Ecology

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Birds and housing62 males and 8 females of house sparrows were caught with mist nets in September and October 2019 in several sites in Kraków, Poland. Before releasing them to the outdoor aviary located on the campus of the Jagiellonian University, Kraków, Poland, each bird was weighed and banded with a metal band. The aviary measured 3.5 m in width, 10.0 m in length, 2.5 m in height, and was outfitted with trees, bushes, perches, wooden shelters, a water source, and food dishes. Initially, birds were maintained with water and a mixture of seeds: wheat, barley, millet, and sunflower seeds, provided ad libitum. Additionally, they had access to sand with shells and sepia.Experimental designAfter a few weeks of acclimation to captivity, the aviary was divided into two separate parts (3.5 × 5 m): aviary no. 1 (A1) and aviary no. 2 (A2). At the same time male individuals were assigned to two crossed experimental treatments, ensuring that in each aviary birds originated from all sampled populations. The experiment comprised of two different treatments conducted simultaneously—one designed to simulate a deficiency in an environmental factor influencing colouration (the quality of available food), the other—to introduce physiological stress and facilitate trade-offs in the allocation of resources limited by the first treatment (an immune response induced by a bacteria-derived compound, S1).The dietary manipulation was achieved by feeding one group of birds with a low-quality protein food (diet reduced in exogenous amino acids, namely phenylalanine and tyrosine content, which are precursors essential for melanin synthesis; PT-reduced diet), and the other one with a wholesome diet (control diet). At the same time, two levels of immune challenge were achieved within each dietary group, by injecting half of the birds with either lipopolysaccharide (LPS) from the cell wall of Escherichia coli, or a 0.9% saline vehicle (as a control). Four females were placed in each group of males to alleviate interspecific conflicts occurring in all-male sparrow flocks, but they did not take part in the experiments. After three weeks of experiment, birds housed in A1 were moved to A2, whereas birds from A2 were moved to A1.Immune challengeBefore receiving injections, birds were first weighed and then transferred from the outdoor aviary to the laboratory. 31 house sparrows (from both dietary groups) were injected intraperitoneally with 0.026 mg LPS (serotype O55:B5, Sigma-Aldrich) diluted in 0.1 mL of 0.9% saline vehicle, so that each bird received a dose of ca. 1 mg/kg body mass, which had previously been shown to induce sickness behaviour in another passerine, the white-crowned sparrow, Zonotrichia leucophrys55. 31 control males were injected with the same volume (0.1 mL) of 0.9% saline vehicle. All individuals were injected twice throughout the experiment with an interval of three weeks between the injections. Birds were always injected at the same time in the morning and early afternoon (between 9:00 am and 12:30 pm).Diet manipulationDuring the six weeks of the experiment (S1), birds received synthetic diet ad libitum, which constituted of a mixture of protein (WPC80, free amino acids and whey protein isolate BiPRO GMP 9000 (Agropur Inc., Appleton, USA)), fats, carbohydrates, and fiber30. The ingredients were thoroughly mixed to produce small pellets (6 mm in diameter) that the sparrows consumed readily. The experimental diet had phenylalanine and tyrosine at 42% (N = 32) of their level in the control diet (N = 30)30. The food pellets were prepared by ZooLab (zoolab.pl/en/home, Sędziszów, Poland). Each bird was weighed before and after the experiment to monitor potential effects of diet on body mass of each animal. Following the experiment, during the next three consecutive days, the amount of food consumed by passerines within every 24 h (starting from 10 am each day to 10 am next day) was noted for both compartments of the aviary. Because of different numbers of individuals per aviary, an overall weight of food consumed in A1 and A2 was calculated per individual, respectively.Feathers samplingMoult of the black bib feathers was stimulated at the end of the moulting period occurring in natural conditions in early November. At day 1 of the dietary/immunological experiment (S1) a small area of the bib (around 25 mm2) was plucked from each male sparrow held in A1. At day 2 the same procedure was performed on individuals from A2. The time difference is orders of magnitude smaller than the timescale of feather growth and hence it would not affect the results in any way.Because the feather growth rate may differ during melanogenesis, with consequences for final colouration (if feathers grow at a faster rate, pigments may be deposited over a larger surface and therefore result in less intense colouration56, we measured the rate of feather development during the course of the experiment. After three weeks of the experiment, three feathers from the upper, central, and lower region of the previously plucked bib were plucked once again. The mass of the collected feathers was determined to the nearest 0.01 mg (XP26 Micro Balance, Mettler-Toledo, Greinfensee, Switzerland). The experiment was completed after six weeks after fully regrown and developed feathers from the bib and PC2 were sampled the second time (S1). Three feathers from the central part of previously plucked bib region were collected to perform transmission electron microscopy (TEM) imaging, whereas the feathers obtained from the rest of the regrown bib area were subjected to electron paramagnetic resonance (EPR) spectroscopy and feather microstructure analyses (greater spatial density of melanized barbs or barbules may affect colouration17.Feathers measurementsReflectance measurementsAn USB4000 spectrophotometer (range 300–700 nm) with the PX-2 Pulsed Xenon Lamp (Ocean Optics, Dunedin, FL, USA) and a bifurcated probe with 7 × 400 μm optical fibres, equipped with a permanently attached 3 mm long black collar, was used to quantify the brightness of the bib feathers collected at the end of the experiment. The measurements were taken with 90 ms integration time and the probe held at 90° to a feather’s surface. Calibration measurements of a Spectralon white standard (Ocean Optics. Largo, FL, USA) were taken every 15 min during measurements. The order in which the samples were measured was randomized in terms of belonging to the experimental group. From each sample (N = 62), seven feathers were chosen and stacked in one pile on a piece of black paper. Ten reflectance measurements were taken on each pile, avoiding distal, brighter parts of the feathers. The obtained spectra were averaged and smoothed in the package ‘pavo’57. Brightness was calculated as a sum of the reflectance values over all wavelengths of a spectrum, and its lower values were interpreted as those indicative of a more melanin-rich feathers (i.e., absorbing more light).Feather developmentEach feather (3 per individual; N = 62 individuals) was laid on a white card and covered by a microscope slide to flatten the naturally curved feathers. Digital photographs were taken using camera (Canon EOS 7D) and imported to ImageJ v1.52a Software (National Institutes of Health, USA). The lengths of fully developed and undeveloped (still in sheath) parts of each feather were measured. To estimate the degree of a feather’s development, the length of the developed part of the vane was divided by its total length (quill with rachis plus the developed vane, Fig. 4A).Figure 4House sparrow feathers sampled from bib after three weeks of the experiment. Feathers during development (A), a TEM cross-sections of feather sampled from bib after the experiment (B).Full size imageFeather densityBarb density measurements were performed on the sampled regrown black bib feathers (N = 2–3 for each individual; N = 62 individuals), but because of their sparser structure we calculated the number of non-down (i.e., rigid) barbs on both sides of the vane, and divided this number by two (to obtain an average single-sided number of barbs) and then by the length of the rachis.Melanosome density (TEM)Feathers sampled from the bib of male sparrows (N = 62) were fixed for transmission electron microscopy (TEM) analysis in a mixture of 0.25 M sodium hydroxide and 0.1% Tween for 20 to 30 min on a bench-top shaker. Next, the feathers were treated with formic acid and ethanol in the ratio of 2:3 for 2.5 h and dehydrated twice for 20 min in 100% ethanol. Samples were embedded in a mixture of the PolyBed 812 resin (20 ml), DDSA (9 ml), NMA (12 ml) and DMP-30 (0.82 ml). Resin infiltration was gradual from 15% resin content in ethanol through 50%, 70% to 100% without alcohol. Each step lasted for 24 h. Then, the feathers were placed in silicone embedding moulds (Agar Scientific) and transferred to an oven. The polymerization proceeded at the temperature of 60 °C for 16 h. The epoxy resin blocks were then trimmed to get rid of excess resin. The surface of each block was prepared by its trimming, starting from the end of the feather, to approximately 5 mm using a glass knife. Next, ultrathin sections (70 nm) were cut with a diamond knife (DIATOME A. G., Berno, Switzerland) on a microtome (UC7, Leica, Wetzlar, Germany) and collected on single slot grids coated with a formvar film. The sections were then contrasted in uranyl acetate and lead citrate for 3 min. They were viewed and photographed with a transmission electron microscope (TEM) JEOL 2100HT (Jeol Ltd, Tokyo, Japan) for the purpose of investigating the number and density of the embedded pigment granules. For each individual three photographs of the cross-sections from a similar feather region were selected. Melanosome density was measured as the number of melanin granules observed in the barb cross-section divided by its area. Images were analysed using Adobe Photoshop (cross-sections area) and ImageJ (number of melanosomes, Fig. 4B).Melanin content: electron paramagnetic resonance (EPR) spectroscopyQuality and quantity of melanin pigments58 in individual feather samples obtained from the bib of house sparrows (N = 57) were characterized using a Varian E3 spectrometer (Varian, Sunnyvale, LA, USA) equipped with a rectangular resonance (TE 102) cavity. Five milligrams of feathers per individual were placed inside the Wilmad finger quartz dewar WG-816-Q (Rototec-Spintec GmbH, Griesheim, Germany). Prior to inserting the vessel into the resonance cavity of the EPR spectrometer, feathers were pressed down the quartz finger to a height of approximately 0.5 cm to ensure comparable volumes of each sample. Measurements were performed at room temperature, at X-band (9.26–9.27 GHz frequency), using the following parameters: magnetic field range 3240–3340 Gs, microwave power 1 mW, modulation frequency 100 kHz, modulation amplitude and time constant—5 Gs and 0.3 s for quantitative analysis, 1 Gs and 0.1 s for qualitative analysis. An EPR signal was recorded as its first derivative, averaged from three consecutive scans, lasting 160 s each (giving a total of 480 s of scan time per EPR spectrum). Then, the following parameters were measured: peak-to-peak amplitude, area under the microwave absorption curve (the integral intensity of the recorded signal) and linewidth of the EPR absorption curve (ΔH;59).Statistical analysesStatistical analysis was performed in R (version 4.0.2,60) using a two-way ANOVA test, with bird’s diet (control vs. PT-reduced) and applied immune challenges (LPS vs. saline-injections) as the independent variables. The following parameters were used as the dependent variables: feathers reflectance (brightness), feather growth rate, feather density (number of barbs per mm), and melanisation level (expressed as the EPR spectrum amplitude measured in arbitrary units [a.u.]). The density of melanosomes was analysed by fitting a linear mixed-effects model. In this model, melanosome density was used as the dependent variable, with diet, immunological challenge, and slice ID as independent variables, and individual ID as a random-effect term. Additionally, to assess the reliability of measurements, the intraclass correlation coefficient (i.e., technical repeatability) was calculated. The models’ residuals were checked for normality and homoscedasticity. Mean food consumption per individual was analysed by the Friedman test. Body mass before and after the experiment was analysed by fitting a linear mixed-effect model. Body mass was used as the dependent variable, whereas diet, immunological challenge, and time as the independent variables, and individual ID as a random-effect term. The model included the following interaction terms: time × diet, time × injection, and diet × injection, and was reduced by removing the non-significant interactions. Results are reported with appropriate statistical tests and estimates (accompanied by standard errors) signifying relevant factor contrasts (relative to the reference group, which in all analyses was diet: control; injection: LPS, body mass: before experiment).
Ethical noteAll applicable national and institutional guidelines for the care and use of animals were followed. The research was performed under permit no. 25/2019 (with a supplementary permit no. 78/2020) from the 2nd Local Institutional Animal Care and Use Committee in Kraków. More

RESEARCH BRIEFINGS
02 November 2022

The world’s largest tropical peatland complex is in the central Congo Basin. A drying of the climate between 5,000 and 2,000 years ago triggered decomposition of peat in the Congo Basin and emission of carbon into the atmosphere. The tipping point at which drought results in carbon release might accelerate future climate change if regional droughts become more common. More

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COVID variant family expandsSince the Omicron variant of SARS-CoV-2 emerged in late 2021, it has spawned a series of subvariants that have sparked global waves of infection. In the past few months, scientists have identified more than a dozen extra subvariants to watch. There are so many that they’re being called a swarm, or ‘variant soup’. BQ.1.1 (a descendant of BQ.1) and XBB seem to be rising to the top, possibly because they have many mutations in a key region of the viral spike protein called the receptor binding domain, which is required to infect cells.

Source: NextStrain

The variants near youIn Europe and North America, SARS-CoV-2 variants in the BQ.1 family are rising quickly and are likely to drive infection waves as these regions enter winter. They are also a common ingredient of the variant soup in South Africa, Nigeria and elsewhere in Africa. XBB, by contrast, looks likely to dominate infections in Asia; it recently drove a wave of infections in Singapore.

Source: Moritz Gerstung, Cov-Spectrum.org and GISAID

Money worries for science studentsEighty-five per cent of graduate students who responded to a Nature survey are worried about the increasing cost of living, and 25% are concerned about their growing student debt. Forty-five per cent said that rising inflation could cause them to reconsider whether to continue their science studies. The survey involved more than 3,200 self-selected PhD and master’s students from around the world.

How species suffer in heatwavesEven a small temperature rise has a severe effect on animal mortality, and understanding this relationship is important for predicting the effects of heatwaves caused by climate change. A paper in Nature used published data to examine how changes in temperature affect the rate of biological processes, such as movement or metabolism, at permissive temperatures — those at which species function normally. They also looked at how higher, stressful temperatures affect the rate of heat failure (irreversible heat injuries that result in death). This graph shows that rising temperatures drive a very rapid increase in heat-failure rates in frogs and molluscs. These high sensitivities suggest that when there is no way to escape hot conditions, species can quickly succumb. More

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GPP dataAs our primary productivity product we used the GOSIF GPP dataset21 which utilizes the linear relationship between GPP and remotely-sensed SIF34. GOSIF GPP is available globally at 0.05° spatial resolution for the period 2000–2021, with the period 2001–2015 selected here (for a short summary of all datasets used in this study see Supplementary Table 3). GOSIF GPP is based on a gridded SIF product (GOSIF)34 which uses MODIS enhanced vegetation index and meteorological data for spatial scaling and is trained with millions of SIF observations from the coarser-resolution Orbiting Carbon Observatory-235. The global coverage of GOSIF and the close relationship between SIF and GPP allow for an independent assessment of how land cover changes affect GPP in different regions around the world. For instance, SIF has been shown to capture the high GPP in the US Corn Belt derived from flux towers, while ecosystem models underestimated it36. While GPP can thus be empirically estimated from satellite SIF observations relatively reliably (even though some assumptions like the linear GPP–SIF relationship and its universality across biomes are still debated20,37,38,39), the calculation of NPP needs additional assumptions of autotrophic respiration. Therefore, we focused our study on GPP, but we included an NPP product in our uncertainty analysis. In addition to that, to account for the challenges and uncertainties in global GPP estimates we included four alternative GPP products in our sensitivity analysis (see below).Land cover mappingGridded land cover was derived from ESA-CCI22, a global land cover product designed for climate science. ESA-CCI is available at 300 m spatial resolution for the 1992–2020 period (https://cds.climate.copernicus.eu/). We first classified ESA-CCI land covers to forests, grasslands, and croplands according to IPCC classification: classes 50–100, 160, 170 forests (2,022,283 grid cells); classes 110 and 130 grasslands (509,297 grid cells); classes 10–40 croplands (950,025 grid cells). We focus on these three major land cover types to facilitate our analysis. We then converted the resulting map to 0.05° resolution by determining the prevalent (i.e., mode) land cover for each grid cell using the aggregate function from the raster package40 and only included grid cells in our training data in which the prevalent land cover was constant over the period 2001–2015. Other classes (e.g., cropland/natural vegetation mosaics) and grid cells where the land cover changed over the 2001–2015 period were not used for the RF training.Random forestsRF is a popular and efficient supervised machine learning technique which can be applied for classification and regression problems41. While complex, it is still easier to interpret compared to other machine learning methods such as Artificial Neural Networks. It has recently been applied to a wide range of ecological research questions, including the prediction of food42 and bioenergy43 crop yields, potential natural vegetation31, forest aboveground biomass44, soil respiration45, and soil carbon emissions from land-use change5 and is thus well suited for our approach. The “Forests” refer to a number of individual decision trees. For each tree, a random sample of the training data is selected and split multiple times based on a random subset of variables from which the one minimizing the weighted variance is selected by the algorithm. Model performance is computed directly on out-of-bag (OOB) data which is randomly omitted from the training data (36.8% of all grid cells). When RF is applied to new data, a weighted prediction of each individual decision tree contributes to the overall prediction. Variance in the individual trees, e.g., by selecting random subsets of the observations and random variables at each node improves the overall RF predictive skill. Model training and prediction were done using the R ranger package46. After initial testing (see Supplementary Fig. S11) we decided to set the number of individual decision trees to 800 and the number of variables to possibly split at in each node to 10. As the good evaluation measures of RF algorithms can be related to spatial autocorrelation24 we also tested a coordinate-only model and performed a leave-one-out cross validation including spatial buffers (Supplementary Discussion 2, Supplementary Fig. S3). Due to the large computational effort we reduced the number of decision trees to 100 for the buffered leave-one-out cross validation.Predictor variablesWe predicted forest, grassland, and cropland potential GPP using the following 20 predictor variables in our RF algorithm: mean annual surface temperature (Tmean), mean diurnal temperature range (Tdiurnal), temperature seasonality (Tseason; standard deviation), minimum temperature of the coldest month (Tmin), annual temperature range (Tannual), mean temperature of the warmest quarter (Twarmest), mean annual precipitation (Pmean), precipitation seasonality (Pseason; coefficient of variation), precipitation of the wettest quarter (Pwettest), precipitation of the driest quarter (Pdriest), precipitation of the warmest quarter (Pwarmest), mean annual solar radiation (SR), growing degree days (GDD), relative humidity (RH), soil clay content (Clay), elevation (EL), nitrogen deposition (Ndep), nitrogen fertilization (NF), pesticide application (Pest), and gross domestic product (GDP; a proxy for agricultural management input other than NF and Pest). Overall Tmean, Tannual, and Pmean were the most important predictor variables (see Supplementary Discussion 3 and Fig. S12). We also tested other predictors (including additional bioclimatic variables, soil pH, irrigation, or phosphate fertilization) but found only negligible improvements in RF evaluation metrics and hence decided to restrict our analysis to the 20 predictors mentioned above.Climate variables were taken from the CHELSA dataset47,48, remapped to 0.05° spatial resolution using the aggregate function from the raster package40. To only include years overlapping with our GPP data we used the CHELSA time-series data for the 2001–2013 period if available and 1979–2013 climatologies elsewise. Clay was derived from the Regridded Harmonized World Soil Database v1.249. Ndep was taken from ISIMIP2b50, bilinear remapped from 0.5° to 0.05° spatial resolution using Climate Data Operators33. Elevation was obtained from WorldClim51. NF and Pest were derived from country-specific FAO data (e.g., https://ourworldindata.org/grapher/pesticide-use-per-hectare-of-cropland), i.e., we used the same value for all grid cells in a country. GDP was obtained from ref.52.Suitable areaFor the comparison of potential forest, grassland, and cropland GPP in Fig. 1g–i we only included grid cells suitable for all three land cover types. For forests, we assumed forest cover possible if the grid cell is currently forested (e.g., all grid cells of our forest training data) or if the potential natural forest cover exceeds 36.3%. This threshold represents the 5th percentile of all currently forested grid cells. Potential natural forest cover was derived from a potential natural vegetation map, available for 20 biomes at 0.00833° spatial resolution31. To convert these biomes into potential natural forest cover we assumed 100% forest cover for the ten forest biomes and 30% forest cover for tropical savannah. Other biomes were not considered. We then aggregated the map to 0.05° spatial resolution by computing the mean of 36 grid cells using the aggregate function form the raster package40 (see Supplementary Fig. S5 for the resulting map). For grasslands and croplands, we computed the 5th percentile of Tmean and Pmean in the training data (− 9.9 °C and 165 mm for grasslands and 2.7 °C and 295 mm for croplands, respectively) and removed all grid cells below those thresholds, assuming these areas to be too cold or too dry for the respective land cover type. Finally, we calculated the land cover with the highest potential GPP for all overlapping grid cells.Sensitivity analysisTo explore the sensitivity and uncertainty of our RF approach we repeated our prediction using different input datasets, potential forest cover, and machine-learning approaches. The importance of the underlying potential forest map was estimated by replacing our potential forest map (Supplementary Fig. S5) by the LUH2 potential forest map (Supplementary Fig. S13)23. To explore the dependency on the land cover product we repeated our RF prediction using the spatially aggregated MODIS land cover map (MCD12C1; IGBP scheme), available at 0.05° spatial resolution53. We classified grid cells of classes 1, 2, 3, 4, 5, (all forests), 8 (woody savannahs) and 9 (savannahs) as forest. Classes 8 and 9 were included in forest because otherwise forest cover would be underestimated in the temperate and boreal zone. Class 10 was classified as grassland and class 12 as cropland. A comparison of ESA-CCI with MODIS reveals a substantially larger cropland area in ECA-CCI but a smaller grassland area (Supplementary Fig. S14).The sensitivity to the climate product was tested by repeating our analysis using predictor variables from the WorldClim climatologies (1970–2000)51, aggregated from 30 s to 0.05° spatial resolution using the aggregate function from the raster package40. In contrast to CHELSA, growing degree days and relative humidity were not available from WorldClim but we included water vapour pressure as additional predictor.We also tested four alternative global GPP products. The vegetation photosynthesis model (VPM) product, available for the period of interest at 0.05° spatial resolution, is based on improved light use efficiency theory and is driven by remotely sensed datasets and reanalysis climate data and land cover classification which also distinguishes C3 vs. C4 photosynthesis pathways54. The second product is derived from remote sensing considering radiation and canopy conductance limitations on GPP and is available at 0.05° resolution for the 2001–2012 period55. Land cover is not an input variable. The third product, FLUXCOM, uses machine learning to scale FLUXNET site GPP to the globe56,57. FLUXCOM is available at 0.0833° resolution and was conservative remapped to 0.05° using Climate Data Operators33 meaning that the GPP of different land cover types might be mixed in regions with heterogeneous land cover patterns. The forth product is the MODIS MOD17A3 GPP product58, available for the 2001–2013 period and aggregated to 0.05° resolution using the raster package40. It is derived from meteorological data, fraction of absorbed photosynthetic active radiation/leaf area index, and land cover. As there is also a MOD17A3 NPP product available we additionally conducted a prediction for potential NPP. The MOD17A3 NPP product is calculated as GPP minus maintenance and growth respiration estimated from allometric relationships linking daily biomass and annual growth of plant tissues to leaf area index58. This leads to additional uncertainty compared to the MOD17A3 GPP product.To test the effect of an alternative RF algorithm we repeated our prediction with the RF algorithm from the Python scikit-learn library59 using the same number of decision trees (800). Additionally, we tested another machine-learning technique, a deep neural network (DNN), using the PyTorch library60. We selected 10 linear layers with 5 times alternating 128 and 256 nodes and a sigmoid output function. All layers were connected using the rectified linear unit activation function. We used the adamW optimizer with 0.0003 learning rate and 2000 epochs of training. To prevent overfitting, we included a 10% dropout after the 7th layer. Lastly, we included a very simple estimate of the most productive land cover based on the nearest neighbour using scikit-learn’s BallTree implementation together with the Haversine formula. For each grid cell we searched for the nearest forest, grassland, and cropland grid cell and assigned the respective GPP also to this grid cell. We thus assumed that environmental conditions are more or less identical in these grid cells, which might be a reasonable assumption for many locations but less reliable in complex terrain or in large homogeneous regions like the central Amazon rainforest where the nearest cropland/grassland grid cell might be located far away.Land-use change scenariosTo estimate the effects of historical and potential future land cover changes on global GPP we applied LUH2 scenarios23 which also serve as input data for ESMs participating in CMIP6. Land-use changes over the historical period are based on the HYDE reconstruction3, while future projections were developed by different Integrated Assessment Models combining various assumptions of socio-economic behaviour (SSPs) with climate mitigation targets (RCPs). Annual fractions for the two land cover classes cropland (sum of 5 crop types) and managed grassland (sum of pasture and rangeland) were available for each scenario at 0.25° resolution (https://luh.umd.edu/). We converted to 0.05° resolution assuming the same land cover fractions for all 25 grid cells around the LUH2 grid cells. We considered the following land cover transitions: forest to managed grassland, forest to cropland, and natural grassland to cropland (and reverse transitions for future scenarios). Transitions in areas suitable for only two land cover types were also included. Conversions of natural grasslands to managed grasslands were assumed not to affect productivity. We assumed the original land cover of a grid cell to be either forest (i.e., potential forest cover  > 36.3%) or natural grassland and accordingly multiplied the converted areas by the differences in potential GPP derived from our RF approach. Our broad forest definition including open tree cover (see above) and the fact that we assumed a change from 100 to 0% forest area in deforested grid cells results in a total historical deforestation area substantially larger than estimated in a recent study (2.4 Mkm2 vs. 1.6 Mkm2)61. These assumptions, however, do not impair our GPP estimate as our approach implicitly accounts for gradients in forest productivity (open forests tend to have lower GPP than closed forests). To test the sensitivity of the resulting GPP reduction we also applied the potential GPP maps from our uncertainty analysis to historical land-use changes (Supplementary Fig. S6). For future land cover changes we investigated changes over the 2015–2100 period for all available LUH2 scenarios: SSP1-1.9, SSP2-2.6, SSP4-3.4, SSP5-3.4, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. Land-use activities in these scenarios range from large-scale deforestation (e.g., SSP3-7.0) to reforestation (e.g., SSP1-1.9) (Supplementary Fig. S7).Earth System ModelsWe compared the potential GPP estimated by our RF algorithm to simulations of eight ESMs participating in CMIP6 (CESM2-CLM562, CNRM-ESM2.1-Surfex 8.0c63, EC-Earth3-Veg-LPJ-GUESSv464, GFDL-ESM4-GFDL-LM4.165, IPSL-CM6A-LR-ORCHIDEEv2.066, MIROC-ES2L-MATSIRO6.0 + VISIT-e ver.1.067, MPI-ESM1-2-LR-JSBACH3.2068, UKESM1-0-LL-JULES-ES-1.069) with an explicit representation of natural vegetation and at least one agricultural land cover class (cropland or managed grassland) in their vegetation sub-model. We selected these ESMs so that all vegetation models implemented in more than one ESM were represented only once (e.g., the JSBACH vegetation model is a component of both MPI-ESM1-2-LR and AWI-ESM). For each ESM, the variable gppLut was downloaded from the CMIP6 archive (https://esgf-data.dkrz.de/search/cmip6-dkrz/) for the historical simulations. These files contain simulated GPP for natural vegetation, pasture, and cropland for which we calculated the 2001–2014 mean (2014 is the last year of the historical period). ESMs use fractional land covers for each grid cell, meaning that climatic drivers are inherently the same for all land cover types within a grid cell and simulated productivities can therefore be directly compared. As ESMs differ in their spatial resolution we bilinear remapped all output to 0.05° resolution using Climate Data Operators33 to allow for a fair comparison across models. To assess the sensitivity of our results to the interpolation method we also tested conservative remapping which results in slightly different maps (Supplementary Fig. S15) and usually larger model biases (Supplementary Table 2). In addition, ESMs differ in where they simulate forests in natural vegetation areas, and therefore we removed all grid cells from the comparison where at least one ESM simulated no tree productivity/cover/biomass in order to avoid comparing the GPP of natural grasslands to managed grasslands. We provide maps based on the original output for each ESM in Supplementary Fig. S10.FLUXNET dataWe compared our predictions of potential GPP to FLUXNET Tier 1 eddy covariance measurements (Supplementary Fig. S16)70. We included all forest, woody savannah (classified as forest), grassland and cropland sites21 which were located in suitable areas for the respective land cover. Mean GPP was calculated as the mean of the GPP estimates based on the night-time (GPP_NT_VUT_REF) and day-time (GPP_DT_VUT_REF) partitioning method. As some sites only had a few years of data, all available years were considered (i.e., site mean GPP was calculated for a different time period than 2001–2015). Comparisons were made with the potential GPP in the respective grid cell in which the site was located (i.e., not calibrated to site conditions). More