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Combined culture-dependent and culture-independent approach identifies the prevalent and viable bacterial community members of the human lung post-transplantTo characterize the bacterial community composition of the lung microbiota post-transplant, we performed 16S rRNA gene amplicon sequencing of 234 longitudinal BALF samples from 64 lung transplant recipients collected over a 49-month period (Fig. 1a, Supplementary Table 1). A total of 7164 operational taxonomic units (OTUs) were identified, excluding OTUs contributing to reads in 11 negative control samples32 (see “Methods”, Supplementary Fig. 1a, Supplementary Data 1 and 2). In accordance with previous studies on BALF samples from healthy non-transplant individuals4,5,6,26, we found that Bacteroidetes and Firmicutes followed by Proteobacteria and Actinobacteria are the most abundant phyla in the post-transplant lung (Fig. 1b). Prevalence analysis across all BALF samples showed that the community composition is highly variable with only 22 OTUs shared by ≥50% of the samples (Supplementary Fig. 1b, Supplementary Data 3). However, these 22 OTUs constituted 42% of the total number of rarefied reads, indicating that they are predominant members of the post-transplant lung microbiota (Fig. 1c, Supplementary Fig. 1c, Supplementary Table 2, Supplementary Data 3). They belonged to the genera Prevotella 7, Streptococcus, Veillonella, Neisseria, Alloprevotella, Pseudomonas, Gemella, Granulicatella, Campylobacter, Porphyromonas and Rothia, the majority of which are also prevailing community members in the healthy human lung3,5,7,26, suggesting a considerable overlap in the overall composition of the lung microbiota between the healthy and the transplanted lung.Fig. 1: Combining BALF amplicon sequencing and bacterial culturing to deduce the microbial ecology of deep lung microbiota.a Schematic of the sampling of Bronchoalveolar lavage fluid (BALF) from lung transplant recipients over time (months post-transplant). b Relative abundances (%) of most abundant phyla across BALF samples. Box plots show median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers) as well as outliers (single points). c Prevalence (% samples) vs contribution to total reads across samples for most abundant phyla. Dot color shows different genera and size show total rarefied reads. Gray dashed horizontal line shows prevalence ≥50%. d Scatter plot shows correlation between number of observed OTUs and bacterial counts per BALF sample obtained by quantifying 16S rRNA gene copies with qPCR. Linear regression is shown by the blue line with gray shaded area showing 95% confidence interval (n = 234, two-sided, F(1, 232) = 91.04, P = 2.2 × 10−16), Coefficient of correlation; R2 = 0.28. e Bar chart shows lung taxa (genera; OTU IDs) that contributed ≥75% of total bacterial biomass across samples (n = 234). Venn diagram inset shows overlap (yellow) between the most prevalent (≥50% incidence, light blue) and the most abundant (≥75% total count, red) taxa in the transplanted lung. Bar colors also show the same.Full size imageDifferences in bacterial loads between samples can skew community analyses when based on relative abundance profiling alone. Therefore, we used qPCR to determine the total copies of the 16S rRNA gene as an estimate for bacterial counts, and normalized the abundances of each OTU across the 234 samples (absolute abundance). We found that the bacterial counts vastly differed between samples, ranging between 101 and 106 gene copies per ml of BALF (Supplementary Fig. 1d). The number of observed OTUs increased with decreasing counts (Fig. 1d) suggesting that a large fraction of the OTUs were detected in samples of low bacterial biomass and hence represent either transient or extremely low-abundant community members, or sequencing artefacts and contaminations. In turn, 19 of the 7164 OTUs constituted >75% of the total bacterial biomass detected across the 234 BALF samples (Fig. 1e). This included 11 of the 22 most prevalent OTUs (see above) plus eight OTUs that were detected in only a few samples but at very high abundance (Staphylococcus; OTU_2, Corynebacterium 1; OTU_16 and OTU_24, Anaerococcus; OTU_49 and OTU_234, Haemophilus; OTU_78, Streptococcus; OTU_6768, Peptoniphilus; OTU_63, Supplementary Table 2). It is important to differentiate these opportunistic colonizers from other community members with low incidence, as they reached very high bacterial counts in some samples with potential implications for lung health.To demonstrate the viability of prevalent lung microbiota members and to establish a reference catalogue of bacterial isolates from the human lung for experimental studies, we complemented the amplicon sequencing with a bacterial culturing approach (Supplementary Fig. 2). We cultivated 21 random BALF samples from 18 individuals, on 15 different semi-solid media (both general and selective) in combination with 3 oxygen concentrations; aerobic, 5% CO2, and anaerobic (See “Methods” and Supplementary Table 3), representing 26 different conditions. We cultured fresh BALF immediately upon extraction (within 2 h), as we observed loss in bacterial diversity upon cultivating frozen samples. This resulted in a total of 300 bacterial isolates, representing 5 phyla, 7 classes, 13 orders, and 17 families from which we built an open-access biobank called the Lung Microbiota culture Collection (LuMiCol, Supplementary Data 4, https://github.com/sudu87/Microbial-ecology-of-the-transplanted-human-lung).To examine the extent of overlap between bacteria in LuMiCol and the diversity obtained by amplicon sequencing, we included 16S rRNA gene sequences from 215 isolates that passed our quality filter into the community analysis, which allowed for the retrieval of OTU-isolate matching pairs32 (Methods). We found that 213 isolates matched to 47 OTUs (Fig. 2a, c, Supplementary Data 5), including 17 of the most prevalent and abundant bacteria (Fig. 1e, Supplementary Table 2). As expected, bacteria with high abundance in the amplicon sequencing-based community analysis were isolated more frequently, with Firmicutes revealing the highest isolate diversity (Fig. 2a–c, Supplementary Data 4, 5) and being recovered under the most diverse culturing conditions.Fig. 2: A lung microbiota culture collection (LuMiCol) reveals extended diversity and phenotypic characteristics of the lower airway bacterial community.a Phylogenetic tree of the 47 OTU-isolate matching pairs inferred with FastTree. Branch bootstrap support values (size of dark gray circles) ≥80% are displayed. b Growth characteristics of each OTU-isolate matching pair in three different oxygen conditions (Anaerobic – light brown, 5% CO2-yellow, aerobic-light blue, n = 3). Column with pie charts shows growth on semi-solid agar. Heatmap shows median change in Optical Density (OD) at 600 nm growth in three different liquid media (THY, RPMI, RPMI without glucose) over 3 days. c Cumulative counts of each OTU-isolate matching pair across all BALF samples (gray). d Number of isolates in Lumicol (black) per OTU-isolate matching pair. Taxa are labeled as genus; OTU ID, with an indication of whether they are prevalent (gray rectangle) or opportunistic (magenta rectangle) in the lower airway community. The names of the closest hit in databases: eHOMD and SILVA are used as species descriptor.Full size imageIn summary, our results from the combined culture-dependent and culture-independent approach show that the lung microbiota post-transplant is highly variable in terms of both bacterial load and community composition with many transient and low-abundant bacterial taxa. However, a few community members display relatively high prevalence and/or abundance suggesting that they represent important colonizers of the human lung.LuMiCol informs on the diversity and metabolic preferences of culturable human lung bacteriaWe characterized the culturable community members of the lower respiratory tract contained in LuMiCol by testing a wide range of growth conditions and phenotypic properties (see “Methods”). The majority of the cultured isolates could taxonomically be assigned at the species level based on genotyping of the 16S rRNA gene V1-V5 region. However, the limited taxonomic resolution offered by this method does not allow to discriminate between closely related strains, which can include both pathogenic and non-pathogenic bacteria. Hence for Streptococcus, we additionally tested for type of hemolysis (alpha, beta, or gamma) and resistance to optochin, which differentiates the pathogenic pneumococcus and the non-pathogenic viridans groups (Fig. 2a, Supplementary Fig. 2b, c). This demonstrated that the 16 Streptococcus OTU-isolate pairs belong to the viridans group of streptococci (VS)33. Interestingly, these isolates exhibited the highest genotypic and phenotypic diversity throughout our collection and belonged to five OTUs among the 22 most prevalent community members, with Streptococcus mitis (OTU_11) present in 93.6% of all samples.BALF from healthy individuals contains amino acids, citrate, urate, fatty acids, and antioxidants such as glutathione but no detectable glucose34, which is associated with increased bacterial load and infection35,36,37. To get insights into basic bacterial metabolism, we assessed the growth of all 47 isolates matching an OTU under different oxygen concentrations. We used undefined rich media (Todd-Hewitt Yeast extract) and defined low-complexity liquid media (RPMI 1640), including a glucose-free version to mimic the deep lung environment (see “Methods”). Despite the presence of oxygen in the human lung, the majority of the isolates were either obligate or facultative anaerobes (Fig. 2a), including some of the most prevalent members (Prevotella melaninogenica (OTU_3), Streptococcus mitis (OTU_11), Veillonella atypica (OTU_6) and Granulicatella adiacens (OTU_17). A similar trend was also observed in liquid media under anaerobic conditions, with the exception of the genera Prevotella, Veillonella and Granulicatella. Most streptococci from the human lung grew best in complex liquid media containing glucose under anaerobic conditions, including the most prevalent species in our cohort, S. mitis (OTU_11) (Fig. 2b). However, noticeable exceptions were S. vestibularis (OTU_34), S. oralis (OTU_3427 and OTU_1567), and S. gordonii (OTU_10031), which grew equally well in the presence of oxygen and in low-complexity liquid medium (Fig. 2b). Most Actinobacteria grew best on rich medium in the presence of 5% CO2, with an exception of Actinomyces odontolyticus (OTU_39), which required anaerobic conditions. Some Actinobacteria grew equally well in anaerobic conditions as in the presence of 5% CO2, i.e., Corynebacterium durum (OTU_501), Actinobacteria sp. oral taxon (OTU_328 and OTU_228).The two most predominant opportunistic pathogens in our lung cohort, P. aeruginosa (OTU_1) and S. aureus (OTU_2), grew best in rich liquid medium in the presence of oxygen (Fig. 2c), although these also grew to lower degree under anaerobic conditions. These results indicate that changes in the physicochemical conditions in the lung may favor the growth of these two opportunistic pathogens. In summary, our observations from the bacterial culture collection provide first insights into the phenotypic properties of human lung bacteria and will serve as a basis for future experimental work.Identification of four compositionally distinct pneumotypes post-transplant using machine learning based on ecological metricsTo detect and characterize differences in bacterial community composition between BALF samples from transplant patients, we clustered the samples using an unsupervised machine learning algorithm based on pairwise Bray–Curtis dissimilarity32 (beta diversity, See “Methods”, Supplementary Data 6). This segregated the samples into four partitions around medoids (PAMs) at both phylum and OTU level (Fig. 3a, b, Supplementary Fig. 3a, b). We refer to these clusters as “pneumotypes” PAM1, PAM2, PAM3, and PAM4 (Supplementary Table 4). PAM1 formed the largest cluster consisting of the majority of samples (n = 115) followed by PAM3 (n = 76), PAM2 (n = 19), and PAM4 (n = 24) (Supplementary Data 7). Examination of various diversity measures (Species occurrence, OTU diversity, OTU richness, Fig. 3c–e), distribution of the dominant community members (Fig. 3f), and bacterial counts (16S rRNA gene copies, Fig. 3g) revealed distinctive characteristics between the four pneumotypes.Fig. 3: Bacterial communities of the lung post-transplant fall into four ‘pneumotypes’ with distinct community characteristics.a, b Principal component analysis shows Partition around medoids (PAMs) at phylum and OTU level respectively generated by k-medoid-based unsupervised machine learning using Bray–Curtis dissimilarity (occurrence and abundance). Pneumotypes are color coded: Balanced (red, n = 115), Staphylococcus (green, n = 19), Microbiota-depleted (MD, blue, n = 76), and Pseudomonas (orange, n = 24). c–g Violin plots show distributions of pairwise species occurrence (Sorenson’s index, PERMANOVA, two-sided, F(3, 229) = 8.49, P = 9.9 × 10−5), OTU diversity (Kruskal–Wallis test, χ2 = 89.2, df = 3, two-sided, P = 2.2 × 10−16), OTU richness (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), proportion of most dominant OTUs (Kruskal–Wallis test, χ2 = 94.45, df = 3, two-sided, P = 2.2 × 10−16), and total bacterial counts (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), respectively, across the four pneumotypes. h, i Enrichment analysis of prevalence (green dotted line ≥50%) and absolute abundance across all samples of the 30 most dominant taxa (i.e., OTUs) in PneumotypeBalanced and PneumotypeMD respectively, when each was compared to the other three combined pneumotypes (gray boxes). 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The experiment underlying the study provides a diversity gradient of 1–60 plant species, established in assemblages randomly chosen from a pool of species typical of Arrhenatheretum grasslands. Recently sown on fertile arable soil and maintained by weeding, this experiment is a highly artificial system that fails to meet the definition of semi-natural grasslands7. Four years after establishment, a management intensity gradient of one to four annual cuts and three fertilization levels was established in subplots randomly assigned to the 1–60-species plots. Data presented in this study were collected in the following year.Intensive management was thus imposed on plant species typical of Arrhenaterethum meadows, a plant community characterized by two annual cuts8. The potential effect size of increased management intensity is thus underestimated by applying the management to a plant community not adapted to it. More importantly, it is unlikely that the species-richness of high-diversity plots could be maintained under increased management intensity over longer periods. In fact, 22% of these subplots managed at very high intensity had to be excluded for missing or insufficient yield after only two years, indicating that their species did not persist under high defoliation frequency and fertilizer levels, even when competitors were excluded by weeding.While the discussion hardly addresses this crucial trade-off between management intensity and plant diversity, Schaub et al.6 do indicate that repeated resowing is likely to be necessary to maintain high diversity under increased management intensities. In contrast to permanent grasslands, whose species composition is shaped by site conditions and management, species selection in (re-)sown grasslands is a conscious choice. To be advantageous, mixtures have to show larger yields than the most productive monoculture, so-called transgressive overyielding. Transgressive overyielding is one of the reasons why mixtures, especially grass-clover mixtures, are frequently used in sown grasslands. A European-scale experiment demonstrated that four-species mixtures showed transgressive overyielding at a wide range of sites under intensive agricultural management9,10. Although Schaub et al.6 generally quantify the diversity effects in comparison to monocultures, they argue that grasslands with the high-diversity characteristic of semi-natural grasslands have benefits not only over monocultures but over low-diversity grasslands, such as the 1–8 species standard mixtures shown in Fig. 6 of their paper. However, their results fail to demonstrate that their high-diversity plots show any transgressive overyielding even over monocultures, not to speak of low-diversity mixtures. As species assemblages of the experiment are randomly drawn from the species pool, monocultures and low-diversity mixtures cannot be expected to include the most productive species or species combinations and thus cannot be used to assess transgressive overyielding. When transgressive overyielding was quantified for one- to eight-species plots of the same experiment under extensive management in 2003, it decreased with species number. While two-species mixtures showed a mean transgressive overyielding of 5%, eight-species mixtures were only 70% as productive as the corresponding best monoculture, on average11.Accordingly, the experimental design fails to capture the real trade-offs faced by grassland managers, either in permanent or in sown grassland. It cannot answer if high levels of diversity and the associated biodiversity benefits can be maintained under intensive management for a longer period than just a few years. Neither can it show a productivity benefit of high-diversity grassland assemblages compared to species-poor mixtures, or even monocultures, when in practice the sown species are deliberately chosen rather than randomly drawn from a species pool. While the underlying biodiversity experiment has made valuable contributions to our fundamental understanding of plant diversity effects on ecosystem functioning, it thus cannot be used to derive direct management recommendations for managed grassland. More

Study areaThis study was conducted using tree census data collected from 119 forest inventory plots (73 tropical, 46 subtropical) situated across a latitudinal range of 7.1°N (Colombia) to 27.8°S (Argentina), a longitudinal range of 79.5° to −63.8° W, and an elevation range of 500–3511 m asl (Fig. 1). The mean annual temperature (MAT) of plots ranged from 7.3 to 23.8 °C (mean = 16.7 ± 4.1 °C; mean ± SD) and mean annual precipitation (MAP) of the plots ranged from 608 to 4313 mm y−1 (mean = 1405.0 ± 623.9 mm y−1) (External Databases 1). The number of plots sampled in each country was: Argentina = 46, Bolivia = 26, Peru = 16, Ecuador = 21, and Colombia = 10 (Fig. 1). The 119 forest plots ranged in size from 0.32 to 1.28 ha and represent a cumulative sample area of 104.4 ha (horizontal areas corrected for slope) that containe more than 63,000 trees with a diameter at breast height (DBH, 1.3 m) ≥10 cm (External Database 1). Ninety-four of the plots (79.0%) were ≥1 ha in size. Neither secondary forests nor plantations were included. However, only seven of the plots (five in Argentina and two in Bolivia) were located in forests >100 km2 in extent41, which suggests that at least the edges and borders of some plots could have experienced some degree of disturbance or degradation. All plots were censused at least twice between 1991 and 2017 (census intervals ranged between 2 and 9 years).In each plot, we tagged, mapped, measured, and collected vouchers of all trees and palms (DBH ≥ 10 cm). DBH was measured 50 cm above buttresses or aerial roots when present (where the stem was cylindrical). During the second or subsequent set of censuses, DBH growth, recruitment, and mortality were recorded. In cases where the recorded DBH growth of the second census was less than −0.1 cm y−1 or greater than 7.5 cm y−1, the DBH of the second census was augmented/reduced in order to match these minimum/maximum values42. To homogenize and validate species names of palms and trees recorded in each country and plot, we submitted the combined list from all plots to the Taxonomic Name Resolution Service (TNRS; http://tnrs.iplantcollaborative.org/) version 3.0. Any species with an unassigned TNRS accepted name or with a taxonomic status of ‘no opinion’, ‘illegitimate’, or ‘invalid’ was manually reviewed. Families and genera were changed in accordance with the new species names. If a full species name was not provided or could not be found, the genus and/or family name from the original file was retained.Aboveground carbon stocksThe aboveground biomass (AGB) of each tree was estimated using the allometric equation proposed by Chave et al43., defined as: AGB = 0.0673 × (WD × DBH2 × H)0.976 where AGB (kg) is the estimated aboveground biomass, DBH (cm) is the diameter of the tree at breast height, H (m) is the estimated total height, and WD (g cm−3) is the stem wood density. To estimate WD, we assigned the WD values available in the literature44 to each species found in each plot. In cases where we could not assign a WD value at the species level, we used the average value at the genus- or family level. For unidentified individuals, we used the average WD value of all other species in the plot. Tree height (H) was estimated (see below) based on the heights measured on a subset of the individual stems in each plot using digital hypsometers or clinometers. The estimated AGB of each tree was then converted to units of aboveground carbon (AGC) by applying a conversion factor of 1 kg AGB = 0.456 kg C45. The AGC per ha was then determined by converting kg to Mg, summing the values for all trees in a plot, and extrapolating or interpolating to a sample area of 1 ha.Estimates of AGB and AGC are highly dependent on tree height. Unfortunately, tree height was difficult or impossible to measure on all stems due to physical and logistical constraints. Therefore, we estimated the height of each stem based on allometric relationships between DBH and tree height that we developed for each plot based on height and DBH measurements taken on a subset of individuals. Although the AGB/AGC estimates are only for trees with DBH ≥ 10, we used trees with DBH ≥ 5 cm to construct the H:DBH models when possible in order to be as comparable as possible with the existing pantropical H:DBH models46. In total, 44,442 trees had their heights measured in the field and were employed to construct the H:DBH models. The percentage of trees with direct field measurements of H (DBH ≥ 5 cm) in each country was: Argentina = 19%, Bolivia = 98%, Peru = 96%, Ecuador = 97%, and Colombia = 46%. In Argentina, 32 of 46 plots did not have any field measurements of H, while all plots in all other countries had field measurements of H for at least a subset of trees.We tested and compared the expected effects of using H:DBH models constructed using the local (plot), country, or pantropical (regional) level data. To select the best model to estimate H from DBH at the plot and country level, we used the function modelHD available in the BIOMASS package for R47. We chose the best allometric model from four candidate models (two log-log polynomial models, the three-parameter Weibull model, and a two-parameter Michaelis-Menten model (Supplementary Table 7)) by selecting the model with the lowest RSE and bias (Supplementary Table 8). At the regional level, we used a pantropical model46. The use of country and pantropical H:DBH allometries underestimates tree heights in the lowlands and overestimates tree heights in highlands, thereby homogenizing AGB estimates along elevational gradients10,48 (Supplementary Figs. 11, 12, 13). Using plot level allometries eliminates this problem. However, in the 32 plots in Argentina where we had no information about tree height, we used the country-level H:DBH model developed with the data available in the remaining 14 plots to estimate the height of each tree, which could have homogenized the AGC estimates along the Argentinian elevational gradient (Supplementary Figs. 11, 12, 13).Aboveground carbon dynamicsThe AGC dynamics of each plot was estimated from the annualized values of AGC mortality, AGC productivity (AGC change due to recruitment + growth), and AGC net change3. The calculations of the separate AGC dynamic components was performed as follows: (i) AGC mortality (Mg ha−1 y−1) = the sum of the AGC of all individuals that died between censuses divided by the time between measurements. (ii) AGC recruitment (Mg C ha−1 y−1) = the sum of the AGC of individuals that recruited into DBH ≥ 10 cm between censuses divided by the time between measurements. However, for each tree recruited (DBH ≥ 10 cm), we subtracted the corresponding AGC associated with a tree of 9.99 cm (i.e. just below the detection limit) in order to avoid overestimations of the overall increase in AGC due to recruitment49. (iii) AGC growth (Mg ha−1 y−1) = the sum of the increase in AGC of all individuals with DBH ≥ 10 cm that survived between censuses divided by the time between censuses. (iv) AGC net change (Mg ha−1 y−1) = the difference between AGC stock in the last census (AGCfinal) and AGC stock in the first census (AGC1) divided by the elapsed time (t; in years) between measurements [(AGC net change = AGCfinal − AGC1)/t]. We recognize that these methods exclude C stored in soils or in belowground tissues9,48; however, quantifying just aboveground C stocks and fluxes provides valuable information about the overall status of these forests as net C sinks or sources.ClimateClimate variables at each plot location were extracted from the CHELSA28 bioclimatic rasters at a resolution of 30-arcsec (~1 km2 at the equator). The climate variables extracted were: Mean Annual Temperature (MAT), Mean Diurnal Range (MDR), Isothermality (Isoth), Temperature Seasonality (TS), Maximum Temperature of Warmest Month (MaxTWarmM), Minimum Temperature of Coldest Month (MinTCM), Temperature Annual Range (TAR), Mean Temperature of Wettest Quarter (MeanTWarmQ), Mean Temperature of Driest Quarter (MeanTDQ), Mean Temperature of Warmest Quarter (MeanTWetQ), Mean Temperature of Coldest Quarter (MeanTCQ), Mean Annual Precipitation (MAP), Precipitation of Wettest Month (PWetM), Precipitation of Driest Month (PDM), Precipitation Seasonality (PS), Precipitation of Wettest Quarter (PWetQ), Precipitation of Driest Quarter (PDQ), Precipitation of Warmest Quarter (PWarmQ), Precipitation of Coldest Quarter (PCQ). We separated all variables associated with temperature (°C) from those associated with precipitation (mm y−1) and applied a Principal Component Analysis (PCA) to the 11 variables associated with temperature (PCAtemp) and a separate PCA to the eight variables associated with precipitation (PCAprec). The first two principal components of both PCAtemp and PCAprec (four PCA axes in total) were selected for use in subsequent analyses. Plot elevations were estimated based on their coordinates and the SRTM 1 ArcSec Global V3 (https://lta.cr.usgs.gov) 30 m resolution digital elevation model (DEM).PCAtemp1 (Supplementary Fig. 1a) explained 53.0% of the total variance of the temperature variables and had high loading from Isothermality and Maximum Temperature of Warmest Month, which was primarily associated with changes in elevation (r = −0.97, p  More

Single plasmid, single-population modelsTo understand the dynamics of parasitic plasmids in complex ecologies, we first need to understand their behavior in simple scenarios. In this section, we analyze the dynamics of plasmids spreading by different HGT mechanisms in single populations. We begin by modeling competition between plasmid-free cells and cells containing a conjugative plasmid. A nutrient, with concentration (C), is supplied to the system at rate (S). Cells grow at a rate proportional to (C) with proportionality constant (alpha) for plasmid-free cells or ((1 ,-, {Delta})alpha) for plasmid-containing cells. Since we are interested in parasitic plasmids, we assume that ({Delta} in (0,1)). Cells of both types die at a rate (delta). When a plasmid-containing cell divides there is a loss probability, (p_ell), for one of the daughter cells to contain no plasmids. As long as a daughter cell contains at least one plasmid, the original plasmid copy number (the number of copies of the plasmid maintained per cell) is regenerated (as depicted in Fig. 1A). Plasmids can spread horizontally by conjugation, as illustrated in Fig. 1B, wherein a plasmid-free cell and a plasmid-containing cell interact to produce two plasmid-containing cells. We model the rate of conjugation by a mass-action term with rate (gamma _{mathrm{c}}). The equations governing the dynamics of conjugation are therefore:$$ frac{{drho }}{{dt}} ,=, alpha Crho ,-, gamma _{mathrm{c}}rho rho _{mathrm{p}} ,+, p_ell (1 ,-, {Delta})alpha Crho _{mathrm{p}} ,-, delta rho ,\ frac{{drho _{mathrm{p}}}}{{dt}} ,=, (1 ,-, {Delta})alpha Crho _{mathrm{p}} ,+, gamma _{mathrm{c}}rho rho _{mathrm{p}} ,-, p_ell (1 ,-, {Delta})alpha Crho _{mathrm{p}} ,-, delta rho _{mathrm{p}},\ frac{{dC}}{{dt}} ,=, S ,-, alpha Crho ,-, (1 ,-, {Delta})alpha Crho _{mathrm{p}}.$$
(1)
Fig. 1: Different modeled mechanisms of plasmid transfer lead to distinct ecological phase diagrams, but all such mechanisms leave individual populations susceptible to runaway plasmid invasion.A At each division, plasmids are randomly segregated between daughter cells. Original plasmid copy number is regenerated if at least one plasmid remains in a daughter cell. B Schematic of plasmid transfer mechanisms. Left: spread of plasmids by plasmid-containing cells conjugating with plasmid-free cells. Right: spread of plasmids by extracellular plasmids infecting plasmid-free cells via transformation. C Phase diagram for conjugative plasmids as a function of plasmid cost, ({Delta}), and (gamma _{mathrm{c}}); (delta ,=, 0.1), (S ,=, 1), (p_ell ,=, 0), and (alpha ,=, 1) (see Eq. 4). D Phase diagram for transformative plasmids as a function of ({Delta}) and (gamma _{mathrm{t}}). Parameters as in C with (delta _{mathrm{p}} ,=, 0.3) and (n_{{mathrm{eff}}} ,=, 0.6) (see Eq. 9). See “Methods” for details. E In model multiplasmid cells, plasmid types segregate independently. If at least one plasmid of a given type remains in a daughter cell, the full copy number of that plasmid type is regenerated. F Fitness cost as a function of number of unique plasmid types in a cell for multiplicative case ({Delta}_{{mathrm{tot}}} ,=, 1 ,-, (1 ,-, {Delta})^m) with ({Delta} ,=, 0.05). G Steady-state distribution of number of plasmid types per cell at different conjugation rates, measured relative to (gamma _{mathrm{c}}^ ast) (the critical conjugation rate necessary for invasion of a single plasmid into a plasmid-free population, see Eq. 4). Results for eight unique plasmid types with (delta ,=, 1), ({Delta} ,=, 0.1), (alpha ,=, 1), (S ,=, 1), and (p_ell ,=, 0.05).Full size imageIn this model, what are the conditions for a parasitic conjugative plasmid to be able to invade a plasmid-free population? Invasibility implies that the equilibrium containing only plasmid-free cells is locally unstable, which occurs when$$qquadqquadqquadgamma _{mathrm{c}}rho ^ ast , > , delta {Delta} ,+, delta p_ell (1 ,-, {Delta}),$$
(2)
where (rho ^ ast ,=, S/delta) is the steady-state abundance of the plasmid-free cells at the plasmid-free equilibrium. This invasibility condition has an intuitive physical interpretation: to invade, the rate of conjugation must overcome losses due to reduced host growth rate as well as plasmid loss during division. This condition is similar to those found in previous studies [15].Given the condition for plasmid invasion in Eq. 4, what is the optimal behavior for a parasitic conjugative plasmid? The left-hand-side of the expression is linear in the plasmid-free population, meaning that it is more difficult for a plasmid to invade smaller populations. To favor invasion, the plasmid can minimize the right-hand-side of the equation. For a plasmid that relies on random segregation upon cell division, both the plasmid cost ({Delta}) and the loss probability (p_ell) are functions of plasmid copy number, (n_{mathrm{p}}), a property controlled by the plasmid itself. If the primary cost of a plasmid is its replication and its gene products, plasmid cost will scale with copy number such that ({Delta} ,=, {Delta}_{mathrm{p}}n_{mathrm{p}}), where ({Delta}_{mathrm{p}}) is the cost of an individual plasmid copy. The loss probability will be (p_ell ,=, 2^{1 ,-, n_{mathrm{p}}}), i.e., the probability that a daughter cell receives zero plasmids from random segregation. The right-hand-side of the invasion condition Eq. 4 is therefore (delta ({Delta}_{mathrm{p}}n_{mathrm{p}} ,+, 2^{1 ,-, n_{mathrm{p}}}(1 ,-, {Delta}_{mathrm{p}}n_{mathrm{p}}))), which has a minimum at finite (n_{mathrm{p}}). The minimum in the invasion boundary at finite (n_{mathrm{p}}) indicates that in our framework optimal conjugative plasmids have a moderate copy number.What kinds of ecological dynamics does our model for a conjugative parasitic plasmid exhibit? To answer this question, we characterize the stability of the system’s equilibria (see SI Appendix 1 for details). For conjugative plasmids with the optimal copy number, the dominant form of loss will be from reduced host fitness (see SI Fig. S1), and thus we characterize the case of negligible loss rate (p_ell ,=, 0) (we consider the case of finite loss rates in SI Fig. S2 and find similar results). In Fig. 1C we show the phase diagram of possible ecological outcomes as a function of plasmid cost ({Delta}) and conjugation rate (gamma _{mathrm{c}}). For high values of plasmid cost and low values of conjugation rate, the plasmid is unable to invade and the plasmid-free equilibrium is the only stable state. As plasmid cost decreases or conjugation rate increases, plasmids are able to invade and there is a state of stable coexistence between plasmid-free and plasmid-containing cells. The range of conjugation rates permitting coexistence is larger for costlier plasmids. Once the plasmid cost is sufficiently low or the conjugation rate is sufficiently high, the unique stable state consists only of plasmid-containing cells (note that for finite values of loss rate (p_ell), this plasmid-only state will contain a small fraction of plasmid-free cells due to plasmid loss).Conjugation is the best studied mechanism of plasmid transmission, but plasmids can instead be transmitted by transformation, whereby plasmid-free cells are infected by free-floating plasmids, as illustrated in Fig. 1B. We therefore consider a model for plasmid-spread via transformation in which cell death results in release of free-floating plasmids which can then infect cells by mass action at rate (gamma _{mathrm{t}}). For every cell death, (n_{{mathrm{eff}}}) free-floating plasmids are released and these plasmids decay at a rate (delta _{mathrm{p}}). The dynamics of transformative plasmids are therefore:$$ frac{{drho }}{{dt}} ,=, alpha Crho – gamma _{mathrm{t}}rho P ,+, p_ell (1 ,-, {Delta})alpha Crho _{mathrm{p}} ,-, delta rho ,\ frac{{drho _{mathrm{p}}}}{{dt}} ,=, (1 ,-, {Delta})alpha Crho _{mathrm{p}} ,+, gamma _{mathrm{t}}rho P ,-, p_ell (1 ,-, {Delta})alpha Crho _{mathrm{p}} ,-, delta rho _{mathrm{p}},\ frac{{dC}}{{dt}} ,=, S ,-, alpha Crho ,-, (1 ,-, {Delta})alpha Crho _{mathrm{p}},\ frac{{dP}}{{dt}} ,=, n_{{mathrm{eff}}}delta rho _{mathrm{p}} ,-, gamma _{mathrm{t}}rho P ,-, delta _{mathrm{p}}P.$$
(3)
What is the condition for transformative plasmid invasion? The plasmid-free equilibrium is unstable if$$qquadqquadquadgamma _{mathrm{t}}rho ^ ast , > , delta _{mathrm{p}}left( {frac{{{Delta} ,+, p_ell (1 ,-, {Delta})}}{{n_{{mathrm{eff}}} ,-, {Delta} ,-, p_ell (1 ,-, {Delta})}}} right).$$
(4)
The left-hand-side of Eq. 9 is similar to the conjugative plasmid invasion condition, with the conjugation rate (gamma _{mathrm{c}}) replaced by the transformation rate (gamma _{mathrm{t}}). The numerator of the right-hand-side is also similar, with the cell death rate (delta) replaced with the plasmid decay rate (delta _{mathrm{p}}). The primary difference is in the denominator, which is the difference between the number of plasmids released on cell death, (n_{{mathrm{eff}}}), and the total replication deficit of plasmid-containing cells. If this denominator is negative, the inequality reverses and the plasmid-free equilibrium is always stable.The invasion condition in Eq. 9 determines the optimal (n_{mathrm{p}}) of transformative plasmids: if each plasmid within a cell has a fixed probability of remaining viable after cell death, (p_{mathrm{v}}), then (n_{{mathrm{eff}}}) will scale linearly with (n_{mathrm{p}}) such that (n_{{mathrm{eff}}} ,=, p_{mathrm{v}}n_{mathrm{p}}). If the denominator of Eq. 9 is positive, the optimal copy number will be (n_{mathrm{p}} ,=, 1/{Delta}_{mathrm{p}}), the point at which the host’s growth rate is driven to zero and the plasmid relies entirely on horizontal transfer to survive. These results are substantially different than in the case of conjugation: instead of restricting itself to a limited portion of the host’s metabolic budget, a transformative parasite maximizes its spread by using as much of the host’s resources as possible. This is reminiscent of the behavior of phages—suggesting a possible evolutionary link between parasitic plasmids and phages.As in the conjugation case, we now explore the ecological outcomes possible with transformative plasmids. We again consider the case of negligible loss rate (p_ell ,=, 0) and characterize the stability of the equilibria (see SI Appendix 1 for details). For (n_{{mathrm{eff}}} , > , 1), the system has similar ecological outcomes to the conjugative case, with the system transitioning through no-plasmid, coexistence, and plasmid-only equilibria as ({Delta}) decreases and (gamma _{mathrm{t}}) increases. Interestingly, when (n_{{mathrm{eff}}} , , 0.} end{array}$$
(5)
Fig. 2: Competition between populations may prevent runaway plasmid invasion.A Illustration of multiple populations, each occupying an isolated “deme”. During each epoch, populations compete for demes, with plasmid invasion occurring randomly (see Eq. 11 for details). In the example shown, in the first epoch, the population with two plasmids is replaced by the population with zero plasmids. In the second epoch, the population with magenta plasmids is invaded by the green plasmid. B Multiplasmid fitness costs for different types of epistasis. With no epistasis, fitness burden is multiplicative as in Fig. 1F. With positive epistasis, fitness burden increases sub-multiplicatively (pictured: ({Delta}_{{mathrm{tot}}} ,=, {Delta}) for (m , > , 0)). For negative epistasis, fitness burden increases super-multiplicatively (pictured: ({Delta}_{{mathrm{tot}}} ,=, 1 ,-, (1 ,-, {Delta})^{m^{3/2}})). C Steady-state distributions of number of plasmid types per cell in the Wright–Fisher model (see SI Appendix 3). Parameters ({Delta} ,=, 0.01) and plasmid invasion probability for each time period (q ,=, 0.005).Full size imageA population’s fitness is dependent on the number of unique plasmid types it contains. Thus far, we have considered a simple multiplicative model. However, it has been demonstrated that plasmid–plasmid interactions can modulate plasmid properties. For example, one study found that the presence of a plasmid can reduce the fitness cost of an invading plasmid [12]. To account for this epistasis between plasmids, we also consider fitness costs that increase sub-multiplicatively (positive epistasis) or super-multiplicatively (negative epistasis). We show examples of positive epistasis, negative epistasis, and no epistasis in Fig. 2B.What is the distribution of unique plasmid types across populations in our model with HGT barriers? We derive the stationary distribution of this model for the three different epistasis functions in Fig. 2B and plot them in Fig. 2C (see SI Appendix 3 for details). For the case of no epistasis, the stationary distribution is Poisson-like. Positive epistasis favors carriage of multiple plasmids and results in an exponential-like distribution with a long tail. Negative epistasis has the opposite effect: it penalizes carriage of multiple plasmids and results in a sub-Poissonian distribution with a reduced tail. Importantly, in all cases the runaway invasion of plasmids is stopped. While there is nothing stopping individual populations from being overrun by invading plasmids, these populations are more likely to be out-competed by populations with fewer plasmids. Thus, the single-population “tragedy of the commons” is counteracted at a higher level of selection.Analysis of natural genomesHow does our predicted distribution of unique plasmid types per cell compare to that in natural genomes? To make this comparison, we downloaded all complete bacterial genomes from NCBI (a total of 17,725 genomes) and analyzed their plasmid content. In Fig. 3A, we show the overall distribution of unique plasmid types per genome and corresponding model fits for both positive and no epistasis cases (see “Methods” for fitting details). The natural distribution is exponential-like and is well-fit by a model with positive epistasis. The model fit with no epistasis has too short a tail to be able to fit the data, and this problem becomes even more severe for negative epistasis. Thus, interestingly, we find that the distribution of unique plasmid types in real-world genomes is consistent with parasitic plasmids that ameliorate each other’s fitness costs. The degree of positive epistasis suggested by the data is quite strong—the distribution is nearly a pure exponential. In our model, this corresponds to the case in which the cost of all plasmids beyond the first is zero, such that for (m , > , 1) the parameters controlling both population replication and plasmid invasion are independent of plasmid number. This means that the ratio between consecutive elements of the distribution is constant, yielding an exponential tail. In order to determine whether our conclusions are influenced by oversampling of clinically relevant species, we excluded 91 genera known to be clinically relevant or human-associated and repeated our analysis. The remaining dataset contains nearly 5000 genomes and still shows clear exponential behavior (see SI Fig. S4). We also analyzed whether the presence of engineered strains within the NCBI database influences our results. We found that there are only a small number of these engineered strains and that removing them had negligible impact on our results (see SI Fig. S5).Fig. 3: Comparison of distributions of number of unique plasmid types per cell in natural genomes to Wright–Fisher model.A Distribution of number of plasmid types per cell in 17,725 complete NCBI genomes. Positive epistasis distribution fit with the fitness function ({Delta}_{{mathrm{tot}}} ,=, {Delta}) for (m , > , 0) (best-fit parameters: ({Delta} ,=, 9.8 ,times, 10^{ – 3}), (q ,=, 5.4 ,times, 10^{ – 3})), no epistasis distribution fit with ({Delta}_{{mathrm{tot}}} ,=, 1 ,-, (1 ,-, {Delta})^m) (best-fit parameters: ({Delta} ,=, 3.9 ,times, 10^{ – 3}), (q ,=, 1.4 ,times, 10^{ – 2})). B Distribution of number of plasmid types per cell in 1153 complete Escherichia genomes, with a positive epistasis fit using the fitness function ({Delta}_{{mathrm{tot}}} ,=, 1 ,-, (1 ,-, {Delta})^{m^a}) (best-fit parameters: ({Delta} ,=, 8.3 ,times, 10^{ – 3}), (q ,=, 8 ,times, 10^{ – 3}), (a ,=, 0.33)). C Distribution of number of plasmid types per cell in 576 complete Klebsiella genomes, with a positive epistasis fit using the fitness function as in (B) (best-fit parameters: ({Delta} ,=, 7 ,times, 10^{ – 3}), (q ,=, 9.7 ,times, 10^{ – 3}), (a ,=, 0.43)). Note that in certain limits of our models, only the ratio of (q) and ({Delta}) can be properly estimated, effectively reducing them to single parameter (see SI Appendix 3). D Distribution of number of plasmid types per cell in genomes containing and not containing cas genes. Genomes are considered cas containing if at least one chromosome or plasmid within the genome contains a cas gene. See “Methods” for details.Full size imageCan our model capture variation within smaller, related groups of genomes? In Fig. 3B we show the distribution of unique plasmid types per cell within the genus Escherichia. As can be seen, the data is very well fit by a model of parasitic plasmids with positive epistasis. However, our model was not able to capture some of the within-genus distributions we encountered. A notable exception is the distribution of unique plasmid types per cell in the genus Klebsiella, shown in Fig. 3C. In this genus, there is a substantial discontinuity between the zero-plasmid class and the rest of the distribution. While our simple Wright–Fisher model with some positive epistasis can capture the tail of the distribution, it then fails to capture the first few classes. Despite such exceptions, we find that the positive epistasis model is generally able to capture the overall trends in plasmid distributions over the bulk of natural genomes (see SI Fig. S6).It should be noted that our current model of constant plasmid invasion probability and strong positive epistasis is not the only Wright–Fisher model that can produce an exponential distribution matching the data. We analyzed a more general form of the Wright–Fisher model in which the invasion probability and total fitness cost are arbitrary functions of unique plasmid number (see SI Appendix). We find that the general condition to yield an exponential is that the plasmid invasion probability and total fitness cost must be comparable regardless of the number of plasmids in the cell. These results indicate that even if there is no epistasis in fitness cost, an exponential can still result if there is positive epistasis in the invasion probability (i.e., if existing plasmids make it more likely for a new plasmid to successfully invade).HGT barriers are not the only mechanism that can plausibly limit runaway plasmid invasion. Cells also have specialized systems to defend against foreign DNA, notably the CRISPR-Cas system [32]. To explore whether CRISPR-Cas is responsible for limiting plasmid invasion in natural genomes, we searched for cas genes within the NCBI complete bacterial genomes using HMMER (see “Methods” for details). We expect that if CRISPR-Cas plays a major role in limiting the spread of plasmids, the distribution of unique plasmid types per cell would be shifted towards lower plasmid numbers in genomes containing cas genes versus those lacking cas genes. In Fig. 3D, we show the distribution of unique plasmid types per genome in genomes containing at least one cas gene and those not containing any cas genes. The distributions are very similar, with no large differences between them. These results suggest that CRISPR-Cas is not a major mechanism limiting the spread of plasmids in bacteria. There are additional defense systems that may also influence plasmid carriage. However, a prior bioinformatics study found results similar to ours for restriction-modification (RM) systems, another defense system that protects against foreign DNA; the study examined the distribution of RM systems in bacterial genomes and found almost no relationship between the number of RM systems a genome encodes and the presence of plasmids (in one subset of data the authors actually found a positive relation) [33]. More

Here we present five current shortcomings identified in China’s biodiversity protection framework.Varying threat-assessment quality and uniform treatment of speciesIn this section, we highlight how the threat classifications of the Catalogue of Wildlife under Special State Conservation can lead to sentences that are not commensurate with the species’ threat level. In recent amendments to the catalogue, insect species occur in the highest protection classes (3 species out of 234 in Class I and 72 species out of 746 in Class II; Fig. 2) with similar sentencing standards as for large mammals and birds. For instance, killing more than six individuals of Class I protected insects is treated equally to killing one giant panda, with a punishment of at least ten years’ imprisonment according to the Judicial Interpretation of Several Questions Concerning the Application of Law in the Trial of Criminal Cases of Destruction of Wildlife Resources.Fig. 2: Example species with the highest protection status but considerably different life histories.a,b, Mammals such as the giant panda (a) and insects such as the butterfly T. aureus (b) both occur in the highest protection category in the Catalogue of Wildlife under Special State Conservation. Credit: Juping Zeng (b).Full size imageIn June 2002, 10 poachers captured 263 adults of the butterfly Teinopalpus aureus, meant to be sold on the black market. As T. aureus is listed in Class I of the Catalogue of Wildlife under Special State Conservation, based on the assumption of being rare, the punishment was 5 to 13 years’ imprisonment20. However, recent observations indicate both a wider distribution range21,22 and larger population sizes than initially assumed23. Further, the reproduction rate of insects is generally much higher than that of mammals, which usually makes insects more resistant to the removal of specimens. This case raised some controversy about the scientific basis for classification and the financial profit that can be made with insects compared with mammals24. On the black market, T. aureus can be sold for 700 Chinese yuan per male (~US$100; US$1 = 6.9932 yuan, 21 July 2020; gross domestic product (GDP) per capita: 30,808 yuan in 2010, 54,139 yuan in 2016) and 3,500 yuan per female (~US$500; personal communication with collectors in 2011), while a pair of giant pandas is usually rented to abroad zoos for about 7 million yuan (~US$1 million) per year25.In 2015, a college student and a farmer took 16 fledglings of the Eurasian hobby (Falco subbuteo), a Class II protected species, and were sentenced to 10.5 and 10 years’ imprisonment and fines of 10,000 and 5,000 yuan, respectively26. However, ecological studies indicate that the distribution range, population density and reproduction rate of F. subbuteo in China seem sufficient for sustaining viable populations27, highlighting the potential of overly harsh punishment when classification lacks scientific basis.In contrast to valuation according to (black) market prices, wild species also provide higher-level socioeconomic benefits28. For instance, the value of insect pollination services in China was estimated to be 886.5 billion yuan (US$131 billion) in 201529. In comparison, the ecosystem services related to the giant panda were estimated at between 18 billion and 48 billion yuan per year (US$2.6–6.9 billion) in 2010, but they seem more indirect via regulating, provisioning and cultural services provided by the panda reserves30. However, pollination services are provided by multiple species within a highly flexible network31,32 and the impact of removing a particular amount of specimens is hard to assess, whereas large mammals, such as the giant panda, are irreplaceable in ecosystems and their roles as umbrella species. Thus, differences between insects and mammals are striking not only in terms of direct financial profit but also in terms of ecological and socioeconomic damage, and therefore it is questionable that they are both listed in the highest protection class with the same stringent punishment.Lack of quantitative sentencing standards for herbaceous plants, fungi and algaeHere, we discuss how limited scientific knowledge for particular species groups can lead to legal uncertainties and consequently to limited protection or overly harsh punishment. The Regulations of the People’s Republic of China on the Protection of Wild Plants identify the legal responsibilities for the protection of wild plants (excluding trees), but have not yet reached the status of a law and thus are without judicial interpretation of the Supreme People’s Court and respective sentencing standards. Instead, stipulations of ‘seriousness’ are used with regard to the sentences used for trees, defined in the Judicial Interpretation of Several Questions Concerning the Specific Application of Law in the Trial of Criminal Cases of Destruction of Forest Resources (Box 1), and respective sentencing standards, defined in the Criminal Law of the People’s Republic of China, are applied (up to seven years’ imprisonment). With this analogy, an offender was sentenced to three years in prison in 2016 (suspended sentence) and a fine of 1,000 yuan for digging out three stems of Cymbidium faberi33, an orchid listed in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES34; Fig. 3d) but with high market value. Some uncertainty in the legal position regarding herbaceous plants is expressed by another case in the same year, in which an offender was sentenced to one year of imprisonment (fine of 5,000 yuan) for digging out 55 stems of C. faberi35, and the later revocation of the sentences given that C. faberi is not listed in the Catalogue of Wild Plants under Special State Conservation36.Fig. 3: Example species with changing threat status.a–d, Wildlife protection laws need to be adaptive to reflect the recovery of formerly threatened species, such as the snow leopard (Panthera uncia; a) or the kiang (E. kiang; b), or the increasing endangerment of initially non-threatened species, such as the butterfly Bhutanitis lidderdalii (c) or the orchid C. faberi (d). Credit: Zhi Lu (a, b); Lixin Zhu (c); Yu Ren (d).Full size imageSimilar to the non-discrimination of large mammals and insects, we find such an approach also questionable for precious trees and other plants. Such analogies might become almost impossible when applied to algae such as Nostoc flagelliforme, an important water and soil conservation and high-priced food algae but under Class I protection37. The main reason for the lack of quantitative sentencing standards for these organisms is limited evidence. Therefore, we think it is necessary to raise the Regulations of the People’s Republic of China on the Protection of Wild Plants to become law with respective judicial interpretations and to establish comprehensive scientific assessments targeting herbaceous plants, fungi and algae to provide a solid basis for the development of sentencing standards.Lack of legislative flexibility to reflect dynamic changes in status and taxonomyWe identified a lack of regular updates of the Catalogues of Wildlife and Wild Plants under Special State Conservation needed to address the dynamic changes in taxonomy and threat status. Since its promulgation, the Wildlife Protection Law of the People’s Republic of China has been revised four times and the Regulations of the People’s Republic of China on the Protection of Wild Plants was amended once in 200138, but the Catalogues of Wildlife and Wild Plants under Special State Conservation have basically remained unchanged for the past 32 and 20 years, respectively, with the exception of a recent amendment of the Catalogue of Wildlife in February 2021 and a pending amendment of the Catalogue of Wild Plants (Box 1). Taxonomies change dynamically, which can lead to considerable incongruences among scientifically accepted species names and those in the respective protection lists39. Until this recent amendment, there has been a mismatch in the names of 25 threatened species as listed under CITES compared with the Catalogue of Wildlife under Special State Conservation, putting them at particular risk because their protection status might be questioned, for example, when species such as the Himalayan goral (Naemorhedus goral), or even genera such as the leaf monkeys (Presbytis spp.), have been split into different units with different names that are not listed in the respective catalogues40. Although the Catalogue of Wildlife under Special State Conservation has been updated very recently, it is still recommended that such updates are done regularly and in a coordinated manner, not only in China but across all CITES signatory nations40.Additional legislative flexibility is also needed when formerly endangered species have recovered11, while others have become endangered16,41 (Fig. 3). Recently, several mammals such as the giant panda, snow leopards or the kiang (Equus kiang)11,42 have considerably recovered and their threat status has been reduced by the International Union for Conservation of Nature (IUCN)11. Although the Chinese government does not follow such a downgrade because of precautionary reasons, we think that the sentencing threshold for such species should be adapted in the Judicial Interpretation of Several Questions Concerning the Application of Law in the Trial of Criminal Cases of Destruction of Wildlife Resources. On the other hand, species whose endangerment has increased since the promulgation of the Catalogues of Wildlife and Wild Plants under Special State Conservation, such as the narrow-ridged finless porpoise43, many birds44, snakes45, turtles46, frogs40, butterflies47 or herbaceous (medicinal) plants2, have long been with low or no protection until the recent amendment. Cultivation can also increase endangerment of wild species by hybridization between the cultivars and the wild populations (for example, rice, wheat, soybean and cotton)48.Outdated punishment standards based on economic profitsSimilar to the lack of flexibility covering species’ taxonomic and threat status, here we highlight that punishment standards are outdated and regular updates are required to reflect economic developments and guarantee balanced sentencing. For instance, according to the Judicial Interpretation of Several Questions Concerning the Application of Law in the Trial of Criminal Cases of Destruction of Wildlife Resources, the illegal purchase, transport and sale of precious and endangered wildlife products will be considered as a ‘serious crime’ if the financial profit is more than 100,000 yuan and as an ‘extremely serious crime’ if the profit is 200,000 yuan or more. The sentencing standard was developed in the year 2000, but with the rapid development of China’s economy, nationwide per capita income has increased more than fourfold from 6,279 yuan in 2000 to 28,228 yuan in 201849. To reflect economic developments, the penalty standards need to be adjusted to comply with the principle of balanced sentencing. In comparison, the Chinese standards for corruption and bribery have been increased from 4,886 yuan in 1997 to currently 30,715 yuan for crimes involving a ‘relatively large amount’, which might serve as a guideline for adapting the sentencing standards for wildlife protection50.Potential for excessive punishment because of non-discrimination between organized and individual wildlife crimeIn this section, we highlight that ignoring the motivational, educational and economic backgrounds of offenders is against the principle of proportionality and may lead to inappropriate deterrence strategies. China’s laws are very strict with quite harsh penalty sentencing; for example, 10.5 years’ imprisonment and a fine of 10,000 yuan for a student taking birds26, 12 years and a fine of 10,000 yuan for a farmer killing a giant panda51 or 13 years and a fine of 2,000 yuan for a farmer taking butterflies20, all cases representing ‘extremely serious crimes’ with a minimum sentencing standard of 10 years’ imprisonment (no maximum defined). Even in comparison with other criminal fields in China and internationally, these standards seem very stringent. For instance, sentences of more than 10 years’ imprisonment apply to larceny only if the value of the stolen goods is larger than 500,000 yuan, or to the theft of first-class cultural relics (all valued in the millions; Criminal Law of the People’s Republic of China, Article 264). Also in comparison, the United Nations Convention Against Transnational Organized Crime52 defines much lower sentencing standards, with at least four years’ imprisonment for a ‘serious crime’. In contrast to China, the wildlife protection laws of Western and many other developing countries prioritize monetary fines over imprisonment. Under European wildlife law53, for example, hunting or destroying Class I protected species is generally punishable by a fine and will be sentenced with fixed-term imprisonment only if the case is ‘extremely serious’. In the United States, the maximum imprisonment is a year, with fines of up to US$50,000 (340,000 yuan)54; in the UK, 6 months and fines of up to £20,000 (177,000 yuan)55,56; in India, 3–7 years and a minimum fine of 25,000 rupees (2,300 yuan)57; or in Brazil, 3 months to a year plus fines58.The wildlife protection laws of such countries may provide useful examples for China, but to adhere to the principle of proportionality, motivational, educational and economic backgrounds, in particular a differentiation between organized wildlife crimes and individual violations needs to be considered. Individual and organized crimes are currently not differentiated in the Criminal Law of the People’s Republic of China. Historically, wildlife crime was considered a local activity performed by single individuals. However, at present criminal networks are highly involved59 and resulting economic damage from environmental crime has been estimated to range between US$91 billion and US$259 billion globally60, with the profits of illegal wildlife trade ranging between US$7 billion and US$23 billion61, which is of similar orders to human trafficking, and arms and drug dealing62. In China, the consumption of illegal wildlife products has increased with growing economic wealth63, while China has also been identified as one of the major exporters of such products64. Key players in both cases are organized crime groups65,66, causing severe ecological damage while making enormous financial profits67. In such cases, high fines might be simply factored in as part of the ‘business model’. Thus, the current focus on severe jail sentences seems appropriate, and the level is comparable to other Southeast Asian countries (Indonesia: 10 years; Singapore: 2 years; Thailand: 7 years; Vietnam: 15 years)68,69.In contrast to organized wildlife crime, we also noticed that many cases of harvesting or poaching protected wildlife happened in remote and less-developed regions, conducted by individuals seeking to earn some extra income but without good knowledge of the protection laws20,51. The resulting ecological damage and profits gained are much lower compared with cases of organized wildlife crime, and thus applying the same harsh punishments, as shown in our earlier examples, is clearly against the principle of proportionality. Moreover, it has been shown that the mentality of different types of offender and how they perceive different punishments (imprisonment, fines or both) need to be considered for designing appropriate deterrence strategies for different offence categories, suggesting that imprisonment as the main policy instrument is inappropriate70. Imprisonment is not necessarily a deterrent for every offender, especially when the price of time in prison falls relative to the price of time outside71. Consequently, a penalty that eliminates any financial gain should eliminate the incentive to engage in such conduct72. A shift in focus from imprisonment to fines, at best coupled with local or regional GDP per capita and in combination with raising public awareness, might not only increase proportionality and effectiveness of environmental laws but also comply with other international standards, where, for example, the Council of Europe’s Recommendation (92)17, concerning consistency in sentencing, paragraph B5(2), states that “custodial sentences should be regarded as a sanction of last resort, and should therefore be imposed only in cases where, taking due account of other relevant circumstances, the seriousness of the offence would make any other sentence clearly inadequate”. More