Ecology

Sayh QatanahSince 1978 the town of Sayh Qatanah has experienced a strong physical expansion, initially driven by the building of secondary houses by families from the oases below. This was increasingly followed by population transfer, family growth, tourism facilities, and general expansion of urban infrastructure. The number of developed plots within the town area rose from 276 in 2009 to 534 in 2018 (+ 90%). During the same period the total plot area increased from 41.6 ha to 73.5 ha (+ 77%). This lead to an increase in the urban area from 206 ha in 2009 by 24 ha in 2014 (+ 13.6%3) to 252 ha in 2018 (+ 8%). At the current rate of growth, the planned urban space of 268 ha will be reached by 2023, likely followed by densification of the built-up area (Fig. 3). To the east of the city centre a new settlement of 8.6 ha has been established, which, in addition to the typical residential buildings and home gardens, contains a new mosque and an olive grove of 0.7 ha.In 2018 the town’s 56.3 ha non-governmental land comprised 19.3 ha private green spaces, 15.2 ha private buildings, and 4.0 ha public green areas. The total irrigated area thus amounted to 23.3 ha (Fig. 4). The size of individual homegardens ranged between 7 and 3590 m2 with an average of 368 m2. Some homegardens were partly outside the property wall and contained fruit trees and annual crops. In total 33 perennial and annual plant species of 16 families were identified (Table 1). Abundance was highest for pomegranate, olive, rose bushes, and vine, but also peach, apricot, pear, and fig trees were encountered. Garlic was cultivated in 14 of the 25 homegardens studied, followed by onion, maize, and some fodder barley.Figure 4Map of Sayh Qatanah (2000 m a.s.l., Al Jabal Al Akhdar, northern Oman) with all buildings and irrigated areas (gardens) in April 2018.Full size imageTable 1 Species occurrence in the homegardens of the town Sayh Qatanah (Al Jabal Al Akhdar, northern Oman) in 25 randomly selected households.Full size tableOur surveys indicated that besides some private cisterns of unknown capacity for rainwater collection, most residents of Sayh Qatanah used tap water from local borewells for irrigation whereby little attention was given to crop-specific water needs. Average monthly water consumption varied from 43 to 213 l m−2 (mean 97 l m−2 ± 49 SD). This translated to a total irrigation water use in the 19.3 ha private homegardens of 224,652 m3 in 2018. Including the public green areas, the annual estimated water consumption of all green areas of the town amounted to 272,054 m3.Terrace gardensExcluding the newly created terrace areas southwest of Ash Sharayjah and the information-free plots of Al ‘Ayn, by 2018 the actively used area of all five oasis systems had declined from 20.3 ha in 2007 to 19.9 ha (− 2.0%). Fallow land increased by 3.5%, while the use of non-perennial crops decreased by 1.9%. The share of perennial crops without underplanting decreased by 5.1%. In contrast, the share of land under agroforestry increased by 2.1% (Table 2). The 2018 plant census yielded an NS of 13,739 with 25 different perennial species from 12 families. The 2007 count resulted in 1150 individuals less, with 24 different species from 14 plant families.Table 2 Landuse of terraces in the oases of Wadi Muyadin, Al Jabal Al Akhdar, northern Oman, in 2007 and 2018. Data of 2007 are from Luedeling and Buerkert11.Full size tableIn 2007 DN was highest for pomegranate (51%), rose (21%), date (9%), true lime (5%), peach (4%), and banana (3%). By 2018, DN increased for pomegranate (52%) and rose (28%), but decreased for date (7%), banana (2%), lime (1%), and peach (1%). The establishment of drip-irrigated olive yielded a DN of 4% in 2018, while this crop was non-existent in 2007. Over the past decade olive has thus become the third most common crop species in the study region.In 2018 the information-free plots of Al ‘Ayn had a similar composition than the other ones, with the three most common species being pomegranate (51%), rose (27%), and olive (6%). Also the newly established Ash Sharayjah terraces were dominated by pomegranate (38%), rose (28%), and olive (23%).From 2007 to 2018 the NA of most species declined. Sapodilla, pigeon pea, almond, prickly pear (Opuntia vulgaris Mill.) and lemon were no longer recorded in the oases. Instead, prickly pear was identified on the newly created terrace areas of Ash Sharayjah and a young almond tree was spotted in Al ‘Ayn. In addition, a sorb tree (Sorbus domestica L.) was discovered in Al ‘Ayn. The stand of pome fruits such as apple and pear decreased by 89% and 86%, respectively, and stone fruits recorded a similar decline. The NA of apricots decreased by 88%, while the decline of peaches was 71% and of plums 64%. Bitter orange, true lime, orange, and Palestinian lime were decimated by 91%, 71%, 63%, and 22%, respectively, while date and banana stocks decreased by 14% and 16%. In contrast, the NA of pomegranate increased by 11% and of rose by 50%.Al ‘AqrAt a constant total terraced area of 1.7 ha the actively used land declined by 3.4% (Fig. 5). Thereof the proportion of agroforestry systems increased by 3.8%, woody plant alone areas declined by 4.8% and annual crops by 3.0%, and fallows increased by 0.8%. Pomegranate and rose were the dominant species in both years (Fig. 5). While the DN of pomegranate decreased from 63.6 to 58.0%, that of rose increased from 22.8 to 39.4%. Whereas the DN of peach fell from 4.5 to 1.6% and bitter orange, orange, lemon, pear and plum completely disappeared, barley, maize, eggplant, and Rhodes grass (Chloris gayana Kunth) continued to be cultivated on the terrace areas.Figure 5Landuse map of the oasis Al ‘Aqr (1,950 m a.s.l.) in Wadi Muaydin (Al Jabal Al Akhdar, Oman) in 2007 and 2018.Full size imageAl ‘AynAlso Al ‘Ayn’s total terraced area of 1.9 ha remained constant. For the 2007 investigation period, information on landuse of ~ 0.3 ha was missing. This was taken into account in the data on relative landuse changes by not considering information-free plots from 2007 which in 2018 contained 20.5% agroforestry systems, 52.0% woody plants only, 1.4% annual crops, and 26.1% fallow land (Fig. 6, Appendix 1).Figure 6Landuse map of the oasis Al ‘Ayn (1900 m a.s.l.) in Wadi Muaydin (Al Jabal Al Akhdar, Oman) in 2007 and 2018.Full size imageDuring the decadal study period the active cultivation area of Al ‘Ayn declined by 0.2%. Areas with agroforestry systems were expanded by 9.3%, while the use of woody plants only recorded a decline of 16.4%, fallow land increased by 25.1%, and the annual cropping area declined by 18.1% (Fig. 6).Between 2007 and 2018, the DN of rose increased from 54.6 to 61.8% and of pomegranate from 28.2 to 30.5%. The DN of peach decreased from 4.3 to 2.1%, and of papaya, lime, and apricot to less than 2.0%. In contrast to 2007, no records of apple and lemon were obtained in 2018. However, barley, garlic, onion, sweet potato, sorghum, and oats continued to be cultivated.Ash SharayjahIn 2007 Ash Sharayjah’s total area was 15.2 ha to which, by 2018, 1.7 ha of newly developed farmland were added and included in our digital mapping (Fig. 7, Appendix 2). For the determination of relative area changes, however, these newly established terraces areas were not taken into account. During the transformation decade the agriculturally used area of Ash Sharayjah decreased by 4.5%. The total area with agroforestry systems increased by 0.2%, woody plants only declined by 4.5%, areas with annual crops decreased by 2.1% and fallow fields expanded by 3.1% (Fig. 7).Figure 7Landuse map of the oasis Ash Sharayjah (1900 m a.s.l.) in Wadi Muaydin (Al Jabal Al Akhdar, Oman) in 2007 and 2018.Full size imageUntil 2018 the DN of roses increased from 21.4 to 26.7%, while if fell for pomegranate from 63.9 to 61.7%, for true lime from 5.6 to 0.9%, and for apricot and peach it declined to  More

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Site descriptionWe define “kelp forest” as rocky-reef habitat within the 5–20 m depth range that supports dense stands of giant kelp, Macrocystis pyrifera. For this study, we considered the Santa Barbara Channel (SBC) to include the mainland region between Point Conception (−120.476° longitude, 34.455° latitude) and Point Mugu (−119.065° longitude, 34.079° latitude), as well the northern and southern sides of the four northern Channel Islands (Fig. 1). Although the SBC is a subset of the Southern California Bight, its strong west-east gradient in cold to warm temperature means the study system includes many of the kelp-forest species throughout California31. This means the SBC kelp-forest food web is a large “metaweb”, characterizing kelp forest meta-communities, rather than a site-specific web. In other words, the system includes cold water and warm water species that might not necessarily co-occur at a single site. However, there are site-specific food webs embedded in the metaweb at particular locations where a subset of species occur.Data sourcesOur goal was to assemble the food web using both published and novel empirical observations. To this end, we first used published data sets and species’ range boundaries to create free-living species lists. The initial list of fishes, algae, and free-living invertebrates was assembled from the Channel Islands National Park Kelp Forest Monitoring program (CINP KFM, annual reports available at https://irma.nps.gov/DataStore/SavedSearch/Profile/1508, accessed March 6, 2017, or visit https://www.nps.gov/im/medn/kelp-forest-communities.htm to contact David Kushner or Joshua Sprague) and the SBC Long Term Ecological Research program’s ongoing kelp-forest community timeseries (SBC LTER, https://sbclter.msi.ucsb.edu/data/catalog/, accessed March 12, 2017). We added to these lists using primary literature, technical reports (e.g., NOAA, USFW), direct observations, expert opinion, crowd-sourced observations (e.g., eBird.org), guidebooks, and grey literature. We sampled the local kelp forest zooplankton and the algae-associated small-invertebrate community, because these organisms were not well represented in surveys (see below).We created initial lists of parasite species using published literature and host-parasite databases. A systematic review was conducted to collect parasite records for each free-living species. We searched the Natural History Museum (NHM) of London host-parasite database (https://www.nhm.ac.uk/research-curation/scientific-resources/taxonomy-systematics/host-parasites/database/search.jsp), the FishPest database32, WoRMs (http://www.marinespecies.org/aphia.php?p = search), BIOSIS citation index (http://webofscience.com), and Google Scholar™(https://scholar.google.com/) (Genus + species + parasit*, expanded to Genus + parasit* if no records were found). For each host species, we recorded the number of records found in BIOSIS and NHM as an estimate of study effort. Although parasites are often reported at the host and parasite species level, we were often able to infer parasite and host life stages based on knowledge about life cycles. We added to these lists by sampling local fish and invertebrates, with a focus on hosts that were common in the system and not well-studied (see below). As for any food-web study, we were most interested in including common or important parasites, rather than rarities.Published diet observations (including in grey literature), direct observations, and inference were used to determine trophic links (see below).Free-living species sampling methodsCertain groups of free-living species were under-represented in published survey data, so we conducted sampling to assess species diversity in the following areas.Zooplankton towsWe conducted vertical zooplankton tows within kelp forests at two island locations (on the same date) and two mainland locations (repeated tows, four dates at one site, three of those dates at a second site, including one nighttime sampling date), for eight site by date samples30. While the vessel was at anchor within a kelp forest, a 30 cm diameter, 200 micron plankton net was dropped to the bottom and pulled to the surface at a rate of 0.33 m per second. Care was taken not to scrape the net against kelp plants. The collection jar attached to the net was kept vertical with a small lead weight to ensure that the net did not collect organisms on the way down to the bottom. The depth and time of collection were recorded30. We held collection jars on ice while in the field, then preserved specimens in 95% ethanol when we returned to the lab (within a few hours of collection). All organisms were counted and identified to species when possible, but some groups were identified to Order or Family, and then cross-checked with lists of known local species. If this was not possible, specimens were assigned to morphospecies, indicating they appeared to be a unique species based on morphology. Representative specimens from each species or morphospecies were photographed and measured.Giant kelp holdfastsGiant kelp holdfasts were sampled for free-living invertebrates. In the field, holdfast circumference and two slant height measures were taken, as well as basal stipe circumference. A subsample of approximately 25% of the holdfast was collected by SCUBA in a large plastic zip bag, and frozen until processing (n = 7). The samples were processed for organisms  > 500 microns, and holdfast tissue was weighed after organisms and debris were removed. Organisms were counted, identified to species or morphospecies when possible, and measured30. Some groups were identified to Family, and then matched to lists of known local species.Taxon-specific methods: gastropodsSmall gastropods are a diverse but overlooked group that lives in benthic turf algae. Algal clumps were collected haphazardly by either laying down a 7 × 7 cm quadrat and collecting all algae within the quadrat, or by collecting clumps of a particular alga and weighing at the lab. All gastropods were removed by hand under a stereomicroscope, counted, identified to species or morphospecies, measured, and photographed30.Parasitological collectionsWe collected fish and invertebrates and dissected them for parasites, with the goal of identifying the most common parasites in the food web. We targeted host groups that are known to transmit trophically-transmitted parasites in other systems. We collected most organisms from mainland sites, and sampled opportunistically at sites on Anacapa, Santa Cruz, and Santa Rosa islands30 (Fig. 2). A list of all species dissected and sample sizes is provided30.Fish collectionsWe prioritized collecting the most common and abundant fish species based on survey data from 2000–2014 (SBC LTER), as well as personal observation, expert opinion, and amount of parasite data in the literature. Other species (lower abundance or higher past study effort) were collected opportunistically. Fish were collected primarily by spear on SCUBA. Specific size classes were not targeted and the spear tips used were appropriate for the focal species. Small benthic fish were collected using dip nets. All fish were collected under UCSB IACUC protocol 549.2. Fish were either stored on ice and processed within 24 hours of collection or frozen until processing.Invertebrate collectionsInvertebrates are necessary intermediate hosts in many parasite life cycles, but relatively few parasite life cycles have been described in marine environments. We targeted invertebrate species that were abundant and potentially important as intermediate hosts for parasites. We did not collect sessile colonial taxa, such as hydroids, gorgonians, sponges, and tunicates, as they were not expected to be hosts for trophically transmitted parasites (but these hosts do merit further study). Most sampled invertebrates were gastropods and small crustaceans, as they host trophically-transmitted parasites in other food webs. Bivalves, large crustaceans, echinoderms, and polychaetes were also dissected. Large invertebrates were collected by hand or using a rock chisel and scraper when appropriate. Small invertebrates were sampled by collecting benthic substrates in plastic or fine mesh bags and removing organisms in the lab. Invertebrates were held live in flow-through seawater until the time of dissection or frozen until processing.Parasitological assessmentFor each host dissection, the exterior and all internal soft tissues were examined for parasite life stages. For larger species, entire host organs were usually searched by pressing soft tissues thin between two glass plates (“squashed”) and examining with a stereomicroscope. However, to increase sample size, bilaterally symmetric organs (e.g. gills) were examined from one randomly determined side, and large organs (e.g. muscle, liver) were subsampled in larger fishes. Small crustaceans and soft-bodied invertebrates were squashed whole. We identified gut contents where feasible to improve host diet data and inform parasite life cycles. We recorded host mass, length (or other species-appropriate measurement), collection method, and host condition at time of dissection (e.g. frozen, fresh). We counted and identified all parasites to the lowest possible taxonomic level and assigned a morphospecies code when species-level identification was not possible. Only a few putative parasites were excluded from additional analysis because they had no identifying features. Dissection data30 includes species not included in the full food web (see below for discussion of justifications for node inclusion).Node list assemblyNodes in the web included free-living species that used the water column and benthic zones within kelp forests as feeding habitat (including transient kelp-forest visitors but excluding rare and vagrant species) and parasites of those free-living species. Species was the preferred taxonomic unit, and life stages were included as separate nodes if that life stage was present in the system and had distinct trophic interactions from the adult stage. The fully-resolved free-living food web was constructed with life stage (e.g., larva, adult) nested within species (or morpho-species) (excepting benthic diatoms, planktonic diatoms, dinoflagellates, foraminifera, free-living nematodes, bacteria, free-living ciliates, copepod nauplii, filamentous algae, and invertebrate eggs, which were aggregate nodes). As various forms of detritus are important to energy flow in kelp forests, detritus was broken into four categories based on the typical feeding modes of detritivores and main sources of detritus: carrion, drift macroalgae, small mixed origin (such as would be consumed by a deposit or suspension feeder, with the recognition that this alone is a complex system deserving further resolution) and dissolved organic material. The “drift macroalgae” component was especially important to distinguish, as certain herbivores (sea urchins) are known to prefer drift algae as food but will turn to feeding on live algae when drift algae are sparse. This is a very distinct type of interaction from suspension feeders, which consume small particles of detritus that may be largely bacteria. “Parasites” are consumers which fit the seven types of parasitism defined by Lafferty and Kuris33. Commensal organisms were also recorded. We limited the parasite species list to metazoan species that use kelp-forest species as hosts for at least one stage in their life cycle. Bacterial, viral, fungal, and protozoan pathogens that are important in kelp-forest food webs merit inclusion in further work.We assigned each node a justification code (see below), confidence level, literature reference, and locality of the reference. Additional node metadata includes site on host (ecto-vs. endoparasite), taxonomic information, and life cycle information30 (see below). The node list contains columns with a species ID, and a species-by-stage ID. To work with the life-stage resolution, select the species-by-stage ID as the node identifier in analyses. To work with the species version, select the species ID as the node identifier in analyses. This will collapse all of the interactions to the species, so all of the trophic interactions are preserved and linked to the species node. Network analysis packages in R (such as Cheddar34) will automatically remove duplicate links if they are generated in this process.Life stages as nodesSpecies were partitioned into life-stage nodes (e.g., larva, juvenile, adult) if a species changed its trophic position from one stage to the other and multiple stages were present in the system. Whether or not a distinct life stage resided in the kelp forest was indicated by various data sources (e.g. dissections, published records), or inferred from species life history or trophic interactions. For example, amphipods brood offspring and have crawl-away juveniles. These juveniles remain in the kelp forest (rather than having a pelagic phase), and due to their small size are subject to different predators than adults (e.g. adults are eaten by fishes, while juveniles are eaten by hydroids). This was justification for juvenile amphipods being a distinct node from adult amphipods. On the other hand, many species have planktonic larvae that develop outside of the kelp forest, so only the adult stages were included at the species level. Larval stages of parasites were included if there was no feasible alternative for the focal host to become infected. We assumed that kelp-forest resident hosts became infected through life-cycle stages found within the kelp-forest food web, but that transient hosts could have acquired some parasites outside the kelp forest (e.g., if intermediate hosts were not known from the kelp forest). Likewise, presence of larval parasites in dissections was evidence for including adult stages. For some species, there was insufficient data on life history to infer additional stages. Metadata in the node list indicates whether parasites have additional life stages inside the kelp forest, outside, or unknown. When comparing this food web with others (which rarely separate species into life stages), using our data it is easy to collapse life-stage nodes into species nodes.Justifications for node inclusionWe used multiple lines of evidence to justify whether or not to include a node in the food web. Free-living species were included if they were known from the SBC (see site description above) and were indicated by the data sources described above (e.g. reports, surveys, published papers, guidebooks, expert opinion, etc.). Species lists from regional guidebooks included non-kelp-forest species, so these lists were compared with species lists from long-term monitoring surveys. Following the methods of Lafferty et al. 2006, we excluded most rare species (0.5, we assumed that an unobserved link actually occurred unless otherwise contradicted by species life history. We also then noted the probability of a false positive link (1 – ({widehat{F}}_{{rm{ij}}})). We further identified those few host and parasite species that generated substantial error in the network. To keep the overall error rate to 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}$$
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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