Philippa Kaur

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Literature studyLiterature considering the effect of pasture degradation on SOC, N, and clay content, as well as bulk density (BD), was assembled by searching (i) Web of Science V.5.22.1, (ii) ScienceDirect (Elsevier B.V.) (iii) Google Scholar, and (iv) the China Knowledge Resource Integrated Database (CNKI). Search terms were “degradation gradient”, “degradation stages”, “alpine meadow”, “Tibetan Plateau”, “soil”, “soil organic carbon”, and “soil organic matter” in different combinations. The criteria for including a study in the analysis were: (i) a clear and comprehensible classification of degradation stages was presented, (ii) data on SOC, N, and/or BD were reported, (iii) a non-degraded pasture site was included as a reference to enable an effect size analysis and the calculation of SOC and N losses, (iv) sampling depths and study location were clearly presented. (v) Studies were only considered that took samples in 10 cm depth intervals, to maintain comparability to the analyses from our own study site. The degradation stages in the literature studies were regrouped into the six successive stages (S0–S5) according to the respective degradation descriptions. In total, we compiled the results of 49 publications published between 2002 and 2020.When SOM content was presented, this was converted to SOC content using a conversion factor of 2.032. SOC and N stocks were calculated using the following equation:$${{{{{rm{Elemental; stock}}}}}}=100* {{{{{rm{content}}}}}}* {{{{{rm{BD}}}}}}* {{{{{rm{depth}}}}}}$$
(1)
where elemental stock is SOC or N stock [kg ha−1]; content is SOC or N content [g kg−1]; BD is soil bulk density [g cm−3] and depth is the soil sampling depth [cm].The effect sizes of individual variables (i.e., SOC and N stocks as well as BD) were quantified as follows:$${{{{{rm{ES}}}}}}=,frac{(D-R)}{R* 100 % }$$
(2)
where ES is the effect size in %, D is the value of the corresponding variable in the relevant degradation stage and R is the value of each variable in the non-degraded stage (reference site). When ES is positive, zero, or negative, this indicates an increase, no change, or decrease, respectively, of the parameter compared to the non-degraded stage.Experimental design of the field studyLarge areas in the study region are impacted by grassland degradation. In total, 45% of the surface area of the Kobresia pasture ecosystem on the TP is already degraded2. The experiment was designed to differentiate and quantify SOC losses by erosion vs. net decomposition and identify underlying shifts in microbial community composition and link these to changes in key microbial functions in the soil C cycle. We categorized the range of Kobresia root-mat degradation from non-degraded to bare soils into six successive degradation stages (S0–S5). Stage S0 represented non-degraded root mats, while stages S1–S4 represented increasing degrees of surface cracks, and bare soil patches without root mats defined stage S5 (Supplementary Fig. 1). All six degradation stages were selected within an area of about 4 ha to ensure equal environmental conditions and each stage was sampled in four field replicates. However, the studied degradation patterns are common for the entire Kobresia ecosystem (Supplementary Fig. 1).Site descriptionThe field study was conducted near Nagqu (Tibet, China) in the late summer 2013 and 2015. The study site of about 4 ha (NW: 31.274748°N, 92.108963°E; NE: 31.274995°N, 92.111482°E; SW: 31.273488°N, 92.108906°E; SE: 31.273421°N, 92.112025°E) was located on gentle slopes (2–5%) at 4,484 m a.s.l. in the core area of the Kobresia pygmaea ecosystem according to Miehe et al.8. The vegetation consists mainly of K. pygmaea, which covers up to 61% of the surface. Other grasses, sedges, or dwarf rosette plants (Carex ivanoviae, Carex spp., Festuca spp., Kobresia pusilla, Poa spp., Stipa purpurea, Trisetum spp.) rarely cover more than 40%. The growing season is strongly restricted by temperature and water availability. At most, it lasts from mid-May to mid-September, but varies strongly depending on the onset and duration of the summer monsoon. Mean annual precipitation is 431 mm, with roughly 80% falling as summer rains. The mean annual temperature is −1.2 °C, while the mean maximum temperature of the warmest month (July) is +9.0 °C2.A characteristic feature of Kobresia pastures is their very compact root mats, with an average thickness of 15 cm at the study site. These consist mainly of living and dead K. pygmaea roots and rhizomes, leaf bases, large amounts of plant residue, and mineral particles. Intact soil is a Stagnic Eutric Cambisol (Humic), developed on a loess layer overlying glacial sediments and containing 50% sand, 33% silt, and 17% clay in the topsoil (0–25 cm). The topsoil is free of carbonates and is of neutral pH (pH in H2O: 6.8)5. Total soil depth was on average 35 cm.The site is used as a winter pasture for yaks, sheep, and goats from January to April. Besides livestock, large numbers of plateau pikas (Ochotona) are found on the sites. These animals have a considerable impact on the plant cover through their burrowing activity, in particular the soil thrown out of their burrows, which can cover and destroy the Kobresia turf.Sampling designThe vertical and horizontal extent of the surface cracks was measured for each plot (Supplementary Table 2). Vegetation cover was measured and the aboveground biomass was collected in the cracks (Supplementary Table 2). In general, intact Kobresia turf (S0) provided high resistance to penetration as measured by a penetrologger (Eijkelkamp Soil and Water, Giesbeek, NL) in 1 cm increments and four replicates per plot.Soil sampling was conducted using soil pits (30 cm length × 30 cm width × 40 cm depth). Horizons were classified and then soil and roots were sampled for each horizon directly below the cracks. Bulk density and root biomass were determined in undisturbed soil samples, using soil cores (10 cm height and 10 cm diameter). Living roots were separated from dead roots and root debris by their bright color and soft texture using tweezers under magnification, and the roots were subsequently washed with distilled water to remove the remaining soil. Because over 95% of the roots occurred in the upper 25 cm5, we did not sample for root biomass below this depth.Additional soil samples were taken from each horizon for further analysis. Microbial community and functional characterization were performed on samples from the same pits but with a fixed depth classification (0–5 cm, 5–15 cm, 15–35 cm) to reduce the number of samples.Plant and soil analysesSoil and roots were separated by sieving (2 mm) and the roots subsequently washed with distilled water. Bulk density and root density were determined by dividing the dry soil mass (dried at 105 °C for 24 h) and the dry root biomass (60 °C) by the volume of the sampling core. To reflect the root biomass, root density was expressed per soil volume (mg cm−3). Soil and root samples were milled for subsequent analysis.Elemental concentrations and SOC characteristicsTotal SOC and total N contents and stable isotope signatures (δ13C and δ15N) were analyzed using an isotope ratio mass spectrometer (Delta plus, Conflo III, Thermo Electron Cooperation, Bremen, Germany) coupled to an elemental analyzer (NA 1500, Fisons Instruments, Milano, Italy). Measurements were conducted at the Centre for Stable Isotope Research and Analysis (KOSI) of the University of Göttingen. The δ13C and δ15N values were calculated by relating the isotope ratio of each sample (Rsample = 13C/12C or 15N/14N) to the international standards (Pee Dee Belemnite 13C/12C ratio for δ13C; the atmospheric 15N/14N composition for δ15N).Soil pH of air-dried soil was measured potentiometrically at a ratio (v/v) of 1.0:2.5 in distilled water.Lignin phenols were depolymerized using the CuO oxidation method25 and analyzed with a gas chromatography-mass spectrometry (GC–MS) system (GC 7820 A, MS 5977B, Agilent Technologies, Waldbronn, Germany). Vanillyl and syringyl units were calculated from the corresponding aldehydes, ketones, and carboxylic acids. Cinnamyl units were derived from the sum of p-coumaric acid and ferulic acid. The sum of the three structural units (VSC = V + S + C) was considered to reflect the lignin phenol content in a sample.DNA extraction and PCRSamples were directly frozen on site at −20 °C and transported to Germany for analysis of microbial community structure. Total DNA was extracted from the soil samples with the PowerSoil DNA isolation kit (MoBio Laboratories Inc., Carlsbad, CA, USA) according to the manufacturer’s instructions, and DNA concentration was determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The extracted DNA was amplified with forward and reverse primer sets suitable for either t-RFLP (fluorescence marked, FAM) or Illumina MiSeq sequencing (Illumina Inc., San Diego, USA): V3 (5’-CCT ACG GGN GGC WGC AG-3’) and V4 (5’-GAC TAC HVG GGT ATC TAA TCC-3’) primers were used for bacterial 16 S rRNA genes whereas ITS1 (5’-CTT GGT CAT TTA GAG GAA GTA A-3’), ITS1-F_KYO1 (5’-CTH GGT CAT TTA GAG GAA STA A-3’), ITS2 (5’-GCT GCG TTC TTC ATC GAT GC-3’) and ITS4 (5’-TCC TCC GCT TAT TGA TAT GC-3’) were used for fungi33,34. Primers for Illumina MiSeq sequencing included adaptor sequences (forward: 5’-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG-3’; reverse: 5’-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA G-3’)33. PCR was performed with the Phusion High-Fidelity PCR kit (New England Biolabs Inc., Ipswich, MA, USA) creating a 50 µl master mix with 28.8 µl H2Omolec, 2.5 µl DMSO, 10 µl Phusion GC buffer, 1 µl of forward and reverse primer, 0.2 µl MgCl2, 1 µl dNTPs, 0.5 µl Phusion HF DNA Polymerase, and 5 µl template DNA. PCR temperatures started with initial denaturation at 98 °C for 1 min, followed by denaturation (98 °C, 45 s), annealing (48/60 °C, 45 s), and extension (72 °C, 30 s). These steps were repeated 25 times, finalized again with a final extension (72 °C, 5 min), and cooling to 10 °C. Agarose gel electrophoresis was used to assess the success of the PCR and the amount of amplified DNA (0.8% gel:1.0 g Rotigarose, 5 µl Roti-Safe Gelstain, Carl Roth GmbH & Co. KG, Karlsruhe, Germany; and 100 ml 1× TAE-buffer). PCR product was purified after initial PCR and restriction digestion (t-RFLP) with either NucleoMag 96 PCR (16 S rRNA gene amplicons, Macherey-Nagel GmbH & Co. KG, Düren, Germany) or a modified clean-up protocol after Moreau (t-RFLP)35: 3× the volume of the reaction solution as 100% ethanol and ¼x vol. 125 mM EDTA was added and mixed by inversion or vortex. After incubation at room temperature for 15 min, the product was centrifuged at 25,000 × g for 30 min at 4 °C. Afterwards the supernatant was removed, and the inverted 96-well plate was centrifuged shortly for 2 min. Seventy microliters ethanol (70%) were added and centrifuged at 25,000 × g for 30 min at 4 °C. Again, the supernatant was removed, and the pallet was dried at room temperature for 30 min. Finally, the ethanol-free pallet was resuspended in H2Omolec.T-RFLP fingerprintingThe purified fluorescence-labeled PCR products were digested with three different restriction enzymes (MspI and BstUI, HaeIII) according to the manufacturer’s guidelines (New England Biolabs Inc., Ipswich, MA, USA) with a 20 µl master mix: 16.75 µl H2Omolec, 2 µl CutSmart buffer, 0.25 or 0.5 µl restriction enzyme, and 1 µl PCR product for 15 min at 37 °C (MspI) and 60 °C (BstUI, HaeIII), respectively. The digested PCR product was purified a second time35, dissolved in Super-DI Formamide (MCLAB, San Francisco, CA, USA) and, along with Red DNA size standard (MCLAB, San Francisco, USA), analyzed in an ABI Prism 3130 Genetic Analyzer (Applied Biosystems, Carlsbad, CA, USA). Terminal restriction fragments shorter than 50 bp and longer than 800 bp were removed from the t-RFLP fingerprints.16 S rRNA gene and internal transcribed spacer (ITS) sequencing and sequence processingThe 16 S rRNA gene and ITS paired-end raw reads for the bacterial and fungal community analyses were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) and can be found under the BioProject accession number PRJNA626504. This BioProject contains 70 samples and 139 SRA experiments (SRR11570615–SRR11570753) which were processed using CASAVA software (Illumina, San Diego, CA, USA) for demultiplexing of MiSeq raw sequences (2 × 300 bp, MiSeq Reagent Kit v3).Paired-end sequences were quality-filtered with fastp (version 0.19.4)36 using default settings with the addition of an increased per base phred score of 20, base-pair corrections by overlap (-c), as well as 5′- and 3′-end read trimming with a sliding window of 4, a mean quality of 20 and minimum sequence size of 50 bp. Paired-end sequences were merged using PEAR v0.9.1137 with default parameters. Subsequently, unclipped reverse and forward primer sequences were removed with cutadapt v1.1838 with default settings. Sequences were then processed using VSEARCH (v2.9.1)39. This included sorting and size-filtering (—sortbylength,—minseqlength) of the paired reads to ≥300 bp for bacteria and ≥140 bp for ITS1, dereplication (—derep_fulllength). Dereplicated sequences were denoised with UNOISE340 using default settings (—cluster_unoise—minsize 8) and chimeras were removed (—uchime3_denovo). An additional reference-based chimera removal was performed (—uchime_ref) against the SILVA41 SSU NR database (v132) and UNITE42 database (v7.2) resulting in the final set of amplicon sequence variants (ASVs)43. Quality-filtered and merged reads were mapped to ASVs (—usearch_global–id 0.97). Classification of ASVs was performed with BLAST 2.7.1+ against the SILVA SSU NR (v132) and UNITE (v7.2) database with an identity of at least 90%. The ITS sequences contained unidentified fungal ASVs after UNITE classification, these sequences were checked (blastn)44 against the “nt” database (Nov 2018) to remove non-fungal ASVs and only as fungi classified reads were kept. Sample comparisons were performed at the same surveying effort, utilizing the lowest number of sequences by random selection (total 15,800 bacteria, 20,500 fungi). Species richness, alpha and beta diversity estimates, and rarefaction curves were determined using the QIIME 1.9.145 script alpha_rarefaction.py.The final ASV tables were used to compute heatmaps showing the effect of degradation on the community using R (Version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria) and R packages “gplots”, “vegan”, “permute” and “RColorBrewer”. Fungal community functions were obtained from the FunGuild database46. Plant mycorrhizal association types were compiled from the literature38,39,40,41,47,48,49,50. If no direct species match was available, the mycorrhizal association was assumed to remain constant within the same genus.Enzyme activityEnzyme activity was measured to characterize the functional activity of the soil microorganisms. The following extracellular enzymes, involved in C, N, and P transformations, were considered: two hydrolases (β-glucosidase and xylanase), phenoloxidase, urease, and alkaline phosphatase. Enzyme activities were measured directly at the sampling site according to protocols after Schinner et al.51. Beta-glucosidase was incubated with saligenin for 3 h at 37 °C, xylanase with glucose for 24 h at 50 °C, phenoloxidase with L-3,4-dihydroxy phenylalanine (DOPA) for 1 h at 25 °C, urease with urea for 2 h at 37 °C and alkaline phosphatase on P-nitrophenyl phosphate for 1 h at 37 °C. Reaction products were measured photometrically at recommended wavelengths (578, 690, 475, 660, and 400 nm, respectively).SOC stocks and SOC lossThe SOC stocks (in kg C m−2) for the upper 30 cm were determined by multiplying the SOC content (g C kg−1) by the BD (g cm−3) and the thickness of the soil horizons (m). SOC losses (%) were calculated for each degradation stage and horizon and were related to the mean C stock of the reference stage (S0). The erosion-induced SOC loss of the upper horizon was estimated by considering the topsoil removal (extent of vertical soil cracks) of all degraded soil profiles (S1–S5) and the SOC content and BD of the reference (S0). To calculate the mineralization-derived SOC loss, we accounted for the effects of SOC and root mineralization on both SOC content and BD. Thus, we used the SOC content and BD from each degradation stage (S1–S5) and multiplied it by the mean thickness of each horizon (down to 30 cm) from the reference site (S0). The disentanglement of erosion-derived SOC loss from mineralization-derived SOC loss was based on explicit assumptions that (i) erosion-derived SOC losses are mainly associated with losses from the topsoil, and (ii) the decreasing SOC contents in the erosion-unaffected horizons were mainly driven by mineralization and decreasing root C input.Statistical analysesStatistical analyses were performed using PASW Statistics (IBM SPSS Statistics) and R software (Version 3.6.1). Soil and plant characteristics are presented as means and standard errors (means ± SE). The significance of treatment effects (S0–S5) and depth was tested by one-way ANOVA at p  More

Sampling sitesLongitudinal observation campaigns for I. ricinus nymph activity were carried out at 11 sampling sites in forest areas from seven different tick observatories across France. Tick observatories are located at the following French municipal areas, where the coordinates of the centre of each municipal area and the climatic types29 are also provided as: (1) La Tour de Salvagny (45° 48′ 50.6″ N 4° 42′ 53.2″ E; Mixed climates); (2) Saint-Genès-Champanelle (45° 43′ 23.8″ N 3°01′ 08.0″ E; Mountain climate); (3) Etiolles (48° 37′ 59.9″ N 2° 28′ 00.1″ E; Degraded oceanic climate); (4) Carquefou (47° 17′ 58.5″ N 1° 29′ 26.0″ W; Oceanic climate); (5) Gardouch (43°23′ 25.7″ N 1° 41′ 02.1″ E; South-West Basin climate); (6) Velaine-en-Haye (48° 42′ 13.4″ N 6° 01′ 16.1″ E; Semi-continental climate); (7) Les Bordes (47° 48′ 47.3″ N 2° 24′ 01.3″ E; Degraded-oceanic climate) (Fig. 1). The observation campaigns were carried out from April/June 2014 to May/June 2021 in most observatories, except for Les Bordes, which began in April 2018.Figure 1The map was created using QGIS version 3.8, Zanzibar (https://www.qgis.org). The climatic region types were previously classified by Joly et al.29.The distribution of tick observatories according to the climatic region types of continental France: (1) Etiolles (degraded oceanic); (2) Velaine-en-Haye (semi-continental); (3) Les Bordes (degraded oceanic); (4) Carquefou (oceanic); (5) La Tour de Salvagny (mixed); (6) Saint-Genès-Champanelle (mountain); (7) Gardouch (south-west basin). Phenological patterns observed at each observatory were also indicated.Full size imageEach tick observatory corresponds to one sampling site except La Tour de Salvagny, Gardouch, and Les Bordes (Table S1). In La Tour de Salvagny, we had to withdraw the observations at the original site (La Tour de Salvagny A) in September 2016 because the site became no longer accessible. In April 2017, we continued our observations at a nearby site, approximately 2 km apart (La Tour de Salvagny B). In Gardouch, the activity of questing nymphs was observed both inside and outside the enclosed area of an experimental station on roe deer (Capreolus capreolus), referred to as Gardouch Inside and Gardouch Outside, respectively. The estimated population density of roe deer in Gardouch Inside (50 individuals per 100 ha) was higher than Gardouch Outside (less than 20 individuals per 100 ha) (H. Verheiden, personal communication, 15th October 2021). Furthermore, three sampling sites in Les Bordes, approximately 1.2 km apart, were referred to as Les Bordes A, B, and C, respectively. Additional sampling sites of these observatories were considered and reported as distinct sampling sites in further analyses, resulting in a total to 11 sampling sites from 7 observatories. Furthermore, due to their geographical proximity, meteorological/climatic factors of different sampling sites from the same observatories were considered identical in subsequent statistical analyses, whereas land cover and topography factors could be varied.Field observation campaigns were planned and carried out by local investigators who had been trained on the sampling protocol. The locations of forests, sampling sites, and passages were chosen where their biotopes are known to be suitable for I. ricinus tick populations around each observatory at the time the field observation campaigns started30. The observations were never carried out during the daytime when the weather was highly unfavourable to questing ticks, e.g., heavy rain, snow, or snow cover.Sampling protocol for questing Ixodes ricinus nymphsActivity of questing I. ricinus nymphs was observed by a cloth-dragging sampling technique31. Within a 1-km radius, a 1 m × 1 m white cloth was dragged over 10 observation units of 10 m short-grass vegetative forest floors, called transects. For each transect, a repeated removal sampling design was used27. The cloth-dragging sampling process was successively repeated three times per sampling. All nymphs found on white cloth in each campaign were removed and collected in a vial for subsequent morphological identification32 by the same acarologists at the corresponding laboratories. As a result, the questing nymph activity of each sampling site was monitored as a total number of confirmed I. ricinus nymphs collected from three repeated sampling on 10 transects, equivalent to a surface area of 100 m2. This measure was considered as an indicator for tick abundance on the day of sampling. The same transects were repeatedly sampled throughout the study period at approximately 1-month intervals.Environmental dataWe tested 28 environmental variables to explain the observed I. ricinus nymph activity (Table 1). These variables could be categorized as: (1) Daytime duration and meteorological variables (time-dependent, 9 variables); (2) Land cover, topography, and bioclimatic variables (time-independent, 19 variables).Table 1 Environmental variables (meteorological, land cover, topography, and bioclimatic variables) used to explain I. ricinus nymph counts per 100 m2 in regression analysis.Full size tableDaytime duration and meteorological variablesDaytime duration ((daytime)) from January 2013 to June 2021 at each sampling site was obtained from the corresponding latitude using geosphere package33. Hourly meteorological data (2-m temperature and relative humidity) were recorded locally at each forest. Subsequently, daily mean, minimum, and maximum values of temperature (({T}_{M}), ({T}_{N}), and ({T}_{X}); in °C) and relative humidity (({U}_{M}), ({U}_{N}), and ({U}_{X}); in %) were derived from these hourly records. The meteorological seasons of the temperate area in northern hemisphere are defined as: (1) Spring, 1st March to 31st May; (2) Summer, 1st June to 31st August; (3) Autumn, 1st September to 30th November; (4) Winter, 1st December to 28th or 29th February.Missing values found on these local daily-level variables were imputed by the random forest algorithm in mice package34. External daily meteorological data, i.e., daily average temperature and relative humidity, derived from neighbouring weather stations (Météo-France or INRAE), as well as month and year information, were used as auxiliary variables (Table S2). As a result, the imputation process creates a total of 500 iterated values for each variable. The median values of 500 imputations were used to replace the missing values.The imputed daily meteorological data were subsequently used to calculate the averaged values in different lagged time intervals for further analysis, called interval-average variables15. The interval-average variables were generated to reduce the uncertainty that might arise during the imputation process and to capture the cumulative effects of the meteorological variables, which were mean temperature ({T}_{M}) and minimum relative humidity ({U}_{N}). The interval-average variables were defined as the average values of a meteorological variable (Min) {({T}_{M}), ({U}_{N})} during a period between ({t}_{1}) to ({t}_{2}) month(s) before the sampling, denoted as ({M}^{{t}_{1}:{t}_{2}}), where 1 month consists of 28 days. As temperature conditions affect several ecological processes of tick populations, particularly developmental and questing rates3, the mean temperature ({T}_{M}) was selected for further analysis to reflect the overall temperature effects. While the minimum relative humidity ({U}_{N}) was chosen for the following reasons: (1) the survival of I. ricinus is highly sensitive to desiccation conditions6,7,8. As a result, when compared to mean or maximum relative humidity, minimum relative humidity is a relatively strong indicator of the effects of desiccation stress; (2) the variation of minimum relative humidity among all sites was higher than that of the mean and maximum relative humidity. This high variation allowed us to better describe meteorological characteristics of each sampling site.Here, we hypothesized that interval-average meteorological conditions influence the dynamics of observed nymph activity at different time lags in different manners. Short-term lags may have an impact on immediate responses, such as the probability of questing. At the same time, long-term lags may influence the dynamics of nymph abundance, which is associated with development and survival rates. Therefore, we explored the impact of each meteorological variable at following time lags on the observed nymphs activity in subsequent regression analysis: (1) 1-month moving average condition, ({M}^{0:1}); (2) previous 3-to-6-month moving average condition, ({M}^{3:6}); (3) 6-month moving average condition, ({M}^{0:6}); (4) 12-month moving average condition, ({M}^{0:12}). For instance, ({T}_{M}^{0:1}) denotes 1-month moving average temperature, representing an average of temperature between 0 and 1 months (0–28 days) before the day of sampling.In addition to the interval-average variables, monthly and seasonal average values of mean temperature and minimum relative humidity during the observation period were also calculated to describe the characteristics of meteorological conditions of each sampling site.Land cover, topography, and bioclimatic variablesWe obtained land cover, topography, and bioclimatic data from a 1-km radius buffer area around the center of each sampling site to capture habitat characteristics across all 10 transects. All the variables were handled and obtained by using QGIS version 3.8.035. The digital elevation model (DEM) data derived from the Shuttle Radar Topography Mission (SRTM) database36 was used to describe the topographic features of sampling sites, which included the mean (({mean}_{elv})) and standard deviation (({sd}_{elv})) of the elevation (in m above sea level), the proportion of flat area (({p}_{flat}); defined by the slope ≤ 2.5%37), the proportion of area facing north (({p}_{north})), east (({p}_{east})), west (({p}_{west})), and south (({p}_{south})), and the catchment area ((catchment)) as a proxy variable for moisture. Bioclimatic variables for each site (historical average conditions during 1970–2000) were derived from the WorldClim database38, including the annual mean temperature (({BIO1}_{Temp}); in °C), the mean diurnal range (({BIO2}_{Diur}); in °C), the maximum temperature of the warmest month (({BIO5}_{maxTemp}); in °C), and the annual precipitation (({BIO12}_{Prec}); in mm). The land cover features of each sampling site were described using the CORINE Land Cover (CLC) 201839, while the characteristics of forests were explained by the BD forêt version 2 data40. The forest fragmentation was characterized by the percentage of forest-covering area (({p}_{Forest})), the forest edge density (({ED}_{Forest}); in m/km2), and the number of forest patches (({n}_{Forest})). While the diversities of the land cover types (level-1 and level-2 CLC) and the forest types were calculated by using the Shannon’s diversity index41 ((H)) as (H=sum_{i=1}^{S}{p}_{i}mathrm{ln}{p}_{i}), where (S) is the total number of land cover/forest types and ({p}_{i}) is the proportion of land cover/forest type (i) within the 1-km radius buffer area. The Shannon’s diversity index for level-1 CLC, level-2 CLC, and forest types were denoted as ({H}_{CLC1}), ({H}_{CLC2}), and ({H}_{Forest}), respectively. Finally, the soil pH data (({pH}_{soil})) was retrieved from the European Soil Data Centre (ESDC) database42.Statistical analysisAll the statistical analyses were carried out using the programming language R version 3.6.043. The variations of questing nymph population of each site were described by using (1) baseline annual nymph counts (spatial variation); (2) phenological patterns (seasonal variation). A baseline annual nymph count of site (i) (({{N}_{base}}_{i})) was defined as a summation of monthly median nymph counts ({varvec{tilde{N}}}_{i}={{tilde{N }}_{i,t}}) across all 12 months (tin left{mathrm{1,2},dots ,12right}) and expressed as: ({{N}_{base}}_{i}=sum_{t=1}^{12}{tilde{N }}_{i,t}). Subsequently, the monthly median nymph counts of each site ({varvec{tilde{N}}}_{i}) were transformed into normalized monthly median nymph counts ({varvec{tilde{N}}}_{i}^{*}={{tilde{N }}_{i,t}^{*}}) following Eq. (1) to have a range value of 0 to 1, which allows us to compare phenological patterns among all sites that have different annual baseline nymph counts.$${tilde{N }}_{i,t}^{*}=frac{{tilde{N }}_{i,t}}{mathrm{max}({stackrel{sim }{{varvec{N}}}}_{i})}$$
(1)
The term (mathrm{max}({stackrel{sim }{{varvec{N}}}}_{i})) denoted the maximum monthly median nymph counts. The normalized median nymph count ({tilde{N }}_{i,t}^{*}) of 1 indicates the maximum nymph activity (peak), while the value ({tilde{N }}_{i,t}^{*}) of 0 designates the absence of nymph activity. Afterwards, the phenological patterns were descriptively classified using the following criteria: (1) the season which the peaks of activity arrive; (2) evidence of reduced activity during winter (November–January); (3) the number of activity waves in a year, whether the pattern is unimodal or bimodal. After assigning phenological patterns to each site, the overall trends of different patterns were derived from medians of the normalized monthly median nymph count ({tilde{N }}_{i,t}^{*}) from all sites that belonged to each pattern. Furthermore, the directional changes in the maximum nymph counts were tested using a Spearman’s rank correlation coefficient, a p-value More

Growth in harvests of crops meant for exports, processing and industrial use, together with their higher yields and faster yield gains, stands out globally; at a more granular level, this was driven by specific global regions that are getting increasingly specialized in harvesting crops for these usages.Changes in global-level harvested areasAt the global scale, we find that crops harvested for direct food utilization have the highest area and have been relatively stable over the study period (Fig. 1a). However, as the total harvested hectares have increased globally (Supplementary Table 1), this has translated into decreasing fractions of crops harvested for direct food utilization, from ~51% in the 1960s (average over 1964 to 1968) to ~37% in the 2010s (average over 2009 to 2013), with a similar reduction in feed crop harvests (Table 1). Conversely, there has been a substantial increase in crops for processing, exports and industrial use (Fig. 1a, Table 1 and Supplementary Table 1). The increase in industrial crop harvests occurred after year 2000. Around the same time, harvested hectares for exported crops ramped up and by the 2010s had surpassed those of crops harvested for feed use (Fig. 1a). Crops harvested for seed usage and losses are relatively minor, and we will not discuss them further. If the global trends observed in the past 20 years continue (Fig. 1a), by 2030, crops harvested for exports, processing and industrial use will account for ~ 23%, 17% and 8% of overall harvested hectares, whereas those for food will decrease to ~29% (Table 1).Fig. 1: Sector-based global crop-utilization trends.a–d, Observed total harvested ha (a), average yield in kcal ha−1 per year (b), average yield in protein ha−1 per year (c) and average yield in fat ha−1 per year (d) in the seven sectors of food, feed, processing, export, other uses (non-food/industrial), seed and losses from 1964 to 2013, annually, and projections to 2030 based on the past 20 years. The shading shows the 90% confidence interval for the significant linear model projections.Full size imageTable 1 Sector-based global crop-utilization changesFull size tableChanges in global-level crop yieldsWe find that crops harvested for direct food usage generally have had lower yields than all other sectors at the global scale over the time period of the study (Fig. 1b–d). This is not a new phenomenon, as crops harvested for direct food utilization have always had lower yields relative to other sectors (Supplementary Table 1). What has changed, however, is the ramping up (steeper positive slopes) of industrial, export and processing crop yields (Fig. 1b–d and Table 1). At these rates, caloric yields of industrial-use crops could increase by 28% from the 2010s to 2030 compared with 24% and 21% yield increases of crops harvested for directly consumed food and for feed use (Fig. 1b). Given that caloric yields of industrial-use crops are already substantially higher than food and feed crops (2× and 1.4×, respectively, in the 2010s), the faster caloric yield increases for industrial-use crops will widen this gap (2.1× and 1.5×, respectively). Yield measurements in other units of protein and fat show similar results (Table 1, Fig. 1c,d and Supplementary Table 1).Changes in the spatial patterns of harvested areas and productionWithin country-level information on harvested areas and productivity based on utilization categories is required for developing more locally effective agricultural policies. Over the course of the study time period 1964 to 2013 (Fig. 2a,b and Supplementary Video 1), we find changes in all continents when spatially analysed at the grid-cell level, except for most parts of Africa. Even in Africa, there are locations with fractional reductions in food crop harvests over the study period, such as parts of Angola, Ghana, Nigeria and South Africa. Within these and other countries, the exact location, magnitude and direction of the change varies from one region to the next (that is, compare Fig. 2a with Fig. 2b).Fig. 2: Sector-based spatial changes in crop harvests.a,b, The fraction of a grid cell in one of seven categories—food, feed, processing, export, other (non-food/industrial use), seed and losses—in each period, 1964–1968 (a) and 2009–2013 (b).Full size imageCrops harvested for direct food utilization have been prevalent in Asia, though much has changed since the 1960s (Fig. 2a,b and Supplementary Video 1). In China, there appears to be an imaginary belt, north and west of which harvests of crops used as directly consumed food decreased between the 1960s (Fig. 2a) and 2010s (Fig. 2b), while those for other uses increased. This belt appears to roughly extend from the northern half of Jiangsu (a province on the Yellow Sea in the east), curving westwards and southwards through northern Anhui, southern Henan, central Hubei and the northern tip of Hunan, and then turning sharply south and splitting Guangdong (a province on the South China Sea) through the middle. The sector gaining from the 10–20% fractional food harvest reduction varies. The increase in crops for feed, processing and industrial usage increases as one moves northward, especially north of Jiangsu and Anhui (Fig. 2a,b and Supplementary Video 1).Similarly, in India, there is a north–south zone encompassing eastern Haryana in the north, moving southwards through eastern Rajasthan, western Madhya Pradesh to eastern Maharashtra in the south, where there was a drastic reduction in crops harvested for direct food utilization over the study period (Fig. 2 and Supplementary Video 1); crops harvested for processing primarily increased. Changes in South and Southeast Asia over the study period are primarily away from once-dominant harvests of directly consumed food crops to feed crops, followed by processing crops, export crops and industrial-use crops, as in Myanmar and Thailand. In Malaysia, the growth was in export and industrial-usage crops, whereas in Indonesia, it was export crops and smaller increases in industrial-utilization crops. Central Asian states, especially Kazakhstan and some parts of Russia, witnessed a large reduction in crops harvested for direct food use over the study period, replaced by the crops destined for exports between the two periods (Fig. 2 and Supplementary Video 1).In Australia in the 1960s, food crops were harvested everywhere, accounting for ~10% of the total, which declined to ~5% by the 2010s. This was accompanied by small reductions in crops harvested for feed and export and balanced mainly by increases in crops for processing and industrial utilization (Fig. 2 and Supplementary Video 1).In Europe in the 1960s, crops were dominantly harvested for food and feed, but by the 2010s, this changed to include crops harvested for processing (Fig. 2 and Supplementary Video 1). In France, major reductions in feed crops have been balanced by growth in processing, export and industrial-use crops. In Spain, the primary change is from crops harvested for direct food to those of feed. In Germany, crops harvested for export have replaced those for direct food utilization.Latin America used to dominantly harvest food crops (as in Mexico) or food and feed crops (as in Brazil and Argentina) (Fig. 2 and Supplementary Video 1). Midwestern Brazil used to harvest only food crops, and feed and processing crop harvests were restricted to the Atlantic states (the 1960s; Fig. 2a), but by the 2010s (Fig. 2b), harvests of food crops had become a negligible fraction in Midwestern Brazil (as in Mato Grosso), and crops harvested for processing and exports are dominant now. In the Atlantic states of Brazil, one of the major changes is the increased proportion of harvests for industrial crops. In Argentina, over the study period, the proportion of crops harvested for food and feed has decreased, and this utilization has been mainly replaced by crops harvested for processing; crops harvested for exports changed, but the direction of change was spatially heterogeneous across Argentina (Fig. 2 and Supplementary Video 1). In Mexico, the primary change is the reduction in the fraction of crops harvested for direct food consumption and the increased harvests of crops for feed.Crops harvested for food and feed are also on the decline proportionally in North America. The United States has experienced a change from the dominance of food and feed crops in the 1960s to processing and industrial-usage crops in the 2010s. Detailed changes in the United States and Canada vary from one location to the next (Fig. 2), though the major change is the lower fraction of crops harvested for direct food consumption.Results are similar when viewed through the lens of calories, protein and fat with local-level differences as yields vary based on the measurement units (Supplementary Fig. 1). Further dramatic changes can be expected if observed linear trends from 1994 to 2013 at each grid cell continued until 2030 (Supplementary Fig. 2).Calories harvested in 2030 and achieving UN SDG 2We compare the extra food calories that will potentially be harvested in 2030 (Fig. 3a and Supplementary Data 2) to those required for both the projected extra population and feeding the projected undernourished population in each country (Fig. 3b and Supplementary Data 2). As an extreme case, we also compared whether total calories (all seven utilization sectors) would be sufficient (Fig. 3c and Supplementary Data 2). Altogether, we evaluated 156 countries, of which 86 had reported undernourished populations (Supplementary Data 2). On the basis of the minimum dietary energy requirement (MDER), we find that countries with reported undernourished populations will have a shortfall of ~675.4 trillion kcal per year to nourish the increased population and the expected undernourished from their extra harvested food calories. However, compared with the more realistic average dietary energy requirement (ADER), this shortfall will be ~993.9 trillion kcal per year (or ~70% from requirements) in 2030 (15 additional scenarios of undernourished populations in 2030 (provided in Supplementary Data 3) show global calorie shortfalls may similarly range from ~587.2 trillion kcal per year to ~1,269.3 trillion kcal per year based on the MDER level of nutrition requirement, and ~880.7 trillion kcal per year to ~1,755.6 trillion kcal per year based on the more realistic ADER level of nutrition requirement in 2030).Fig. 3: Meeting UN SDG goal 2 in 2030.a, Same as Fig. 2 but for the projected kcal ha−1 per year in 2030 per utilization sector and then mapping the fraction of total kcal ha−1 per year projected as harvested. b, Shortfall or gap from kcal per year harvested in 2030 as crops for direct food use and those to plug the gap from population growth and/or undernourished population. Computed based on the 2018 to 2020 ADER number for the country. c, Same as b but the kcal per year harvested used for computation is the total across all the seven sectors and shortfall is from whether the total calories harvested were used for direct food consumption (little to no processing).Full size imageCountries reporting undernourishment can, however, meet their requirement of extra calories in 2030 for both population change and those for the undernourished if calories from other utilization sectors are diverted and consumed directly as food calories (Fig. 3c and Supplementary Data 2 and 3). Though at the global scale, it appears that countries with high levels of undernourishment in 2030 can divert just a portion of their total harvested calories and meet some of the requirements of UN’s SDG 2 (ref. 4). In reality, many of the individual countries concentrated in sub-Saharan Africa have limited scope of diversion of calories from other sectors such as feed, processing or exports as crops for direct food use, as they already harvest most crops for direct food consumption (Fig. 2 and Supplementary Figs. 1 and 2). As such, many countries in this region may see deepening reliance on food imports. Note that the UN’s second SDG goal is broader in scope, including efforts to end malnutrition and increase agricultural productivity, among other goals4. Reconfiguration planning19 can use our spatially detailed information (Figs. 2 and 3, Supplementary Figs. 1 and 2 and Supplementary Data 2 and 3) in conjunction with policies that incentivize increased food crop harvests globally and ensure their equitable distribution to undernourished regions when local production is not sufficient20,21. This will require supply chain management22,23 and detailed analysis of optimization scenarios24 with our maps and tables as an important step linking specific production regions with the initial use of that production. More

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Spillover events, in which a pathogen that originates in animals jumps into people, have probably triggered every viral pandemic that’s occurred since the start of the twentieth century1. What’s more, an August 2021 analysis of disease outbreaks over the past four centuries indicates that the yearly probability of pandemics could increase several-fold in the coming decades, largely because of human-induced environmental changes2.Fortunately, for around US$20 billion per year, the likelihood of spillover could be greatly reduced3. This is the amount needed to halve global deforestation in hotspots for emerging infectious diseases; drastically curtail and regulate trade in wildlife; and greatly improve the ability to detect and control infectious diseases in farmed animals.That is a small investment compared with the millions of lives lost and trillions of dollars spent in the COVID-19 pandemic. The cost is also one-twentieth of the statistical value of the lives lost each year to viral diseases that have spilled over from animals since 1918 (see ‘Spillovers: a growing threat’), and less than one-tenth of the economic productivity erased per year1.

Source: Ref. 1

Yet many of the international efforts to better defend the world from future outbreaks, prompted by the COVID-19 pandemic, still fail to prioritize the prevention of spillover. Take, for example, the Independent Panel for Pandemic Preparedness and Response, established by the World Health Organization (WHO). The panel was convened in September 2020, in part to ensure that any future infectious-disease outbreak does not become another pandemic. In its 86-page report released last May, wildlife is mentioned twice; deforestation once.We urge the decision-makers currently developing three landmark international endeavours to make the prevention of spillover central to each.First, the G20 group of the world’s 20 largest economies provisionally agreed last month to create a global fund for pandemics. If realized, this could provide funding at levels that infectious-disease experts have been recommending for decades — around $5 per person per year globally (see go.nature.com/3yjitwx). Second, an agreement to improve global approaches to pandemics is under discussion by the World Health Assembly (WHA), the decision-making body of the WHO. Third, a draft framework for biodiversity conservation — the post-2020 global biodiversity framework — is being negotiated by parties to the Convention on Biological Diversity.Designed in the right way, these three international endeavours could foster a more proactive global approach to infectious diseases. This opportunity — to finally address the factors that drive major disease outbreaks, many of which also contribute to climate change and biodiversity loss — might not present itself again until the world faces another pandemic.Four actions The risk of spillover is greater when there are more opportunities for animals and humans to make contact, for instance in the trade of wildlife, in animal farming or when forests are cleared for mining, farming or roads. It is also more likely to happen under conditions that increase the likelihood of infected animals shedding viruses – when they are housed in cramped conditions, say, or not fed properly.Decades of research from epidemiology, ecology and genetics suggest that an effective global strategy to reduce the risk of spillover should focus on four actions1,3.First, tropical and subtropical forests must be protected. Various studies show that changes in the way land is used, particularly tropical and subtropical forests, might be the largest driver of emerging infectious diseases of zoonotic origin globally4. Wildlife that survives forest clearance or degradation tends to include species that can live alongside people, and that often host pathogens capable of infecting humans5. For example, in Bangladesh, bats that carry Nipah virus — which can kill 40–75% of people infected — now roost in areas of high human population density because their forest habitat has been almost entirely cleared6.Furthermore, the loss of forests is driving climate change. This could in itself aid spillover by pushing animals, such as bats, out of regions that have become inhospitable and into areas where many people live7.Yet forests can be protected even while agricultural productivity is increased — as long as there is enough political will and resources8. This was demonstrated by the 70% reduction in deforestation in the Amazon during 2004–12, largely through better monitoring, law enforcement and the provision of financial incentives to farmers. (Deforestation rates began increasing in 2013 due to changes in environmental legislation, and have risen sharply since 2019 during Jair Bolsonaro’s presidency.)Second, commercial markets and trade of live wild animals that pose a public-health risk must be banned or strictly regulated, both domestically and internationally.Doing this would be consistent with the call made by the WHO and other organizations in 2021 for countries to temporarily suspend the trade in live caught wild mammals, and to close sections of markets selling such animals. Several countries have already acted along these lines. In China, the trade and consumption of most terrestrial wildlife has been banned in response to COVID-19. Similarly, Gabon has prohibited the sale of certain mammal species as food in markets.

A worker in a crowded chicken farm in Anhui province, China.Credit: Jianan Yu/Reuters

Restrictions on urban and peri-urban commercial markets and trade must not infringe on the rights and needs of Indigenous peoples and local communities, who often rely on wildlife for food security, livelihoods and cultural practices. There are already different rules for hunting depending on the community in many countries, including Brazil, Canada and the United States.Third, biosecurity must be improved when dealing with farmed animals. Among other measures, this could be achieved through better veterinary care, enhanced surveillance for animal disease, improvements to feeding and housing animals, and quarantines to limit pathogen spread.Poor health among farmed animals increases their risk of becoming infected with pathogens — and of spreading them. And nearly 80% of livestock pathogens can infect multiple host species, including wildlife and humans9.Fourth, particularly in hotspots for the emergence of infectious diseases, people’s health and economic security should be improved.People in poor health — such as those who have malnutrition or uncontrolled HIV infection — can be more susceptible to zoonotic pathogens. And, particularly in immunosuppressed individuals such as these, pathogens can mutate before being passed on to others10.What’s more, some communities — especially those in rural areas — use natural resources to produce commodities or generate income in a way that brings them into contact with wildlife or wildlife by-products. In Bangladesh, for example, date palm sap, which is consumed as a drink in various forms, is often collected in pots attached to palm trees. These can become contaminated with bodily substances from bats. A 2016 investigation linked this practice to 14 Nipah virus infections in humans that caused 8 deaths11.Providing communities with both education and tools to reduce the risk of harm is crucial. Tools can be something as simple as pot covers to prevent contamination of date palm sap, in the case of the Bangladesh example.In fact, providing educational opportunities alongside health-care services and training in alternative livelihood skills, such as organic agriculture, can help both people and the environment. For instance, the non-governmental organization Health in Harmony in Portland, Oregon, has invested in community-designed interventions in Indonesian Borneo. During 2007–17, these contributed to a 90% reduction in the number of households that were reliant on illegal logging for their main livelihood. This, in turn, reduced local rainforest loss by 70%. Infant mortality also fell by 67% in the programme’s catchment area12.Systems-oriented interventions of this type need to be better understood, and the most effective ones scaled up.Wise investmentSuch strategies to prevent spillover would reduce our dependence on containment measures, such as human disease surveillance, contact tracing, lockdowns, vaccines and therapeutics. These interventions are crucial, but are often expensive and implemented too late — in short, they are insufficient when used alone to deal with emerging infectious diseases.The COVID-19 pandemic has exposed the real-world limitations of these reactive measures — particularly in an age of disinformation and rising populism. For example, despite the US federal government spending more than $3.7 trillion on its pandemic response as of the end of March, nearly one million people in the United States — or around one in 330 — have died from COVID-19 (see go.nature.com/39jtdfh and go.nature.com/38urqvc). Globally, between 15 million and 21 million lives are estimated to have been lost during the COVID-19 pandemic beyond what would be expected under non-pandemic conditions (known as excess deaths; see Nature https://doi.org/htd6; 2022). And a 2021 model indicates that, by 2025, $157 billion will have been spent on COVID-19 vaccines alone (see go.nature.com/3jqds76).

A farmer in Myanmar gathers sap from a palm tree to make wine. Contamination of the collection pots with excretions from bats can spread diseases to humans.Credit: Wolfgang Kaehler/LightRocket via Getty

Preventing spillover also protects people, domesticated animals and wildlife in the places that can least afford harm — making it more equitable than containment. For example, almost 18 months since COVID-19 vaccines first became publicly available, only 21% of the total population of Africa has received at least one dose. In the United States and Canada, the figure is nearly 80% (see go.nature.com/3vrdpfo). Meanwhile, Pfizer’s total drug sales rose from $43 billion in 2020 to $72 billion in 2021, largely because of the company’s COVID-19 vaccine, the best-selling drug of 202113.Lastly, unlike containment measures, actions to prevent spillover also help to stop spillback, in which zoonotic pathogens move back from humans to animals and then jump again into people. Selection pressures can differ across species, making such jumps a potential source of new variants that can evade existing immunity. Some researchers have suggested that spillback was possibly responsible for the emergence of the Omicron variant of SARS-CoV-2 (see Nature 602, 26–28; 2022).Seize the dayOver the past year, the administration of US President Joe Biden and two international panels (one established in 2020 by the WHO and the other in 2021 by the G20) have released guidance on how to improve approaches to pandemics. All recommendations released so far acknowledge spillover as the predominant cause of emerging infectious diseases. None adequately discusses how that risk might be mitigated. Likewise, a PubMed search for the spike protein of SARS-CoV-2 yields thousands of papers, yet only a handful of studies investigate coronavirus dynamics in bats, from which SARS-CoV-2 is likely to have originated14.Spillover prevention is probably being overlooked for several reasons. Upstream animal and environmental sources of pathogens might be being neglected by biomedical researchers and their funders because they are part of complex systems — research into which does not tend to lead to tangible, profitable outputs. Also, most people working in public health and biomedical sciences have limited training in ecology, wildlife biology, conservation and anthropology.There is growing recognition of the importance of cross-sectoral collaboration, including soaring advocacy for the ‘One Health’ approach — an integrated view of health that recognizes links between the environment, animals and humans. But, in general, this has yet to translate into action to prevent pandemics.Another challenge is that it can take decades to realize the benefits of preventing spillover, instead of weeks or months for containment measures. Benefits can be harder to quantify for spillover prevention, no matter how much time passes, because, if measures are successful, no outbreak occurs. Prevention also runs counter to individual, societal and political tendencies to wait for a catastrophe before taking action.The global pandemic fund, the WHA pandemic agreement and the post-2020 global biodiversity framework all present fresh chances to shift this mindset and put in place a coordinated global effort to reduce the risk of spillover alongside crucial pandemic preparedness efforts.Global fund for pandemicsFirst and foremost, a global fund for pandemics will be key to ensuring that the wealth of evidence on spillover prevention is translated into action. Funding for spillover prevention should not be folded into existing conservation funds, nor draw on any other existing funding streams.Investments must be targeted to those regions and practices where the risk of spillover is greatest, from southeast Asia and Central Africa to the Amazon Basin and beyond. Actions to prevent spillover in these areas, particularly by reducing deforestation, would also help to mitigate climate change and reduce loss of biodiversity. But conservation is itself drastically underfunded. As an example, natural solutions (such as conservation, restoration and improved management of forests, wetlands and grasslands) represent more than one-third of the climate mitigation needed by 2030 to stabilize warming to well below 2 °C15. Yet these approaches receive less than 2% of global funds for climate mitigation16. (Energy systems receive more than half.)In short, the decision-makers backing the global fund for pandemics must not assume that existing funds are dealing with the threat of spillover — they are not. The loss of primary tropical forest was 12% higher in 2020 than in 2019, despite the economic downturn triggered by COVID-19. This underscores the continuing threat to forests.Funding must be sustained for decades to ensure that efforts to reduce the risk of spillover are in place long enough to yield results.WHA pandemic agreementIn 2020, the president of the European Council, Charles Michel, called for a treaty to enable a more coordinated global response to major epidemics and pandemics. Last year, more than 20 world leaders began echoing this call, and the WHA launched the negotiation of an agreement (potentially, a treaty or other international instrument) to “strengthen pandemic prevention, preparedness, and response” at the end of 2021.Such a multilateral agreement could help to ensure more-equitable international action around the transfer of scientific knowledge, medical supplies, vaccines and therapeutics. It could also address some of the constraints currently imposed on the WHO, and define more clearly the conditions under which governments must notify others of a potential disease threat. The COVID-19 pandemic exposed the shortcomings of the International Health Regulations on many of these fronts17. (This legal framework defines countries’ rights and obligations in the handling of public-health events and emergencies that could cross borders.)We urge negotiators to ensure that the four actions to prevent spillover outlined here are prioritized in the WHA pandemic agreement. For instance, it could require countries to create national action plans for pandemics that include reducing deforestation and closing or strictly regulating live wildlife markets. A reporting mechanism should also be developed to evaluate progress in implementing the agreement. This could build on experience from existing schemes, such as the WHO Joint External Evaluation process (used to assess countries’ capacities to handle public-health risks) and the verification regime of the Chemical Weapons Convention.Commitments to expand pathogen surveillance at interfaces between humans, domesticated animals and wildlife — from US mink farms and Asian wet markets to areas of high deforestation in South America — should also be wrapped into the WHA agreement. Surveillance will not prevent spillover, but it could enable earlier detection and better control of zoonotic outbreaks, and provide a better understanding of the conditions that cause them. Disease surveillance would improve simply through investing in clinical care for both people and animals in emerging infectious-disease hotspots.Convention on Biological DiversityWe are in the midst of the sixth mass extinction, and activities that drive the loss of biodiversity, such as deforestation, also contribute to the emergence of infectious disease. Meanwhile, epidemics and pandemics resulting from the exploitation of nature can lead to further conservation setbacks — because of economic damage from lost tourism and staff shortages affecting management of protected areas, among other factors18. Also, pathogens that infect people can be transmitted to other animals and decimate those populations. For instance, an Ebola outbreak in the Republic of Congo in 2002–03 is thought to have killed 5,000 gorillas19.Yet the global biodiversity framework currently being negotiated by the Convention on Biological Diversity fails to explicitly address the negative feedback cycle between environmental degradation, wildlife exploitation and the emergence of pathogens. The first draft made no mention of pandemics. Text about spillover prevention was proposed in March, but it has yet to be agreed on.Again, this omission stems largely from the siloing of disciplines and expertise. Just as the specialists relied on for the WHA pandemic agreement tend to be those in the health sector, those informing the Convention on Biological Diversity tend to be specialists in environmental science and conservation.The global biodiversity framework, scheduled to be agreed at the Conference of the Parties later this year, must strongly reflect the environment–health connection. This means explicitly including spillover prevention in any text relating to the exploitation of wildlife and nature’s contributions to people. Failing to connect these dots weakens the ability of the convention to achieve its own objectives around conservation and the sustainable use of resources.Preventive health careA reactive response to catastrophe need not be the norm. In many countries, preventive health care for chronic diseases is widely embraced because of its obvious health and economic benefits. For instance, dozens of colorectal cancer deaths are averted for every 1,000 people screened using colonoscopies or other methods20. A preventive approach does not detract from the importance of treating diseases when they occur.With all the stressors now being placed on the biosphere — and the negative implications this has for human health — leaders urgently need to apply this way of thinking to pandemics. More

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