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    Soil, leaf and fruit nutrient data for pear orchards located in the Circum-Bohai Bay and Loess Plateau regions

    Orchard site selectionThe survey was conducted from 2018 to 2019 in the Circum-Bohai Bay region, which included Shandong, Hebei, and Liaoning provinces and Beijing, and the Loess Plateau region, which included Shanxi and Shaanxi provinces. Five typical production counties were selected in each province or city. Representative orchards were selected according to the production of the main varieties in each county (orchard area was greater than 1.0 ha; the pear trees were 15 to 25 years old; and the yield of orchards ranged from 40 to 60 t ha−1). A total of 225 orchards were investigated (Fig. 1), including 150 in the Circum-Bohai Bay region and 75 in the Loess Plateau region (Table 1).Fig. 1The locations of the 225 pear orchards.Full size imageTable 1 Numbers of pear orchard and main cultivated varieties investigated in Circum-Bohai Bay and Loess Plateau.Full size tableSample collection and pretreatmentSoil and leaf samples were collected at the stage in which the growth of new shoots ceased, from July 1 to July 1510. Eleven sampling sites were determined in each orchard according to an “S” shape sampling method (Fig. 2), and soil samples from the 0–20 cm, 20–40 cm and 40–60 cm layers were collected. The soil samples of the same soil layer at each sampling site were mixed into one sample. Then, the soil samples were air-dried, ground and sifted with a nylon sieve for determination of nutrient concentrations.Fig. 2The “S” shape sampling method. The red dots are the sampling locations.Full size imageTen to fifteen pear trees in each orchard of the same size and vigour and 5 to 10 mature leaves from the middle of a long shoot from the periphery of each tree were selected for leaf sampling11. Then, all the leaves from the same orchard were mixed into one leaf sample. The leaves were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the leaf samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Fruit samples were collected at the ripening stage. Pear trees from which leaf samples were collected from each orchard were selected for fruit sample collection. Three to five peripheral fruits of the same size were collected from each tree, and fruit samples from the same orchard were mixed into one sample. The fruits were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again, cut into slices and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the fruit samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Sample determinationVarious indicators of soil and plant samples were determined according to the method of Cui et al.12 and Bao13.Soil pH determinationA potentiometric method was used to measure soil pH. Carbon dioxide-free water was added to soil that had been passed through a 2 mm sieve at a water-soil ratio of 2.5:1. The soil solution was stirred for 1 min and left undisturbed for 30 min. Each soil sample was measured more than three times with a pH meter (FE20K PLUS PH, Mettler-Toledo, Switzerland), and the difference in the parallel determination results was less than 0.2 pH units. The electrode was washed with deionized water and dried with filter paper after each sample measurement. A calibration solution was used to calibrate the electrode between measurements after every 10 soil samples.Soil organic matter determinationSoil organic matter was measured according to the Schollenberger method using chromic acid redox titration. Five millilitres of a 0.8 M 1/6 K2Cr2O7 solution was added to a test tube with approximately 0.5000 g of soil that had been passed through a 0.25 mm sieve. The mixture was then added to 5 mL concentrated sulfuric acid and shaken gently to disperse the soil. The tube was placed in a phosphoric acid bath, heated to 170 °C and boiled for 5 min. To condense the water vapour that escaped during the heating process, a small funnel was placed on the top of the test tube. The substances in the test tube and funnel were transferred to a conical flask after cooling. Then, the solution was added to 1,10-phenanthroline hydrate and titrated with 0.2 M FeSO4 until it turned maroon. A blank experiment was performed when each batch of samples was measured. The soil organic matter content was calculated according to the following formula:$${rm{omega }}left({rm{OM}}right)=frac{left({rm{V}}-{rm{V}}0right)times {rm{c}}times 3times 1.724times {rm{f}}}{{rm{m}}}$$
    (1)
    ω(OM): soil organic matter content; c: standard FeSO4 solution concentration; V: volume of the standard FeSO4 used in titration; V0: volume of standard FeSO4 used in titrating control sample; 3: molar mass of a quarter of carbon; 1.724: the conversion factor from organic carbon to organic matter; f: oxidation correction coefficient (the value was 1.1); m: mass of oven-dried soil sample.Soil total N determinationTotal N was determined by the semitrace Kjeldahl method. Approximately 1.0000 g of air-dried soil that had been passed through a 0.25 mm sieve was added to a digestion tube. Meanwhile, the soil moisture content was measured to calculate the mass of the oven-dried soil. Two grams of accelerator and 5 mL of concentrated sulfuric acid were added to the tube. The tube was then covered with a small funnel, and the sample was digested at 360 °C for 15–20 min. The mixture was digested for 1 h until the colour changed from brown to greyish green or greyish white. Two digested soilless samples were used as controls. After the digestion tube cooled, it was placed in a distiller, and a small amount of deionized water was added. Five millilitres of a 2% boric acid indicator was added to a 150 mL conical flask, and the flask was placed at the end of the condenser tube. Then, the digestion solution was distilled until the distillate volume was approximately 75 mL. The distillate was titrated with 0.01 M standard hydrochloric acid to a purplish red colour endpoint. The soil total N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
    (2)
    ω(N): soil total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried soil sample.Soil alkaline hydrolysable N determinationApproximately 2.00 g of air-dried soil that have been passed through a 2 mm sieve was placed in the outer chamber of a diffuser. The diffuser was gently rotated to evenly distribute the soil in the outer chamber. Two millilitres of H3BO3 indicator was placed in the inner chamber of the diffusion dish. The edge of the frosted glass surface of the diffuser was coated with alkaline glycerin and covered with frosted glass. The diffuser was covered tightly and secured with rubber bands after 10.00 mL of 1 M NaOH was injected into the diffuser through a hole in the frosted glass. The diffuser was placed in a 40 °C incubator for alkaline hydrolysis diffusion for 24 h. Then, the mixture was titrated with 0.01 M standard hydrochloric acid until it turned purplish red. A blank test was performed at the same time as the samples. The soil alkaline hydrolysable N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
    (3)
    ω(N): soil alkaline hydrolysable N concentration; c: standard acid solution concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of air-dried soil sample.Soil available P determinationApproximately 2.50 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle and 50 mL of 0.5 M NaHCO3 was added. After the bottle was shaken for 30 min, the mixture was immediately filtered with phosphorus-free filter paper. Ten millilitres of the filtrate was accurately measured into a conical flask, and 5.00 mL of Mo-Sb-Vc colour developer and 10 mL of deionized water were added. The absorbance of the mixture was measured at approximately 700 nm after 30 min using a UV-Vis spectrophotometer (UV1900PC, AuCy Instrument, Shanghai, China). Finally, the P concentration was calculated according to a standard curve prepared with solutions of different P concentrations. A blank test was performed at the same time that the samples were determined.Soil available K determinationApproximately 5.00 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle, and 50 mL of 1.0 M NH4OAc was added. After the sample was shaken for 30 min, the mixture was immediately filtered with dry filter paper. The concentration of K in the filtrate was determined directly by a flame photometer (LM12-FP6430, Haifuda, China) according to a standard curve prepared with solutions of different K concentrations. A blank test was performed at the same time that the samples were determined.Leaf and fruit N determinationApproximately 0.3000 g of plant powder that had been passed through a 0.5 mm sieve was placed into a digestion tube and 5 mL concentrated sulfuric acid was added. Then, the digestion tube was placed onto a digestion stove at 360 °C after two doses of 2 mL H2O2, and the sample was digested until the mixture turned brown. After the tube cooled, 2 mL H2O2 was added, and the digestion was continued for 5 min. This process was repeated until the mixture turned clear. The mixture was diluted to 100 mL in a volumetric flask for testing after it cooled. Then, 5 to 10 mL of the liquid to be tested was accurately measured into a distiller for distillation. The distillation and titration processes were the same as those used for ammonium in the Soil total N determination section. A blank test was performed at the same time as sample measurement. The leaf or fruit N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14times {rm{V}}1}{{rm{m}}times {rm{V}}2}$$
    (4)
    ω(N): total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried sample; V1: volume of the digestion solution after constant volume; V2: measured volume of digestion solution after constant volume.Leaf and fruit P, K, Ca, Fe, Mn, Cu, Zn, B determinationApproximately 0.5000 g of plant powder that had been passed through a 0.5 mm sieve was placed in a digestion tube and a 10 mL mixture of concentrated nitric acid and hypochlorous acid (4:1) was added. After the sample was left undisturbed for more than 4 h, it was placed onto a digestion stove and heated to 150 °C so that NO2 could volatilize slowly. Then, the temperature was appropriately increased to a temperature not higher than 250 °C until the digestive solution was transparent and approximately 2 mL remained. The solution was transferred into a volumetric flask after cooling and adjusted to a constant volume of 50 mL. The solution was then filtered, and the concentration of each element in the solution was determined by a plasma emission spectrometer (ICP-OES, OPTIMA 3300 DV, 75 Perkin-Elmer, USA). A blank test was performed at the same time as sample measurement. The leaf or fruit P, K, Ca, Fe, Mn, Cu, Zn, and B concentrations were calculated according to the following formula:$${rm{omega }}({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})=frac{rho ({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})times {rm{V}}times {rm{f}}}{{rm{m}}}$$
    (5)
    ω(P, K, Ca, Fe, Mn, Cu, Zn, B): P, K, Ca, Fe, Mn, Cu, Zn, B concentration in leaf or fruit; ρ(P, K, Ca, Fe, Mn, Cu, Zn, B): the concentration of P, K, Ca, Fe, Mn, Cu, Zn or B in the liquid to be measured; V: volume of the liquid to be measured after constant volume; f: dilution ratio of the liquid to be measured; m: mass of oven-dried sample. More

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    Genomic architecture of migration timing in a long-distance migratory songbird

    Davidson, S. C. et al. Ecological insights from three decades of animal movement tracking across a changing arctic. Science 370, 712–715 (2020).ADS 
    CAS 

    Google Scholar 
    Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Chang. 8, 224–228 (2018).ADS 

    Google Scholar 
    Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).ADS 
    CAS 

    Google Scholar 
    Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Sci. B. 278, 3437–3443 (2011).
    Google Scholar 
    González, A. M., Bayly, N. J. & Hobson, K. A. Earlier and slower or later and faster: spring migration pace linked to departure time in a Neotropical migrant songbird. J. Anim. Ecol. 89, 2840–2851 (2020).
    Google Scholar 
    Liedvogel, M., Åkesson, S. & Bensch, S. The genetics of migration on the move. Trends Ecol. Evol. 26, 561–569 (2011).
    Google Scholar 
    Caprioli, M. et al. Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow. PLoS ONE 7, e35140 (2012).ADS 
    CAS 

    Google Scholar 
    Mettler, R., Segelbacher, G. & Schaefer, M. H. Interactions between a candidate gene for migration (ADCYAP1), morphology and sex predict spring arrival in blackcap populations. PLoS ONE 10, e0144587 (2015).
    Google Scholar 
    Bazzi, G. et al. Clock gene polymorphism and scheduling of migration: a geolocator study of the barn swallow Hirundo rustica. Sci. Rep. 5, 12443 (2015).ADS 

    Google Scholar 
    Saino, N. et al. Polymorphism at the Clock gene predicts phenology of long-distance migratoin in birds. Mol. Ecol. 24, 1758–1773 (2015).CAS 

    Google Scholar 
    Bossu, C. M. et al. Clock-linked genes underlie seasonal migratory timing in a diurnal raptor. Proc. R. Soc. B. 289, 20212507 (2022).CAS 

    Google Scholar 
    O’Malley, K. G., Ford, M. J. & Hard, J. J. Clock polymorphism in Pacific salmon: evidence for variable selection along a latitudinal gradient. Proc. R. Soc. B. 277, 3703–3714 (2010).
    Google Scholar 
    Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Research 2 (2013).McKinnon, E. A. & Ten Love, O. P. years tracking the migrations of small landbirds: Lessons learned in the golden age of bio-logging. Auk 135, 834–856 (2018).
    Google Scholar 
    Fraser, K. C. et al. Continent-wide tracking to determine migratory connectivity and tropical habitat associations of a declining aerial insectivore. Proc. R. Soc. B. 279, 4901–4906 (2012).
    Google Scholar 
    Neufeld, L. R. et al. Breeding latitude is associated with the timing of nesting and migration around the annual calendar among purple martin Progne subis populations. J. Ornithol. 162, 1009–1024 (2021).
    Google Scholar 
    Peona, V. et al. Identifying the causes and consequences of assembly gaps using a multiplatform genome assembly of a bird-of-paradise. Mol. Ecol. 21(1), 263–286 (2020).
    Google Scholar 
    Coelho, L. A., Musher, L. J. & Cracraft, J. A multireference-based whole genome assembly for the obligate ant-following antbird, Rhegmatorhina melanosticta (Thamnophilidae). Diversity 11(19), 144 (2019).CAS 

    Google Scholar 
    Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).CAS 

    Google Scholar 
    Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: Towards a genomic predictor of bleaching. Science 369(6501) (2019).Jones, S., Pfister-Genskow, M., Benca, R. M. & Cirelli, C. Molecular correlates of sleep and wakefulness in the brain of the white-crowned sparrow. J. Neurochem. 105, 46–62 (2008).CAS 

    Google Scholar 
    Ma, C. et al. Sleep regulation by neurotensinergic neurons in a thalamo-amygdala circuit. Neuron 103 (2019).Wong, J. M. & Eirin-Lopez, J. M. Evolution of methyltransferase-like (METTL) proteins in metazoan: a complex gene family involved in epitranscriptomic regulation and other epigenetic processes. Mol. Biol. Evol. 38, 5309–5327 (2021).CAS 

    Google Scholar 
    Jia, Z. et al. ACSS3 in brown fast drives propionate catabolism and its deficiency leads to autophagy and systemic metabolic dysfunction. Clin. Transl. Med. 12, e665 (2022).CAS 

    Google Scholar 
    Muller, F. et al. Towards a conceptual framework for explaining variation in nocturnal departure time of songbird migrants. Mov. Ecol. 4, 24 (2016).
    Google Scholar 
    Fraser, K. C. et al. Individual variability in migration timing can explain long-term population-level advances in a songbird. Front. Ecol. Evol. 7, 324 (2019).ADS 

    Google Scholar 
    Barret, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23(1), 38–44 (2008).
    Google Scholar 
    Colodro-Conde, L. et al. A direct test of the diathesis-stress model for depression. Mol. Psychiatry 23, 1590–1596 (2017).
    Google Scholar 
    Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLOS Genetics 9(4) (2013).Lavallée, C. D. et al. The use of nocturnal flights for barrier crossing in a diurnally migrating songbird. Mov. Ecol. 9, 21 (2021).
    Google Scholar 
    Saino, N. et al. Migration phenology and breeding success are predicted by methylation of a photoperiodic gene in the barn swallow. Sci. Rep. 7, 45412 (2017).ADS 
    CAS 

    Google Scholar 
    Henry, R. A. et al. Changing the selectivity of p300 by acetyl-CoA modulation of histone acetylation. ACS Chem. Biol 10, 146–156 (2015).CAS 

    Google Scholar 
    Sun, H., Skorgerbø, G., Wang, Z., Liu, W. & Li, Y. Structural relationships between highly conserved elements and genes in vertebrate genomes. PLoS ONE 3, e3727 (2008).ADS 

    Google Scholar 
    Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 

    Google Scholar 
    Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013).CAS 

    Google Scholar 
    Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 

    Google Scholar 
    Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).ADS 

    Google Scholar 
    Coombe, L. et al. ARKS: Chromosome-scale scaffolding of human genome drafts with linked read kmers. BMC Bioinform. 19, 1–10 (2018).
    Google Scholar 
    Campbell, M. S., Holt, C., Moore, B. & Yandell, M. Genome annotation and curation using MAKER and MAKER‐P. Curr. Protocols Bioinform. 48, 4.11.1–4.11.39 (2014).Malmberg, M. M. et al. Evaluation and recommendations for routine genotyping using skim whole genome re-sequencing in canola. Front. Plant. Sci. 9 (2018).Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).CAS 

    Google Scholar 
    Golicz, A. A., Bayer, P. E. & Edwards, D. Skim-based genotyping by sequencing. Methods Mol. Biol. 1245, 257–270 (2015).CAS 

    Google Scholar 
    Hill, R. D. Theory of geolocation by light levels. In B. J. L. Boeuf, & R. M. Laws (Ed.), Elephant seals: Population ecology, behaviour and physiology, pp. 227–236. Berkeley, CA: University of California Press (1994).Wotherspoon, S., Summer, M. & Lisovski, S. BAStag: basic data processing for light based geolocation archival tags. Version 0.1.3. (2016).Lisovski, S. & Hahn, S. GeoLight-processing and anslysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
    Google Scholar 
    Gompert, Z., Lucas, L. K., Nice, C. C. & Buerkle, C. A. Genome divergence and the genetic architecture of barriers to gene flow between Lycaeides idas and L. melissa. Evolution 67, 2498–2514 (2013).
    Google Scholar 
    Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).CAS 

    Google Scholar 
    Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 

    Google Scholar 
    Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analysis. Nat Protoc 15, 2759–2772 (2020).CAS 

    Google Scholar 
    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 

    Google Scholar 
    Cruickshank, T. E. & Hahn, M. W. Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol. Ecol. 23, 3133–3157 (2014).
    Google Scholar 
    Vijay, N. et al. Evolution of heterogeneous genome differentiation across multiple contact zones in a crow species complex. Nat. Commun. 7, 13195 (2016).ADS 
    CAS 

    Google Scholar 
    Delmore, K. et al. The evolutionary history and genomics of European blackcap migration. eLife 9, e54462 (2020). More

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    Mangrove reforestation provides greater blue carbon benefit than afforestation for mitigating global climate change

    Literature search and screeningOur analysis included a systematic literature search and was conducted by following the PRISMA protocol55 (Supplementary Fig. 7). We searched through Web of Science and China National Knowledge Infrastructure (CNKI) platforms by using keywords listed in Supplementary Table 3. A total of 3299 potentially relevant articles were found (Mandarin and English). The availability of peer-reviewed datasets associated with these published articles11,15,56,57,58,59 and online databases (The Sustainable Wetlands Adaptation and Mitigation Program (SWAMP) database, https://www2.cifor.org/swamp) were also considered. We then removed a significant number of articles through title screening, leaving 551 articles for further inspection.For these remaining articles, we used a four-step critique process to screen their title, abstract, and full text. We determined that firstly, they must provide carbon density data for at least one of the four mangrove carbon pools (i.e., aboveground biomass, belowground biomass, sediment organic carbon, or total ecosystem carbon). Secondly, articles needed to state the forest age or the starting date of the restoration action. For those studies providing only age intervals (e.g., 10–25 years, >66 years), we excluded them from the analysis. Thirdly, a description of prior land use was required. From these, mangrove restoration could be divided into two categories—reforestation and afforestation—on whether mangroves previously existed in that location. For reforestation, the initial conditions for inclusion were: (1) abandoned agricultural/aquacultural sites built previously by excavating mangrove forests, (2) clear-felled mangrove lands after wars, timber harvest, and silvicultural management, and (3) mangrove forests with mortality due to spraying of defoliants and hydrological alteration caused by the construction of embankments. We compared the carbon densities of reforested mangroves among sites with different causes of degradation/deforestation, and no significant difference is found (Supplementary Fig. 9). For those reforested mangroves, we assumed they would be protected and conserved by local governments and non-government organizations, so that there will not be human-driven degradation or deforestation in the near future. However, we acknowledge that a fraction of mangrove reforestation is managed for wood production, which means logging would happen at a certain interval after reforestation at these sites. For these logging sites, we used their reported measurements after clear-cut, such as 0-, 5-, 10-, 15-, and 25-year post-harvest sites in Sundarbans, Bangladesh60. On the other hand, the future occurrence of natural-driven deforestation (e.g., cyclones) is difficult to predict, and thus not considered in our study. For afforestation, the initial condition for inclusion was the presence of non-mangrove habitat immediately before afforestation began, such as mudflats, seagrass, saltmarsh, coral reef, or denuded areas. In most cases, reforestation and afforestation were undertaken through active planting without much re-engineering4, but for reforestation, natural regeneration could have, and in many places likely did, augment recruitment61. Moreover, we only considered mangrove succession that started from near-barren land with an insignificant amount of biomass, and introductions of exotic species to degraded areas with sparse trees were not incorporated. Lastly, if the forest age or prior land use type was not given, the articles needed to specify the location of sampling plots (latitude, longitude). With the coordinates matching, prior land use type and establishment dates were sometimes identifiable through remote sensing (Supplementary Fig. 10). For those articles sharing the same restoration sites but showing different aspects of the data collection, we combined the results and considered the collective work as one source. Based on the space-for-time method, data in the control sites before mangrove restoration actions were also collected as a paired site of restoration (e.g., abandoned ponds before mangrove reforestation; mudflats before mangrove afforestation). In total, we obtained data from 379 mangrove restoration sites described by 106 articles.Data extractionWe extracted aboveground living biomass carbon (AGC), belowground living biomass carbon (BGC), sediment carbon (SCS), and total ecosystem carbon (TECS) density from the 106 original data sources. In most cases, numeric values were provided. For those data not provided numerically but graphed, we determined values from figures with the application of GetData Graph Digitizer (http://getdata-graph-digitizer.com/).Among the articles, aboveground and belowground biomass (Mg ha−1) data were obtained using either a harvesting method (empirical) or an allometric method (calculation). Aboveground biomass represented the sum of stem, leaf, and branch dry weight, and we included prop root biomass when Rhizophora spp. were present. For soil coring methods that determined belowground biomass or sediment carbon density, belowground biomass was considered the dry weight of living coarse and fine roots multiplied by the ratio of core area to land surface area62. For allometric methods, trunk diameter at breast height (DBH, ~1.3 m) and tree height were used to calculate aboveground and belowground biomass by species-specific or common allometric equations63. These equations were also used to calculate the belowground biomass when articles provided plot information (DBH, height) but not belowground biomass (Supplementary Table 4). Total biomass was calculated as the sum of aboveground and belowground biomass. Deadwood and pneumatophore biomass were not included in our analysis; these data are rarely provided and/or methods of determination are inconsistent among global studies64. Some articles provided total biomass and shoot/root biomass ratio (S/R), and in such cases, above- and belowground biomass data were obtained through calculation as follows:$${{{{{rm{Aboveground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{frac{S}{R}}{frac{S}{R}+1}$$
    (1)
    $${{{{{rm{Belowground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{1}{frac{S}{R}+1}$$
    (2)
    For those articles measuring carbon content, study-specific carbon conversion factors were used to transform biomass to biomass carbon density (Mg C ha−1). If carbon content data were not provided, we converted aboveground and belowground biomass to carbon density by applying a conversion of 0.47 and 0.39, respectively65. The aboveground biomass carbon density was divided by its corresponding age to get the average aboveground biomass carbon accumulation rate (Mg C ha−1 yr−1).For sediment carbon density (SCS, Mg C ha−1), we selected the top 1 m because this depth equated to the most commonly reported depth and could reflect the impact of root mass input in the deeper depth66, which is also consistent with recent blue carbon standing stock assessment guidance64,67. Sediment carbon stock was calculated by multiplying sediment organic carbon content (SOC, %) by bulk density (BD, g cm−3), integrated over depth (cm). For studies that reported sediment carbon stock to More

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    Family before work: task reversion in workers of the red imported fire ant, Solenopsis invicta in the presence of brood

    Wilson, E. O. The Insect Societies (Oxford University Press, 1971).
    Google Scholar 
    Beshers, S. N. & Fewell, J. H. Models of division of labor in social insects. Annu. Rev. Entomol. 46, 413–440 (2001).CAS 

    Google Scholar 
    Seeley, T. D. Adaptive significance of the age polyethism schedule in honeybee colonies. Behav. Ecol. Sociobiol. 4, 287–293 (1982).
    Google Scholar 
    Tallamy, D. W. Insect parental care. Bioscience 34, 20–24. https://doi.org/10.2307/1309421 (1984).Article 

    Google Scholar 
    Queller, D. C. Extended parental care and the origin of eusociality. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 256, 105–111. https://doi.org/10.1098/rspb.1994.0056 (1994).Article 
    ADS 

    Google Scholar 
    Bigley, W. S. & Vinson, S. B. Characterization of a brood pheromone isolated from the sexual brood of the imported fire ant, Solenopsis invicta 1,2. Ann. Entomol. Soc. Am. 68, 301–304 (1975).CAS 

    Google Scholar 
    Endler, A. et al. Surface hydrocarbons of queen eggs regulate worker reproduction in a social insect. Proc. Natl. Acad. Sci. USA 101, 2945–2950. https://doi.org/10.1073/pnas.0308447101 (2004).Article 
    ADS 
    CAS 

    Google Scholar 
    Maisonnasse, A., Lenoir, J. C., Beslay, D., Crauser, D. & Le Conte, Y. E-beta-ocimene, a volatile brood pheromone involved in social regulation in the honey bee colony (Apis mellifera). PLoS ONE 5, e13531. https://doi.org/10.1371/journal.pone.0013531 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Schultner, E., Oettler, J. & Helantera, H. The role of brood in eusocial hymenoptera. Q. Rev. Biol. 92, 39–78. https://doi.org/10.1086/690840 (2017).Article 

    Google Scholar 
    Amdam, G. V., Hartfelder, K., Norberg, K., Hagen, A. & Omholt, S. W. Altered physiology in worker honey bees (Hymenoptera: Apidae) infested with the mite Varroa destructor (Acari: Varroidae): A factor in colony loss during overwintering? J. Econ. Entomol. 97, 741–747 (2004).
    Google Scholar 
    Calabi, P. & Traniello, J. F. Behavioral flexibility in age castes of the ant Pheidole dentata. J. Insect Behav. 2, 663–677 (1989).
    Google Scholar 
    Gordon, D. W. Dynamics of task switching in harvester ants. Anim. Behav. 38, 194–204 (1989).
    Google Scholar 
    Robinson, G. E. Regulation of division of labor in insect societies. Annu. Rev. Entomol. 37, 637–665. https://doi.org/10.1146/annurev.en.37.010192.003225 (1992).Article 
    CAS 

    Google Scholar 
    Robinson, E. J., Feinerman, O. & Franks, N. R. Flexible task allocation and the organization of work in ants. Proc. R. Soc. B: Biol. Sci. 276, 4373–4380 (2009).
    Google Scholar 
    Nijhout, H. F. & Wheeler, D. E. Juvenile-hormone and the physiological-basis of Insect polymorphisms. Q. Rev. Biol. 57, 109–133. https://doi.org/10.1086/412671 (1982).Article 
    CAS 

    Google Scholar 
    Herb, B. R. et al. Reversible switching between epigenetic states in honeybee behavioral subcastes. Nat. Neurosci. 15, 1371–1373. https://doi.org/10.1038/nn.3218 (2012).Article 
    CAS 

    Google Scholar 
    Kensuke, N. Age polyethism, idiosyncrasy and behavioural flexibility in the queenless ponerine ant, Diacamma sp. J. Ethol. 13, 113–123 (1995).
    Google Scholar 
    Kensuke, N. Does behavioral flexibility compensate or constrain colony productivity? Relationship among age structure, labor allocation, and production of workers in ant colonies. J. Insect Behav. 9, 557–569 (1996).
    Google Scholar 
    Shimoji, H., Kasutani, N., Ogawa, S. & Hojo, M. K. Worker propensity affects flexible task reversion in an ant. Behav. Ecol. 74, 1–8 (2020).
    Google Scholar 
    Bernadou, A., Busch, J. & Heinze, J. Diversity in identity: Behavioral flexibility, dominance, and age polyethism in a clonal ant. Behav. Ecol. Sociobiol. 69, 1365–1375 (2015).
    Google Scholar 
    Kohlmeier, P., Feldmeyer, B. & Foitzik, S. Vitellogenin-like A—Associated shifts in social cue responsiveness regulate behavioral task specialization in an ant. PLoS Biol. 16, e2005747 (2018).
    Google Scholar 
    Tripet, F. & Nonacs, P. Foraging for work and age-based polyethism: The roles of age and previous experience on task choice in ants. Ethology 110, 863–877 (2004).
    Google Scholar 
    Kohlmeier, P., Alleman, A. R., Libbrecht, R., Foitzik, S. & Feldmeyer, B. Gene expression is more strongly associated with behavioural specialisation than with age or fertility in ant workers. Mol. Ecol. https://doi.org/10.1111/mec.14971 (2018).Article 

    Google Scholar 
    Levenbook, L. & Bauer, A. C. The fate of the larval storage protein calliphorin during adult development of Calliphora vicina. Insect Biochem. 14, 77–86 (1984).CAS 

    Google Scholar 
    Zhou, X., Oi, F. M. & Scharf, M. E. Social exploitation of hexamerin: RNAi reveals a major caste-regulatory factor in termites. Proc. Natl. Acad. Sci. 103, 4499–4504 (2006).ADS 
    CAS 

    Google Scholar 
    Zhou, X., Tarver, M. R., Bennett, G., Oi, F. & Scharf, M. Two hexamerin genes from the termite Reticulitermes flavipes: Sequence, expression, and proposed functions in caste regulation. Gene 376, 47–58 (2006).CAS 

    Google Scholar 
    Hawkings, C., Calkins, T. L., Pietrantonio, P. V. & Tamborindeguy, C. Caste-based differential transcriptional expression of hexamerins in response to a juvenile hormone analog in the red imported fire ant (Solenopsis invicta). PLoS ONE 14, e0216800 (2019).CAS 

    Google Scholar 
    Hoffman, E. A. & Goodisman, M. A. Gene expression and the evolution of phenotypic diversity in social wasps. BMC Biol. 5, 1–9 (2007).
    Google Scholar 
    Hunt, J. H., Buck, N. A. & Wheeler, D. E. Storage proteins in vespid wasps: Characterization, developmental pattern, and occurrence in adults. J. Insect Physiol. 49, 785–794 (2003).CAS 

    Google Scholar 
    Colgan, T. J. et al. Polyphenism in social insects: Insights from a transcriptome-wide analysis of gene expression in the life stages of the key pollinator, Bombus terrestris. BMC Genom. 12, 1–20 (2011).
    Google Scholar 
    Cremer, S., Armitage, S. A. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, R693–R702 (2007).CAS 

    Google Scholar 
    Cremer, S., Pull, C. D. & Fuerst, M. A. Social immunity: Emergence and evolution of colony-level disease protection. Annu. Rev. Entomol. 63, 105–123 (2018).CAS 

    Google Scholar 
    Danihlík, J., Aronstein, K. & Petřivalský, M. Antimicrobial peptides: A key component of honey bee innate immunity: Physiology, biochemistry, and chemical ecology. J. Apic. Res. 54, 123–136 (2015).
    Google Scholar 
    Koch, S. I. et al. Caste-specific expression patterns of immune response and chemosensory related genes in the leaf-cutting ant, Atta vollenweideri. PLoS ONE 8, e81518 (2013).ADS 

    Google Scholar 
    Chardonnet, F. et al. Food searching behaviour of a Lepidoptera pest species is modulated by the foraging gene polymorphism. J. Exp. Biol. 217, 3465–3473 (2014).
    Google Scholar 
    Scheiner, R., Page, R. E. Jr. & Erber, J. Responsiveness to sucrose affects tactile and olfactory learning in preforaging honey bees of two genetic strains. Behav. Brain Res. 120, 67–73 (2001).CAS 

    Google Scholar 
    Wang, Z. et al. Visual pattern memory requires foraging function in the central complex of Drosophila. Learn. Mem. 15, 133–142 (2008).
    Google Scholar 
    Zhou, Y., Lei, Y., Lu, L. & He, Y. Temperature-and food-dependent foraging gene expression in foragers of the red imported fire ant Solenopsis invicta Buren (Hymenoptera: Formicidae). Physiol. Entomol. 45, 1–6 (2020).
    Google Scholar 
    Ingram, K. K. et al. Context-dependent expression of the foraging gene in field colonies of ants: The interacting roles of age, environment and task. Proc. R. Soc. B: Biol. Sci. 283, 20160841 (2016).
    Google Scholar 
    Ingram, K. K., Oefner, P. & Gordon, D. M. Task-specific expression of the foraging gene in harvester ants. Mol. Ecol. 14, 813–818 (2005).CAS 

    Google Scholar 
    Lucas, C. & Sokolowski, M. B. Molecular basis for changes in behavioral state in ant social behaviors. Proc. Natl. Acad. Sci. 106, 6351–6356 (2009).ADS 
    CAS 

    Google Scholar 
    Ben-Shahar, Y. The foraging gene, behavioral plasticity, and honeybee division of labor. J. Comp. Physiol. A. 191, 987–994 (2005).CAS 

    Google Scholar 
    Daugherty, T., Toth, A. & Robinson, G. Nutrition and division of labor: Effects on foraging and brain gene expression in the paper wasp Polistes metricus. Mol. Ecol. 20, 5337–5347 (2011).CAS 

    Google Scholar 
    Morrison, L. W., Porter, S. D., Daniels, E. & Korzukhin, M. D. Potential global range expansion of the invasive fire ant, Solenopsis invicta. Biol. Invasions 6, 183–191 (2004).
    Google Scholar 
    Valles, S. M., Wetterer, J. K. & Porter, S. D. The red imported fire ant (Hymenoptera: Formicidae) in the West Indies: Distribution of natural enemies and a possible test bed for release of self-sustaining biocontrol agents. Fls. Entomol. 98, 1101–1105 (2015).
    Google Scholar 
    Greenberg, L., Vinson, S. & Ellison, S. Nine-year study of a field containing both monogyne and polygyne red imported fire ants (Hymenoptera: Formicidae). Ann. Entomol. Soc. Am. 85, 686–695 (1992).
    Google Scholar 
    Keller, L. & Ross, K. G. Selfish genes: A green beard in the red fire ant. Nature 394, 573–575 (1998).ADS 
    CAS 

    Google Scholar 
    Vinson, S. B. Impact of the invasion of the imported fire ant. Insect Sci. 20, 439–455 (2013).
    Google Scholar 
    Tschinkel, W. R. The Fire Ants (Harvard University Press, 2006).
    Google Scholar 
    Cassill, D. L. & Tschinkel, W. R. Task selection by workers of the fire ant Solenopsis invicta. Behav. Ecol. Sociobiol. 45, 301–310 (1999).
    Google Scholar 
    Mirenda, J. T. & Vinson, S. B. Division of labour and specification of castes in the red imported fire ant Solenopsis invicta Buren. Anim. Behav. 29, 410–420 (1981).
    Google Scholar 
    Wilson, E. O. Division of labor in fire ants based on physical castes (Hymenoptera: Formicidae: Solenopsis). J. Kansas Entomol. Soc. 51, 615–636 (1978).
    Google Scholar 
    Sorensen, A., Busch, T. M. & Vinson, S. B. Behavioral flexibility of temporal subcastes in the fire ant, Solenopsis invicta in response to food. Psyche 91, 319–331 (1984).
    Google Scholar 
    Bigley, W. S. & Vinson, S. B. Characterization of a brood pheromone isolated from the sexual brood of the imported fire ant, Solenopsis invicta. Ann. Entomol. Soc. Am. 2, 301–304 (1975).
    Google Scholar 
    Bajracharya, P., Lu, H. L. & Pietrantonio, P. V. The red imported fire ant (Solenopsis invicta Buren) kept Y not F: Predicted sNPY endogenous ligands deorphanize the short NPF (sNPF) receptor. PLoS ONE 9(10), e109590 (2014).ADS 

    Google Scholar 
    Castillo, P. Short neuropeptide F receptor in the worker brain of the red imported fire ant (Solenopsis invicta Buren) and methodology for RNA interference M.S. thesis, Texas A&M University (2015).Castillo, P. & Pietrantonio, P. V. Differences in sNPF receptor-expressing neurons in brains of fire ant (Solenopsis invicta Buren) worker subcastes: Indicators for division of labor and nutritional status? PLoS ONE 8, e83966 (2013).ADS 

    Google Scholar 
    Cassill, D. L. & Tschinkel, W. R. Allocation of liquid food to larvae via trophallaxis in colonies of the fire ant, Solenopsis invicta. Anim. Behav. 3, 801–813 (1995).
    Google Scholar 
    Cassill, D. L., Stuy, A. & Buck, R. G. Emergent properties of food distribution among fire ant larvae. J. Theor. Biol. 3, 371–381 (1998).ADS 

    Google Scholar 
    Dussutour, A. & Simpson, S. J. Communal nutrition in ants. Curr. Biol. 19, 740–744. https://doi.org/10.1016/j.cub.2009.03.015 (2009).Article 
    CAS 

    Google Scholar 
    Petralia, R. S. & Vinson, S. B. Feeding in the larvae of the imported fire ant, Solenopsis invicta: Behavior and morphological adaptations. Ann. Entomol. Soc. Am. 71, 643–648 (1978).
    Google Scholar 
    Petralia, R. S. & Vinson, S. B. Developmental morphology of larvae and eggs of the imported fire ant, Solenopsis invicta. Ann. Entomol. Soc. Am. 72, 472–484 (1979).
    Google Scholar 
    Chen, J. Advancement on techniques for the separation and maintenance of the red imported fire ant colonies. Insect Sci. 14, 1–4 (2007).
    Google Scholar 
    Banks, W. A. et al. (Agricultural Research (Southern Region), Science and Education…, 1981).Valles, S. M. & Porter, S. D. Identification of polygyne and monogyne fire ant colonies (Solenopsis invicta) by multiplex PCR of Gp-9 alleles. Insectes Soc. 2, 199–200 (2003).
    Google Scholar 
    Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 3, 1101 (2008).CAS 

    Google Scholar 
    Cheng, D., Zhang, Z., He, X. & Liang, G. Validation of reference genes in Solenopsis invicta in different developmental stages, castes and tissues. PLoS ONE 8, e57718. https://doi.org/10.1371/journal.pone.0057718 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Qiu, H.-L., Zhao, C.-Y. & He, Y.-R. On the molecular basis of division of labor in Solenopsis invicta (Hymenoptera: Formicidae) workers: RNA-seq analysis. J. Insect Sci. 17, 48 (2017).
    Google Scholar 
    Chen, J. et al. Role of the foraging gene in worker behavioral transition in the red imported fire ant, Solenopsis invicta (Hymenoptera: Formicidae). Pest Manag. Sci. https://doi.org/10.1002/ps.6921 (2022).Article 

    Google Scholar 
    Shorter, J. R. & Tibbetts, E. A. The effect of juvenile hormone on temporal polyethism in the paper wasp Polistes dominulus. Insectes Soc. 56, 7–13 (2009).
    Google Scholar 
    Pankiw, T., Page, R. E. Jr. & Kim Fondrk, M. Brood pheromone stimulates pollen foraging in honey bees (Apis mellifera). Behav. Ecol. Sociobiol. 44, 193–198. https://doi.org/10.1007/s002650050531 (1998).Article 

    Google Scholar 
    Smedal, B., Brynem, M., Kreibich, C. D. & Amdam, G. V. Brood pheromone suppresses physiology of extreme longevity in honeybees (Apis mellifera). J. Exp. Biol. 212, 3795–3801. https://doi.org/10.1242/jeb.035063 (2009).Article 
    CAS 

    Google Scholar 
    Solis, C. R. & Strassmann, J. E. Presence of brood affects caste differentiation in the social wasp, Polistes exclamans Viereck (Hymenoptera, Vespidae). Funct. Ecol. 4, 531–541. https://doi.org/10.2307/2389321 (1990).Article 

    Google Scholar 
    Traynor, K. S. Decoding Brood Pheromone: The Releaser and Primer Effects of Young and Old Larvae on Honey Bee (Apis mellifera) Workers (Arizona State University, 2014).
    Google Scholar 
    Wagoner, K. M., Spivak, M. & Rueppell, O. Brood affects hygienic behavior in the honey bee (Hymenoptera: Apidae). J. Econ. Entomol. 111, 2520–2530. https://doi.org/10.1093/jee/toy266 (2018).Article 
    CAS 

    Google Scholar 
    Nijhout, H. F. & Wheeler, D. E. Juvenile hormone and the physiological basis of insect polymorphisms. Q. Rev. Biol. 57, 109–133 (1982).CAS 

    Google Scholar  More

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    What it would take to bring back the dodo

    The flightless dodo went extinct in the seventeenth century. Biotech company Colossal Biosciences plans to resurrect it.Credit: Hart, F/Bridgeman Images

    A biotech company announced an audacious effort to ‘de-extinct’ the dodo last week. The flightless birds vanished from the island of Mauritius — in the Indian Ocean — in the late seventeenth century, and became emblematic of humanity’s negative impacts on the natural world. Could the plan actually work?Colossal Biosciences, based in Dallas, Texas, has landed US$225 million in investment (including funds from the celebrity Paris Hilton) — having previously announced plans to de-extinct thylacines, an Australian marsupial, and create elephants with woolly mammoth traits. But Colossal’s plans depend on huge advances in genome editing, stem-cell biology and animal husbandry, making success far from certain.“It’s incredibly exciting that there’s that kind of money available,” says Thomas Jensen, a cell and molecular reproductive physiologist at Wells College in Aurora, New York. “I’m not sure that the end goal they’re going for is something that’s super feasible in the near future.”Iridescent pigeonsColossal’s plan starts with the dodo’s closest living relative, the iridescent-feathered Nicobar pigeon (Caloenas nicobarica). The company plans to isolate and culture specialized primordial germ cells (PGCs) — which make sperm and egg-producing cells — from developing Nicobars. Colossal’s scientists would edit DNA sequences in the PGCs to match those of dodos using tools such as CRISPR. These gene-edited PGCs would then be inserted into embryos from a surrogate bird species to generate chimeric — those with DNA from both species — animals that make dodo-like egg and sperm. These could potentially produce something resembling a dodo (Raphus cucullatus).To gene-edit Nicobar pigeon PGCs, scientists first need to identify the conditions that allow these cells to flourish in the laboratory, says Jae Yong Han, an avian-reproduction scientist at Seoul National University. Researchers have done this with chickens, but it will take time to identify the appropriate culture conditions that suit other birds’ PGCs.A greater challenge will be determining the genetic changes that could transform Nicobar pigeons into Dodos. A team including Beth Shapiro, a palaeogeneticist at the University of California, Santa Cruz, who is advising Colossal on the dodo project, has sequenced the dodo genome but has not yet published the results. Dodos and Nicobar pigeons shared a common ancestor that lived around 30 million to 50 million years ago, Shapiro’s team reported in 20161. By comparing the nuclear genomes of the two birds, the researchers hope to identify most of the DNA changes that distinguish between them.Insights from ratsTom Gilbert, an evolutionary biologist at the University of Copenhagen, who also advises Colossal, expects the dodo genome to be of high quality — it comes from a museum sample he provided to Shapiro. But he says that finding all the DNA differences between the two birds is not possible. Ancient genomes are cobbled together from short sequences of degraded DNA, and so are filled with unavoidable gaps and errors. And research he published last year comparing the genome of the extinct Christmas Island rat (Rattus macleari) with that of the Norwegian brown rat (Rattus norvegicus)2 suggests that gaps in the dodo genome could lie in the very DNA regions that have changed the most since its lineage split from that of Nicobar pigeons.Even if researchers could identify every genetic difference, introducing the thousands of changes to PGCs would not be simple. “I’m not sure it’s feasible in the near future,” says Jensen, whose team is encountering difficulties making a single genetic change to the genomes of quail.Focusing on only a subset of DNA changes, such as those that alter protein sequences, could slash the number of edits needed. But it’s still not clear that this would yield anything resembling a wild dodo, says Gilbert. “My worry is that Paris Hilton thinks she’s going to get a dodo that looks like a dodo,” he says.A further problem will be the need to find a large bird, such as an emu (Dromaius novaehollandiae), that can act as the surrogate, says Jensen. “Dodo eggs are much, much larger than Nicobar pigeon eggs, you couldn’t grow a dodo inside of a Nicobar egg.”Chicken embryos are fairly receptive to PGCs from other birds, and Jensen’s team has created chimeric chickens that can produce quail sperm — efforts to generate eggs have failed so far. But he thinks it will be far more challenging to transfer PGCs — particularly heavily gene-edited ones — from one wild bird into another.Conservation boon?Colossal chief executive Ben Lamm acknowledges these hurdles, but argues they aren’t dealbreakers. Work towards dodo de-extinction will help with conservation efforts for other birds, he adds. “It will bring a lot of new technologies to the field of bird conservation,” agrees Jensen.Vikash Tatayah, conservation director at the Mauritian Wildlife Foundation in Vacoas-Phoenix, is also enthusiastic about the attention dodo de-extinction could bring to conservation. “It’s something we would like to embrace,” he says.But he points out that the predators that threatened the dodo in the seventeeth century haven’t gone away, whereas most of its habitat has. “You do have to ask,” he says, “if we could have such money, wouldn’t it be better spent on restoring habitat on Mauritius and preventing species from going extinct?” More

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    Flickering flash signals and mate recognition in the Asian firefly, Aquatica lateralis

    Flash recordingAll field recording and experiments were performed at the paddy field in the Northern Chita Peninsula, Aichi Prefecture, central Japan, in June and July between 2003 and 2016. The ambient temperature at the firefly’s active period was measured using a thermometer. The flashes were recorded with a digital video camera (NV-GS-400, Panasonic, Japan) mounted on a tripod at a height of 30–50 cm from ground and a distance of 1.0–1.5 m away from the specimen. Isolated specimens were selected for recording to exclude the background light from other nontarget specimens. When another specimen appeared near the target specimen, the video recording was cancelled. When a female copulated during video recording in the field, her flashes until 1 min before copulation were regarded as those of a ‘receptive female’. To record the flashes of a ‘mated female’, the female specimens already mated were prepared in aquariums (because virgin and mated females cannot be distinguished in the field): the eggs were obtained from wild female specimens collected one year before at the same field and reared to adults; immediately after emergence the virgin female was confined in a small container with two cultured males for two nights to facilitate copulation. As the parents of the reared specimens were collected from the observation field (same genetic background), the rearing temperature was almost the same as that of the natural field, the emergence period of the cultured specimens overlapped with that of the natural population, the adult body sizes of the reared and natural specimens were indistinguishable, and the flash pattern of the cultured mated females was indistinguishable from that of the wild (potentially) mated females. Thus, we believe that there was no influence of different rearing environments, i.e., the flash behavior of the cultured mated female specimens is expected to be substantially the same as that of wild mated female specimens. To distinguish them from wild (potentially) mated females, the elytra of cultured mated females were marked with colored ink before placing them in the field, and after three days, the flashes of ink-marked specimens were recorded. Of note, we never observed male attraction and copulation in any of the mated females used for field observation; thus, the mated females were unreceptive.Waveform analysisSequential still images were captured from video files at 30 frames per second using VirtualDub (GPL), and then the light intensities in the images were qualified (8-bit linear gray scaling from black to white at 0–255) using ImageJ software. In this study, we defined ‘flash’ as a luminescent waveform from baseline to baseline and ‘flickering’ as fluctuation above baseline in a single flash. The waveforms containing a saturated signal (255, white) were omitted. The waveforms of the maximum signal value lower than 50 were also omitted because of the difficulty in separating signal and noise. Approximately 10–90 waveforms per individual were analyzed; thus, the effect of the occasional interruption of the flash recording by the specimen’s movement and/or vegetation swinging between the specimen and the video camera is statistically ignorable. FD is defined as the time interval between the beginning and the end of a flash (Fig. S1). Flicker intensity (FI) was defined as$${text{FI}} = left{ {begin{array}{*{20}l} {mathop {max }limits_{1 le i le n} left( {frac{{{text{min}}left( {p_{i} ,p_{i + 1} } right) – t_{i} }}{{min left( {p_{i} , p_{i + 1} } right) + t_{i} }}} right)} hfill & {{text{if}} , n ge 1} hfill \ 0 hfill & {{text{if}} , n = 0} hfill \ end{array} } right.$$where p, t, and n denote the peak and the trough (local extrema) in the waveform of a flash and the number of toughs in the flash, respectively (Fig. S1). In total, we measured the FD and FI values of 347, 94, and 355 waveforms from 13 sedentary males, 7 receptive females, and 8 mated females, respectively. We did not consider the flash brightness as a factor because the measured value of the light intensity depends largely on the distance between the light source and the detector; thus, the actual brightness of the lantern cannot be practically measured in the field.e-FireflyFor male attraction experiments, we built an electronic LED device, the e-firefly, to generate patterned flashes with various FDs and FIs using a chip LED (green type, λmax = 568 nm, Everlight Electronics, Taiwan; Figs. S2 and S3) with a microcontroller PIC16F628A (Microchip Technology, USA) (see Figs. S4-S5). An example of the program for the microcontroller is shown in Supplementary Data S1. The brightness was constant in all programs. Flickering frequency ranged between 5–12 Hz, which corresponds to that of sedentary male flashes (approximately 10 Hz)15. To prevent direct access of the attracted specimen to the light source, the chip LED was covered by a steel net painted green (see Fig. S2). For flying male attraction experiments, when the male landed within a 100-mm distance from the e-firefly, we judged the attraction to be a success; otherwise, it was a failure. For sedentary male attraction experiments, the e-firefly was placed 200–300 mm away from the sedentary male. When the approaching male touched the steel net covering the e-firefly, to warrant a positive approach, we measured the time the male remained on the net. If the male did not move away from the net for more than 2 min, we judged the attraction to be a success (strict criterion for judgment); otherwise, it was a failure.Spectral measurementThe luminescence spectra of e-firefly and A. lateralis were measured using a Flame-S spectrophotometer (Ocean Insight, USA). The living A. lateralis specimens were anesthetized on ice and frozen at − 20 °C until use. The lantern started luminescence by thawing at room temperature, and the spectrum was measured during luminescence (within 5 min).Statistical analysisFirst, we considered a discriminant analysis using a logistic regression model that discriminates between receptive females and others in the observational data. We fitted several models with combinations of FD and FI, quadratic terms of FD and FI (FD2, FI2), interaction of FD and FI (FD (times) FI), and temperature (T) as explanatory variables. Based on Akaike’s information criteria (AIC) values and model simplicity, we chose the logistic regression model with FD, FI, FD2 and T as explanatory variables. Let (p)(({varvec{x}})) denote the conditional probability that a flash is from a receptive female given ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and (widehat{p})(({varvec{x}})) denote its estimate. The coefficients of the logistic regression model are estimated as follows.
    [Model for the observational data with temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 32.26 + 69.69 times FD – 43.47 times FI – 76.63 times FD^{2} + 0.87 times T} hfill \ {~quad left( {6.50} right)quad quad left( {15.37} right)quad quad quad left( {8.56} right)quad quad quad quad left( {17.44} right)quad quad quad left( {0.19} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 84}}{text{.75}} hfill \ end{gathered}$$[Model for the observational data without temperature (T)]$$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 7.69~ + 47.57 times FD~ – 38.29 times FI~ – 52.86 times FD^{2} ~} hfill \ {~;left( {1.86} right)quad quad left( {9.68} right)quad quad quad left( {7.08} right)quad quad quad quad left( {11.38} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 114}}{text{.89}} hfill \ end{gathered}$$where values in parentheses indicate standard deviations. The same applies hereafter. Temperature (T) is included in the model not because it affects the occurrence of receptive females but because it affects the FD and/or FI of receptive females. The AIC value increased by 30, which is substantial, when temperature was excluded from the model.Figure 2 shows the FD and FI of each flash from receptive females, mated females and males with the discriminant boundaries of receptive females from others for (p=0.5).We next considered a discriminant analysis for the experimental data. Let ({q}^{f}({varvec{x}})) denote the conditional probability that a flying male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and lands, and ({widehat{q}}^{f}({varvec{x}})) denote its estimate. Among several models we fit, the smallest AIC value is attained by the logistic regression model with FD, FI and T as explanatory variables, but the AIC is not much different from the model with FD and FI only.
    [Model for flying males with temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 0.74~~ – 2.42 times FD – 16.82 times FI + 0.31 times T} hfill \ {~;left( {4.01} right)quad quad left( {0.83} right)quad quad quad left( {4.88} right)quad quad quad quad left( {0.20} right)~} hfill \ end{array} hfill \ quad {text{AIC}}:66.96 hfill \ end{gathered}$$

    [Model for flying males without temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 5.36~ – 1.72 times FD – 13.69 times FI} hfill \ {~;left( {1.49} right)quad quad left( {0.63} right)~quad quad left( {4.09} right)~~} hfill \ end{array} hfill \ quad {text{AIC}}:67.61 hfill \ end{gathered}$$
    For sedentary males, the model with the smallest AIC value includes all the quadratic terms of FI and FD but not temperature. Let ({q}^{s}({varvec{x}})) denote the conditional probability that a sedentary male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and ({widehat{q}}^{s}left({varvec{x}}right)) denote its estimate. The logistic regression model for ({q}^{s}({varvec{x}})) with the best AIC value is given as follows.
    [Model for sedentary males]
    $${text{log}}frac{{hat{q}~^{s} }}{{1 – hat{q}~^{s} }} = begin{array}{*{20}l} { – 0.68~ + 7.84 times FD~ + 48.17 times FI – 5.35 times FD^{2} – 166.70 times FI^{2} – 65.67 times FD times FI} hfill \ {;left( {0.97} right)quad quad quad left( {2.99} right)quad quad quad left( {17.74} right)quad quad quad left( {1.74} right)quad quad quad quad left( {72.34} right)quad quad quad quad left( {17.67} right)~} hfill \ end{array}$$
    Figure 3 shows successes and failures of attraction of flying males on the left and sedentary males on the right with estimated discriminant boundaries.Let us now estimate probabilities that a flying male is attracted and lands or a sedentary male is attracted to a flash when a flash is from a receptive female or when a flash is either from a sedentary male or mated female. The probability that a flying male is attracted and lands when a flash is from a receptive female is a conditional probability and is expressed as follows.$$begin{aligned} Pleft(left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} right) & = frac{{Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) }}{{Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right)}}, \ Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} = mathop int_{Omega }pleft( {varvec{x}} right) fleft( {varvec{x}} right)d{varvec{x}} hspace{5mm}{text{and}} \ Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|varvec{x} right)Pleft(left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} \ & = mathop int_{Omega } pleft( varvec{x} right)q^{f} left( {varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}}mathbf{.} \ end{aligned}$$Integrals are taken over the domain (Omega) of ({varvec{x}}=(FD, FI, T)) of all females and males, and (f({varvec{x}})) is the joint density function of ({varvec{x}}.) Because (f({varvec{x}})) is unknown, we use the empirical distribution of the observational data, and conditional probabilities given ({varvec{x}}) are replaced with their estimates by logistic regression models. Let ({{varvec{x}}}_{i}=left(F{D}_{i}, F{I}_{i}, {T}_{i}right), i=mathrm{1,2},dots N) denote the (i) th observation in the observational data. The estimates of probabilities are given as follows:$$begin{aligned} hat{P}left( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} }right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hspace{15mm} {text{and}} \ hat{P}left( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right). \ end{aligned}$$Thus,$$hat{P}left( left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right) = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n}hat{p}left(varvec{x}_i right)}}.$$Similarly, we have$$begin{aligned} hat{P}left( left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right)) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right))}} \ hat{P}left( left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right)& = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{s} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} hat{p}left( varvec{x}_{i} right)}}hspace{15mm} {text{ and}} \hat{P}left(left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right) hat{q}^{s} left( {varvec{x}_{i} } right)}}{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right)} . \ end{aligned}$$The estimated probabilities are shown in Table 1.Table 1 Estimated probabilities of a flying male and a sedentary male being attracted to flashes from a receptive female and from others.Full size table More

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    Ecological niche model transferability of the white star apple (Chrysophyllum albidum G. Don) in the context of climate and global changes

    IPBES (2019): Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 p.FAO. in Global Forest Resources Assessment 2020: Main report. Rome. https://doi.org/10.4060/ca9825en (2020).Millennium Ecosystem Assessment (MA). Ecosystems and Human Well-Being: Synthesis. Island Press, Washington (2005)CBD. Considerations for Implementing International Standards and Codes of Conduct in National Invasive Species. Strategies and Plans. CBD (2011).Semper-Pascual, A. et al. Using occupancy models to assess the direct and indirect impacts of agricultural expansion on species’ populations. Biodivers. Conserv. 29, 3669–3688 (2020).
    Google Scholar 
    IPCC. Provisional State of the Global Climate. 2022. https://storymaps.arcgis.com/stories/5417cd9148c248c0985a5b6d028b0277, Accessed 23rd December 2022.Nunez, S. & Alkemade, R. Exploring interaction effects from mechanisms between climate and land-use changes and the projected consequences on biodiversity. Biodivers. Conserv. 30, 3685–3696 (2021).
    Google Scholar 
    Liu, C., White, M. & Newell, G. Measuring and comparing the accuracy of species distribution models with presence absence data. Ecography 34, 232–243. https://doi.org/10.1111/j.1600-0587.2010.06354.x (2011).Article 
    CAS 

    Google Scholar 
    Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558. https://doi.org/10.1111/ecog.04890 (2020).Article 

    Google Scholar 
    Pearson, G. R., Raxworthy, J. C., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr 34, 102–117 (2007).
    Google Scholar 
    Thuiller, W. et al. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob. Chang. Biol. 11, 2234–2250 (2005).ADS 

    Google Scholar 
    He, Y. et al. Predicting potential global distribution and risk regions for potato cyst nematodes (Globodera rostochiensis and Globodera pallida). Sci. Rep. 12(1), 1–10 (2022).ADS 
    CAS 

    Google Scholar 
    Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).
    Google Scholar 
    Ashraf, U., Chaudhry, M. N. & Peterson, A. T. Ecological niche models of biotic interactions predict increasing pest risk to olive cultivars with changing climate. Ecosphere 12, e03714. https://doi.org/10.1002/ecs2.3714 (2021).Article 

    Google Scholar 
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
    Google Scholar 
    Ganglo, J. C. et al. Ecological niche modeling and strategies for the conservation of Dialium guineense Willd. (Black velvet) in West Africa. Int. J. Biodivers. Conserv. 9, 373–388 (2017).
    Google Scholar 
    Djotan, A. K. G. et al. How far can climate changes help to conserve and restore Garcinia kola Heckel, an extinct species in the wild in Benin (West Africa). Int. J. Biodivers. Conserv. 10, 203–213 (2018).
    Google Scholar 
    Kakpo, S. B. et al. Spatial distribution and impacts of climate change on Milicia excelsa in Benin, West Africa. J. For. Res. 32, 143–150. https://doi.org/10.1007/s11676-019-01069-7 (2021).Article 

    Google Scholar 
    Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7(1), 1–8 (2020).MathSciNet 

    Google Scholar 
    Poor, E. E., Scheick, B. K. & Mullinax, J. M. Multiscale consensus habitat modeling for landscape level conservation prioritization. Sci. Rep. 10(1), 1–13 (2020).
    Google Scholar 
    Schüßler, D., Mantilla-Contreras, J., Stadtmann, R., Ratsimbazafy, J. H. & Radespiel, U. Identification of crucial stepping stone habitats for biodiversity conservation in northeastern Madagascar using remote sensing and comparative predictive modeling. Biodivers. Conserv. 29, 2161–2184 (2020).
    Google Scholar 
    Campos-Cerqueira, M. et al. Climate change is creating a mismatch between protected areas and suitable habitats for frogs and birds in Puerto Rico. Biodivers. Conserv. 30, 3509–3528 (2021).
    Google Scholar 
    Biddle, R. et al. The value of local community knowledge in species distribution modelling for a threatened Neotropical parrot. Biodivers. Conserv. 30, 1803–1823 (2021).
    Google Scholar 
    Costa, A. et al. Modelling the amphibian chytrid fungus spread by connectivity analysis: Towards a national monitoring network in Italy. Biodivers. Conserv. 30(10), 2807–2825 (2021).
    Google Scholar 
    Konowalik, K. & Nosol, A. Evaluation metrics and validation of presence-only species distribution models based on distributional maps with varying coverage. Sci. Rep. 11(1), 1–15 (2021).
    Google Scholar 
    Borgelt, J., Sicacha-Parada, J., Skarpaas, O. & Verones, F. Native range estimates for red-listed vascular plants. Sci. Data 9(1), 1–12 (2022).
    Google Scholar 
    Brychkova, G. et al. Climate change and land-use change impacts on future availability of forage grass species for Ethiopian dairy systems. Sci. Rep. 12(1), 1–16 (2022).
    Google Scholar 
    Carrara, R. & Roig-Juñent, S. A. Maps of potential biodiversity: when the tools for regional conservation planning clash with species ecological niches. Biodivers. Conserv. 31(2), 651–665 (2022).
    Google Scholar 
    Critchlow, R. et al. Multi-taxa spatial conservation planning reveals similar priorities between taxa and improved protected area representation with climate change. Biodivers. Conserv. 31(2), 683–702 (2022).
    Google Scholar 
    González-Orozco, C. E., Porcel, M., Rodriguez-Medina, C. & Yockteng, R. Extreme climate refugia: A case study of wild relatives of cacao (Theobroma cacao) in Colombia. Biodivers. Conserv. 31(1), 161–182 (2022).
    Google Scholar 
    Karami, S., Ejtehadi, H., Moazzeni, H., Vaezi, J. & Behroozian, M. Minimal climate change impacts on the geographic distribution of Nepeta glomerulosa, medicinal species endemic to southwestern and central Asia. Sci. Rep. 12(1), 1–10 (2022).ADS 
    CAS 

    Google Scholar 
    Montemayor, S. I., Besteiro, S. I. & del Río, M. G. Integrating ecological and biogeographical tools for the identification of conservation areas in two Neotropical biogeographic provinces in Argentina based on phytophagous insects. Biodivers. Conserv. 31(7), 1969–1986 (2022).
    Google Scholar 
    da Silva, L. B. et al. How future climate change and deforestation can drastically affect the species of monkeys endemic to the eastern Amazon, and priorities for conservation. Biodivers. Conserv. 31(3), 971–988 (2022).
    Google Scholar 
    Yousefi, M. & Naderloo, R. Global habitat suitability modeling reveals insufficient habitat protection for mangrove crabs. Sci. Rep. 12(1), 1–9 (2022).
    Google Scholar 
    Yudaputra, A. et al. Habitat preferences, spatial distribution and current population status of endangered giant flower Amorphophallus titanum. Biodivers. Conserv. 31(3), 831–854 (2022).
    Google Scholar 
    Gomes, V. H. et al. Species distribution modelling: Contrasting presence-only models with plot abundance data. Sci. Rep. 8(1), 1–12 (2018).
    Google Scholar 
    Hoveka, L. N., van der Bank, M., Bezeng, B. S. & Davies, T. J. Identifying biodiversity knowledge gaps for conserving South Africa’s endemic flora. Biodivers. Conserv. 29, 2803–2819 (2020).
    Google Scholar 
    Macdonald, D. W. et al. Predicting biodiversity richness in rapidly changing landscapes: Climate, low human pressure or protection as salvation?. Biodivers. Conserv. 29, 4035–4057 (2020).
    Google Scholar 
    Peng, Y., Feng, J., Sang, W. & Axmacher, J. C. Geographical divergence of species richness and local homogenization of plant assemblages due to climate change in grasslands. Biodivers. Conserv. 31(3), 797–810 (2022).
    Google Scholar 
    Rincón, V. et al. Connectivity of Natura 2000 potential natural riparian habitats under climate change in the Northwest Iberian Peninsula: Implications for their conservation. Biodivers. Conserv. 31(2), 585–612 (2022).MathSciNet 

    Google Scholar 
    Leta, S. et al. Modeling the global distribution of Culicoides imicola: An Ensemble approach. Sci. Rep. 9(1), 1–9 (2019).ADS 
    CAS 

    Google Scholar 
    Messina, J. P. et al. The current and future global distribution and population at risk of dengue. Nat. Microbiol. 4(9), 1508–1515 (2019).CAS 

    Google Scholar 
    Redding, D. W. et al. Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa. Nat. Commun. 10, 4531 (2019).ADS 

    Google Scholar 
    Klitting, R. et al. Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades. Nat. Commun. 13(1), 1–15 (2022).
    Google Scholar 
    Li, Y. P., Gao, X., An, Q., Sun, Z. & Wang, H. B. Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China. Sci. Rep. 12(1), 1–11 (2022).ADS 

    Google Scholar 
    Oppel, S., Schaefer, H. M., Schmidt, V. & Schröder, B. How much suitable habitat is left for the last known population of the Pale-headed Brush-Finch?. The Condor 106, 429–434 (2004).
    Google Scholar 
    Heikkinen, R. K., Marmion, M. & Luoto, M. Does the interpolation accuracy of species distribution models come at the expense of transferability?. Ecography 35, 276–288 (2012).
    Google Scholar 
    Manzoor, S. A., Griffiths, G. & Lukac, M. Species distribution model transferability and model grain size–finer may not always be better. Sci. Rep. 8(1), 1–9 (2018).
    Google Scholar 
    Yates, K. L. et al. Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 33, 790–802. https://doi.org/10.1016/j.tree.2018.08.001 (2018).Article 

    Google Scholar 
    Gantchoff, M. G. et al. Distribution model transferability for a wide-ranging species, the Gray Wolf. Sci. Rep. 12(1), 1–11 (2022).
    Google Scholar 
    Lyam, P. T., Adeyemi, T. O. & Ogundipe, O. T. Distribution modelling of Chrysophyllum albidum G. Don. in South-West Nigeria. J. Nat. Environ. Sci. 3, 7–14 (2012).
    Google Scholar 
    Orwa, C., Mutua, A., Kindt, R., Jamnadass, R., & Simons, A. Agroforestree Database: a tree reference and selection guide version 4.0. World Agroforestry Centre, Kenya. http://www.worldagroforestry.org/af/treedb/ (2009).Bolanle-Ojo, O. T. & Onyekwelu, J. C. Socio-economic importance of Chrysophyllum albidum G. Don. Rainforest and derived savanna ecosystems of Ondo state, Nigeria. Eur. J. Agric. For. Res. 2, 43–51 (2014).
    Google Scholar 
    Ugwu, J. A. & Umeh, V. C. Assessment of African star apple (Chrysophyllum albidum) fruit damage due to insect pests in Ibadan Southwest Nigeria. Res. J. For. 9, 87–92 (2015).
    Google Scholar 
    Akoegninou, A., Van der Burg, W. J. & Van der Maesen, L. J. G. in Flore Analytique du Bénin (No. 06.2). Backhuys Publishers. (2006).Houessou, L. G., Lougbegnon, T. O., Gbesso, F. G., Anagonou, L. E. & Sinsin, B. Ethno-botanical study of the African star apple (Chrysophyllum albidum G. Don) in the Southern Benin (West Africa). J. Ethnobiol. Ethnomed. 8, 1–10 (2012).
    Google Scholar 
    Lougbégnon, O. T., Nassi, K. M. & Gbesso, G. H. F. Ethnobotanique quantitative de l’usage de Chrysophyllum albidum G. Don par les populations locales au Bénin. J. Appl. Biosci. 95, 9028–9038 (2015).
    Google Scholar 
    Nartey, D., Gyesi, J. N., & Borquaye, L. S. Chemical composition and biological activities of the essential oils of Chrysophyllum albidum G. Don (African star apple). Biochem. Res. Int. 2021 (2021).Olajide, O., Udo, E. S., & Out, D. O. Diversity and population of timber tree species producing valuable non-timber products in two tropical rainforests in cross river state, Nigeria. J. Agric. Soc. Sci. ISSN Print 1813–2235 (2008)Platts, P. J., Omeny, P. & Marchant, R. AFRICLIM: High-resolution climate projections for ecological applications in Africa. Afr. J. Ecol. 53, 103–108 (2015).
    Google Scholar 
    Hajima, T. et al. Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci. Model Dev. 13, 2197–2244 (2020).ADS 

    Google Scholar 
    Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
    Google Scholar 
    Lannuzel, G., Balmot, J., Dubos, N., Thibault, M. & Fogliani, B. High-resolution topographic variables accurately predict the distribution of rare plant species for conservation area selection in a narrow-endemism hotspot in New Caledonia. Biodivers. Conserv. 30, 963–990 (2021).
    Google Scholar 
    Scales, K. L. et al. Scale of inference: On the sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data. Ecography 40, 210–220 (2017).
    Google Scholar 
    Fick, S. E. & Hijmans, R. J. (2017) WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).
    Google Scholar 
    Center for International Earth Science Information Network: CIESIN—Columbia University. 2021. Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Last Accessed 7th December, 2021. https://doi.org/10.7927/H4BC3WMT (2018)Eyring, V. et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016 (2016).Article 
    ADS 

    Google Scholar 
    Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    Naimi, B. & Araújo, M. B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375. https://doi.org/10.1111/ecog.01881 (2016).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2020)Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Google Scholar 
    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).Article 
    MATH 

    Google Scholar 
    Zheng, B. & Agresti, A. Summarizing the predictive power of a generalized linear model. Stat. Med. 19, 1771–1781 (2000).CAS 

    Google Scholar 
    Hastie, T. J. in Generalized Additive Models, Statistical models, 249–307 (Routledge, 2017).Friedman, J. H. Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991).MathSciNet 
    MATH 

    Google Scholar 
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
    Google Scholar 
    Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).
    Google Scholar 
    QGIS Development Team. QGIS geographic information system. Open Source Geospatial Foundation Project. http://qgis.osgeo.org (2021)Fandohan, B. et al. Women’s traditional knowledge, use value, and the contribution of tamarind (Tamarindus indica L.) to rural households’ cash income in Benin. Econ. Bot. 64, 248–259 (2010).
    Google Scholar 
    Gouwakinnou, G. N., Lykke, A. M., Assogbadjo, A. E. & Sinsin, B. Local knowledge, pattern and diversity of use of Sclerocarya birrea. J. Ethnobiol. Ethnomed. 7, 1–9 (2011).
    Google Scholar 
    O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geological Surv. Data Ser. 691, 4–9 (2012).
    Google Scholar 
    United Nations. 2022. World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100, Accessed 25th December 2022 .Gbesso, F. H. G., Tente, B. H. A., Gouwakinnou, G. N. & Sinsin, B. A. Influence des changements climatiques sur la distribution géographique de Chrysophyllum albidum G. Don (Sapotaceae) au Benin. Int. J. Biol. Chem. Sci. 7, 2007–2018 (2013).
    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
    Google Scholar 
    Mi, C., Huettmann, F., Guo, Y., Han, X. & Wen, L. Why choose random forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. Peer J. 5, e2849. https://doi.org/10.7717/peerj.2849 (2017).Article 

    Google Scholar 
    Segurado, P. & Araujo, M. B. An evaluation of methods for modelling species distributions. J. Biogeogr. 31, 1555–1568 (2004).
    Google Scholar 
    Pearson, R. G. et al. Model-based uncertainty in species range prediction. J. Biogeogr. 33, 1704–1711 (2006).
    Google Scholar 
    Dambros, C. et al. The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia. Biodivers. Conserv. 29, 3609–3634 (2020).
    Google Scholar  More

  • in

    Multilayer networks of plasmid genetic similarity reveal potential pathways of gene transmission

    WHO. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021; https://apps.who.int/iris/bitstream/handle/10665/341666/9789240027336-eng.pdfVan Boeckel TP, Glennon EE, Chen D, Gilbert M, Robinson TP, Grenfell BT, et al. Reducing antimicrobial use in food animals. Science. 2017;357:1350–2. https://doi.org/10.1126/science.aao1495Article 
    CAS 

    Google Scholar 
    ONeill J. Antimicrobials in agriculture and the environment: reducing unnecessary use and waste. Rev Antimicrob Resistance; 2015:1–28.Van Boeckel TP, Brower C, Gilbert M, Grenfell BT, Levin SA, Robinson TP, et al. Global trends in antimicrobial use in food animals. Proc Natl Acad Sci USA. 2015;112:5649–54. https://doi.org/10.1073/pnas.1503141112Article 
    CAS 

    Google Scholar 
    Managaki S, Murata A, Takada H, Tuyen BC, Chiem NH. Distribution of macrolides, sulfonamides, and trimethoprim in tropical waters: ubiquitous occurrence of veterinary antibiotics in the Mekong Delta. Environ Sci Technol. 2007;41:8004–10. https://doi.org/10.1021/es0709021Article 
    CAS 

    Google Scholar 
    Woolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140083 https://doi.org/10.1098/rstb.2014.0083Article 
    CAS 

    Google Scholar 
    Noyes NR, Yang X, Linke LM, Magnuson RJ, Cook SR, Zaheer R, et al. Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems. Sci Rep. 2016;6:24645. https://doi.org/10.1038/srep24645Article 
    CAS 

    Google Scholar 
    Agga GE, Cook KL, Netthisinghe AMP, Gilfillen RA, Woosley PB, Sistani KR. Persistence of antibiotic resistance genes in beef cattle backgrounding environment over two years after cessation of operation. PLoS One. 2019;14:e0212510. https://doi.org/10.1371/journal.pone.0212510Article 
    CAS 

    Google Scholar 
    Hudson JA, Frewer LJ, Jones G, Brereton PA, Whittingham MJ, Stewart G. The agri-food chain and antimicrobial resistance: a review. Trends Food Sci Technol. 2017;69:131–47. https://doi.org/10.1016/j.tifs.2017.09.007Article 
    CAS 

    Google Scholar 
    Gillings MR. Lateral gene transfer, bacterial genome evolution, and the Anthropocene. Ann NY Acad Sci. 2017;1389:20–36. https://doi.org/10.1111/nyas.13213Article 

    Google Scholar 
    Rodríguez-Beltrán J, DelaFuente J, León-Sampedro R, MacLean RC, San Millán Á. Beyond horizontal gene transfer: the role of plasmids in bacterial evolution. Nat Rev Microbiol. 2021;6:347–59. https://doi.org/10.1038/s41579-020-00497-1Article 
    CAS 

    Google Scholar 
    Zhang T, Zhang XX, Ye L. Plasmid metagenome reveals high levels of antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS One. 2011;6:e26041. https://doi.org/10.1371/journal.pone.0026041Article 
    CAS 

    Google Scholar 
    Li AD, Li LG, Zhang T. Exploring antibiotic resistance genes and metal resistance genes in plasmid metagenomes from wastewater treatment plants. Front Microbiol. 2015;6:1025. https://doi.org/10.3389/fmicb.2015.01025Article 

    Google Scholar 
    Bukowski M, Piwowarczyk R, Madry A, Zagorski-Przybylo R, Hydzik M, Wladyka B. Prevalence of antibiotic and heavy metal resistance determinants and virulence-related genetic elements in plasmids of Staphylococcus aureus. Front Microbiol. 2019;10:805. https://doi.org/10.3389/fmicb.2019.00805Article 

    Google Scholar 
    Ramírez-Díaz MI, Díaz-Magaña A, Meza-Carmen V, Johnstone L, Cervantes C, Rensing C. Nucleotide sequence of Pseudomonas aeruginosa conjugative plasmid pUM505 containing virulence and heavy-metal resistance genes. Plasmid. 2011;66:7–18. https://doi.org/10.1016/j.plasmid.2011.03.002Article 
    CAS 

    Google Scholar 
    Haenni M, Poirel L, Kieffer N, Châtre P, Saras E, Métayer V, et al. Co-occurrence of extended spectrum β lactamase and MCR-1 encoding genes on plasmids. Lancet Infect Dis. 2016;16:281–2. https://doi.org/10.1016/S1473-3099(16)00007-4Article 
    CAS 

    Google Scholar 
    Peter S, Bosio M, Gross C, Bezdan D, Gutierrez J, Oberhettinger P, et al. Tracking of antibiotic resistance transfer and rapid plasmid evolution in a hospital setting by nanopore sequencing. mSphere. 2020;5. https://doi.org/10.1128/mSphere.00525-20Halary S, Leigh JW, Cheaib B, Lopez P, Bapteste E. Network analyses structure genetic diversity in independent genetic worlds. Proc Natl Acad Sci USA. 2010;107:127–32. https://doi.org/10.1073/pnas.0908978107Article 

    Google Scholar 
    Bosi E, Fani R, Fondi M. The mosaicism of plasmids revealed by atypical genes detection and analysis. BMC Genom. 2011;12:403. https://doi.org/10.1186/1471-2164-12-403Article 
    CAS 

    Google Scholar 
    Pesesky MW, Tilley R, Beck DAC. Mosaic plasmids are abundant and unevenly distributed across prokaryotic taxa. Plasmid. 2019;102:10–18. https://doi.org/10.1016/j.plasmid.2019.02.003Article 
    CAS 

    Google Scholar 
    Casjens SR, Gilcrease EB, Vujadinovic M, Mongodin EF, Luft BJ, Schutzer SE, et al. Plasmid diversity and phylogenetic consistency in the Lyme disease agent Borrelia burgdorferi. BMC Genom. 2017;18:165. https://doi.org/10.1186/s12864-017-3553-5Article 
    CAS 

    Google Scholar 
    Madec JY, Haenni M. Antimicrobial resistance plasmid reservoir in food and food-producing animals. Plasmid. 2018;99:72–81. https://doi.org/10.1016/j.plasmid.2018.09.001Article 
    CAS 

    Google Scholar 
    Ceccarelli D, Kant A, van Essen-Zandbergen A, Dierikx C, Hordijk J, Wit B, et al. Diversity of plasmids and genes encoding resistance to extended spectrum cephalosporins in commensal escherichia coli from dutch livestock in 2007–2017. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.00076Auffret MD, Dewhurst RJ, Duthie CA, Rooke JA, John Wallace R, Freeman TC, et al. The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. Microbiome. 2017;5:159. https://doi.org/10.1186/s40168-017-0378-zArticle 

    Google Scholar 
    Sabino YNV, Santana MF, Oyama LB, Santos FG, Moreira AJS, Huws SA, et al. Characterization of antibiotic resistance genes in the species of the rumen microbiota. Nat Commun. 2019;10:5252. https://doi.org/10.1038/s41467-019-13118-0Article 
    CAS 

    Google Scholar 
    Brown Kav A, Benhar I, Mizrahi I. Rumen plasmids. In: Gophna U, editor. Lateral gene transfer in evolution. New York, NY: Springer New York; 2013. p. 105–20.Mizrahi I, Wallace RJ, Moraïs S. The rumen microbiome: balancing food security and environmental impacts. Nat Rev Microbiol. 2021;19:553–66. https://doi.org/10.1038/s41579-021-00543-6Article 
    CAS 

    Google Scholar 
    Dionisio F, Zilhão R, Gama JA. Interactions between plasmids and other mobile genetic elements affect their transmission and persistence. Plasmid. 2019;102:29–36. https://doi.org/10.1016/j.plasmid.2019.01.003Article 
    CAS 

    Google Scholar 
    Brown Kav A, Sasson G, Jami E, Doron-Faigenboim A, Benhar I, Mizrahi I. Insights into the bovine rumen plasmidome. Proc Natl Acad Sci USA. 2012;109:5452–7. https://doi.org/10.1073/pnas.1116410109Article 

    Google Scholar 
    Kav AB, Rozov R, Bogumil D, Sørensen SJ, Hansen LH, Benhar I, et al. Unravelling plasmidome distribution and interaction with its hosting microbiome. Environ Microbiol. 2020;22:32–44. https://doi.org/10.1111/1462-2920.14813Article 

    Google Scholar 
    Jørgensen TS, Xu Z, Hansen MA, Sørensen SJ, Hansen LH. Hundreds of circular novel plasmids and DNA elements identified in a rat cecum metamobilome. PLoS One. 2014;9:e87924. https://doi.org/10.1371/journal.pone.0087924Article 
    CAS 

    Google Scholar 
    He Q, Pilosof S, Tiedje KE, Ruybal-Pesántez S, Artzy-Randrup Y, Baskerville EB, et al. Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum. Nat Commun. 2018;9:1817. https://doi.org/10.1038/s41467-018-04219-3Article 
    CAS 

    Google Scholar 
    Acman M, van Dorp L, Santini JM, Balloux F. Large-scale network analysis captures biological features of bacterial plasmids. Nat Commun. 2020;11:2452. https://doi.org/10.1038/s41467-020-16282-wArticle 
    CAS 

    Google Scholar 
    Redondo-Salvo S, Fernández-López R, Ruiz R, Vielva L, de Toro M, Rocha EPC, et al. Pathways for horizontal gene transfer in bacteria revealed by a global map of their plasmids. Nat Commun. 2020;11:3602. https://doi.org/10.1038/s41467-020-17278-2Article 
    CAS 

    Google Scholar 
    Savary P, Foltête JC, Moal H, Vuidel G, Garnier S. Analysing landscape effects on dispersal networks and gene flow with genetic graphs. Mol Ecol Resour. 2021;21:1167–85. https://doi.org/10.1111/1755-0998.13333Article 

    Google Scholar 
    Pilosof S, He Q, Tiedje KE, Ruybal-Pesántez S, Day KP, Pascual M. Competition for hosts modulates vast antigenic diversity to generate persistent strain structure in Plasmodium falciparum. PLoS Biol. 2019;17:e3000336. https://doi.org/10.1371/journal.pbio.3000336Article 
    CAS 

    Google Scholar 
    Brilli M, Mengoni A, Fondi M, Bazzicalupo M, Liò P, Fani R. Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network. BMC Bioinform. 2008;9:551. https://doi.org/10.1186/1471-2105-9-551Article 
    CAS 

    Google Scholar 
    Fondi M, Karkman A, Tamminen MV, Bosi E, Virta M, Fani R, et al. “Every gene is everywhere but the environment selects”: global geolocalization of gene sharing in environmental samples through network analysis. Genome Biol Evol. 2016;8:1388–1400. https://doi.org/10.1093/gbe/evw077Article 

    Google Scholar 
    Tamminen M, Virta M, Fani R, Fondi M. Large-scale analysis of plasmid relationships through gene-sharing networks. Mol Biol Evol. 2012;29:1225–40. https://doi.org/10.1093/molbev/msr292Article 
    CAS 

    Google Scholar 
    Yamashita A, Sekizuka T, Kuroda M. Characterization of antimicrobial resistance dissemination across plasmid communities classified by network analysis. Pathogens. 2014;3:356–76. https://doi.org/10.3390/pathogens3020356Article 
    CAS 

    Google Scholar 
    Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A. Epidemic processes in complex networks. Rev Mod Phys. 2015;87:925–79. https://doi.org/10.1103/RevModPhys.87.925Article 

    Google Scholar 
    Pilosof S, Morand S, Krasnov BR, Nunn CL. Potential parasite transmission in multi-host networks based on parasite sharing. PLoS One. 2015;10:e0117909 https://doi.org/10.1371/journal.pone.0117909Article 
    CAS 

    Google Scholar 
    VanderWaal KL, Atwill ER, Isbell LA, McCowan B.Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).J Anim Ecol.2014;83:406–14. https://doi.org/10.1111/1365-2656.12137Article 

    Google Scholar 
    Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, et al. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface. 2022;19:20210690. https://doi.org/10.1098/rsif.2021.0690Article 

    Google Scholar 
    Dallas TA, Han BA, Nunn CL, Park AW, Stephens PR, Drake JM. Host traits associated with species roles in parasite sharing networks. Oikos. 2019;128:23–32. https://doi.org/10.1111/oik.05602Article 

    Google Scholar 
    Matlock W, Chau KK, AbuOun M, Stubberfield E, Barker L, Kavanagh J, et al. Genomic network analysis of environmental and livestock F-type plasmid populations. ISME J. 2021;15:2322–35. https://doi.org/10.1038/s41396-021-00926-wArticle 
    CAS 

    Google Scholar 
    Pilosof S, Porter MA, Pascual M, Kéfi S. The multilayer nature of ecological networks. Nat Ecol Evol. 2017;1:0101. https://doi.org/10.1038/s41559-017-0101Article 

    Google Scholar 
    Paull SH, Song S, McClure KM, Sackett LC, Kilpatrick AM, Johnson PTJ. From superspreaders to disease hotspots: linking transmission across hosts and space. Front Ecol Environ. 2012;10:75–82. https://doi.org/10.1890/110111Article 

    Google Scholar 
    Hutchinson MC, Bramon Mora B, Pilosof S, Barner AK, Kéfi S, Thébault E, et al. Seeing the forest for the trees: putting multilayer networks to work for community ecology. Funct Ecol. 2019;33:206–17. https://doi.org/10.1111/1365-2435.13237Article 

    Google Scholar 
    Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. J Complex Netw. 2014;2:203–71. https://doi.org/10.1093/comnet/cnu016Article 

    Google Scholar 
    Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005;438:355–9. https://doi.org/10.1038/nature04153Article 
    CAS 

    Google Scholar 
    Fortuna MA, Popa-Lisseanu AG, Ibáñez C, Bascompte J. The roosting spatial network of a bird-predator bat. Ecology. 2009;90:934–44. https://doi.org/10.1890/08-0174.1Article 

    Google Scholar 
    Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;69:026113. https://doi.org/10.1103/PhysRevE.69.026113Article 
    CAS 

    Google Scholar 
    Rosvall M, Bergstrom CT. Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA. 2008;105:1118–23. https://doi.org/10.1073/pnas.0706851105Article 

    Google Scholar 
    De Domenico M, Lancichinetti A, Arenas A, Rosvall M. Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys Rev X. 2015;5:011027. https://doi.org/10.1103/PhysRevX.5.011027Article 
    CAS 

    Google Scholar 
    Farage C, Edler D, Eklöf A, Rosvall M, Pilosof S. Identifying flow modules in ecological networks using Infomap. Methods Ecol Evol. 2021;12:778–86. https://doi.org/10.1111/2041-210x.13569Article 

    Google Scholar 
    Popa O, Hazkani-Covo E, Landan G, Martin W, Dagan T. Directed networks reveal genomic barriers and DNA repair bypasses to lateral gene transfer among prokaryotes. Genome Res. 2011;21:599–609. https://doi.org/10.1101/gr.115592.110Article 
    CAS 

    Google Scholar 
    Smillie C, Garcillán-Barcia MP, Francia MV, Rocha EPC, de la Cruz F. Mobility of plasmids. Microbiol Mol Biol Rev. 2010;74:434–52. https://doi.org/10.1128/MMBR.00020-10Article 
    CAS 

    Google Scholar 
    Garcillán-Barcia MP, Francia MV, de la Cruz F. The diversity of conjugative relaxases and its application in plasmid classification. FEMS Microbiol Rev. 2009;33:657–87. https://doi.org/10.1111/j.1574-6976.2009.00168.xArticle 
    CAS 

    Google Scholar 
    Coluzzi C, Guédon G, Devignes MD, Ambroset C, Loux V, Lacroix T, et al. A glimpse into the world of integrative and mobilizable elements in streptococci reveals an unexpected diversity and novel families of mobilization proteins. Front Microbiol. 2017;8:443. https://doi.org/10.3389/fmicb.2017.00443Article 

    Google Scholar 
    Moraïs S, Mizrahi I. Islands in the stream: from individual to communal fiber degradation in the rumen ecosystem. FEMS Microbiol Rev. 2019;43:362–79. https://doi.org/10.1093/femsre/fuz007Article 
    CAS 

    Google Scholar 
    León-Sampedro R, DelaFuente J, Díaz-Agero C, Crellen T, Musicha P, Rodríguez-Beltrán J, et al. Pervasive transmission of a carbapenem resistance plasmid in the gut microbiota of hospitalized patients. Nat Microbiol. 2021;6:606–16. https://doi.org/10.1038/s41564-021-00879-yArticle 
    CAS 

    Google Scholar 
    Rocha LEC, Singh V, Esch M, Lenaerts T, Liljeros F, Thorson A. Dynamic contact networks of patients and MRSA spread in hospitals. Sci Rep. 2020;10:9336. https://doi.org/10.1038/s41598-020-66270-9Article 
    CAS 

    Google Scholar 
    Lerner A, Adler A, Abu-Hanna J, Cohen Percia S, Kazma Matalon M, Carmeli Y. Spread of KPC-producing carbapenem-resistant Enterobacteriaceae: the importance of super-spreaders and rectal KPC concentration. Clin Microbiol Infect. 2015;21:470.e1–7. https://doi.org/10.1016/j.cmi.2014.12.015Article 
    CAS 

    Google Scholar 
    Stein RA, Katz DE. Escherichia coli, cattle and the propagation of disease. FEMS Microbiol Lett. 2017;364. https://doi.org/10.1093/femsle/fnx050.de Freslon I, Martínez-López B, Belkhiria J, Strappini A, Monti G. Use of social network analysis to improve the understanding of social behaviour in dairy cattle and its impact on disease transmission. Appl Anim Behav Sci. 2019;213:47–54. https://doi.org/10.1016/j.applanim.2019.01.006Article 

    Google Scholar 
    Rushmore J, Caillaud D, Hall RJ, Stumpf RM, Meyers LA, Altizer S. Network-based vaccination improves prospects for disease control in wild chimpanzees. J R Soc Interface. 2014;11:20140349. https://doi.org/10.1098/rsif.2014.0349Article 

    Google Scholar 
    Xue H, Cordero OX, Camas FM, Trimble W, Meyer F, Guglielmini J, et al. Eco-evolutionary dynamics of episomes among ecologically cohesive bacterial populations. MBio. 2015;6:e00552–15. https://doi.org/10.1128/mBio.00552-15Article 
    CAS 

    Google Scholar 
    Evans DR, Griffith MP, Sundermann AJ, Shutt KA, Saul MI, Mustapha MM, et al. Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital. Elife. 2020;9. https://doi.org/10.7554/eLife.53886Abe R, Oyama F, Akeda Y, Nozaki M, Hatachi T, Okamoto Y, et al. Hospital-wide outbreaks of carbapenem-resistant Enterobacteriaceae horizontally spread through a clonal plasmid harbouring bla IMP-1 in children’s hospitals in Japan. J Antimicrob Chemother. 2021;76:3314–7.Article 
    CAS 

    Google Scholar 
    Bingen EH, Desjardins P, Arlet G, Bourgeois F, Mariani-Kurkdjian P, Lambert-Zechovsky NY, et al. Molecular epidemiology of plasmid spread among extended broad-spectrum beta-lactamase-producing Klebsiella pneumoniae isolates in a pediatric hospital. J Clin Microbiol. 1993;31:179–84. https://doi.org/10.1128/jcm.31.2.179-184.1993.Bai H, He LY, Wu DL, Gao FZ, Zhang M, Zou HY, et al. Spread of airborne antibiotic resistance from animal farms to the environment: dispersal pattern and exposure risk. Environ Int. 2022;158:106927 https://doi.org/10.1016/j.envint.2021.106927Article 
    CAS 

    Google Scholar 
    Boyland NK, Mlynski DT, James R, Brent LJN, Croft DP. The social network structure of a dynamic group of dairy cows: from individual to group level patterns. Appl Anim Behav Sci. 2016;174:1–10. https://doi.org/10.1016/j.applanim.2015.11.016Article 

    Google Scholar 
    Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: longitudinal answers to standing questions in microbiome research. Am J Primatol. 2019;81:e22970. https://doi.org/10.1002/ajp.22970Article 

    Google Scholar 
    Dib JR, Wagenknecht M, Farías ME, Meinhardt F. Strategies and approaches in plasmidome studies—uncovering plasmid diversity disregarding of linear elements? Front Microbiol. 2015;6. https://doi.org/10.3389/fmicb.2015.00463Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77. https://doi.org/10.1089/cmb.2012.0021Article 
    CAS 

    Google Scholar 
    Rozov R, Brown Kav A, Bogumil D, Shterzer N, Halperin E, Mizrahi I, et al. Recycler: an algorithm for detecting plasmids from de novo assembly graphs. Bioinformatics. 2017;33:475–82. https://doi.org/10.1093/bioinformatics/btw651Article 
    CAS 

    Google Scholar 
    Orlek A, Stoesser N, Anjum MF, Doumith M, Ellington MJ, Peto T, et al. Plasmid classification in an era of whole-genome sequencing: application in studies of antibiotic resistance epidemiology. Front Microbiol. 2017;8:182. https://doi.org/10.3389/fmicb.2017.00182Article 

    Google Scholar 
    Komsta L, Novomestky F. Moments, cumulants, skewness, kurtosis and related tests. R package version. 2015;14.Rosvall M, Axelsson D, Bergstrom CT. The map equation. Eur Phys J Spec Top. 2010;178:13–23. https://doi.org/10.1140/epjst/e2010-01179-1Article 

    Google Scholar 
    Bascompte J, Jordano P, Melián CJ, Olesen JM. The nested assembly of plant–animal mutualistic networks. Proc Natl Acad Sci USA. 2003;100:9383–7. https://doi.org/10.1073/pnas.1633576100Article 
    CAS 

    Google Scholar 
    Vázquez DP, Poulin R, Krasnov BR, Shenbrot GI. Species abundance and the distribution of specialization in host–parasite interaction networks. J Anim Ecol. 2005;74:946–55.Article 

    Google Scholar 
    Fortuna MA, Stouffer DB, Olesen JM, Jordano P, Mouillot D, Krasnov BR, et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J Anim Ecol. 2010;79:811–7. https://doi.org/10.1111/j.1365-2656.2010.01688.xArticle 

    Google Scholar 
    Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81:2340–61. https://doi.org/10.1021/j100540a008Article 
    CAS 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing; 2021. More