More stories

  • in

    Hummingbird plumage color diversity exceeds the known gamut of all other birds

    The avian plumage color gamut is much more diverse than previously estimated2. We demonstrate that hummingbird barbule structural colors contribute substantially to the total color diversity of living birds, occurring in areas of the avian color space that were sparsely occupied in Stoddard and Prum2, which most notably included saturated blues, greens, and true purples (blue + red). Such regions of the avian color space were suggested to be unoccupied because these colors are challenging to create, rather than because they might function poorly for communication2. Our results support this hypothesis because hummingbird coloration densely occupies these regions of the avian color gamut (Fig. 2d), using plumage patches that generally play particularly important roles in hummingbird communication, such as throat and crown plumage patches (Supplementary Fig. 5)16,17. The greater color diversity uncovered by our study suggests that barbule structural coloration is the most versatile class of all plumage coloration mechanisms and poses the least constraints on the evolvability of plumage color diversity. Barbule structural colors evolve through changes in the size, shape, spacing, and refractive index of barbule melanosome nanostructures, but little is known about how changes in these parameters themselves evolve18.The UV/V + green region of avian color space remains mostly unoccupied (Fig. 2c, d). It is challenging to create colors with separate reflectance peaks within the wavelength sensitivities of non-adjacent color cones because the peaks must be highly saturated to avoid stimulating neighboring cones2. However, this idea does not explain why there are far more true purple (blue + red) than UV/V + green plumage colors. Notably, birds particularly fail to fill the more UV/V regions (those closer to the UV/V vertex) of UV/V + green color space, which might indicate that it is difficult to create spectra with uv/v wavelength peaks higher than those in the m wavelengths.The differences between our methods and those of Stoddard and Prum2 likely contribute in part to the larger gamut size when comparing species data but not overall data. While the number of species included in our study was comparable to that of Stoddard and Prum2 (114 vs 111 species, respectively), we measured almost twice as many plumage patches as they did (+1600 vs. 965 patches). To prevent erroneous distortion to iridescent colors we did not average the three measurements per patch. Both studies measured six standard patches for all species and additional patches if necessary to capture other plumage color variation. The larger number of plumage patches we measured reflects how color diverse hummingbird plumages are. Our methods preserved the natural variation in hue due to iridescence and avoided the distorted flattening caused by averaging highly saturated peaks with slightly different peak hues. Although our methods are biased toward increasing variation, they are necessary to accurately capture the phenomenon of iridescent hummingbird coloration.There are multiple reasons why the hummingbird color gamut is so diverse. The size of the hummingbird color gamut, like the achieved color gamut of any clade, constitutes a combination of the history of selection on color function, the clade’s evolved capacities for color production, the age of the clade, and the number of species. Hummingbirds excel at all these criteria. The 336 species of extant hummingbirds have radiated rapidly over the last 22 million years19. Hummingbird plumage color diversity has evolved through a long history of persistent sexual and social selection on plumage coloration. Hummingbirds have polygynous breeding systems characterized by female only parental care, female mate choice, and often elaborate male courtship displays. Intersexual selection in hummingbirds has contributed to elaborate radiation in brilliant plumage coloration as well as vocalizations and non-vocal feather sounds14,16,20. Hummingbird plumage color evolution rates have even been shown to positively correlate with hummingbird speciation rates14. Furthermore, in some species, brilliant monomorphic plumage ornaments apparently function in aggressive, intra- and interspecific defense of floral resources21 and appear to be associated with socioecological features related to resource competition19. Our finding that crown and throat patches, which flash brilliantly when the head of the bird is oriented toward the observer, are more diverse in coloration than other plumage regions highlights the role of plumage coloration in direct inter-individual communication and social interactions.The mechanistic properties of hummingbird barbule structural color further explain the exceptional diversity of hummingbird plumage coloration. Hummingbird barbule structural coloration is among the most complex plumage coloration mechanisms, comprised of stacks of hollow, air-filled melanosomes, surrounded by a thin superficial, solid keratin cortex as well as sometimes superficial, miniature melanin platelets which lie just beneath this cortex9,10,11,12,13. Complex nanostructures allow for independent tuning of multiple components, and, hence, greater achievable color diversity12,18,22. Barbule structural color permits the production of any peak-reflected wavelength by varying the thickness of melanosome arrays, which can produce a diversity of single-peak spectra-hues, such as the unusual diversity of greens, blues, and blue + greens seen in hummingbirds (Fig. 2b). Hummingbird melanosomes are among the most unusual in birds in being both disc-shaped and air-filled9,10,11,12,13,23. The air in the center of hummingbird melanosomes approaches the maximum possible biological difference in refractive index (air = 1.0, melanin = ~1.7), which results in the efficient production of brilliant colors with the fewest layers of melanosomes, such that resulting spectra are narrow and near saturation13,24. Such spectra can thereby create colors that extend further in color space (Fig. 2a–c).Barbule structural color also allows for the production of plumage spectra with multiple saturated peaks, creating saturated color combinations that are not as commonly produced via other plumage coloration mechanisms. However, researchers have yet to identify exactly how hummingbird multipeak spectra are produced12,13, emphasizing the need for further analyses of the optics of hummingbird feathers. Many hummingbird melanosome arrays are non-ideal– i.e., the products of the thicknesses and refractive indices of the melanin and air cavity layers are not equal25. Non-ideal thin films can create more highly saturated, pure tone colors of the primary peak while also introducing additional, harmonic spectral peaks at shorter wavelengths25, which allows for complex reflectance spectra with multiple bright peaks within the avian visible spectrum. Also, melanosome arrays with a large average layer thickness ( >~300 nm) can create colors with fundamental interference peaks in the infrared and multiple, harmonic peaks in the avian visible range (300–700 nm). The presence of minute, superficial melanin platelets below the cortex in hummingbird barbules is also correlated with secondary, lower wavelength reflectance peaks, but the precise optical mechanism remains to be established12. These different nanostructural elements all contribute to distinctive multipeak reflectance spectra that can stimulate non-adjacent color cone combinations, which Stoddard and Prum2 identified as particularly difficult to accomplish: UV/V-purple (uv/v + s + l wavelengths; Schistes geoffroyi cheek, Fig. 4g); true purple (s + l wavelengths; Atthis ellioti gorget, Fig. 4h); UV/V-green (uv/v + m; Schistes geoffroyi crown, Fig. 4a); and UV/V-red (uv/v + l; Heliangelus viola, Fig. 4b). With multipeak spectra the potential for creating new and different colors is greatly expanded, allowing for a more versatile evolution of novel colors.Unexpectedly, the hummingbird plumage color gamut is larger in volume when modeled with the VS-type (34.2%) than with the UVS-type (29.6%) visual system. This apparently unique result contrasts notably with both Stoddard and Prum’s2 and our revised estimate of the color gamut of all birds combined– VS gamut = 40.5%; UVS gamut = 47.3%. Multiple previous analyses have shown that the UVS cone-type visual system does a more efficient job of discriminating the colors of natural objects because of the broader separation between the peak spectral sensitivities of the uv and s (blue) cone types2,26,27. Because the UVS-type visual system produces an even greater increase in color volume for a diverse plant color data set over the VS-type visual system, Stoddard and Prum2 rejected the hypothesis that the UVS-type visual system had specifically evolved to expand the diversity of avian color stimuli.However, our observations that the hummingbird plumage gamut is substantially greater in volume with the VS-visual system than with the more efficient UVS-visual system strongly suggests another hypothesis: Hummingbird plumage may have specifically evolved to be more diverse within the hummingbird VS-type color visual system via selection for highly saturated plumage colors. Given diversity in hue, the way to achieve greater color gamut volume, i.e., greater plumage color diversity, is through highly chromatic color vectors that extend toward the limits of the color space. The two visual systems map variation in wavelength to different maximum potential chroma—i.e., wavelengths with color vectors that extend toward the edges, faces, and vertices of the tetrahedron6. Color vectors that extend towards the vertices, i.e., plumage that best corresponds to a singular cone type’s peak sensitivity, have the highest maximum potential chroma because vertices are the regions furthest away from the tetrahedron’s center. Thus, hummingbird plumages may have specifically evolved to have maximum chroma within their own VS-visual system via peaks that correspond most closely to the peak sensitivities of the VS- rather than the UVS-visual system. For example, when comparing the UVS and VS plumage color gamuts for hummingbirds, it is notable that hummingbird coloration extends much further into the UV/V regions of color space for the VS-visual system (Supplementary Fig. 2). While in the VS system these color points map toward the v vertex, in the UVS-visual system they map towards the uv-s edge and the uv-s-l face. Such color vectors that contribute to expanded color volume of the VS gamut could have evolved by sexual or social selection for highly saturated plumage colors that are near in hue to the specific sensitivity peaks of hummingbird receptor cone types. Such selection could note preferences within some hummingbird species for hues with maximally possible chroma, not merely for maximal chroma of a given hue.Hummingbirds have tetrachromatic color vision with substantial sensitivity in the near ultraviolet28,29. Recently, Stoddard et al.30 used a series of elegant experiments with hummingbird feeders and LED lights to demonstrate for the first time that hummingbirds can distinguish non-spectral colors distributed throughout the tetrachromatic color space. However, the presence of this remarkably proficient four-color vision in hummingbirds poses an interesting evolutionary conundrum. Recent phylogenetic analyses have established that hummingbirds and swifts are phylogenetically embedded within the nocturnal caprimulgiforms31,32. The most parsimonious hypothesis is that the immediate ancestors of swifts and hummingbirds were extensively nocturnal for approximately 8 million years before they re-evolved diurnal ecology and behavior31. Given that an evolutionary history of nocturnality can lead to the degradation or loss of opsin genes33,34, it should be a high priority to establish what effect that ancestral nocturnality may have had on the molecular physiology and anatomy of the hummingbird color visual system.Our attempt to document the color diversity of an avian family has revealed that current estimates of the total avian color gamut are likely inaccurately low. Similar studies sampling from other color-diverse families, such as sunbirds (Nectariniidae), parrots (Psittacidae), tanagers (Thraupidae), birds of paradise (Paradiseidae), manakins (Pipridae), and starlings (Sturnidae), most of which have already been studied for their plumage coloration35,36,37,38,39, would help us obtain a better estimate of the true avian color gamut. More

  • in

    Global and seasonal variation of marine phosphonate metabolism

    Proteobacteria are major contributors to marine microbial phosphonate cyclingDatabases for all putative sequences of genes for phosphonate production (pepM, aepY, phpC, mpnS, hepD), substrate-specific catabolism (phnAWXYZ, palA), and broad-specificity catabolism (phnIJM) were created using available public genomes from JGI IMG/MER and GORG-Tropics. Gene identity was verified by the presence of catalytically essential residues (Supplementary Table S2). Phosphonate genes were identified in 10,337 genomes of bacteria and archaea spanning over 100 unique classes, suggesting a wide variety of microorganisms mediate phosphonate production and catabolism (Fig. 2, Supplementary Dataset S1). A high proportion of all collected sequences affiliated with Proteobacteria (Gamma, Alpha, and Beta classes), averaging 52% of the production genes, 78% of substrate-specific catabolism genes, and 88% of broad-specificity catabolism genes before dereplication (Fig. 2).Fig. 2: Phosphonate gene and genome count with taxonomic distribution.Number of sequences and genomes collected for study (A, D, G) with distribution of class-level taxa for all redundant sequences (B, E, H) and marine redundant (C, F, I) sequences. Results are shown for selected genes representing phosphonate (A–C) production, (D–F) substrate-specific catabolism, and (G–I) broad-specificity catabolism. The taxa shown are the 15 classes with the highest representation across all databases.Full size imageOf the 10,337 genomes, 1556 (15%) were confirmed to be marine organisms from 35 different classes (Fig. 2, Supplementary Dataset S1). Proteobacteria had even greater representation in the subset of marine genomes, averaging 65% of marine production genes, 88% of marine substrate-specific catabolism genes, and 96% of marine broad-specificity catabolism genes from the redundant databases (Fig. 2). The dominance of Alphaproteobacteria in the marine subset may be attributed to the wide variety of Pelagibacterales bacterium captured in the database, making up 426 (27%) of the 1556 genomes involved in all three categories of phosphonate cycling. Rhodobacterales (Ascidiaceihabitans sp., Roseovarius sp., Sulfitobacter sp., Labrenzia sp., and Phaeobacter sp.) alongside Rhodospirillales (Thalassobaculum sp., Thalassospira sp., Roseospira sp., Varunaivibrio sp., and Oceanibaculum sp.) were also highly represented among the marine subset with 214 (14%) and 251 (16%) genomes, respectively (Supplemental Dataset S1), though these taxa primarily show potential for phosphonate catabolism rather than production. Vibrionales were well represented in the JGI IMG/MER marine genome subset with 107 (7%) genomes spanning 59 different species including Vibrio lentus, Vibrio breoganii, and Vibrio splendidus.Diverse taxa encode the capacity to produce phosphonate derivativesPhosphonate production is widespread and distributed throughout many different bacteria and archaea. Genes responsible for the first two steps in phosphonate production, pepM and aepY, had the broadest taxonomic distribution within the redundant databases (Shannon indices of 2.66 and 2.76) for all genes in this study, distributed with 0.59 and 0.61 evenness from 70 and 72 unique, verified classes, respectively. Their broad distribution further highlights the ubiquity and necessity of phosphonate compounds to microbial life and function across all environments. Within the marine setting, both pepM and aepY have reference sequences from 22 unique, verified classes which is the second highest class representation in the marine genome subset (Fig. 2). The marine subset of pepM and aepY also have the highest Shannon indices (1.76 and 1.92) distributed with 0.53 and 0.58 evenness, respectively. A majority (87%) of the Alphaproteobacteria phosphonate producers are Pelagibacterales bacterium with other notable taxa including Bacteria: Candidatus Actinomarinaceae, Prochlorococcus sp., Synechococcus sp., Nitrosococcus sp., and MG-I Archaea: Candidatus Nitrosomarinus catalina, Nitrosopumilus maritimus, alongside other unidentified Crenarchaeota and Thaumarchaeota genomes.The gene phpC was found in less than half the number of genomes than pepM and aepY, and encoded by fewer classes in both the general database (47) and marine subset (10). In the full databases, the distribution of retrieved phpC sequences are similar to pepM and aepY with respect to taxonomic ranking, Shannon index (2.49), and evenness (0.60) (Fig. 2A–C, Supplementary Table 5). Within the marine subset, phpC has less Shannon index (1.61) but greater evenness (0.61) than the marine subset of pepM and aepY. All three upstream phosphonate production genes (pepM, aepY, phpC) are found together within Pelagibacterales bacterium, Prochlorococcus sp., Thaumarchaeota, and Crenarchaoeta alongside other taxa such as Oceanospirillales sp., Arenimonas donghaensis, Desulfuromusa kysingii, and Cellulosilyticum lentocellum.We further investigated the relationship between pepM, aepY, and phpC by examining co-occurrence in genomes and synteny with the general, redundant databases. The first two steps in phosphonate biosynthesis are intimately linked (Fig. 3). Out of all genomes with pepM, 86% have aepY, and out of all genomes with aepY, 90% have pepM. By contrast, phpC is not as closely tied to pepM and phosphonate production. We found phpC in just over 20% of genomes with the capability of phosphonate production (Fig. 3), implying that a majority of bacterial and archaeal phosphonate production stops at the production of phosphonoacetaldehyde or 2-AEP (Fig. 1A). Furthermore, half of the phpC genes were not associated with phosphonate production, given 53% of genomes with phpC did not have pepM and 54% did not have aepY (Fig. 3). In these instances, microbes may use phpC within a 2-AEP substrate-specific catabolism operon (Fig. 3) that allows phosphonate compounds to be synthesized by transforming 2-AEP with phnW and phpC into 2-HEP (Figs. 1A and 3). By repurposing 2-AEP, individuals can still create the specific compound needed while bypassing the energetically unfavourable first step of phosphonate production.Fig. 3: Co-occurrence of phosphonate cycling genes within the same genome and examples of genetic organization of phosphonate cycling genes.The heatmap displays co-occurrence of phosphonate cycling genes. Each column represents the subset of all genomes which contain the source gene and the heatmap value represents the fraction of the source genomes which also contain the co-occurring gene. Heatmap values are not symmetrical due to differing number of genomes represented in each column, database size listed above each column. Examples for phosphonate cycling genomic neighbourhoods were chosen to maximize diversity in synteny with examples from both Bacteria and Archaea where applicable. Several phosphonate-specific ABC transport system clusters are labelled as follows: phnC = phosphonate transport system ATPase; phnD = phosphonate transport system substrate-binding; phnE = phosphonate transport system permease; phnS = 2-AEP transport system substrate-binding; phnT = 2-AEP transport system ATP-binding; phnV = 2-AEP transport system permease; palC = transport system permease; palD = transport system ATP-binding; palE = transport system permease. Genes are colour coded by: red = lyase; orange = transcriptional regulator; yellow = hydrolase; green = transferase; light blue = oxidoreductase; dark blue = transaminase; purple = kinase; pink = isomerase; brown = transport; white = synthase; black = uncharacterized protein; grey = unknown.Full size imageA narrow but diverse selection of taxa encoded MpnS, the marker gene for Mpn production and a key determinant in marine methane production. We observed distinct clades of this enzyme in autotrophic archaea and heterotrophic bacteria (Fig. 2B, C). Within the marine ecosystems, Pelagibacterales, Rhodospirillales, Rickettsiales, Oceanospirillales, Flavobacteriales, and Synechococcales are bacterial candidates for MPn production alongside Thaumarchaeota and Crenarchaeota archaeon (Fig. 2B, C). While six of the bacterial genomes with MpnS also encoded genes for phosphonate catabolism, none of the archaeal MPn producers showed capacity for catabolism (Supplementary Dataset S1). The genomic neighbourhoods for general phosphonate production (pepM, aepY, phpC) and MPn production (mpnS) in both bacteria and archaea include genes such as glycosyltransferase, lipopolysaccharide choline phosphotransferase, choline kinase, adenylyltransferase, and arylsulfatase A (Fig. 3) suggesting the potential for synthesis of (methyl)phosphonate esters [93]. This is consistent with previous analysis [29] of the Nitrosopumilus maritimus SCM1 MPn production genomic neighbourhood and biophysical evidence that MPn producing archaea synthesize an exopolysaccharide modified with MPn similar to 2-AEP modified polymers.Contrary to the diversity of the other phosphonate production databases, the hepD database has low Shannon index (0.62) and evenness (0.45) with 79% of sequences mapping to Actinomycetia including Streptomycetales and Corynebacteriales (Fig. 2B, C). The marine subset has lower Shannon index (0.28) and evenness (0.41) where all sequences derive from Pelagibacterales except one from Prochlorococcus sp. The genomic neighbourhood of HMP production may contain genes for cell surface modification such as acetyltransferase, peptidoglycan biosynthesis, and adenylylsulfate transferase, suggesting that some organisms may use HMP as a conjugate for membrane-associated or exported macromolecules similar to theories on MPn utilization. Other examples of hepD synteny contain more specific genes such as the HMP dehydrogenase or other enzymes for downstream modification (Fig. 3).Marine proteobacteria encode genes for substrate-specific and broad-specificity phosphonate catabolismGenes for marine substrate-specific phosphonate catabolism were widespread among Proteobacterial classes, and to a lesser extent amongst other classes including Bacilli, Planctomycetes, and Synechococcus (Fig. 2E,F). Marine substrate-specific catabolism has lower average Shannon index (1.00) and evenness (0.43) than the three general production genes (pepM, aepY, phpC). The most widespread of these genes was phnW, likely due to its pivotal role in 2-AEP transformations as a precursor reaction to phnAY or phnX (Fig. 1, Supplementary Table 5). Marine hydrolases for 2-AEP catabolism, phnA, phnX, and phnZ, have similar Shannon indices (mean: 1.11 ± 0.05) and evenness (mean: 0.41 ± 0.03) (Fig. 2E, F, Supplementary Table 5).While not exclusive, sequenced references demonstrate a strong taxonomic partition between Proteobacterial classes for 2-AEP catabolism pathways phnAWY and phnWX. Over 74% of marine genomes with phnAWY are Alphaproteobacteria, in particular Rhodobacterales species such as Roseovarius nubinhibens, Marivita geojedonensis, and Pelagicola litoralis. On the contrary, ~80% of marine genomes with phnWX are Gammaproteobacteria, specifically of Vibrionales, Oceanospirillales, and Alteromonadales including a wide range of species from Vibrio, Photobacterium, Marinobacterium, Halomonas, and Pseudoalteromonas.Taxonomic distribution for marine phnZ was 72% Alphaproteobacteria with Pelagibacterales making up 45% of marine phnZ sequences. Note that phnZ has the most (17) reference sequences from marine Cyanobacteriia, specifically Prochlorococcus sp., than any other phosphonate catabolizing gene. Lack of marine sequence representatives for catabolism of phosphonopyruvate by palA suggests that either the substrate is uncommon, therefore the function unnecessary, or marine microbes have other methods of catabolizing phosphonopyruvate, perhaps by the C-P lyase. Overall taxonomic distribution of phosphonate substrate-specific catabolism, specifically targeting 2-AEP, suggests said function is essential to many marine heterotrophs within Alphaproteobacteria and Gammaproteobacteria. However 2-AEP catabolism appears to be less universally important than phosphonate production to marine microbial life since the required genes are found in a less diverse selection of taxa.Genetic organization for substrate-specific catabolism genes, particularly those targeting 2-AEP, varied widely in line with the numerous options for 2-AEP catabolism (Fig. 3). Though some bacteria specialize in a single 2-AEP degradation pathway such as only containing phnWAY, others contained multiple hydrolases for 2-AEP catabolism with some incorporating phpC into a 2-AEP specific catabolism operon (Fig. 3). When a genome has two hydrolases for phosphonate catabolism, often phnZ was paired with either phnA or phnX. Co-occurrence between phnZ and either phnA or phnX ranged between 30-50%, whereas co-occurrence between phnA and phnX was between 6-12% (Fig. 3). This discrepancy in co-occurrence may be due to the metabolic similarity between phnA and phnX, where having both may be redundant. Both of these enzymes rely on phnW for 2-AEP catabolism and produce carbon metabolites, whereas phnZ does not need phnW and produces the amino acid glycine (Fig. 1B).C-P lyase genes representing substrate non-specific catabolism were overwhelmingly attributed to Alphaproteobacteria which consisted over 75% of all collected marine sequences for phnIJM (Fig. 2H, I). A wide variety of Rhodobacterales, spanning 55 different genus are the most numerous representatives, followed by Pelagibacterales and Rhodospirillales. The genes in all three databases have very high genome co-occurrence, 89–99%, as expected given all three operate within the same enzyme complex (Fig. 3). Gene co-occurrence, Shannon index, and evenness is lower for phnM than the other two C-P lyase components, phnI and phnJ, likely due to instances of organisms containing two copies of phnM where one copy lies outside the C-P lyase operon [94]. C-P lyase gene databases have lower Shannon index (mean 0.81 ± 0.11) than phosphonate production and 2-AEP substrate-specific catabolism genes (phnAWXZ) (Fig. 3D, G), suggesting broad-specificity phosphonate catabolism by the C-P lyase is a narrowly distributed function (Supplementary Table 5). Organization of C-P lyase operons held the most consistency between example genomes, likely due to the high number of genes simultaneously utilized for lyase construction. These operons encoded a consecutive string of lyase subunits, including a generic phosphonate transporter (phnCDE) and GntR transcriptional regulator (Fig. 3). C-P lyase genes had low genomic co-occurrence with all other phosphonate cycling genes with notable co-occurrence between phnW at 26%, phnZ at 21%, and phnX and 18% (Fig. 3). The low rate of co-occurrence may be due to redundancy in function for P harvesting between the C-P lyase and substrate-specific catabolism. In some cases there are instances of a substrate-specific hydrolase gene located within the C-P lyase operon (Fig. 3).Phosphonate biosynthesis genes are globally prevalent in oceans and increase in mesopelagic watersFollowing curation of phn-gene databases, we analysed 121 metagenomes and 91 metatranscriptomes from the publicly available TARA Oceans expedition (spanning samples from the Atlantic, Indian, Pacific, and Southern Ocean and Red Sea) to investigate the global potential for marine phosphonate cycling. Measuring the proportion of the community capable of performing specific tasks through metagenomics indicates the long-term selective pressures that shape P-cycling and microbial communities.Potential for phosphonate production (pepM) was globally ubiquitous across all depths, with 14–17% of the community encoding in the surface waters and deep chlorophyll maximum (DCM), increasing to 45% in mesopalagic waters (Fig. 4A, Supplementary Tables S6 and S7), highlighting the importance of phosphonate compounds to marine microbial communities. Relative abundance of phpC was 64–76% that of pepM and aepY across all depths (Fig. 4A). We observed significant increase in relative abundance between the surface and mesopelagic for pepM (ANOVA: F = 1262, p  More

  • in

    Major biodiversity summit will go ahead in Canada not China: what scientists think

    Deforestation, in places such as the Amazon, contributes to biodiversity loss.Credit: Ivan Valencia/Bloomberg/Getty

    Researchers are relieved that a pivotal summit to finalize a new global agreement to save nature will go ahead this year, after two-years of delays because of the pandemic. But they say the hard work of negotiating an ambitious deal lays ahead.The United Nations Convention on Biological Diversity (CBD) announced yesterday that the meeting will move from Kunming in China to Montreal in Canada. The meeting of representatives from almost 200 member states of the CBD — known as COP15 — will now run from 5 to 17 December. China will continue as president of the COP15 and Huang Runqiu, China’s minister of ecology and environment, will continue as chairman.Conservation and biodiversity scientists were growing increasingly concerned that China’s strict ‘zero COVID’ strategy, which uses measures such as lockdowns to quash all infections, would force the host nation to delay the meeting again. Researchers warned that another setback to the agreement, which aims to halt the alarming rate of species extinctions and protect vulnerable ecosystems, would be disastrous for countries’ abilities to meet ambitious targets to protect biodiversity over the next decade.“We are relieved and thankful that we have a firm date for these critically important biodiversity negotiations within this calendar year,” says Andrew Deutz, an expert in biodiversity law and finance at the Nature Conservancy, a conservation group in Virginia, US. “The global community is already behind in agreeing, let alone implementing, a plan to halt and reverse biodiversity loss by 2030,” he says.With the date now set, Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services, says the key to success in Montreal will be for the new global biodiversity agreement to focus on the direct and indirect drivers of nature loss, and the behaviors that underpin them. “Policy should be led by science, action adequately resourced and change should be transformative,” she adds.New locationThe decision to move the meeting came about after representatives of the global regions who make up the decision-making body of the COP reached a consensus to shift it to Montreal. China and Canada then thrashed out the details of how the move would work. The CBD has provisions that if a host country is unable to hold a COP, the meeting shifts to the home of the convention’s secretariat, Montreal.Announcing the decision, Elizabeth Mrema, executive secretary of the CBD, said in a statement, “I want to thank the government of China for their flexibility and continued commitment to advancing our path towards an ambitious post 2020 Global Biodiversity Framework.”In a statement, Runqiu said, “China would like to emphasize its continued strong commitment, as COP president, to ensure the success of the second part of COP 15, including the adoption of an effective post 2020 Global Biodiversity Framework, and to promote its delivery throughout its presidency.”China also agreed to pay for ministers from the least developed countries and small Island developing states to travel to Montreal to participate in the meeting.Work aheadPaul Matiku, an environmental scientist and head of Nature Kenya, a conservation organization in Nairobi, Kenya, says the move “is a welcome decision” after “the world lost patience after a series of postponements”.But he says that rich nations need to reach deeper into their pockets to help low- and middle-income countries — which are home to much of the world’s biodiversity — to implement the deal, including meeting targets such as protecting at least 30% of the world’s land and seas and reducing the rate of extinction. Disputes over funding already threaten to stall the agreement. At a meeting in Geneva in March, nations failed to make progress on the new deal because countries including Gabon and Kenya argued that the US$10 billion of funding per year proposed in the draft text of the agreement was insufficient. They called for $100 billion per year in aid.“The extent to which the CBD is implemented will depend on the availability of predictable, adequate financial flows from developed nations to developing country parties,” says Matiku.Talks on the agreement are resuming in Nairobi from 21-26 June, where Deutz hopes countries can find common ground on key issues such as financing before heading to Montreal. Having a firm date set for the COP15 will help push negotiations forward, he says.“Negotiators only start to compromise when they are up against a deadline. Now they have one,” he says. More

  • in

    Incongruences between morphology and molecular phylogeny provide an insight into the diversification of the Crocidura poensis species complex

    Foote, M. The evolution of morphological diversity. Annu. Rev. Ecol. Syst. 28, 129–152 (1997).Article 

    Google Scholar 
    Félix, M. A. Phenotypic evolution with and beyond genome evolution. Curr. Top. Dev. Biol. 119, 291–347 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    Carroll, S. B. Evo-devo and an expanding evolutionary synthesis: A genetic theory of morphological evolution. Cell 134, 25–36 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Harvey, P. & Pagel, M. The Comparative Method in Evolutionary Biology. (Oxford University Press, 1991).Huxley, J. S. & Teissier, G. Terminology of relative growth. Nature 137, 780–781 (1936).ADS 
    Article 

    Google Scholar 
    Klingenberg, C. P. Size, shape, and form: Concepts of allometry in geometric morphometrics. Dev. Genes Evol. 226, 113–137 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Russell, E. S. Form and Function: A Contribution to the History of Animal Morphology. (John Murray, 1916).Goswami, A. & Polly, P. D. Methods for studying morphological integration and modularity. Paleontol. Soc. Pap. 16, 213–243 (2010).Article 

    Google Scholar 
    Vidal-García, M., Byrne, P. G., Roberts, J. D. & Keogh, J. S. The role of phylogeny and ecology in shaping morphology in 21 genera and 127 species of Australo-Papuan myobatrachid frogs. J. Evol. Biol. 27, 181–192 (2014).PubMed 
    Article 

    Google Scholar 
    Erwin, D. H. Disparity: Morphological pattern and developmental context. Palaeontology 50, 57–73 (2007).Article 

    Google Scholar 
    Fišer, C., Robinson, C. T. & Malard, F. Cryptic species as a window into the paradigm shift of the species concept. Mol. Ecol. 27, 613–635 (2018).PubMed 
    Article 

    Google Scholar 
    Wilson, D. E. & Mittermeier, R. A. Handbook of the Mammals of the World: Volume 8: Insectivores. vol. 8 (Lynx Edicions, 2018).Jacquet, F. et al. Phylogeography and evolutionary history of the Crocidura olivieri complex (Mammalia, Soricomorpha): From a forest origin to broad ecological expansion across Africa. BMC Evol. Biol. 15, 71. https://doi.org/10.1186/s12862-015-0344-y (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ceríaco, L. M. P. et al. Description of a new endemic species of shrew (Mammalia, Soricomorpha) from PrÍncipe Island (Gulf of Guinea). Mammalia 79, 325–341 (2015).Article 

    Google Scholar 
    Nicolas, V. et al. Multilocus phylogeny of the Crocidura poensis species complex (Mammalia, Eulipotyphla): Influences of the palaeoclimate on its diversification and evolution. J. Biogeogr. 46, 871–883 (2019).Article 

    Google Scholar 
    Konečný, A., Hutterer, R., Meheretu, Y. & Bryja, J. Two new species of Crocidura (Mammalia: Soricidae) from Ethiopia and updates on the Ethiopian shrew fauna. J. Vertebr. Biol. 69, 20064.1. https://doi.org/10.25225/jvb.20064 (2020).Article 

    Google Scholar 
    Couvreur, T. L. P. et al. Tectonics, climate and the diversification of the tropical African terrestrial flora and fauna. Biol. Rev. 96, 16–51 (2021).PubMed 
    Article 

    Google Scholar 
    Mayr, E. & O’Hara, R. J. The biogeographic evidence supporting the Pleistocene forest refuge hypothesis. Evolution 40, 55–67 (1986).PubMed 
    Article 

    Google Scholar 
    Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

    Google Scholar 
    Smith, T. B., Wayne, R. K., Girman, D. J. & Bruford, M. W. A role for ecotones in generating rainforest biodiversity. Science 276, 1855–1857 (1997).CAS 
    Article 

    Google Scholar 
    Needham, A. E. & Hardy, A. C. The form-transformation of the abdomen of the female pea-crab, Pinnotheres pisum Leach. Proc. R Soc. Lond. Ser. B Biol. Sci. 137, 115–136 (1950).ADS 
    CAS 

    Google Scholar 
    Hanken, J. & Hall, B. K. The Skull, Volume 3: Functional and Evolutionary Mechanisms. (University of Chicago Press, 1993).Hautier, L., Lebrun, R. & Cox, P. G. Patterns of covariation in the masticatory apparatus of hystricognathous rodents: Implications for evolution and diversification. J. Morphol. 273, 1319–1337 (2012).PubMed 
    Article 

    Google Scholar 
    Aristide, L. et al. Multiple factors behind early diversification of skull morphology in the continental radiation of New World monkeys. Evolution 72, 2697–2711 (2018).PubMed 
    Article 

    Google Scholar 
    Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Denys, C. et al. Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species. Zoosystema 43, 729–757 (2021).Article 

    Google Scholar 
    Estevo, C. A., Nagy-Reis, M. B. & Nichols, J. D. When habitat matters: Habitat preferences can modulate co-occurrence patterns of similar sympatric species. PLoS One 12, e0179489. https://doi.org/10.1371/journal.pone.0179489 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spaeth, P. A. Morphological convergence and coexistence in three sympatric North American species of Microtus (Rodentia: Arvicolinae). J. Biogeogr. 36, 350–361 (2009).Article 

    Google Scholar 
    Adams, D. C., Berns, C. M., Kozak, K. H. & Wiens, J. J. Are rates of species diversification correlated with rates of morphological evolution?. Proc. R. Soc. B Biol. Sci. 276, 2729–2738 (2009).Article 

    Google Scholar 
    Caumul, R. & Polly, P. D. Phylogenetic and environmental components of morphological variation: Skull, mandible, and molar shape in marmots (marmota, Rodentia). Evolution 59, 2460–2472 (2005).PubMed 
    Article 

    Google Scholar 
    Da Silva, F. O. et al. The ecological origins of snakes as revealed by skull evolution. Nat. Commun. 9, 376. https://doi.org/10.1038/s41467-017-02788-3 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hirano, T., Kameda, Y., Kimura, K. & Chiba, S. Substantial incongruence among the morphology, taxonomy, and molecular phylogeny of the land snails Aegista, Landouria, Trishoplita, and Pseudobuliminus (Pulmonata: Bradybaenidae) occurring in East Asia. Mol. Phylogenet. Evol. 70, 171–181 (2014).PubMed 
    Article 

    Google Scholar 
    Ge, D., Yao, L., Xia, L., Zhang, Z. & Yang, Q. Geometric morphometric analysis of skull morphology reveals loss of phylogenetic signal at the generic level in extant lagomorphs (Mammalia: Lagomorpha). Contrib. Zool. 84, 267–284 (2015).Article 

    Google Scholar 
    Zou, Z. & Zhang, J. Morphological and molecular convergences in mammalian phylogenetics. Nat. Commun. 7, 12758. https://doi.org/10.1038/ncomms12758 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ananjeva, N. B. Current state of the problems in the phylogeny of squamate reptiles (Squamata, Reptilia). Biol. Bull. Rev. 9, 119–128 (2019).Article 

    Google Scholar 
    Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57, 591–601 (2008).PubMed 
    Article 

    Google Scholar 
    Klingenberg, C. P. & Marugán-Lobón, J. Evolutionary covariation in geometric morphometric data: Analyzing integration, modularity, and allometry in a phylogenetic context. Syst. Biol. 62, 591–610 (2013).PubMed 
    Article 

    Google Scholar 
    Cardini, A. & Polly, P. D. Larger mammals have longer faces because of size-related constraints on skull form. Nat. Commun. 4, 2458. https://doi.org/10.1038/ncomms3458 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Esquerré, D., Sherratt, E. & Keogh, J. S. Evolution of extreme ontogenetic allometric diversity and heterochrony in pythons, a clade of giant and dwarf snakes. Evolution 71, 2829–2844 (2017).PubMed 
    Article 

    Google Scholar 
    Marroig, G. & Cheverud, J. M. Size as a line of least evolutionary resistance: Diet and adaptive morphological radiation in New World monkeys. Evolution 59, 1128–1142 (2005).PubMed 
    Article 

    Google Scholar 
    Cornette, R., Tresset, A., Houssin, C., Pascal, M. & Herrel, A. Does bite force provide a competitive advantage in shrews? The case of the greater white-toothed shrew. Biol. J. Linn. Soc. 114, 795–807 (2015).Article 

    Google Scholar 
    Rodgers, G. M., Downing, B. & Morrell, L. J. Prey body size mediates the predation risk associated with being “odd”. Behav. Ecol. 26, 242–246 (2015).Article 

    Google Scholar 
    Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).ADS 
    Article 

    Google Scholar 
    Verschuren, D. Decadal and century-scale climate variability in tropical Africa during the past 2000 years. In Past Climate Variability Through Europe and Africa (eds. Battarbee, R. W., Gasse, F. & Stickley, C. E.) 139–158 (Springer Netherlands, 2004). https://doi.org/10.1007/978-1-4020-2121-3_8.Smith, T. B., Schneider, C. J. & Holder, K. Refugial isolation versus ecological gradients. Genetica 112, 383–398 (2001).PubMed 
    Article 

    Google Scholar 
    Brown, W. L. Jr. & Wilson, E. O. Character displacement. Syst. Biol. 5, 49–64 (1956).
    Google Scholar 
    Vogel, P. et al. Genetic identity of the critically endangered Wimmer’s shrew Crocidura wimmeri. Biol. J. Linn. Soc. 111, 224–229 (2014).Article 

    Google Scholar 
    Esselstyn, J. A. et al. Fourteen new, endemic species of shrew (genus Crocidura) from Sulawesi reveal a spectacular island radiation. Bull. Am. Mus. Nat. Hist. 454, 1–108 (2021).Article 

    Google Scholar 
    Evin, A., Bonhomme, V. & Claude, J. Optimizing digitalization effort in morphometrics. Biol. Methods Protoc. 5, bpaa023. https://doi.org/10.1093/biomethods/bpaa023 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Blomberg, S. P., Garland, T. & Ives, A. R. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57, 717–745 (2003).PubMed 
    Article 

    Google Scholar 
    Adams, D. C. A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data. Syst. Biol. 63, 685–697 (2014).PubMed 
    Article 

    Google Scholar 
    Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Revell, L. J. phytools: Phylogenetic Tools for Comparative Biology (and Other Things). (2021).Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439 (2018).Article 

    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. (2020).Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).Article 

    Google Scholar 
    Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R. (Springer, 2018).Dray, S. et al. adespatial: Multivariate Multiscale Spatial Analysis. (2021).Collyer, M. & Adams, D. RRPP: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure. (2021).Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. (2021).Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).Article 

    Google Scholar 
    Rohlf, F. J. & Corti, M. Use of two-block partial least-squares to study covariation in shape. Syst. Biol. 49, 740–753 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schlager, S., Jefferis, G. & Ian, D. Morpho: Calculations and Visualisations Related to Geometric Morphometrics. (2020). More

  • in

    Index system of rural human settlement in rural revitalization under the perspective of China

    Parra-Lopez, C., Groot, J. C. J., Carmona-Torres, C. & Rossing, W. A. H. Integrating public demands into model-based design for multifunctional agriculture: an application to intensive Dutch dairy landscapes. Ecol. Econ. 67(4), 538–551 (2008).Article 

    Google Scholar 
    Pinto-Correia, T., Guiomar, N., Guerra, C.A. et al. Assessing the ability of rural (2016)UPA. Desarrollo rural. Oportunidades Desaprovechadas La Tierra 254, 31–33 (2016).
    Google Scholar 
    Abreu, I., Nunes, J. M. & Mesias, F. J. Can rural development Be measured? Design and application of a synthetic index to Portuguese municipalities. Soc. Indic. Res. 145, 1107–1123 (2019).Article 

    Google Scholar 
    Doxiadis, C. A. Ekistics: an introduction to the science of human settlements (Oxford University Press, 1968).
    Google Scholar 
    Algeciras, J. A. R., Coch, H. & Perez, G. D. L. P. Human thermal comfort conditions and urban planning in hot-humid climates-The case of Cuba. Int. J. Biometeorol. 60, 1151–1164 (2016).Article 

    Google Scholar 
    Zhang, H., Zhang, S. & Liu, Z. Evolution and influencing factors of China’s rural population distribution patterns since 1990. PLoS ONE 15, e0233637 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eurostat. Eurostat Regional Yearbook. 2019 Edition. Publications Office of the European Union, Luxembourg (2019)Overview of CAP Reform 2014–2020. Agricultural Policy Perspectives. Brief, no. 5, December 2013. European Commission.Marsden, T. & Sonnino, R. Rural development and the regional state: denying multifunctional agriculture in the UK. J. Rural Stud. 24(4), 422–431 (2008).Article 

    Google Scholar 
    Adamowicz, M. Normative Aspects of Rural Development Strategy and Policy in The European Union Normative Aspects of Rural Development Strategy and Policy in The European Union, (2018)Léon, Y. Rural development in Europe: a research frontier for agricultural economists. Eur. Rev. Agric. Econ. 32, 301–317 (2005).Article 

    Google Scholar 
    Biegańska, J., Środa-Murawska, S., Kruzmetra, Z. & Swiaczny, F. Peri-Urban development as a significant rural development trend. Quaest. Geogr. 37, 125–140 (2018).Article 

    Google Scholar 
    Lee I.-H. Change of rural development policy in South Korea After Korean War. J. Reg City Plan, 2021.Oh, Y.-Y. et al. The selection of proper resource and change of salinity in Helianthus tuberosus L. cultivated in Saemangeum reclaimed tidal land. Korean J. Environ. Agric. 37, 73–78 (2018).Article 

    Google Scholar 
    Yoon, J.-Y., Jeong, J.-H. & Choi, S.-K. Validation of reference genes for quantifying changes in physiological gene expression in apple tree under cold stress and virus infection. Radiat. Prot. Dosim. 26, 144–158 (2020).
    Google Scholar 
    Faradiba, F. & Zet, L. The impact of climate factors, disaster, and social community in rural development. J. Asian Finance Econ. Bus. 7, 707–717 (2020).Article 

    Google Scholar 
    Kaneko, M., Ohta, R., Vingilis, E. & Mathews, M. Thomas Robert freeman; systematic scoping review of factors and measures of rurality: toward the development of a rurality index for health care research in Japan. BMC Health Services Res. 21, 1–11 (2021).Article 

    Google Scholar 
    Yokoyama, S. Sustainable activities for rural development, New Frontiers in Regional Science: Asian Perspectives, (2019).Georgios, C., & Barraí, H. Social innovation in rural governance: a comparative case study across the marginalised rural EU. J. Rural Stud. (2021)Michalek, J. & Zarnekow, N. Application of the rural development index to analysis of rural regions in Poland and Slovakia. Soc. Indic. Res. 105, 1–37 (2012).Article 

    Google Scholar 
    Liu, Y., Wang, G. & Zhang, F. Spatio-temporal dynamic patterns of rural area development in eastern coastal China. Chin. Geogr. Sci. 23, 173–181 (2013).Article 

    Google Scholar 
    Kim, T.-H. & Yang, S.-R. Construction of the rural development index: the case of Vietnam. J. Rural Dev. 39, 113–142 (2016).
    Google Scholar 
    Houkai, W. Current top ten frontier issues [J]in the study of agriculture. Rural Areas Rural Econ. China 4, 2–6 (2019).
    Google Scholar 
    Hu, Y., Fu, R. & Jin, S. Ecological environment concern in the organic link between poverty alleviation and rural revitalization. Reform 10, 141–148 (2019).
    Google Scholar 
    Zhang H., Gao L., & Yan K. On the theoretical origin, main innovation and realization path of rural revitalization thought rural economy in China, (11), 2–16 (2018).Feiwei, S. & Zegan, X. Construction and empirical analysis of the index system of beautiful villages in Zhejiang Province. J. Huazhong Agric. Univ. (Social Sciences Edition) 02, 45-51+132 (2017).
    Google Scholar 
    Research Group of Shanghai Rural Revitalization Index. Construction and evaluation of the index system of rural revitalization in Shanghai. Sci. Dev. 9, 56–63 (2020).World Bank, Expanding the Measure of Wealth. Indicators of Environmentally Sustainable Development. World Bank, Washington, D.C. (1997)Hicks, D. A. The inequality-adjusted human development index: a constructive proposal. World Dev. 25, 1283–1298 (1997).ADS 
    Article 

    Google Scholar 
    Zhao, R., Shao, C. & He, R. Spatiotemporal evolution of ecosystem health of China’s Provinces based on SDGs. Int. J Environ. Res. Public Health 18, 10569 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kageyama, A., Desenvolvimento rural : conceitos e aplicaçao ao caso brasileiro. UFRGS Editora, Porto Alegre (Brasil) (2008).Abreu, I. Construçao de um índice de desenvolvimento rural e sua aplicaçao ao Alto Alentejo. Instituto Polit´ecnico de Portalegre (2014)Castellani, V. & Sala, S. Sustainable performance index for tourism policy development. Tour. Manag. 31, 871–880 (2009).Article 

    Google Scholar 
    Hashemi, N. The role of ecotourism in sustainable rural development. J. Rural Dev. Stud. 13(3), 173–188 (2010).
    Google Scholar 
    Alkire, S., Conconi, A., & Seth, S. Multidimensional Poverty Index 2013: Brief methodological note and results. Oxford Poverty and Human Development Initiative (OPHI) (2014b)Alkire, S., Foster, J. & Santos, M. E. Where did identification go?. J. Econ. Inequal. 9, 501–505 (2011).Article 

    Google Scholar 
    Alkire, S. et al. Multidimensional poverty measurement and analysis (Oxford University Press, Oxford, 2015).MATH 
    Book 

    Google Scholar 
    Alkire, S., & Seth, S. Identifying destitution through linked subsetsof multidimensionally poor: an ordinal approach, OPHI Working Paper 99. University of Oxford (2016)Li, X., Yang, H., Jia, J., Shen, Y. & Liu, J. Index system of sustainable rural development based on the concept of ecological livability. Environ. Impact Assess. Rev. 86, 1–12 (2021).
    Google Scholar 
    Guo, X. & Hu, Y. Construction of evaluation index system of rural revitalization level. Agric. Econ. Manag. 05, 5–15 (2020).
    Google Scholar 
    Kong, X. & Xia, J. The value logic relation and synergy path choice between rural revitalization strategy and rural integration development. J. Northwest Univ. (Philosophy and Social Sciences Edition) 49(02), 10–18 (2019).
    Google Scholar 
    Wen, T. Three hundred years: the context and development of Chinese rural construction. Open Age. (04) (2016)Ren, C. A Study on the basis, constraints and institutional supply of industrial prosperity. Acad. Acad. 07, 15–27 (2018).
    Google Scholar 
    Ye, X., Cheng, Y., Zhao, J. & Ning, X. Rural revitalization during the 14th Five-year plan: trend judgment, general thinking and safeguard mechanism. Rural Econ. 09, 1–9 (2020).
    Google Scholar 
    Zhu, Q. The sociological explanation of the prosperity of rural industry-industry in the context of rural revitalization. J. China Agric. Univ. (Social Sciences Edition) 35(03), 89–95 (2018).
    Google Scholar 
    Bithas, K. A bioeconomic approach to sustainability with ecological thresholds as an operational indicator. Environ. Sustain. Indic. 6, 100027 (2020).Article 

    Google Scholar 
    Zheng, X. The East Asian experience of Rural Revitalization and its Enlightenment to China—Take Japan and South Korea as an example. Lanzhou J. 11, 200–208 (2019).ADS 

    Google Scholar 
    Srivastava, P. K., Kulshreshtha, K., Monhanty, C. S., Pushpangadan, P. & Singh, A. Stakeholder-based SWOT analysis for successful municipal solid waste managementin Lucknow, India. Waste Manag 25(5), 531–537 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Alcon, F., Tapsuwan, S., Martínez-Paz, J. M., Brouwer, R. & Miguel, M. Forecasting deficit irrigation adoption using a mixed stakeholder assessment methodology. Technol. Forecast. Soc. Change 83, 183–193. https://doi.org/10.1016/j.techfore.2013.07.003 (2014).Article 

    Google Scholar 
    Chen, Y., Yang, G., Sweeney, S. & Feng, Y. Household biogas use in rural China: a study of opportunities and constraints. Renew. Sust. Energ. Rev. 14(1), 545–549 (2010).Article 

    Google Scholar 
    Yeh, C. H. A problem-based selection of multi-attribute decision-making methods. Int. Trans. Oper. Res. 9(2), 169–181 (2002).MATH 
    Article 

    Google Scholar 
    Chakraborty, S., & Yeh, C. -H. A simulation comparison of normalization procedures for TOPSIS, In 2009 International Conference on Computers & Industrial Engineering, IEEE, 2009.Yoon, K. & Hwang, C. L. Multiple attribute decision making. Eur. J. Oper. Res. 4(4), 287–288 (1995).
    Google Scholar 
    Sharma, N., Khan, Z.A., Siddiquee, A.N., Wahid, M.A. Proc. Inst. Mech. Eng. Part C:J. Mech. Eng. Sci. 233 (2019) 1–10.Maniraj, S. & Thanigaivelan, R. Optimization of electrochemical micromachining process parameters for machining of AMCs with different % compositions of GGBS using Taguchi and TOPSIS methods. Trans. Indian Inst. Met. 72, 3057–3066 (2019).CAS 
    Article 

    Google Scholar 
    Martinez-Fernandez, C. et al. Shrinking cities in Australia, Japan, Europe and the USA: From a global process to local policy responses. Prog. Plan. 105, 1–48 (2016).Article 

    Google Scholar 
    Mo, G. Green poverty reduction: the value orientation and realization path of ecological poverty alleviation in the battle against poverty—a series of studies on the mechanism of improving the performance of precision poverty alleviation. Modern Econ. Discuss. 11, 10–14 (2016).
    Google Scholar 
    Wu, L. Echanism and path selection of poverty alleviation in deep-poverty areas. Chin. Soft Sci. 7, 63–70 (2018).
    Google Scholar 
    Shengzu, Gu. et al. Countermeasure reflections on advancing the poverty relief in the 13th five-year plan. China Financial Res 2, 7–16 (2016).
    Google Scholar 
    Xie, Z. & Xunwu, J. Giving full play to the advantages of financial financing to help lift out of poverty. China Finance 3, 83–84 (2018).
    Google Scholar 
    Chen, W. A Way to realize the effective linkage between poverty relief and rural revitalization. Guizhou Soc. Sci. 1, 11–14 (2020).
    Google Scholar 
    Bijker, R. & Haartsen, T. More than counterurbanisation: migration to popular andless-popular rural areas in the Netherlands. Popul. Space Place 18, 643–657 (2012).Article 

    Google Scholar  More

  • in

    Top-down control of planktonic ciliates by microcrustacean predators is stronger in lakes than in the ocean

    Sherr, E. B. & Sherr, B. F. Role of microbes in pelagic food webs: A revised concept. Limnol. Oceanogr. 33, 1225–1227 (1988).ADS 
    Article 

    Google Scholar 
    Weisse, T. Pelagic microbes—Protozoa and the microbial food web. In The Lakes Handbook, Vol. 1—Limnology and Limnetic Ecology (eds O’Sullivan, P. & Reynolds, C. S.) 417–460 (Blackwell Science Ltd, 2004).
    Google Scholar 
    Foissner, W. Protist diversity: Estimates of the near-imponderable. Protist 150, 363–368 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sommer, U. & Sommer, F. Cladocerans versus copepods: The cause of contrasting top–down controls on freshwater and marine phytoplankton. Oecologia 147, 183–194 (2006).ADS 
    PubMed 
    Article 

    Google Scholar 
    Wiackowski, K., Brett, M. T. & Goldman, C. R. Differential effects of zooplankton species on ciliate community structure. Limnol. Oceanogr. 39, 486–492 (1994).ADS 
    Article 

    Google Scholar 
    Armengol, L., Calbet, A., Franchy, G., Rodríguez-Santos, A. & Hernández-León, S. Planktonic food web structure and trophic transfer efficiency along a productivity gradient in the tropical and subtropical Atlantic Ocean. Sci. Rep. 9, 2044. https://doi.org/10.1038/s41598-019-38507-9 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Carrick, H. J., Fahnenstiel, G. L., Stoermer, E. F. & Wetzel, R. G. The importance of zooplankton-protozoan trophic couplings in Lake Michigan. Limnol. Oceanogr. 36, 1335–1345. https://doi.org/10.4319/lo.1991.36.7.1335 (1991).ADS 
    Article 

    Google Scholar 
    Jack, J. D. & Gilbert, J. J. Effects of metazoan predators on ciliates in freshwater plankton communities. J. Eukaryot. Microbiol. 44, 194–199. https://doi.org/10.1111/j.1550-7408.1997.tb05699.x (1997).Article 

    Google Scholar 
    Sanders, R. W. & Wickham, S. A. Planktonic protozoa and metazoa: Predation, food quality and population control. Mar. Microb. Food Webs 7, 197–223 (1993).
    Google Scholar 
    Kiørboe, T. How zooplankton feed: Mechanisms, traits and trade-offs. Biol. Rev. 86, 311–339. https://doi.org/10.1111/j.1469-185X.2010.00148.x (2011).Article 
    PubMed 

    Google Scholar 
    Gliwicz, Z. M. Zooplankton. The Lakes Handbook: Limnology and Limnetic Ecology Vol. 1 (eds P. O’Sullivan & C. S. Reynolds) 461–516 (Blackwell Science Ltd, 2004).Wickham, S. A. The direct and indirect impact of Daphnia and cyclops on a freshwater microbial food web. J. Plankton Res. 20, 739–755 (1998).Article 

    Google Scholar 
    Gilbert, J. J. Suppression of rotifer populations by Daphnia: A review of the evidence, the mechanisms, and the effects on zooplankton community structure. Limnol. Oceanogr. 33, 1286–1303 (1988).ADS 
    Article 

    Google Scholar 
    Lampert, W. & Muck, P. Multiple aspects of food limitation in zooplankton communities: The Daphnia-Eudiaptomus example. Ergebnisse der Limnologie/Adv. Limnol. 21, 311–322 (1985).
    Google Scholar 
    Kiørboe, T. What makes pelagic copepods so successful?. J. Plankton Res. 33, 677–685. https://doi.org/10.1093/plankt/fbq159 (2011).Article 

    Google Scholar 
    Paffenhöfer, G.-A. Heterotrophic protozoa and small metazoa: Feeding rates and prey-consumer interactions. J. Plankton Res. 20, 121–133 (1998).Article 

    Google Scholar 
    Forró, L., Korovchinsky, N. M., Kotov, A. A. & Petrusek, A. Global diversity of cladocerans (Cladocera; Crustacea) in freshwater. In Freshwater Animal Diversity Assessment 177–184 (Springer, 2007).Jack, J. D. & Gilbert, J. J. Susceptibilities of different-sized ciliates to direct suppression by small and large cladocerans. Freshw. Biol. 29, 19–29 (1993).Article 

    Google Scholar 
    Jürgens, K. Impact of Daphnia on planktonic microbial food webs—A review. Mar. Microb. Food Webs 8, 295–324 (1994).
    Google Scholar 
    Calbet, A. & Saiz, E. The ciliate-copepod link in marine ecosystems. Aquat. Microb. Ecol. 38, 157–167. https://doi.org/10.3354/ame038157 (2005).Article 

    Google Scholar 
    Saiz, E. & Calbet, A. Scaling of feeding in marine calanoid copepods. Limnol. Oceanogr. 52, 668–675 (2007).ADS 
    Article 

    Google Scholar 
    Steinberg, D. K. & Landry, M. R. Zooplankton and the ocean carbon cycle. Ann. Rev. Mar. Sci. 9, 413–444 (2017).PubMed 
    Article 

    Google Scholar 
    Pierce, R. W. & Turner, J. T. Ecology of planktonic ciliates in marine food webs. Rev. Aquat. Sci. 6, 139–181 (1992).
    Google Scholar 
    Oghenekaro, E. U. & Chigbu, P. Population dynamics and life history of marine cladocera in the maryland coastal bays, USA. J. Coast. Res. 35, 1225–1236 (2019).Article 

    Google Scholar 
    Pestorić, B., Lučić, D & Joksimović, D. Cladocerans spatial and temporal distribution in the coastal south Adriatic waters (Montenegro). Stud. Mar. 25, 101–120 (2011).Adrian, R. & Schneider-Olt, B. Top-down effects of crustacean zooplankton on pelagic microorganisms in a mesotrophic lake. J. Plankton Res. 21, 2175–2190. https://doi.org/10.1093/plankt/21.11.2175 (1999).Article 

    Google Scholar 
    Burns, C. W. & Schallenberg, M. Relative impacts of copepods, cladocerans and nutrients on the microbial food web of a mesotrophic lake. J. Plankton Res. 18, 683–714. https://doi.org/10.1093/plankt/18.5.683 (1996).Article 

    Google Scholar 
    Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237–240 (1998).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Lewis, W. M. Jr. Global primary production of lakes: 19th Baldi Memorial Lecture. Inland Waters 1, 1–28 (2011).Article 

    Google Scholar 
    Moore, C. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710. https://doi.org/10.1038/NGEO1765 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Gilbert, J. J. Jumping behavior in the oligotrich ciliates Strobilidium velox and Halteria grandinella and its significance as a defense against rotifers. Microb. Ecol. 27, 189–200 (1994).CAS 
    PubMed 
    Article 

    Google Scholar 
    Weisse, T. & Sonntag, B. Ciliates in planktonic food webs: communication and adaptive response. In Biocommunication of Ciliates (eds Witzany, G. & Nowacki, M.) 351–372 (Springer International Publishing, 2016).
    Google Scholar 
    Burns, C. W. & Gilbert, J. J. Predation on ciliates by freshwater calanoid copepods: Rates of predation and relative vulnerabilities of prey. Freshw. Biol. 30, 377–393. https://doi.org/10.1111/j.1365-2427.1993.tb00822.x (1993).Article 

    Google Scholar 
    Lampert, W. & Sommer, U. Limnoecolgy 2nd edn. (Oxford University Press, 2007).
    Google Scholar 
    Almeda, R., Someren Gréve, H. & Kiørboe, T. Prey perception mechanism determines maximum clearance rates of planktonic copepods. Limnol. Oceanogr. 63, 2695–2707. https://doi.org/10.1002/lno.10969 (2018).ADS 
    Article 

    Google Scholar 
    Holling, C. S. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can. Entomol. 91, 293–320 (1959).Article 

    Google Scholar 
    Fenchel, T. Ecology of protozoa. The Biology of Free-living Phagotrophic Protists (Science Tech./Springer, 1987).
    Google Scholar 
    Weisse, T. et al. Functional ecology of aquatic phagotrophic protists—Concepts, limitations, and perspectives. Eur. J. Protistol. 55, 50–74. https://doi.org/10.1016/j.ejop.2016.03.003 (2016).Article 
    PubMed 

    Google Scholar 
    Wickham, S. A. Cyclops predation on ciliates: Species-specific differences and functional responses. J. Plankton Res. 17, 1633–1646 (1995).Article 

    Google Scholar 
    Coats, D. W. & Bachvaroff, T. R. Parasites of tintinnids. In The Biology and Ecology of Tintinnid Ciliates: Models for Marine Plankton (eds Dolan, R. et al.) 145–170 (Wiley, 2012).Chapter 

    Google Scholar 
    Guillou, L. et al. Widespread occurrence and genetic diversity of marine parasitoids belonging to Syndiniales (Alveolata). Environ. Microbiol. 10, 3349–3365. https://doi.org/10.1111/j.1462-2920.2008.01731.x (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Brun, P. G., Payne, M. R. & Kiørboe, T. A trait database for marine copepods. Earth Syst. Sci. Data 9, 99–113. https://doi.org/10.5194/essd-9-99-2017 (2017).ADS 
    Article 

    Google Scholar 
    Armengol, L., Franchy, G., Ojeda, A., Santana-del Pino, Á. & Hernández-León, S. Effects of copepods on natural microplankton communities: Do they exert top-down control?. Mar. Biol. 164, 136. https://doi.org/10.1007/s00227-017-3165-2 (2017).Article 

    Google Scholar 
    Moriarty, R. & O’Brien, T. Distribution of mesozooplankton biomass in the global ocean. Earth Syst. Sci. Data 5, 45–55 (2013).ADS 
    Article 

    Google Scholar 
    Landry, M. R., Al-Mutairi, H., Selph, K. E., Christensen, S. & Nunnery, S. Seasonal patterns of mesozooplankton abundance and biomass at Station ALOHA. Deep Sea Res. Part II Top. Stud. Oceanogr. 48, 2037–2061 (2001).ADS 
    Article 

    Google Scholar 
    Turner, J. T. The importance of small planktonic copepods and their roles in pelagic marine food webs. Zool. Stud. 43, 255–266 (2004).
    Google Scholar 
    Heneghan, R. F. et al. A functional size-spectrum model of the global marine ecosystem that resolves zooplankton composition. Ecol. Model. 435, 109265. https://doi.org/10.1016/j.ecolmodel.2020.109265 (2020).CAS 
    Article 

    Google Scholar 
    Wang, Q. et al. Predicting temperature impacts on aquatic productivity: Questioning the metabolic theory of ecology’s “canonical” activation energies. Limnol. Oceanogr. 64, 1172–1185. https://doi.org/10.1002/lno.11105 (2019).ADS 
    Article 

    Google Scholar 
    Montagnes, D. J. Ecophysiology and behavior of tintinnids. In The Biology and Ecology of Tintinnid Ciliates: Models for Marine Plankton (eds Dolan, R. et al.) 85–121 (Wiley, 2012).Chapter 

    Google Scholar 
    McManus, G. B. & Santoferrara, L. F. Tintinnids in microzooplankton communities. In The Biology and Ecology of Tintinnid Ciliates: Models for Marine Plankton (eds Dolan, R. et al.) 198–213 (Wiley, 2012).Chapter 

    Google Scholar 
    Fileman, E., Petropavlovsky, A. & Harris, R. Grazing by the copepods Calanus helgolandicus and Acartia clausi on the protozooplankton community at station L4 in the Western English Channel. J. Plankton Res. 32, 709–724. https://doi.org/10.1093/plankt/fbp142 (2010).CAS 
    Article 

    Google Scholar 
    Zeldis, J. R. & Décima, M. Mesozooplankton connect the microbial food web to higher trophic levels and vertical export in the New Zealand Subtropical Convergence Zone. Deep Sea Res. Part I Oceanogr. Res. Pap. 155, 103146. https://doi.org/10.1016/j.dsr.2019.103146 (2020).CAS 
    Article 

    Google Scholar 
    Stoecker, D. K. Predators of tintinnids. In The Biology and Ecology of Tintinnid Ciliates: Models for Marine Plankton (eds Dolan, J. R. et al.) 122–144 (Wiley, 2012).Chapter 

    Google Scholar 
    Levinsen, H. & Nielsen, T. G. The trophic role of marine pelagic ciliates and heterotrophic dinoflagellates in arctic and temperate coastal ecosystems: A cross-latitude comparison. Limnol. Oceanogr. 47, 427–439. https://doi.org/10.4319/lo.2002.47.2.0427 (2002).ADS 
    Article 

    Google Scholar 
    Gallienne, C. & Robins, D. Is Oithona the most important copepod in the world’s oceans?. J. Plankton Res. 23, 1421–1432. https://doi.org/10.1093/plankt/23.12.1421 (2001).Article 

    Google Scholar 
    Stoecker, D. K. & Egloff, D. A. Predation by Acartia tonsa Dana on planktonic ciliates and rotifers. J. Exp. Mar. Biol. Ecol. 110, 53–68 (1987).Article 

    Google Scholar 
    Stoecker, D. & Pierson, J. Predation on protozoa: Its importance to zooplankton revisited. J. Plankton Res. 41, 367–373. https://doi.org/10.1093/plankt/fbz027 (2019).Article 

    Google Scholar 
    Diehl, S. & Feissel, M. Intraguild prey suffer from enrichment of their resources: A microcosm experiment with ciliates. Ecology 82, 2977–2983 (2001).Article 

    Google Scholar 
    Broglio, E., Saiz, E., Calbet, A., Trepat, I. & Alcaraz, M. Trophic impact and prey selection by crustacean zooplankton on the microbial communities of an oligotrophic coastal area (NW Mediterranean Sea). Aquat. Microb. Ecol. 35, 65–78 (2004).Article 

    Google Scholar 
    Sommer, U. et al. Beyond the Plankton Ecology Group (PEG) Model: Mechanisms driving plankton succession. Annu. Rev. Ecol. Evol. Syst. 43, 429–448. https://doi.org/10.1146/annurev-ecolsys-110411-160251 (2012).Article 

    Google Scholar 
    IGKB. Jahresbericht der Internationalen Gewässerschutzkommission für den Bodensee: Limnologischer Zustand des Bodensees Nr. 43 (2018–2019), 128 https://www.igkb.org/oeffentlichkeitsarbeit/limnologischer-zustand-des-sees-gruene-berichte/. (2020).Wetzel, R. G. Limnology—Lake and River Ecosystems 3rd edn. (Academic Press, 2001).
    Google Scholar 
    Kumar, R. Effects of Mesocyclops thermocyclopoides (Copepoda: Cyclopoida) predation on the population growth patterns of different prey species. J. Freshw. Ecol. 18, 383–393. https://doi.org/10.1080/02705060.2003.9663974 (2003).Article 

    Google Scholar 
    Porter, K. G., Pace, M. L. & Battey, F. J. Ciliate protozoans as links in freshwater planktonic food chains. Nature 277, 563–565 (1979).ADS 
    Article 

    Google Scholar 
    Landry, M. & Fagerness, V. Behavioral and morphological influences on predatory interactions among marine copepods. Bull. Mar. Sci. 43, 509–529 (1988).
    Google Scholar 
    Krainer, K.-H. & Müller, H. Morphology, infraciliature and ecology of a nerw planktonic ciliate, Histiobalantium bodamicum n. sp. (Scuticociliatida: Histiobalantiidae). Eur. J. Protistol. 31, 389–395 (1995).Article 

    Google Scholar 
    Lu, X., Gao, Y. & Weisse, T. Functional ecology of two contrasting freshwater ciliated protists in relation to temperature. J. Eukaryot. Microb. 68, e12823. https://doi.org/10.1111/jeu.12823 (2021).CAS 
    Article 

    Google Scholar 
    Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579. https://doi.org/10.4319/lo.2000.45.3.0569 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    Bergkemper, V. & Weisse, T. Phytoplankton response to the summer heat wave 2015—A case study from Lake Mondsee, Austria. Inland Waters 7, 88–99. https://doi.org/10.1080/20442041.2017.1294352 (2017).CAS 
    Article 

    Google Scholar 
    Crosbie, N. D., Teubner, K. & Weisse, T. Flow-cytometric mapping provides novel insights into the seasonal and vertical distributions of freshwater autotrophic picoplankton. Aquat. Microb. Ecol. 33, 53–66. https://doi.org/10.3354/ame033053 (2003).Article 

    Google Scholar 
    Dokulil, M. T. & Teubner, K. Deep living Planktothrix rubescens modulated by environmental constraints and climate forcing. Hydrobiologia 698, 29–46 (2012).CAS 
    Article 

    Google Scholar 
    Weisse, T., Lukić, D. & Lu, X. Container volume may affect growth rates of ciliates and clearance rates of their microcrustacean predators in microcosm experiments. J. Plankton Res. 43, 288–299. https://doi.org/10.1093/plankt/fbab017 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bergkemper, V. & Weisse, T. Do current European lake monitoring programmes reliably estimate phytoplankton community changes? Hydrobiologia 824, 143–162. https://doi.org/10.1007/s10750-017-3426-6 (2018).CAS 
    Article 

    Google Scholar 
    Rosen, R. A. Length-dry weight relationships of some freshwater zooplanktona. J. Freshw. Ecol. 1, 225–229 (1981).Article 

    Google Scholar 
    Frost, B. W. Effects of size and concentration of food particles on the feeding behavior of the marine planktonic copepod Calanus pacificus. Limnol. Oceanogr. 17, 805–815 (1972).ADS 
    Article 

    Google Scholar 
    RStudio Team. RStudio: Integrated Development Environment for R.RStudio, http://www.rstudio.com/ (PBC, 2021).Burnham, K. P. & Anderson, D. R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304. https://doi.org/10.1177/0049124104268644 (2004).MathSciNet 
    Article 

    Google Scholar 
    Hansen, P. J., Bjørnsen, P. K. & Hansen, B. W. Zooplankton grazing and growth: Scaling within the 2–2,000-μm body size range. Limnol. Oceanogr. 42, 687–704. https://doi.org/10.4319/lo.1997.42.4.0687 (1997).ADS 
    Article 

    Google Scholar  More

  • in

    Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour

    Breed, M. F. et al. The potential of genomics for restoring ecosystems and biodiversity. Nat. Rev. Genet. 20, 615–628 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Carpenter, S. R., Stanley, E. H. & Vander Zanden, M. J. State of the world’s freshwater ecosystems: Physical, chemical, and biological changes. Annu. Rev. Environ. Resour. 36, 75–99 (2011).Article 

    Google Scholar 
    Geist, J. Integrative freshwater ecology and biodiversity conservation. Ecol. Indic. 11, 1507–1516 (2011).Article 

    Google Scholar 
    Jeppesen, E., Søndergaard, M., Meerhoff, M., Lauridsen, T. L. & Jensen, J. P. Shallow lake restoration by nutrient loading reduction–some recent findings and challenges ahead. Hydrobiologia 584, 239–252 (2007).CAS 
    Article 

    Google Scholar 
    Søndergaard, M. & Jeppesen, E. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. J. Appl. Ecol. 44, 1089–1094 (2007).Article 

    Google Scholar 
    Marburg, A. E., Turner, M. G. & Kratz, T. K. Natural and anthropogenic variation in coarse wood among and within lakes. J. Ecol. 94, 558–568 (2006).Article 

    Google Scholar 
    Schindler, D. W. Recent advances in the understanding and management of eutrophication. Limnol. Oceanogr. 51, 356–363 (2006).ADS 
    Article 

    Google Scholar 
    Lau, S. S. S. & Lane, S. N. Continuity and change in environmental systems: The case of shallow lake ecosystems. Prog. Phys. Geogr. Earth Environ. 25, 178–202 (2001).Article 

    Google Scholar 
    Brinkhurst, R. O. Distribution and abundance of Tubificid (Oligochaeta) species in Toronto harbour, Lake Ontario. J. Fish. Res. Board Can. 27, 1961–1969 (1970).Article 

    Google Scholar 
    Wood, L. W. & Chua, K. E. Glucose flux at the sediment-water interface of Toronto Harbour, Lake Ontario, with reference to pollution stress. Can. J. Microbiol. 19, 413–420 (1973).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nriagu, J. O., Wong, H. K. T. & Snodgrass, W. J. Historical records of metal pollution in sediments of Toronto and Hamilton harbours. J. Gt. Lakes Res. 9(3), 365–373 (1983).CAS 
    Article 

    Google Scholar 
    Toronto & Region Remedial Action Plan. Metro Toronto and Region Remedial Action Plan (1989).Dahmer, S. C., Matos, L. & Morley, A. Restoring Toronto’s waters: Progress toward delisting the Toronto and Region area of concern. Aquat. Ecosyst. Health Manag. 21, 229–233 (2018).Article 

    Google Scholar 
    Munawar, M., Norwood, W., McCarthy, L. & Mayfield, C. In situ bioassessment of dredging and disposal activities in a contaminated ecosystem: Toronto Harbour. Hydrobiologia https://doi.org/10.1007/978-94-009-1896-2_62 (1989).Article 

    Google Scholar 
    Dahmer, S. C., Matos, L. & Jarvie, S. Assessment of the degradation of aesthetics beneficial use impairment in the Toronto and region area of concern. Aquat. Ecosyst. Health Manag. 21, 276–284 (2018).Article 

    Google Scholar 
    Metro Toronto and Region Remedial Action Plan. Within Reach: 2015 Toronto an Region Remedial Action Plan Progress Report (2016).Burniston, D. & Waltho, J. Report on Sediment Quality in the Toronto Inner Harbour 2007 (2011).Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8, 1265–1275 (2017).Article 

    Google Scholar 
    Emilson, C. E. et al. DNA metabarcoding and morphological macroinvertebrate metrics reveal the same changes in boreal watersheds across an environmental gradient. Sci. Rep. 7, 12777 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Aylagas, E., Borja, Á., Muxika, I. & Rodríguez-Ezpeleta, N. Adapting metabarcoding-based benthic biomonitoring into routine marine ecological status assessment networks. Ecol. Indic. 95, 194–202 (2018).Article 

    Google Scholar 
    Bush, A. et al. Studying ecosystems with DNA metabarcoding: Lessons from biomonitoring of aquatic macroinvertebrates. Front. Ecol. Evol. 7, 434 (2019).Article 

    Google Scholar 
    Serrana, J. M., Miyake, Y., Gamboa, M. & Watanabe, K. Comparison of DNA metabarcoding and morphological identification for stream macroinvertebrate biodiversity assessment and monitoring. Ecol. Indic. 101, 963–972 (2019).Article 

    Google Scholar 
    Fernández, S., Rodríguez-Martínez, S., Martínez, J. L., Garcia-Vazquez, E. & Ardura, A. How can eDNA contribute in riverine macroinvertebrate assessment? A metabarcoding approach in the Nalón River (Asturias, Northern Spain). Environ. DNA 1, 385–401 (2019).Article 

    Google Scholar 
    Hajibabaei, M. et al. Watered-down biodiversity? A comparison of metabarcoding results from DNA extracted from matched water and bulk tissue biomonitoring samples. PLoS ONE 14, e0225409 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).PubMed 
    Article 

    Google Scholar 
    Hajibabaei, M., Baird, D. J., Fahner, N. A., Beiko, R. & Golding, G. B. A new way to contemplate Darwin’s tangled bank: How DNA barcodes are reconnecting biodiversity science and biomonitoring. Philos. Trans. R. Soc. B. Biol. Sci. 371, 20150330 (2016).Article 
    CAS 

    Google Scholar 
    Beermann, A. J., Zizka, V. M. A., Elbrecht, V., Baranov, V. & Leese, F. DNA metabarcoding reveals the complex and hidden responses of chironomids to multiple stressors. Environ. Sci. Eur. 30, 26 (2018).Article 

    Google Scholar 
    Bush, A. et al. DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness. Proc. Natl. Acad. Sci. 117, 8539–8545 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Compson, Z. G. et al. Chapter Two—Linking DNA Metabarcoding and Text Mining to Create Network-Based Biomonitoring Tools: A Case Study on Boreal Wetland Macroinvertebrate Communities. In Advances in Ecological Research Vol. 59 (eds Bohan, D. A. et al.) 33–74 (Academic Press, 2018).
    Google Scholar 
    Fernandes, K. et al. DNA metabarcoding—A new approach to fauna monitoring in mine site restoration. Restor. Ecol. 26, 1098–1107 (2018).Article 

    Google Scholar 
    Fernandes, K. et al. Invertebrate DNA metabarcoding reveals changes in communities across mine site restoration chronosequences. Restor. Ecol. 27, 1177–1186 (2019).Article 

    Google Scholar 
    Poikane, S. et al. Benthic macroinvertebrates in lake ecological assessment: A review of methods, intercalibration and practical recommendations. Sci. Total Environ. 543, 123–134 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Macher, J.-N. et al. Comparison of environmental DNA and bulk-sample metabarcoding using highly degenerate cytochrome c oxidase I primers. Mol. Ecol. Resour. 18, 1456–1468 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Marshall, N. T. & Stepien, C. A. Macroinvertebrate community diversity and habitat quality relationships along a large river from targeted eDNA metabarcode assays. Environ. DNA 2, 572–586 (2020).Article 

    Google Scholar 
    Metro Toronto and Region Remedial Action Plan. Updates on Actions 2013–2014. (2013).López-López, E. & Sedeño-Díaz, J. E. Biological indicators of water quality: The role of fish and macroinvertebrates as indicators of water quality. In Environmental Indicators (eds Armon, R. H. & Hänninen, O.) 643–661 (Springer Netherlands, 2015). https://doi.org/10.1007/978-94-017-9499-2_37.Chapter 

    Google Scholar 
    Berry, O. et al. A Comparison of Morphological and DNA Metabarcoding Analysis of Diets in Exploited Marine Fishes (2015).Sweeney, B. W., Battle, J. M., Jackson, J. K. & Dapkey, T. Can DNA barcodes of stream macroinvertebrates improve descriptions of community structure and water quality?. J. N. Am. Benthol. Soc. 30, 195–216 (2011).Article 

    Google Scholar 
    Banerji, A. et al. Spatial and temporal dynamics of a freshwater eukaryotic plankton community revealed via 18S rRNA gene metabarcoding. Hydrobiologia 818, 71–86 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Porter, T. M. et al. Widespread occurrence and phylogenetic placement of a soil clone group adds a prominent new branch to the fungal tree of life. Mol. Phylogenet. Evol. 46, 635–644 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rosling, A. et al. Archaeorhizomycetes: Unearthing an ancient class of ubiquitous soil fungi. Science 333, 876–879 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Mandaville, S. M. Benthic Macroinvertebrates in Freshwaters—Taxa Tolerance Values, Metrics, and Protocols, vol. 128. http://lakes.chebucto.org/H-1/tolerance.pdf (2002).Trzcinski, M. K. et al. The effects of food web structure on ecosystem function exceeds those of precipitation. J. Anim. Ecol. 85, 1147–1160 (2016).PubMed 
    Article 

    Google Scholar 
    Liu, X. & Wang, H. Contrasting patterns and drivers in taxonomic versus functional diversity, and community assembly of aquatic plants in subtropical lakes. Biodivers. Conserv. 27(12), 3103–3118 (2018).Article 

    Google Scholar 
    Kovalenko, K. E., Brady, V. J., Ciborowski, J. J. H., Ilyushkin, S. & Johnson, L. B. Functional changes in littoral macroinvertebrate communities in response to watershed-level anthropogenic stress. PLoS ONE 9, e101499 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Luiza-Andrade, A., Montag, L. F. A. & Juen, L. Functional diversity in studies of aquatic macroinvertebrates community. Scientometrics 111, 1643–1656 (2017).Article 

    Google Scholar 
    MacMillan, G. A., Chételat, J., Heath, J. P., Mickpegak, R. & Amyot, M. Rare earth elements in freshwater, marine, and terrestrial ecosystems in the eastern Canadian Arctic. Environ. Sci. Process. Impacts 19, 1336–1345 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pastorino, P. et al. Macrobenthic invertebrates as tracers of rare earth elements in freshwater watercourses. Sci. Total Environ. 698, 134282 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Kulaš, A. et al. Ciliates (Alveolata, Ciliophora) as bioindicators of environmental pressure: A karstic river case. Ecol. Indic. 124, 107430 (2021).Article 

    Google Scholar 
    Persaud, D., Lomas, T., Boyd, D. & Mathai, S. Historical Development and Quality of the Toronto Waterfront Sediments (1985).Milani, D. & Grapentine, L. Assessment of Sediment Quality in the Bay of Quinte Area Of Concern (2000).Reynoldson, T. B., Bailey, R. C., Day, K. E. & Norris, R. H. Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state. Aust. J. Ecol. 20(1), 198–219 (1995).Article 

    Google Scholar 
    Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Zhan, A. et al. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 4, 558–565 (2013).Article 

    Google Scholar 
    Gibson, J. et al. Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics. Proc. Natl. Acad. Sci. 111, 8007–8012 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gibson, J. F. et al. Large-scale biomonitoring of remote and threatened ecosystems via high-throughput sequencing. PLoS ONE 10, e0138432 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Porter, T. M. & Hajibabaei, M. METAWORKS: A flexible, scalable bioinformatic pipeline for multi-marker biodiversity assessments. bioRxiv https://doi.org/10.1101/2020.07.14.202960 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).PubMed 
    Article 
    CAS 

    Google Scholar 
    Anon. Conda. (2016).Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep. 8, 4226 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Porter, T. M. Eukaryote CO1 Reference set for the RDP Classifier (Zenodo, 2017) https://doi.org/10.5281/zenodo.4741447.Book 

    Google Scholar 
    Porter, T. M. SILVA 18S Reference Set for the RDP Classifier(Zenodo, 2018) https://doi.org/10.5281/zenodo.4741433.Book 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2020).
    Google Scholar 
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009). https://doi.org/10.1007/978-0-387-98141-3.Book 
    MATH 

    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package (2020).Komsta, L. & Novomestky, F. moments: Moments, cumulants, skewness, kurtosis and related tests (2015).U.S. Environmental Protection Agency. Freshwater Biological Traits Database (Final Report) EPA/600/R-11/038F. (2012)U.S. Environmental Protection Agency. Freshwater Biological Traits Database (2012).Schmidt-Kloiber, A. & Hering, D. An online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol. Indic. 53, 271–282 (2015).Article 

    Google Scholar 
    Moog, O. Fauna Aquatica Austriaca – Catalogue for autecological Classification of Austrian Aquatic Organisms (1995).Tachet, H., Bournaud, M., Richoux, P., Usseglio-Polatera, P. Invertébrés d’eau douce – systématique, biologie, écologie (2010).Nally, R. M. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. https://doi.org/10.1023/B:BIOC.0000009515.11717.0b (2004).Article 

    Google Scholar  More

  • in

    Participatory mapping identifies risk areas and environmental predictors of endemic anthrax in rural Africa

    Study areaThe NCA encompasses an area of 8292 km2 and in 2020 had approximately 87,000 inhabitants23, who are primarily dependent on livestock for their livelihoods. It is a multiple-use area where people coexist with wildlife and livestock, and practise pastoralism with transhumance, characterised by seasonal movements of livestock for accessing resources such as grazing areas and water. The NCA comprises eleven administrative wards: Alailelai, Endulen, Eyasi, Laitole, Kakesio, Misigiyo, Ngorongoro, Naiyobi, Nainokanoka, Ngoile and Olbalbal (Fig. 1). The NCA was chosen for our study as it is known to be hyperendemic for anthrax4,17,20. In addition, informal consultations we held prior to the study, as well as tailored data collection at the community and household level, indicated that local communities have a good understanding of the disease in humans and animals, and of practices around carcass and livestock management that increase risks, particularly in certain locations and periods of the year24.Figure 1Locations of participatory mapping. Map showing the 11 administrative wards of the Ngorongoro Conservation Area in northern Tanzania and the locations where participatory mapping sessions took place (red dots). The maps were produced in QGIS 2.18.2 using data from the National Bureau of Statistics, Tanzania (http://www.nbs.go.tz/).Full size imageEthics approval and consent to participateThe study received approval from the National Institute for Medical Research, Tanzania, with reference number NIMRJHQ/R.8a/Vol. IX/2660; the Tanzania Commission for Science and Technology (numbers 2016-94-NA-2016-88 (O. R. Aminu), 2016-95-NA-2016-45 (T. L. Forde) and 2018-377-NA-2016-45 (T. Lembo)); Kilimanjaro Christian Medical University College Ethics Review Committee (certificate No. 2050); and the University of Glasgow College of Medical Veterinary & Life Sciences Ethics Committee (application number 200150152). Approval and permission to access communities and participants were also obtained from relevant local authorities. Written informed consent was obtained from all participants involved in the study. All data collected were analysed anonymously, ensuring the confidentiality of participants. All research activities were performed in accordance with relevant guidelines and regulations.Participatory mappingA participatory mapping approach based on methodology previously tested in East Africa25 was employed to define areas of anthrax risk for animals in the NCA based on community knowledge. Georeferenced maps of the NCA were produced using data from Google and DigitalGlobe (2016). The maps used datum Arc 1960/UTM zone 36S and grid intervals of 1000 km and were produced at 1:10,000 and 1:50,000 scales, in order to provide participants with a choice. Ten participatory mapping focus groups were held at ward administrative level (Fig. 1) in order to identify areas in the NCA that communities perceive as posing a high risk of anthrax. One mapping exercise was held in each ward. Ngoile and Olbalbal wards were covered at the same time and treated as one, as they had only recently (in 2015) been split from one ward (Olbalbal). Each session had between ten and thirteen participants, who consisted of village and ward administrators, animal health professionals (including community animal health workers and livestock field officers), community leaders, and selected community members. These participants represented members of the community concerned with animal health and owning livestock and, as such, were likely to hold in-depth knowledge relating to community experience of animal health and disease, including anthrax. Participants were recruited by consulting with animal health professionals as well as village and ward administrators, who gave permission to conduct the mapping sessions.The mapping sessions were conducted in Swahili and translated into English by an interpreter. Participants’ general knowledge of the area was first verified by testing whether they could correctly identify popular locations such as health centres, places of worship, markets and schools. Subsequently, participants discussed among themselves and came to a consensus about areas they considered to be at high risk of anthrax. Specifically, we asked them to identify locations they perceived as areas where they considered their animals to be at risk of being exposed to anthrax. These areas were drawn on the maps provided (Fig. 2). While they did not locate areas where the animals had succumbed to disease, we also asked for generic information on locations where anthrax outbreaks had occurred in the past to define areas that could be targeted for active surveillance of cases. In order to improve the fidelity of the data, participants defined risk areas in relation to their own locality (ward) and locations where their animals access resources. Therefore, the areas were not defined by administrative boundaries, as communities may access locations outside their wards, for instance for grazing or watering. The resulting maps were scanned, digitised and analysed as detailed in the following sections. Further detail on the participatory mapping process is provided in the Supplementary Methods (Additional File 1).Figure 2Participatory mapping of anthrax risk areas in the Ngorongoro Conservation Area. Images show (A) the set-up of a mapping session, (B) participants engaged during a session and (C) an example of a 1:50,000 scale map annotated by participants. The map was created with QGIS opensource mapping software. The basemap used was a scanned and geo-referenced full colour 1:50,000 scale topographic map produced by the Surveys & Mapping Division, Ministry of Lands, Housing & Human Settlements, Dar es Salaam, Tanzania. The grid is based on the Arc1960 UTM 36S projection and datum. The map was exported from QGIS in Acrobat Pdf format to enable it to be printed at suitable sizes for using in the fieldwork and to be manually annotated during the participatory mapping.Full size imageDigitisation of maps and generation of random pointsScanned maps were saved as PDF files and converted to high resolution TIFF files for digitisation in QGIS 2.18.2-Las Palmas free OpenSource software26. All maps were georeferenced with geographical coordinates during production and reference points were available to enable the precise mapping of all locations. The digitization was carried out using the QGIS digitizing tools and by creating polygon layers of the defined risk areas.Sourcing data on the environmental predictors of anthraxAvailable soil and environmental data (250 m grid) for Tanzania were obtained from various sources (Table 1). From the available data, we selected the following seven variables which have previously been shown to contribute to or explain the risk of anthrax based on the biology of B. anthracis (Table 1).Table 1 Environmental factors with potential to influence anthrax occurrence.Full size tableCation exchange capacity (CEC)Measured in cmol/kg, CEC is the total capacity of the soil to retain exchangeable cations such as Ca2+, Mg2+ etc. It is an inherent soil characteristic and is difficult to alter significantly. It influences the soil’s ability to hold on to essential nutrients and provides a buffer against soil acidification27. CEC has been reported to be positively correlated with anthrax risk. In addition, CEC is a proxy for calcium content, which may contribute to anthrax risk in a pH-dependent manner as explained below19,22.Predicted topsoil pH (pH)Soil pH below 6.0 (acidic soil) is thought to inhibit the viability of spores19 thus a positive effect of higher pH on the risk of anthrax is expected. It has been suggested that the exosporium of B. anthracis is negatively charged in soils with neutral to slightly alkaline pH. This negative charge attracts positively charged cations in soil, mainly calcium, enabling the spores to be firmly attached to soil particles and calcium to be maintained within the spore core, thereby promoting the viability of B. anthracis19,28.Distance to inland water bodies (DOWS)Both the distance from water and proximity to water may increase anthrax risk. Distance to inland water may indicate the degree to which an area is dry/arid. Anthrax outbreaks have been shown to occur in areas with very dry conditions19. Although anthrax occurrence has also been associated with high soil moisture, this relates more to the spore germination in the environment (a mechanism that is disputed) and the concentration of spores in moist humus that amount to an infectious dose18,29. Spores will survive much longer in soils with low moisture content19. Low moisture may also be associated with low vegetation which results in animals grazing close to the soil, increasing the risk of ingesting soil with spores. Hampson et al. reported that anthrax outbreaks occurred close to water sources in the Serengeti ecosystem of Tanzania in periods of heavy rainfall20, and Steenkamp et al. found that close proximity to water bodies was key to the transmission of B. anthracis spores in Kruger National Park, South Africa22. Water is an important resource for livestock and a large number of animals may congregate at water sources during dry seasons. The close proximity of a water source to a risk area may increase the chance of infection, particularly during periods of high precipitation which might unearth buried spores.Average enhanced vegetation index (EVI)Vegetation density may influence the likelihood of an animal ingesting soil or inhaling dust that may be contaminated with spores. Grazing animals are more likely to encounter bacteria in soil with low vegetation density20, although there is a possibility that spores can be washed onto higher vegetation by the action of water19. Vegetation index may also reflect the moisture content of soil. Arid/dry conditions favour the formation and resistance of spores in the environment, thus lower vegetation may be associated with the occurrence of anthrax.Average daytime land surface temperature (LSTD)Anthrax has been more commonly reported to occur in regions with warmer climates worldwide. Minett observed that under generally favourable conditions and at 32 °C to 37 °C, sporulation of B. anthracis occurs readily but vegetative cells are more likely to disintegrate at temperatures below 21 °C30. Another hypothesis for the association of high temperature with anthrax occurrence is altered host immune response to disease due to stress caused by elevated temperatures19. In addition, elevated temperatures are usually associated with arid areas where vegetation is low, limiting access to adequate nutrition, which in turn affects immunity. Similarly, in hotter climates where infectious diseases occur more often, host interactions with other pathogens may modulate immune response to anthrax31. In this case, a lower infectious and lethal dose of spores would be sufficient to cause infection and death, respectively19. Contact with and ingestion of soil, spores and abrasive pasture is also higher with low vegetation in hot and arid areas19,32. In boreal regions such as in northern Canada, where anthrax occurs in wood bison, and Siberia, the disease is more commonly reported in the summer19. We therefore hypothesised a positive effect of LSTD on the risk of anthrax.SlopeSpores of B. anthracis are hypothesized to persist more easily in flat landscapes that are characterised by shallow slopes19, as it is thought that wind and water may disperse spores more easily along areas with a higher slope gradient, thereby decreasing the density of spores to levels that may be insufficient to cause infection in a susceptible host. Therefore, we expected a negative relationship between slope and the risk of anthrax.Predicted topsoil organic carbon content (SOC)Organic matter (g/kg) may aid spore persistence by providing mechanical support. The negatively charged exosporium of spores is attracted to the positive charges on hummus-rich soil, thus anthrax is thought to persist in soil rich in organic matter18. Based on available evidence, we expected a positive effect of SOC on the risk of anthrax.Creating the datasetThe annotated and digitised maps yielded polygons of high-risk areas within the NCA (Fig. 3). After digitization, 5000 random points were generated33 to cover the 8292 km2 area of the NCA. This enabled us to obtain distinct points allowed by the 250 m grid resolution of the environmental variables. Points falling within the defined risk areas were selected to represent risk areas while those falling outside represented low-risk areas. Measures of the environmental characteristics associated with individual points were obtained with the ‘add Raster data to points’ feature in QGIS.Figure 3Ngorongoro Conservation Area map showing (A) defined risk areas (in red) and (B) distance to settlements. For analysis, 5000 random points were generated throughout the area; points falling within 4.26 km of human settlements (the average distance herds are moved from settlements in a day as determined through interviews of resident livestock owners) were retained for analysis (n = 2173, shown in blue in 3a). The maps were created in QGIS 2.18.2 using data from the National Bureau of Statistics, Tanzania (http://www.nbs.go.tz/).Full size imageIn order to focus on areas of greatest risk to humans and livestock and to exclude locations that are not accessible, only points within a certain range of distance from settlements were included (Fig. 3). On average, herders in the NCA move their livestock 4.26 km away from settlements for grazing and watering during the day (unpublished data obtained through a cross-sectional survey of 209 households). Thus, only points falling within this distance from settlements were selected, providing us with data on areas where infection is most likely to occur. Data on locations of settlements were obtained from satellite imagery and included permanent residences as well as temporary settlements (e.g. seasonal camps set up after long distance movement away from permanent settlements, typically in the dry season, in search of pasture and water). These data were collated from the Center for International Earth Science Information Network (CIESIN).After adjusting for accessibility of resource locations using the average distance moved by livestock, 2173 points were retained for analysis, of which 239 (11%) fell within high-risk areas.Data analysisAll statistical analyses were carried out in R (v 4.1.0) within the RStudio environment34. The aims of the statistical analysis were to infer the relationship between anthrax risk areas as determined through participatory mapping and the environmental factors identified in Table 1, and to use this relationship to make spatial predictions of anthrax risk across the study area. We achieved both aims by modelling the binary risk status (high or low) of the randomly generated points as a function of their environmental characteristics in a Bayesian spatial logit-binomial generalised linear mixed-effects model (GLMM), implemented in the package glmmfields35. Spatial autocorrelation (residual non-independence between nearby points) was accounted for by including spatial random effects in the GLMM. We chose relatively non-informative priors for the intercept and the covariates, using Student’s t-distributions centred at 0 and wide variances (intercept: df = 3, location = 0, scale = 10; betas: df = 3, location = 0, scale = 3). For the spatial Gaussian Process and the observation process scale parameters, we adopted the default glmmfields settings and used half-t priors (both gp_theta and gp_sigma: df = 3, location = 0, scale = 5), and 12 knots. To achieve convergence, the models were run for 5000 iterations35.First, univariable models were fitted to estimate unadjusted associations between each environmental factor (CEC, pH, DOWS, EVI, LSTD, slope, and SOC; Table 1; Supplementary Table S1) and high- and low-risk areas. Second, we constructed multivariable models by fitting multiple environmental variables (Supplementary Table S2). Three variables, SOC, slope and EVI showed a strongly right-skewed distribution and were therefore log-transformed prior to GLMM analysis to prevent excessive influence of outliers. All predictor variables were centred to zero mean and scaled to unit standard deviation for analysis, and odds ratios were rescaled back to the original units for ease of interpretation. Prior to fitting the multivariable GLMM, the presence of collinearity among the predictor variables—which were all continuous—was assessed using variance inflation factors (VIFs)36, calculated with the car package and illustrated using scatter plots (Supplementary Fig. S1)36. Three predictor variables showed a VIF greater than 3 (LSTD, ln EVI and pH with VIFs of 6.8, 4.2 and 3.5, respectively). Removal of LSTD and ln EVI reduced all VIFs to below 3, therefore these two variables were excluded from the multivariable regression analysis37.The model performance was assessed by calculating the area under the receiver operating characteristic curve. The predicted probability of being an anthrax high-risk area was determined and depicted on a map of the NCA using a regular grid of points generated throughout the NCA with one point sampled every 500 m.Consent for publicationPermission to publish was granted by the National Institute for Medical Research, Tanzania. More