evidence of Plasmodium falciparum-like malaria parasites in wild greater spot-nosed monkeys (Cercopithecus nictitans). Int. J. Parasitol. 42, 709–713 (2012).PubMed 
PubMed Central 

Google Scholar 
Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogenetics Evolution 47, 261–273 (2008).CAS 

Google Scholar 
Thurber, M. I. et al. Co-infection and cross-species transmission of divergent Hepatocystis lineages in a wild African primate community. Int. J. Parasitol. 43, 613–619 (2013).PubMed 
PubMed Central 

Google Scholar 
Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics (Cambridge University Press, 2008).Stanisic, D. I. et al. Acquisition of antibodies against Plasmodium falciparum merozoites and malaria immunity in young children and the influence of age, force of infection, and magnitude of response. Infect. Immun. 83, 646–660 (2015).PubMed 
PubMed Central 

Google Scholar 
Taylor, R. R., Allen, S. J., Greenwood, B. M. & Riley, E. M. IgG3 antibodies to Plasmodium falciparum merozoite surface protein 2 (MSP2): increasing prevalence with age and association with clinical immunity to malaria. Am. J. Tropical Med. Hyg. 58, 406–413 (1998).CAS 

Google Scholar 
World Malaria Report (World Health Organization, 2015).Shaman, J. Letter to the Editor: Caution needed when using gridded meteorological data products for analyses in Africa. Eur. Surveill. 19, 20930 (2014).
Google Scholar 
Tatem, A. J., Goetz, S. J. & Hay, S. I. Terra and Aqua: new data for epidemiology and public health. Int. J. Appl. Earth Observation Geoinf. 6, 33–46 (2004).
Google Scholar 
Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167 (2003).
Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 

Google Scholar 
Carter, R. & Mendis, K. N. Evolutionary and historical aspects of the burden of malaria. Clin. Microbiol. Rev. 15, 564–594 (2002).PubMed 
PubMed Central 

Google Scholar 
Kwiatkowski, D. P. How malaria has affected the human genome and what human genetics can teach us about malaria. Am. J. Hum. Genet. 77, 171–192 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tarello, W. A fatal Plasmodium reichenowi infection in a chimpanzee? Rev. de. Med. Veterinaire 156, 503–505 (2005).
Google Scholar 
Taylor, D. W. et al. Parasitologic and immunologic studies of experimental Plasmodium falciparum infection in nonsplenectomized chimpanzees (Pan troglodytes). Am. J. Tropical Med. Hyg. 34, 36–44 (1985).CAS 

Google Scholar 
Krief, S., Martin, M., Grellier, P., Kasenene, J. & Sevenet, T. Novel antimalarial compounds isolated in a survey of self-medicative behavior of wild chimpanzees in Uganda. Antimicrobial Agents Chemother. 48, 3196–3199 (2004).CAS 

Google Scholar 
Cox-Singh, J. et al. Plasmodium knowlesi malaria in humans is widely distributed and potentially life threatening. Clin. Infect. Dis. 46, 165–171 (2008).CAS 
PubMed 

Google Scholar 
Singh, B. & Daneshvar, C. Human infections and detection of Plasmodium knowlesi. Clin. Microbiol. Rev. 26, 165–184 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brasil, P. et al. Outbreak of human malaria caused by Plasmodium simium in the Atlantic Forest in Rio de Janeiro: a molecular epidemiological investigation. Lancet Global Health 5, e1038–e1046 (2017).Krief, S. et al. On the diversity of malaria parasites in African apes and the origin of Plasmodium falciparum from bonobos. PLoS Pathog. 6, e1000765 (2010).Pacheco, M. A., Cranfield, M., Cameron, K. & Escalante, A. A. Malarial parasite diversity in chimpanzees: the value of comparative approaches to ascertain the evolution of Plasmodium falciparum antigens. Malar. J. 12, 328 (2013).PubMed 
PubMed Central 

Google Scholar 
Etienne, L. et al. Noninvasive follow-up of simian immunodeficiency virus infection in wild-living nonhabituated western lowland gorillas in Cameroon. J. Virol. 86, 9760–9772 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Keele, B. F. et al. Chimpanzee reservoirs of pandemic and nonpandemic HIV-1. Science 313, 523–526 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Keele, B. F. et al. Increased mortality and AIDS-like immunopathology in wild chimpanzees infected with SIVcpz. Nature 460, 515–519 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. et al. Eastern chimpanzees, but not bonobos, represent a simian immunodeficiency virus reservoir. J. Virol. 86, 10776–10791 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neel, C. et al. Molecular epidemiology of simian immunodeficiency virus infection in wild-living gorillas. J. Virol. 84, 1464–1476 (2010).CAS 
PubMed 

Google Scholar 
Rudicell, R. S. et al. Impact of simian immunodeficiency virus infection on chimpanzee population dynamics. PLoS Pathog. 6, 1–17 (2010).
Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bates, D. & Maechler, M. Lme4: linear mixed-effects models using s4 classes. Cran R Project Website (2010). 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

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

Glass eels collection (Anguilla anguilla, L.)Three hundred European glass eels ( 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

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

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