Philippa Kaur

Chemicals and reagentsThe fluopyram standard was purchased from the Environmental Protection Monitoring Institute of the Ministry of Agriculture of China at a concentration of 1000 mg/L. Analytical grade acetonitrile, acetone, dichloromethane, and sodium chloride were purchased from the Guangzhou Chemical Reagent Factory. Chromatographic grade Methanol and n-hexane were available from Thermo Fisher Scientific. Purified water was prepared using a Milli-Q reverse osmosis system (Millipore, Milford, MA, USA). Strata Florisil (FL-PR) 500 mg/6 mL SPE manufactured by Strata™ (5.0 mL n-hexane–acetone (9:1, V/V) solution pre-rinsing cartridge).A standard solution of 1000 μg/mL fluopyram was diluted in n-hexane, and the matrix extract of the blank sample was obtained by the extraction method. The matrix standard solutions of 0.025, 0.05, 0.10, 0.15 and 0.50 μg/mL were obtained by the step dilution. All prepared solutions were stored at temperature of 4 °C until further use.Soil sample collectionHainan latosol was collected from the Bailian banana experimental base in Chengmai (Hainan), Yunnan sandy soil was collected from Taoyuan banana experimental base in Longtou Street, Kunming (Yunnan) and Fujian plain alluvial soil was collected from the Zhangzhou banana experimental base (Fujian). 5–10 soil sampling points were randomly selected in each banana experimental base; the soil samples were collected from depths of 0–10 cm, and debris such as gravel, weeds, and plant roots were removed from each sample. The soil samples were obtained by the quarter method after mixing, dried, and stored after 20 mesh screening.Extraction and purification of flupyramSoil sample extraction was conducted as follows: in a 200 mL conical flask, 20.0 g of the drying soil sample and 40.0 mL acetonitrile was added. After shaking on a reciprocating shaker for 2 h, the mixture was filtered through filter paper. The filtrate was transferred to a stoppered measuring cylinder with 6.0 g NaCl. The stopper was inserted, and the mixture was vigorously shaken for 2 min. The mixture was left at 25 ± 2 °C for more than 30 min to separate the acetonitrile and aqueous solutions. Meanwhile, 10.0 mL of the supernatant were accurately transferred into a 100 mL round-bottom flask and concentrated by a rotatory evaporator at 40 °C to near dryness, which was dissolved in a 5.0 mL n-hexane–acetone (9:1, v/v) solution, vortexed, and mixed well for purification.Water sample extraction is shown below. A 20 mL water sample was transferred to a separatory funnel with 40.0 mL dichloromethane. After vigorously shaking it for 2 min and then letting it stand for 30 min, the lower layer solution was collected in a 100 mL round-bottom flask. The collected fluid was concentrated by a rotatory evaporator at 40 °C to near dryness and dissolved in 5.0 mL n-hexane–acetone (9:1, v/v) solution, vortexed, and mixed well for purification.Sample purification is described below. A 5.0 mL n-hexane–acetone (9:1, v/v) was used to preach the Strata Florisil (FL-PR) 500 mg/6 mL extraction column. When the leaching solvent level reached the surface of the column adsorption layer, the solution sample was immediately poured into the column be purified. Then, the purified solution was collected in a 100 mL round-bottom flask. A 5.0 mL n-hexane–acetone (9:1, v/v) solution was used to rinse the round-bottom flask residuum, after which the rinse solution was applied to elute the Florisil column. The rinsing and elution steps were repeated three times. The collected fluid was concentrated by a rotatory evaporator at 40 °C to near dryness and dissolved in 2.5 mL n-hexane for analysis.Instrumental conditionThe test was performed using the Theomer DSQII gas chromatography-mass spectrometer (GC–MS) with Xcalibur 2.0, software for data acquisition and analysis. A SLB-5MS analytical column (30 m × 0.25 mm × 0.25 μm) was used as chromatographic column. The injection volume was 1 μL without split injection, the carrier gas was helium (He, ≥ 99.999% purity), and the carrier gas flow rate was set to 1.0 mL/min. The protective gas was nitrogen (N2, ≥ 99.999% purity), and the injection port temperature was 250 °C. The chromatographic column temperature program was set as follows: the initial temperature at 80 °C was maintained for 1 min; then it was raised to 240 °C at a speed of 20 °C/min and maintained for 3 min; finally, the temperature was raised at a rate of 50 °C/min until 280 °C, where it was maintained for 7 min.The MS was operated in electron ionisation (EI) mode with an ionising energy of 70 eV. MS data were acquired in both full scan (m/z 50–500) mode for identification and selected ion monitoring (SIM) mode for quantification. The temperatures of the ion source and transfer line were 250 °C and 280 °C, respectively. The retention time of fluopyram was 10.59 min. The quantifier ions were m/z 223, and the qualifier ions were m/z 195 and m/z 173.Analytical method validationFirst, we addressed the linearity. The matrix standard of fluopyram was prepared in the range of 0.025–0.50 μg/mL and the determination was carried out, with the concentration of fluopyram matrix standard solution as the abscissa and the peak area obtained from the GC–MS as the ordinate. Linearity was calculated by plotting the relationship between the concentration and the peak area.The sensitivity analysis relied on the LOD and the limit of quantitation (LOQ). To evaluate the sensitivity of the method, they were obtained by adding the standard solution of fluopyram at the lowest concentration level in line with the requirements of the analytical method for blank samples. The LOD was the corresponding concentration when the signal-to-noise ratio (S/N) was 3, and S/N = 10 corresponds to the LOQ.Accuracy and precision were estimated as well. To determine the reliability of the method, fluopyram standard solutions with different concentrations were added to the blank sample for the recovery experiment. Fluopyram standard solutions with concentrations of 0.008, 0.600, and 1.000 mg/kg were added to the blank samples. This procedure was repeated five times for each concentration. The samples were subjected to extract, purify and analysis under the method the same conditions as described above. The recovery was calculated for the accuracy of the method, and the RSD was calculated for the precision.Soil dissipation experimentIn a number of 100 mL clean and sterilized conical flasks with covers, 20.0 g of soil was added (net weight converted by water content); then, 0.1 mL 1000 μg/mL fluopyram standard solution was pipetted into the conical flasks. Ultrapure water was added. The water was controlled to occupy 60% of the total volume. The flasks were shaken on a constant temperature oscillator for 2 min to mix the fluopyram evenly. Then, they were placed in an artificial climate incubator and exposed to light at 25 ± 2 °C for 12 h per day. According to the different soil types, they were divided into three treatment groups: Hainan, Yunnan, and Fujian. Each treatment group had three parallels and three blanks. The detection intervals were 2 h, 1, 3, 7, 14, 21, 28, 42 and 60 day, while the detection of fluopyram was performed based on the interval according to the shown methods. The dissipation kinetics of fluopyram in banana planting soil conformed to the first-order kinetic equation Ct = C0e−kt, where Ct is a pesticide concentration (mg/kg) at different times (day), C0 is an initial concentration (mg/kg), and k is the dissipation rate constant. The half-life of fluopyram is determined using Eq. (1).$$T_{1/2} = , ln 2/k$$
(1)
Soil adsorption experimentUsing the oscillation balance method, 5.0 g of soil was put into the 250 mL conical flasks with cover, which contained 25 mL fluopyram aqueous solutions with mass concentrations of 0.02, 0.1, 0.5, 2.5 and 4.0 mg/L (containing 0.01 mol/L CaCl2), respectively. The soils were divided into three treatment groups: Hainan, Yunnan, and Fujian (based on the different soil types). The fluopyram aqueous solution and the blank soil aqueous solution (both containing 0.01 mol/L CaCl2) were used as controls. Each treatment group had three replicates. The conical flasks were then placed in a constant temperature oscillator at 25 ± 2 °C for 24 h to prepare the suspension. The suspension was transferred to a centrifuge tube for high-speed centrifugation, and 80% of the total volume of the supernatant was used for determination. The fluopyram in the supernatant was extracted and determined under the methods as described above, and the Freundlich equation model (see Eq. 2) was used to describe the adsorption law for fluopyram in soil.$${text{Freundlich: }}C_{s} = K_{f} times C_{e}^{1/n}$$
(2)
where Cs is adsorption content of pesticide in soil (mg/kg), Ce is concentration of the pesticide in aqueous solution at adsorption equilibrium (mg/L), Kf is the soil adsorption coefficient of the Freundlich model (L/kg), indicating the pesticide adsorption capacity of the soil and 1/n is a slope rate of the curve between Cs and Ce, reflecting the heterogeneity of the adsorbent surface.The relationship between the adsorption free energy of soil to pesticides (ΔG, kJ/mol) and the soil adsorption coefficient Koc is expressed using Eq. (3).$$Delta G , = – RTln K_{oc}$$
(3)
where Koc is the soil adsorption coefficient (Koc = Kf/OC × 100) expressed by organic carbon content (L/kg), OC is soil organic carbon content (%), R is the molar gas constant (J/K mol), and T is absolute temperature (K).Soil leaching experimentA plexiglass tube with an inner diameter of 5 cm and a length of 40 cm was used as a packed column. A layer of cotton, a 1 cm thick quartz sand layer, and a layer of filter paper were added at the bottom of the column. Dry soil (700–800.0 g) was weighed for filling, and the column was fully wetted with ultrapure water to prepare a 30 ± 0.2 cm high leaching soil column. 0.1 mL of 1000 μg/mL fluopyram solution was further added to 5.0 g of soil. After the solution completely volatilized, it was evenly spread on the top of the soil column, and a layer of filter paper and a layer of 1 cm thick quartz sand were added to the top of the soil. During the test, ultrapure water was used for washing the soil column for 10 h at a speed of 30 mL/h, and the leaching solution was collected. After washing, the soil column was removed and was cut into four sections of 1–5, 5–10, 10–20 and 20–30 cm. The residues of fluopyram in the soil samples and leaching solutions were extracted and determined under the methods as described above. According to the three soil types, they were divided into Hainan, Yunnan and Fujian treatment groups, where each group received another parallel treatment. More

1.Gray, M., Gordon, J. & Brown, E. Geodiversity and the ecosystem approach: The contribution of geoscience in delivering integrated environmental management. Proc. Geol. Assoc. 124, 659–673 (2013).Article 

Google Scholar 
2.Gray, M. Valuing geodiversity in an ‘ecosystem services’ context. Scott. Geogr. J. 128, 177–194 (2012).Article 

Google Scholar 
3.Warren, A. & French, J. R. Habitat Conservation: Managing the Physical Environment (Wiley, Hoboken, 2001).
Google Scholar 
4.Gordon, J. E., Barron, H. F., Hansom, J. D. & Thomas, M. F. Engaging with geodiversity—Why it matters. Proc. Geol. Assoc. 123, 1–6 (2012).Article 

Google Scholar 
5.Hjort, J., Gordon, J. E., Gray, M. & Hunter, M. L. Why geodiversity matters in valuing nature’s stage. Conserv. Biol. 29, 630–639 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Gray, M. Geodiversity: Valuing and Conserving Abiotic Nature 448 (Wiley, 2004).
Google Scholar 
7.Serrano, E. & Ruiz-Flano, P. Geodiversity. A theoretical and applied concept. Geogr. Helv. Jg 62, 140–147 (2007).Article 

Google Scholar 
8.Comer, P. J. et al. Incorporating geodiversity into conservation decisions. Conserv. Biol. 29, 692–701 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Pătru-Stupariu, I. et al. Integrating geo-biodiversity features in the analysis of landscape patterns. Ecol. Indic. 80, 363–375 (2017).Article 

Google Scholar 
10.Chakraborty, A. & Gray, M. A call for mainstreaming geodiversity in nature conservation research and praxis. J. Nat. Conserv. 56, 125862 (2020).Article 

Google Scholar 
11.Poesen, J., Torri, D. & Bunte, K. Effects of rock fragments on soil erosion by water at different spatial scales: A review. CATENA 23, 141–166 (1994).Article 

Google Scholar 
12.Zhang, Y., Zhang, M., Niu, J., Li, H. & Xiao, R. Rock fragments and soil hydrological processes: Significance and progress. CATENA 147, 153–166 (2016).Article 

Google Scholar 
13.Xia, L. et al. Effects of rock fragment cover on hydrological processes under rainfall simulation in a semi-arid region of China. Hydrol. Process. 32, 792–804 (2018).ADS 
Article 

Google Scholar 
14.Lavee, H. & Poesen, J. W. A. Overland flow generation and continuity on stone-covered soil surfaces. Hydrol. Process. 5, 345–360 (1991).ADS 
Article 

Google Scholar 
15.Agassi, M. & Levy, G. Stone cover and rain intensity—Effects on infiltration, erosion and water splash. Soil Res. 29, 565–575 (1991).Article 

Google Scholar 
16.Mandal, U. K. et al. Soil infiltration, runoff and sediment yield from a shallow soil with varied stone cover and intensity of rain. Eur. J. Soil Sci. 56, 435–443 (2005).Article 

Google Scholar 
17.Cerdà, A. Effects of rock fragment cover on soil infiltration, interrill runoff and erosion. Eur. J. Soil Sci. 52, 59–68 (2001).Article 

Google Scholar 
18.Jury, W. A. & Bellantuoni, B. Heat and water movement under surface rocks in a field soil: I. Thermal effects. Soil Sci. Soc. Am. J. 40, 505–509 (1976).ADS 
Article 

Google Scholar 
19.Yuan, C., Lei, T., Mao, L., Liu, H. & Wu, Y. Catena soil surface evaporation processes under mulches of different sized gravel. CATENA 78, 117–121 (2009).CAS 
Article 

Google Scholar 
20.Poesen, J. & Lavee, H. Rock fragments in top soils: Significance and processes. CATENA 23, 1–28 (1994).Article 

Google Scholar 
21.Yizhaq, H., Stavi, I., Shachak, M. & Bel, G. Geodiversity increases ecosystem durability to prolonged droughts. Ecol. Complex. 31, 96–103 (2017).Article 

Google Scholar 
22.Stavi, I., Rachmilevitch, S. & Yizhaq, H. Geodiversity effects on soil quality and geo-ecosystem functioning in drylands. CATENA 176, 372–380 (2019).CAS 
Article 

Google Scholar 
23.Preisler, Y. et al. Mortality versus survival in drought-affected Aleppo pine forest depends on the extent of rock cover and soil stoniness. Funct. Ecol. 33, 901–912 (2019).Article 

Google Scholar 
24.Sauer, T. J. & Logsdon, S. D. Hydraulic and Physical Properties of Stony Soils in a Small Watershed. Soil Sci. Soc. Am. J. 66, 1947–1956 (2002).25.Arnau-Rosalén, E., Calvo-Cases, A., Boix-Fayos, C., Lavee, H. & Sarah, P. Analysis of soil surface component patterns affecting runoff generation. An example of methods applied to Mediterranean hillslopes in Alicante (Spain). Geomorphology 101, 595–606 (2008).ADS 
Article 

Google Scholar 
26.Ceacero, C. J., Díaz-Hernández, J. L., de Campo, A. D. & Navarro-Cerrillo, R. M. Soil rock fragment is stronger driver of spatio-temporal soil water dynamics and efficiency of water use than cultural management in holm oak plantations. Soil Tillage Res. 197, 104495 (2020).Article 

Google Scholar 
27.Burnett, M. R., August, P. V., Brown, J. H. & Killingbeck, K. T. The influence of geomorphological heterogeneity on biodiversity I. A patch-scale perspective. Conserv. Biol. 12, 363–370 (2008).Article 

Google Scholar 
28.Engelbrecht, B. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Stavi, I., Rachmilevitch, S. & Yizhaq, H. Small-scale geodiversity regulates functioning, connectivity, and productivity of shrubby, semi-arid rangelands. L. Degrad. Dev. 29, 205–209 (2018).Article 

Google Scholar 
30.Dubinin, V., Stavi, I., Svoray, T., Dorman, M. & Yizhaq, H. Hillslope geodiversity improves the resistance of shrubs to prolonged droughts in semiarid ecosystems. J. Arid Environ. 188, 104462 (2021).ADS 
Article 

Google Scholar 
31.Ochoa-Hueso, R. et al. Soil fungal abundance and plant functional traits drive fertile island formation in global drylands. J. Ecol. 106, 242–253 (2018).CAS 
Article 

Google Scholar 
32.Stavi, I., Rachmilevitch, S., Hjazin, A. & Yizhaq, H. Geodiversity decreases shrub mortality and increases ecosystem tolerance to droughts and climate change. Earth Surf. Process. Landforms 43, 2808–2817 (2018).ADS 
Article 

Google Scholar 
33.Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).ADS 
Article 

Google Scholar 
34.Bailey, J. J., Boyd, D. S. & Field, R. Models of upland species’ distributions are improved by accounting for geodiversity. Landscape Ecol. https://doi.org/10.1007/s10980-018-0723-z (2018).Article 

Google Scholar 
35.Lenoir, J. et al. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob. Change Biol. 19, 1470–1481 (2013).ADS 
Article 

Google Scholar 
36.Lawler, J. J. et al. The theory behind, and the challenges of, conserving nature’s stage in a time of rapid change. Conserv. Biol. 29, 618–629 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Nichols, W. F., Killingbeck, K. T. & August, P. V. The influence biodiversity of geomorphological heterogeneity: II. A landscape perspective. Soc. Conserv. Biol. 12, 371–397 (1998).Article 

Google Scholar 
38.Alahuhta, J., Toivanen, M. & Hjort, J. Geodiversity–biodiversity relationship needs more empirical evidence. Nat. Ecol. Evol. 4, 2–3 (2020).PubMed 
Article 

Google Scholar 
39.Evenari, M., Shanan, L., Tadmor, N. & Shkolnik, A. The Negev: The Challenge of a Desert (Harvard University Press, 1982).Book 

Google Scholar 
40.Akttani, H., Trimborn, P. & Ziegler, H. Photosynthetic pathways in Chenopodiaceae from Africa, Asia and Europe with their ecological, phytogeographical and taxonomical importance. Plant Syst. Evol. 206, 187–221 (1997).Article 

Google Scholar 
41.Harley, J. The Biology of Mycorrhiza (Leonard Hill, 1969).
Google Scholar 
42.Mejsti, V. K. & Cudlin, P. Mycorrhiza in some plant desert species in Algeria. Plant Soil 71, 363–366 (1983).Article 

Google Scholar 
43.Segoli, M., Ungar, E. D. & Shachak, M. Shrubs enhance resilience of a semi-arid ecosystem by engineering and regrowth. Ecohydrology 1, 330–339 (2008).Article 

Google Scholar 
44.Gilad, E., Von Hardenberg, J., Provenzale, A., Shachak, M. & Meron, E. Ecosystem engineers: From pattern formation to habitat creation. Phys. Rev. Lett. 93, 098105 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Wright, J. P., Jones, C. G., Boeken, B. & Shachak, M. Predictability of ecosystem engineering effects on species richness across environmental variability and spatial scales. J. Ecol. 94, 815–824 (2006).Article 

Google Scholar 
46.Katra, I., Blumberg, D. G., Lavee, H. & Sarah, P. Spatial distribution dynamics of topsoil moisture in shrub microenvironment after rain events in arid and semi-arid areas by means of high-resolution maps. Geomorphology 86, 455–464 (2007).ADS 
Article 

Google Scholar 
47.Hoffman, O., de Falco, N., Yizhaq, H. & Boeken, B. Annual plant diversity decreases across scales following widespread ecosystem engineer shrub mortality. J. Veg. Sci. 27, 578–586 (2016).Article 

Google Scholar 
48.Shachak, M. et al. Woody species as landscape modulators and their effect on biodiversity patterns. Bioscience 58, 209–221 (2008).Article 

Google Scholar 
49.Madrigal-González, J., García-Rodríguez, J. A. & Alarcos-Izquierdo, G. Testing general predictions of the stress gradient hypothesis under high inter- and intra-specific nurse shrub variability along a climatic gradient. J. Veg. Sci. 23, 52–61 (2012).Article 

Google Scholar 
50.Boeken, B. & Shachak, M. The dynamics of abundance and incidence of annual plant species during colonization in a desert. Ecography (Cop.) 21, 63–73 (1998).Article 

Google Scholar 
51.Golodets, C. & Boeken, B. Moderate sheep grazing in semiarid shrubland alters small-scale soil surface structure and patch properties. CATENA 65, 285–291 (2006).Article 

Google Scholar 
52.Boeken, B. & Shachak, M. Desert plant communities in human-made patches-implications for management. Ecol. Appl. 4, 702–716 (1994).Article 

Google Scholar 
53.Hoffman, O., Yizhaq, H. & Boeken, B. Small-scale effects of annual and woody vegetation on sediment displacement under field conditions. CATENA 109, 157–163 (2013).Article 

Google Scholar 
54.Zaady, E., Arbel, S., Barkai, D. & Sarig, S. Long-term impact of agricultural practices on biological soil crusts and their hydrological processes in a semiarid landscape. J. Arid Environ. 90, 5–11 (2013).ADS 
Article 

Google Scholar 
55.Zaady, E., Stavi, I. & Yizhaq, H. Hillslope geodiversity effects on properties and composition of biological soil crusts in drylands. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.13097 (2021).Article 

Google Scholar 
56.Feinbrun-Dothan, N. & Danin, A. Analytical Flora of Eretz-Israel (Cana Publishing Ltd, 1991).
Google Scholar 
57.Solovchenko, A., Merzlyak, M. N., Khozin-Goldberg, I., Cohen, Z. & Boussiba, S. Coordinated carotenoid and lipid syntheses induced in parietochloris incisa (chlorophyta, trebouxiophyceae) mutant deficient in δ5 desaturase by nitrogen starvation and high light. J. Phycol. 46, 763–772 (2010).CAS 
Article 

Google Scholar 
58.Shannon, C. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).MathSciNet 
MATH 
Article 

Google Scholar 
59.Simpson, E. Measurement of diversity. Nature 163, 688 (1949).ADS 
MATH 
Article 

Google Scholar 
60.R Core Team. R: A language and environment for statistical Computing. R Foundation for Statistical  Computing, Vienna, Austria (2020).61.Richerson, P. J. & Lum, K. Patterns of plant species diversity in California: Relation to weather and topography. Am. Nat. 116, 504–536 (1980).Article 

Google Scholar 
62.Kerr, J. T. & Packer, L. Habitat heterogeneity as a determinant of mammal species richness in high-energy regions. Nature 385, 252–254 (1997).ADS 
CAS 
Article 

Google Scholar 
63.Alahuhta, J. et al. The role of geodiversity in providing ecosystem services at broad scales. Ecol. Indic. 91, 47–56 (2018).Article 

Google Scholar 
64.Zarnetske, P. L. et al. Towards connecting biodiversity and geodiversity across scales with satellite remote sensing. Wiley Online Libr. 28, 548–556 (2019).
Google Scholar 
65.Schrodt, F. et al. To advance sustainable stewardship, we must document not only biodiversity but geodiversity. Proc. Natl. Acad. Sci. U. S. A. 116, 16155–161658 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Read, Q. D. et al. Beyond counts and averages: Relating geodiversity to dimensions of biodiversity. Wiley Online Libr. 29, 696–710 (2020).
Google Scholar 
67.Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).ADS 
CAS 
Article 

Google Scholar 
68.Knudson, C., Kay, K. & Fisher, S. Appraising geodiversity and cultural diversity approaches to building resilience through conservation. Nat. Clim. Change 8, 678–685 (2018).ADS 
Article 

Google Scholar 
69.Beier, P., Hunter, M. L. & Anderson, M. Special section: Conserving nature’s stage. Conserv. Biol. 29, 613–617 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Dubinin, V., Svoray, T., Stavi, I. & Yizhaq, H. Using LANDSAT 8 and VENµS data to study the effect of geodiversity on soil moisture dynamics in a semiarid shrubland. Remote Sens. 12, 3377 (2020).ADS 
Article 

Google Scholar 
71.Renne, R. R. et al. Soil and stand structure explain shrub mortality patterns following global change–type drought and extreme precipitation. Ecology 100, e02889 (2019).PubMed 
PubMed Central 

Google Scholar 
72.Gutterman, Y., Golan, T. & Garsani, M. Porcupine diggings as a unique ecological system in a desert environment. Oecologia 85, 122–127 (1990).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Armas, C., Pugnaire, F. I. & Sala, O. E. Patch structure dynamics and mechanisms of cyclical succession in a Patagonian steppe (Argentina). J. Arid Environ. 72, 1552–1561 (2008).ADS 
Article 

Google Scholar 
74.Pickett, S. & White, P. The Ecology of Natural Disturbance and Patch Dynamics (Academic Press, 1985). https://doi.org/10.1016/C2009-0-02952-3.Book 

Google Scholar 
75.Segoli, M., Ungar, E. D., Giladi, I., Arnon, A. & Shachak, M. Untangling the positive and negative effects of shrubs on herbaceous vegetation in drylands. Landsc. Ecol. 27, 899–910 (2012).Article 

Google Scholar 
76.Rodríguez, F., Mayor, Á. G., Rietkerk, M. & Bautista, S. A null model for assessing the cover-independent role of bare soil connectivity as indicator of dryland functioning and dynamics. Ecol. Indic. 94, 512–519 (2018).Article 

Google Scholar 
77.Zelnik, Y. R., Kinast, S., Yizhaq, H., Bel, G. & Meron, E. Regime shifts in models of dryland vegetation. Philos. Trans. R. Soc. A https://doi.org/10.1098/rsta.2012.0358 (2013).Article 
MATH 

Google Scholar 
78.Walker, M. D. et al. Plant community responses to experimental warming across the tundra biome. PNAS 103, 1342–1346 (2006).79.Kardol, P. et al. Climate change effects on plant biomass alter dominance patterns and community evenness in an experimental old-field ecosystem. Glob. Change Biol. 16, 2676–2687 (2010).ADS 
Article 

Google Scholar 
80.Hillebrand, H., Bennett, D. M. & Cadotte, M. W. Consequences of dominance: A review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
81.Stavi, I., Yizhaq, H., Szitenberg, A. & Zaady, E. Patch-scale to hillslope-scale geodiversity alleviates susceptibility of dryland ecosystems to climate change: Insights from the Israeli Negev. Curr. Opin. Environ. Sustain. 50, 129–137 (2021).Article 

Google Scholar 
82.Loarie, S. R. et al. Climate change and the future of California’s endemic flora. PLoS ONE 3, 2502 (2008).ADS 
Article 
CAS 

Google Scholar 
83.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Ashcroft, M. B., Chisholm, L. A. & French, K. O. Climate change at the landscape scale: Predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation. Glob. Change Biol. 15, 656–667 (2009).ADS 
Article 

Google Scholar 
85.Correa-Metrio, A., Meave, J. A., Lozano-García, S. & Bush, M. B. Environmental determinism and neutrality in vegetation at millennial time scales. J. Veg. Sci. 25, 627–635 (2014).Article 

Google Scholar 
86.Baumgartner, J., Esperon-Rodriguez, M. & Beaumont, L. Identifying in situ climate refugia for plant species. Ecography (Cop.) 41, 1850–1863 (2018).Article 

Google Scholar 
87.Alahuhta, J. et al. The Role of Geodiversity in Providing Ecosystem Services at Broad Scales (Elsevier, 2018).Book 

Google Scholar 
88.Parks, K. E. & Mulligan, M. On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns. Biodivers. Conserv. 19, 2751–2766 (2010).Article 

Google Scholar 
89.Keppel, G. et al. The capacity of refugia for conservation planning under climate change. Front. Ecol. Environ. 13, 106–112 (2015).Article 

Google Scholar 
90.Mokany, K. et al. Past, present and future refugia for Tasmania’s palaeoendemic flora. J. Biogeogr. 44, 1537–1546 (2017).Article 

Google Scholar  More

Data collectionWe systematically searched all peer-reviewed publications that were published prior to May 2021, which investigated the effects of plant diversity on terrestrial C:N:P ratios (i.e., plants, soils, soil microbial biomass, and extracellular enzymes) using the Web of Science (Core Collection; http://www.webofknowledge.com), Google Scholar (http://scholar.google.com), and the China National Knowledge Infrastructure (CNKI; https://www.cnki.net) using the search term: “C:N or C:P or N:P or C:N:P AND plant OR soil OR microbial biomass OR extracellular enzyme OR exoenzyme AND plant diversity OR richness OR mixture OR pure OR polyculture OR monoculture OR overyielding”, and also searched for references within these papers. Our survey also included studies summarized in previously published diversity-ecosystem functioning meta-analyses15,17,20,33. The literature search was performed following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) (Moher, Liberati44; Supplementary Fig. 5).We employed the following criteria to select the studies: (i) they were purposely designed to test the effects of plant diversity on C:N:P ratios, (ii) they had at least one species mixture treatment and corresponding monocultures, (iii) they had the same initial climatic and soil properties in the monoculture and mixture treatment plots. In thirteen publications, several experiments, each with independent controls, were conducted at different locations and were considered to be distinct studies. In total, 169 studies met these criteria (Supplementary Fig. 5 and Supplementary Table 3). When different publications included the same data, we recorded the data only once. When a study included plant species mixtures of different numbers of species, we considered them as distinct observations.For each site, we extracted the means, the number of replications, and standard deviations of the C:N, N:P, and C:P ratios of plants (including leaves, shoots, fine roots, total roots), soils, soil enzymes as well as soil microbial biomass C:N ratios, if reported. Similar to Zhou and Staver45, we collected nine types of soil enzymes and integrated individual soil enzymes into combined enzymes to represent proxies targeting specific resource acquisitions: C-acquisition (average of Invertase, α-Glucosidase, β-1,4-Glucosidase, Cellobiohydrolase, β-1,4-Xylosidase), N-acquisition (average of β-1,4-N-acetylglucosaminidase, Leucine-aminopeptidase, Urease), and P-acquisition (phosphatase). The ratios of each type of enzyme were subsequently calculated, referred to as soil enzyme C:N, C:P, and N:P. When an original study reported the results graphically, we used Plot Digitizer version 2.0 (Department of Physics at the University of South Alabama, Mobile, AL, USA) to extract data from the figures. This resulted in 52 studies for plant C:N ratios, 35 studies for plant N:P ratios, 17 studies for plant C:P ratios, 83 studies for soil C:N ratios, 42 studies for soil N:P ratios, 19 studies for soil C:P ratios, 33 studies for soil microbial biomass C:N ratios, 41 studies for soil enzyme C:N ratios, 40 studies for soil enzyme N:P ratios and 34 studies for soil enzyme C:P ratios (Supplementary Table 3).We also extracted species compositions in mixtures, latitude, longitude, stand age, ecosystem type (i.e, forest, grassland, cropland, pot), mean annual temperature (MAT, °C), management practice (fertilization or not), soil type (FAO classification) and sampled soil depth from original or cited papers, or cited data sources. The mean annual aridity index and solar radiation data were retrieved from the CGIAR-CSI Global Aridity Index data set46 and WorldClim Version 247 using location information. The annual aridity index was calculated as the ratio of the mean annual precipitation to mean annual potential evapotranspiration48. Stand age (SA) was recorded as the number of years since stand establishment following stand-replacing disturbances in forests, and the number of years between the initiation and measurements of the experiments in grasslands, croplands, and pots. Observations were averaged if multiple measurements were conducted during different seasons within a year. The species proportions in plant mixtures were based on the stem density in forests and pots, coverage in croplands, and sown seeds in grasslands. Soil depth was recorded as the midpoint of each soil depth interval49. We employed the weighted averages of soil C:N, C:P, and N:P ratios of monocultures in each study as proxies for the status of background nutrients. For studies that did not report soil C:N, C:P, and N:P ratios of monocultures, we used the initial soil C:N, C:P, and N:P ratios (before experiment establishment, if reported) as proxies for the status of background nutrients. When a study reported the soils, soil microbial biomass or soil enzyme C:N:P data from multiple soil depths, we used the soil C:N, C:P, and N:P ratios of the corresponding depths as background nutrient proxies. For plant C:N:P data, we used the uppermost soil layer C:N, C:P, and N:P ratios as background nutrient proxies, since it contains the majority of the available nutrients essential for plant growth50. We compared the estimates for the data sets with and without pot studies and found that both data sets yielded qualitatively similar results (Supplementary Tables 2 and 4). Thus, we reported results based on the whole data set.We employed two key functional traits to describe the functional composition: ‘leaf nitrogen content per leaf dry mass’ (Nmass, mg g−1), and “specific leaf area” (SLA, mm2 mg−1; i.e., leaf area per leaf dry mass), as they are expected to be related to plant growth rate, resource uptake and use efficiency27, and are available for large numbers of species. We obtained the mean trait values of Nmass and SLA data by using all available measurements for each plant species from the TRY Plant Trait Database51 except for two studies that included the data in their original publication52, or related publications in the same sites53. Functional diversity (FDis) was calculated as functional dispersion, which is the mean distance of each species to the centroid of all species in the functional trait space, based on the two traits together54. The calculation of FDis was conducted using the FD package54.Data analysisThe natural log-transformed response ratio (lnRR) was employed to quantify the effects of plant mixture following Hedges, Gurevitch55:$${{{{{{mathrm{ln}}}}}}}{RR}={{{{{{mathrm{ln}}}}}}}({bar{X}}_{{{{{{mathrm{t}}}}}}}/{bar{X}}_{{{{{{mathrm{c}}}}}}})={{{{{{{mathrm{ln}}}}}}}bar{X}}_{{{{{{mathrm{t}}}}}}}-{{{{{{{mathrm{ln}}}}}}}bar{X}}_{{{{{{mathrm{c}}}}}}}$$
(1)
where ({overline{X}}_{{{{{{rm{t}}}}}}}) and ({overline{X}}_{{{{{{rm{c}}}}}}}) are the observed values of a selected variable in the mixture and the expected value of the mixture in each study, respectively. If a study has multiple richness levels in mixtures (for example, 1, 4, 8, and 16), lnRR was calculated for the species richness levels 4, 8, and 16, respectively. We calculated ({overline{X}}_{{{{{{rm{c}}}}}}}) based on weighted values of the component species in monocultures following Loreau and Hector39:$$overline{{X}_{{{{{{mathrm{c}}}}}}}}=sum ({p}_{i}times {m}_{i})$$
(2)
where mi is the observed value of the selected variable of the monoculture of species i and pi is the proportion of species i density in the corresponding mixture. When a study reported multiple types of mixtures (species richness levels) and experimental years, ({overline{X}}_{{{{{{rm{t}}}}}}}) and ({overline{X}}_{{{{{{rm{c}}}}}}}) were calculated separately for each mixture type and experimental year.In our data set, sampling variances were not reported in 37 of the 169 studies, and no single control group mean estimate is present with standard deviation or the standard error reported. Like the previous studies6,56, we employed the number of replications for weighting:$${W}_{{{{{{mathrm{r}}}}}}}=({N}_{{{{{{mathrm{c}}}}}}}times {N}_{{{{{{mathrm{t}}}}}}})/({N}_{{{{{{mathrm{c}}}}}}}+{N}_{{{{{{mathrm{t}}}}}}})$$
(3)
where Wr is the weight associated with each lnRR observation, and Nc and Nt are the number of replications in monocultures and corresponding mixtures, respectively.The C:N, N:P, and C:P ratios of plants, soils, and soil enzymes, as well as soil microbial biomass C:N ratios were considered as response variables and analyzed separately. To validate the linearity assumption for the continuous predictors, we initially graphically plotted the lnRR vs. individual predictors, including FDis, SA, and background nutrient status (N, i.e., C:N, C:P, and N:P ratios of soil) and identified logarithmic functions as an alternative to linear functions. We also statistically compared the linear and logarithmic functions with the predictor of interest as the fixed effect, and “study” and measured plant parts (i.e., leaves, shoots, fine roots, total roots) or soil depth as the random effects, using Akaike information criterion (AIC). The random factors were used to account for the autocorrelation among observations within each “Study”, and potential influences of variation in measured plant parts and soil depth. We found that the linear FDis, SA, and N resulted in lower, or similar AIC values (∆AIC  More

1.Jiang, X., Yang, G. P., Wei, Q. W. & Zou, G. W. Analysis of the genetic structure of spotted sea bass (Lateolabrax maculatus) inhabiting the Chinese Coast. Period. Ocean Univ. China 39, 271–274 (2009) (In Chinese with English abstract).
Google Scholar 
2.Clifford, S. L., McGinnity, P. & Ferguson, A. Genetic changes in Atlantic salmon (Salmo salar) populations of northwest Irish rivers resulting from escapes of adult farm salmon. Can. J. Fish. Aquat. Sci. 55, 358–363 (1998).Article 

Google Scholar 
3.Bourret, V., O’Reilly, P. T., Carr, J. W., Berg, P. R. & Bernatchez, L. Temporal change in genetic integrity suggests loss of local adaptation in a wild Atlantic salmon (Salmo salar) population following introgression by farmed escapees. Heredity 106, 500–510 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Hutchings, J. A. & Fraser, D. J. The nature of fisheries- and farming-induced evolution. Mol. Ecol. 17, 294–313 (2008).PubMed 
Article 

Google Scholar 
5.Reiss, H., Hoarau, G., Dickey-Collas, M. & Wolff, W. J. Genetic population structure of marine fish: Mismatch between biological and fisheries management units. Fish Fish. 10, 361–395 (2009).Article 

Google Scholar 
6.Giantsis, I. A., Mucci, N., Randi, E., Abatzopoulos, T. J. & Apostolidis, A. P. Microsatellite variation of mussels (Mytius galloprovincialis) in Central and Eastern Mediterranean: Genesc panmixia in the Aegean and the Ionian Sea. J. Mar. Biol. Assoc. U.K. 94, 797–809 (2014).Article 

Google Scholar 
7.Liu, J. X., Gao, T. X., Yokogawa, K. & Zhang, Y. P. Differential population structuring and demographic history of two closely related fish species, Japanese sea bass (Lateolabrax japonicus) and spotted sea bass (Lateolabrax maculatus) in Northwestern Pacific. Mol. Phylogenet. Evol. 39(3), 799–811 (2006).CAS 
PubMed 
Article 

Google Scholar 
8.Hu, Z. M., Gao, T. X., Han, Z. Q. & Song, L. Studies on genetic differentiation of the spotted sea bass (Lateolabrax maculatus) and Japanese sea bass (Lateolabrax japonicus). Period. Ocean Uni. China 37(3), 413–418 (2007) (In Chinese with English abstract).CAS 

Google Scholar 
9.Nakayama, K., Kineshita, I. & Seikai, T. Morphologlcal comparisons during early stage rearing of Chinese and Japanese forms of the temperate bass, Lateolabrax japonicus. Jpn. J. Ichthyol. 43(1), 13–20 (1996).
Google Scholar 
10.Lou, D., Gao, T. X. & Zhang, X. M. The advances on germplasm resources study of Lateolabrax. J. Zhejiang Ocean Univ. (Natural Science) 19(2), 162–167 (2000) (Chinese in English abstract).
Google Scholar 
11.Lou, D. et al. Study on genetic variation in Chinese and Japanese sea bass. Journal of Ocean University of Qingdao 33(1), 22–28 (2003) (In Chinese in English abstract).
Google Scholar 
12.Li, M. Y., Zhao, M. Z., Zhong, A. H. & Xue, L. Y. The analysis of genetic variation of Lateolabrax japonicus from Rizhao of Shandong and Xiamen of Fujian by isozyme and RAPD methods. Acta Oceanol. Sin. 27(3), 119–123 (2005) (In Chinese with English abstract).CAS 

Google Scholar 
13.Liu, M. Y., Jiang, Q. C. & Yang, J. X. Analysis on mitochondrial DNA cytochrome b gene of Lateolabrax japonicas from different seas. J. Nanjing Normal Univ. (Natural Science Edition) 33(1), 102–106 (2010) (In Chinese with English abstract).
Google Scholar 
14.Wang, W. et al. Population genetic diversity of Chinese sea bass (Lateolabrax maculatus) from southern coastal regions of China based on mitochondrial COI gene sequences. Biochem. Syst. Ecol. 71, 114–140 (2017).CAS 
Article 

Google Scholar 
15.Wang, G. X. et al. Genetic variability in six Lateolabrax maculatus populations inhabiting the Chinese coast. J. Fish. Sci. China 24(2), 395–402 (2017) (In Chinese with English abstract).
Google Scholar 
16.Chapuis, M. P., Loiseau, A., Michalakis, Y., Lecoq, M. & Estoup, A. Characterization and PCR multiplexing of polymorphic microsatellite loci for the locust Locusta migratoria. Mol. Ecol. Notes 5(3), 554–557 (2005).CAS 
Article 

Google Scholar 
17.Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24(3), 621–631 (2007).CAS 
PubMed 
Article 

Google Scholar 
18.Tan, Y., Fang, L., Qiu, M., Huo, Z. M. & Yan, Y. W. Population genetic of the Manila clam (Ruditapes philippinarum) in East Asia. Sci. Rep. 10, 21890 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Agulló, M. et al. Genetic analyses reveal temporal stability and connectivity pattern in blue and red shrimp Aristeus antennatus populations. Sci. Rep. 10, 21505 (2020).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.An, H. S., Lee, J. W. & Kim, H. Y. Genetic differences between wild and hatchery populations of Korean spotted sea bass (Lateolabrax maculatus) inferred from microsatellite markers. Genes Genom. 35(5), 671–680 (2013).Article 

Google Scholar 
21.An, H. S., Kim, H. Y. & Kim, J. B. Genetic characterization of hatchery populations of Korean spotted sea bass (Lateolabrax maculatus) using multiplex polymerase chain reaction assays. Genet. Mol. Res. 13(3), 6701–6715 (2014).CAS 
PubMed 
Article 

Google Scholar 
22.Shaw, P. W., Turan, C., Wright, J. M., O’Connell, M. & Carvalho, G. R. Microsatellite DNA analysis of population structure in Atlantic herring (Clupea harengus), with direct comparison to allozyme and mtDNA RFLP analyses. Heredity 83, 490–499 (1999).CAS 
PubMed 
Article 

Google Scholar 
23.Shao, C. W., Chen, S. L., Xu, G. B., Liao, X. L. & Tian, Y. S. Eighteen novel microsatellite markers for the Chinese sea perch, Lateolabrax maculatus. Conserv. Genet. 10, 623–625 (2009).CAS 
Article 

Google Scholar 
24.Zhang, H. R., Niu, S. F., Wu, R. X., Zhai, Y. & Tian, L. T. Development and characterization of 26 polymorphic microsatellite markers in Lateolabrax maculatus and cross-species amplification for the phylogenetically related taxa. Biochem. Syst. Ecol. 66, 326–330 (2016).CAS 
Article 

Google Scholar 
25.Zhao, Y. et al. Isolation of microsatellite markers for Lateolabrax japonicus and polymorphic analysis. Zool. Res. 32(5), 515–520 (2011) (In Chinese with English abstract).CAS 

Google Scholar 
26.Botstein, D., White, R. L., Skolnick, M. & Davis, R. W. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32, 314–331 (1980).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Wan, N. W., Rolland, J. L., Bonhomme, F. & Phongdara, A. Population genetic structure of Penaeus merguiensis, Thailand based on nuclear DNA variation. J. Exp. Mar. Biol. Ecol. 311(1), 63–78 (2004).Article 
CAS 

Google Scholar 
28.Xiao, Q. Z. et al. Genetic diversity analysis of wild and cultured megalopa population of Eriocheir sinensis from Yangtze River. Genom. Appl. Biol. 36(5), 1935–1945 (2017) (In Chinese with English abstract).
Google Scholar 
29.Liu, F. et al. High genetic diversity and substantial population differentiation in grass carp (Ctenopharyngodon idella) revealed by microsatellite analysis. Aquaculture 297, 51–56 (2009).CAS 
Article 

Google Scholar 
30.Halasz, J., Pedryc, A., Ercisli, S., Yilmaz, K. U. & Hegedus, A. S-genotyping supports the genetic relationships between Turkish and Hungarian apricot germplasm. J. Am. Soc. Hortic. Sci. 135(5), 410–417 (2010).Article 

Google Scholar 
31.Wang, S. H., Zhang, C., Shang, M., Wu, X. G. & Cheng, Y. X. Genetic diversity and population structure of native mitten crab (Eriocheir sensu stricto) by microsatellite markers and mitochondrial COI gene sequence. Gene 693, 101–113 (2019).CAS 
PubMed 
Article 

Google Scholar 
32.Li, M. Y., Zhao, M. Z., Zhong, A. H. & Xue, L. Y. Comparative analysis of genetic variation by isozymes between two stocks of the sea Bass (Lateolabrax japonicus) from Rizhao & Xiamen sea areas, China. J. Zhejiang Ocean Univ. Nat. Sci. 22(2), 121–124 (2003) (In Chinese with English abstract).CAS 

Google Scholar 
33.Li, M. Y., Zhao, M. Z., Zhong, A. H. & Xue, L. Y. Comparative analysis of RAPD genetic variation between two stocks of sea bass (Lateolabrax aponicus) from Shandong Rizhao and Fujian Xiamen sea areas. Oceanol. Limnol. Sin. 34(6), 618–624 (2003) (In Chinese with English abstract).CAS 

Google Scholar 
34.Machado-Schiaffino, G. & Garcia-Vazquez, E. Isolation and characterization of microsatellite loci in Merluccius australis and cross-species amplification. Mol. Ecol. Resour. 9(2), 585–587 (2009).CAS 
PubMed 
Article 

Google Scholar 
35.Freeland, J. R. Molecular ecology 112–116 (Wiley, Chichester, 2005).
Google Scholar 
36.Li, D. Y., Yin, Q. Q., Hou, N., Sun, X. W. & Liang, L. Q. Genetic diversity of different ecologo-geographical populations of yellow catfish Pelteobagrus eupogon. Oceanol. Limnol. Sin. 40(4), 460–469 (2009) (In Chinese with English abstract).CAS 

Google Scholar 
37.Shoji, J. & Tanaka, M. Influence of spring river flow on the recruitment of Japanese sea perch Lateolabrax japonicus into the Chikugo estuary, Japan. . Sci. Mar. 70(2), 159–164 (2006).Article 

Google Scholar 
38.Liu, B., Kuang, Y. Y., Tong, G. X. & Yin, J. S. Analysis of genetic diversity on 9 wild stocks of Taimen (Hucho taimen) by microsatellite markers. Zool. Res. 32(6), 597–604 (2011) (In Chinese with English abstract).CAS 

Google Scholar 
39.Clegg, S. M. et al. Genetic consequences of sequential founder events by an island-colonizing bird. Proc. Natl. Acad. Sci. USA 99, 8127–8132 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Pruett, C. L. & Winker, K. Northwestern song sparrow populations show genetic effects of sequential colonization. Mol. Ecol. 14, 1421–1434 (2005).CAS 
PubMed 
Article 

Google Scholar 
41.An, H. S. et al. Population genetic structure of the sea bass (Lateolabrax japonicus) in Korea based on multiplex PCR assays with 12 polymorphic microsatellite markers. Genes Genom. 36, 247–259 (2014).CAS 
Article 

Google Scholar 
42.Van Oosterhout, C., Hutchinson, W. F., Willis, D. P. M. & Shipley, P. F. Microchecker: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 
CAS 

Google Scholar 
43.Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B 39, 1–38 (1977).MathSciNet 
MATH 

Google Scholar 
44.Raymond, M. & Rousset, F. GENEPOP (version 12): Population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).Article 

Google Scholar 
45.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
Article 

Google Scholar 
46.Wang, S. H. et al. Identification and genetic diversity analysis of Chinese mitten crab (Eriocheir sinensis) in the Liao river area. J. Northeast. Agric. Univ. 25, 43–53 (2018).
Google Scholar 
47.Tamura, K. et al. MEGA 5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol. 28, 2731–2739 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Tamura, K. & Nei, M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512–526 (1993).CAS 
PubMed 

Google Scholar 
49.Maruyama, T. & Fuerst, P. A. Population bottlenecks and nonequilibrium models in population genetics. I. Allele numbers when populations evolve from zero variability. Genetics 111, 675–689 (1985).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multi locus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Jombart, T. Adegenet: An R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 

Google Scholar  More

1.Smith, P. A. et al. Status and trends of tundra birds across the circumpolar Arctic. Ambio 49, 732–748 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Callaghan, T. V. et al. Arctic tundra and polar desert ecosystems. In Arctic Climate Impact Assessment (eds Symon, C. et al.) 243–352 (Cambridge University Press, 2005).
Google Scholar 
3.Serreze, M. C. & Francis, J. A. The Arctic amplification debate. Clim. Change 76, 241–264 (2006).ADS 
CAS 
Article 

Google Scholar 
4.Hodgkins, R. The twenty-first-century Arctic environment: Accelerating change in the atmospheric, oceanic and terrestrial spheres. Geogr. J. 180, 429–436 (2014).Article 

Google Scholar 
5.Meltofte, H. et al. Effects of climate variation on the breeding ecology of Arctic shorebirds. Medd. Grønl. Biosci. 59, 1–48 (2007).
Google Scholar 
6.Saalfeld, S. T. et al. Phenological mismatch in Arctic-breeding shorebirds: Impact of snowmelt and unpredictable weather conditions on food availability and chick growth. Ecol. Evol. 9, 6693–6707 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
7.McKinnon, L., Picotin, M., Bolduc, E., Juillet, C. & Bêty, J. Timing of breeding, peak food availability, and effects of mismatch on chick growth in birds nesting in the High Arctic. Can. J. Zool. 90, 961–971 (2012).Article 

Google Scholar 
8.Kwon, E. et al. Geographic variation in the intensity of warming and phenological mismatch between Arctic shorebirds and invertebrates. Ecol. Monogr. https://doi.org/10.1002/ecm.1383 (2019).Article 

Google Scholar 
9.Reneerkens, J. et al. Effects of food abundance and early clutch predation on reproductive timing in a high Arctic shorebird exposed to advancements in arthropod abundance. Ecol. Evol. 6, 7375–7386 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Senner, N. R., Stager, M. & Sandercock, B. K. Ecological mismatches are moderated by local conditions for two populations of a long-distance migratory bird. Oikos 126, 61–72 (2017).Article 

Google Scholar 
11.Grabowski, M. M., Doyle, F. I., Reid, D. G., Mossop, D. & Talarico, D. Do Arctic-nesting birds respond to earlier snowmelt? A multi-species study in north Yukon, Canada. Polar Biol. 36, 1097–1105 (2013).Article 

Google Scholar 
12.Gill, J. A. et al. Why is timing of bird migration advancing when individuals are not?. Proc. R. Soc. Biol. Sci. Ser. B https://doi.org/10.1098/rspb.2013.2161 (2014).Article 

Google Scholar 
13.Liebezeit, J. R., Gurney, K. E. B., Budde, M., Zack, S. & Ward, D. Phenological advancement in Arctic bird species: Relative importance of snow melt and ecological factors. Polar Biol. 37, 1309–1320 (2014).Article 

Google Scholar 
14.Saalfeld, S. T. & Lanctot, R. B. Multispecies comparisons of adaptability to climate change: A role for life- history characteristics?. Ecol. Evol. 7, 10492–10502 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Tulp, I. & Schekkerman, H. Has prey availability for Arctic birds advanced with climate change? Hindcasting the abundance of tundra arthropods using weather and seasonal variation. Arctic 61, 48–60 (2008).Article 

Google Scholar 
16.Braegelman, S. D. Seasonality of Some Arctic Alaskan Chironomids (North Dakota State University, 2016).
Google Scholar 
17.Piersma, T., Brugge, M., Spaans, B. & Battley, P. F. Endogenous circannual rhythmicity in body mass, molt, and plumage of Great Knots (Calidris tenuirostris). Auk 125, 140–148 (2008).Article 

Google Scholar 
18.Karagicheva, J. et al. Seasonal time keeping in a long-distance migrating shorebird. J. Biol. Rhythms 31, 509–521 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Danks, H. V. Life cycles in polar arthropods—Flexible or programmed?. Eur. J. Entomol. 96, 83–102 (1999).
Google Scholar 
20.Bolduc, E. et al. Terrestrial arthropod abundance and phenology in the Canadian Arctic: Modelling resource availability for Arctic-nesting insectivorous birds. Can. Entomol. 145, 155–170 (2013).Article 

Google Scholar 
21.McKinnon, L., Nol, E. & Juillet, C. Arctic-nesting birds find physiological relief in the face of trophic constraints. Sci. Rep. https://doi.org/10.1038/srep01816 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Ruthrauff, D. R. & McCaffery, B. J. Survival of Western Sandpiper broods on the Yukon-Kuskokwim Delta, Alaska. Condor 107, 597–604 (2005).Article 

Google Scholar 
23.Pearce-Higgins, J. W. & Yalden, D. W. Variation in the growth and survival of Golden Plover Pluvialis apricaria chicks. Ibis 144, 200–209 (2002).Article 

Google Scholar 
24.Holmes, R. T. Breeding ecology and annual cycle adaptations of the Red-backed Sandpiper (Calidris alpina) in northern Alaska. Condor 68, 3–46 (1966).Article 

Google Scholar 
25.Lanctot, R. B. Blood sampling in juvenile Buff-breasted Sandpipers: Movement, mass change and survival. J. Field Ornithol. 65, 534–542 (1994).
Google Scholar 
26.Jamieson, S. E. Pacific Dunlin Calidris alpina pacifica show a high propensity for second clutch production. J. Ornithol. 152, 1013–1021 (2011).Article 

Google Scholar 
27.Colwell, M. A. Shorebird Ecology, Conservation, and Management (University of California Press, 2010).Book 

Google Scholar 
28.Machín, P., Fernández-Elipe, J. & Klaassen, R. H. G. The relative importance of food abundance and weather on the growth of a sub-arctic shorebird chick. Behav. Ecol. Sociobiol. 72, 42. https://doi.org/10.1007/s00265-018-2457-y (2018).Article 

Google Scholar 
29.Corkery, C. A., Nol, E. & McKinnon, L. No effects of asynchrony between hatching and peak food availability on chick growth in Semipalmated Plovers (Charadrius semipalmatus) near Churchill, Manitoba. Polar Biol. 42, 593–601 (2019).Article 

Google Scholar 
30.Naves, L. C., Lanctot, R. B., Taylor, A. R. & Coutsoubos, N. P. How often do Arctic shorebirds lay replacement clutches?. Wader Study Gr. Bull. 115, 2–9 (2008).
Google Scholar 
31.Swift, R. J., Anteau, M. J., Ring, M. M., Toy, D. L. & Sherfy, M. H. Low renesting propensity and reproductive success make renesting unproductive for the threatened Piping Plover (Charadrius melodus). Condor https://doi.org/10.1093/condor/duz066 (2020).Article 

Google Scholar 
32.Gates, H. R., Lanctot, R. B. & Powell, A. N. High renesting rates in Arctic-breeding Dunlin (Calidris alpina): A clutch-removal experiment. Auk 130, 372–380 (2013).Article 

Google Scholar 
33.Richter-Menge, J., Druckenmiller, M. L. & Jeffries, M. (eds.) Arctic Report Card 2019. https://www.arctic.noaa.gov/Report-Card. (2019).34.Weiser, E. L. et al. Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain. Condor https://doi.org/10.1093/condor/duaa026 (2020).Article 

Google Scholar 
35.Sandercock, B. K. Estimation of survival rates for wader populations: A review of mark-recapture methods. Wader Study Gr. Bull. 100, 163–174 (2003).
Google Scholar 
36.Ottvall, R. & Härdling, R. Sensitivity analysis of a migratory population of Redshanks Tringa totanus: A forewarning of a population decline?. Wader Study Gr. Bull. 107, 40–45 (2005).
Google Scholar 
37.Hitchcock, C. L. & Gratto-Trevor, C. Diagnosing a shorebird local population decline with a stage-structured population model. Ecology 78, 522–534 (1997).Article 

Google Scholar 
38.Weiser, E. L. et al. Environmental and ecological conditions at Arctic breeding sites have limited effects on true survival rates of adult shorebirds. Auk 135, 29–43 (2018).Article 

Google Scholar 
39.Studds, C. E. et al. Rapid population decline in migratory shorebirds relying on Yellow Sea tidal mudflats as stopover sites. Nat. Commun. 8, 14895 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Piersma, T. et al. Simultaneous declines in summer survival of three shorebird species signals a flyway at risk. J. Appl. Ecol. 53, 479–490 (2016).Article 

Google Scholar 
41.Amano, T., Székely, T., Koyama, K., Amano, H. & Sutherland, W. J. A framework for monitoring the status of populations: An example from wader populations in the East Asian-Australasian flyway. Biol. Conserv. 143, 2238–2247 (2010).Article 

Google Scholar 
42.Amano, T., Székely, T., Koyama, K., Amano, H. & Sutherland, W. J. Addendum to “A framework for monitoring the status of populations: An example from wader populations in the East Asian-Australasian flyway”. Biological Conservation, 143, 2238–2247. Biol. Conserv. 145, 278–295 (2012).Article 

Google Scholar 
43.Pearce-Higgins, J. W. & Yalden, D. W. Habitat selection, diet, arthropod availability and growth of a moorland wader: The ecology of European Golden Plover Pluvialis apricaria chicks. Ibis 146, 335–346 (2004).Article 

Google Scholar 
44.Schekkerman, H., Tulp, I., Piersma, T. & Visser, G. H. Mechanisms promoting higher growth rate in Arctic than in temperate shorebirds. Oecologia 134, 332–342 (2003).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Tulp, I. & Schekkerman, H. Studies on Breeding Shorebirds at Medusa Bay, Taimyr, in Summer 2000 (Alterra, Green World Research, 2001).
Google Scholar 
46.Schekkerman, H., van Roomen, M. W. J. & Underhill, L. G. Growth, behaviour of broods and weather-related variation in breeding productivity of Curlew Sandpipers Calidris ferruginea. Ardea 86, 153–168 (1998).
Google Scholar 
47.Tjørve, K. M. C. et al. Growth and energetics of a small shorebird species in a cold environment: The Little Stint Calidris minuta on the Taimyr Peninsula, Siberia. J. Avian Biol. 38, 552–563 (2007).Article 

Google Scholar 
48.Pearce-Higgins, J. W. & Yalden, D. W. Golden Plover Pluvialis apricaria breeding success on a moor managed for shooting Red Grouse Lagopus lagopus. Bird Study 50, 170–177 (2003).Article 

Google Scholar 
49.Loonstra, A. H. J., Verhoeven, M. A. & Piersma, T. Sex-specific growth in chicks of the sexually dimorphic Black-tailed Godwit. Ibis 160, 89–100 (2018).Article 

Google Scholar 
50.Taylor, A. R., Lanctot, R. B., Powell, A. N., Kendall, S. J. & Nigro, D. A. Residence time and movements of postbreeding shorebirds on the northern coast of Alaska. Condor 113, 779–794 (2011).Article 

Google Scholar 
51.Meltofte, H., Høye, T. T., Schmidt, N. M. & Forchhammer, M. C. Differences in food abundance cause inter-annual variation in the breeding phenology of High Arctic waders. Polar Biol. 30, 601–606 (2007).Article 

Google Scholar 
52.Visser, G. H. & Ricklefs, R. E. Development of temperature regulation in shorebirds. Physiol. Zool. 66, 771–792 (1993).Article 

Google Scholar 
53.Colwell, M. A., Hurley, S. J., Hall, J. N. & Dinsmore, S. J. Age-related survival and behavior of Snowy Plover chicks. Condor 109, 638–647 (2007).Article 

Google Scholar 
54.Powell, A. N. The Effects of Early Experience on the Development, Behavior, and Survival of Shorebirds (University of Minnesota, 1992).
Google Scholar 
55.Ackerman, J. T., Herzog, M. P., Takekawa, J. Y. & Hartman, C. A. Comparative reproductive biology of sympatric species: Nest and chick survival of American Avocets and Black-necked Stilts. J. Avian Biol. 45, 609–623 (2014).Article 

Google Scholar 
56.Catlin, D. H., Fraser, J. D. & Felio, J. H. Demographic responses of Piping Plovers to habitat creation on the Missouri River. Wildl. Monogr. 192, 1–42 (2015).Article 

Google Scholar 
57.Dinsmore, S. J., Gaines, E. P., Pearson, S. F., Lauten, D. J. & Castelein, K. A. Factors affecting Snowy Plover chick survival in a managed population. Condor 119, 34–43 (2017).Article 

Google Scholar 
58.Dinsmore, S. J. Influence of drought on annual survival of the Mountain Plover in Montana. Condor 110, 45–54 (2008).Article 

Google Scholar 
59.Soikkeli, M. Breeding cycle and population dynamics in the Dunlin (Calidris alpina). Ann. Zool. Fenn. 4, 158–198 (1967).
Google Scholar 
60.Blomqvist, D. & Johansson, O. C. Distribution, reproductive success, and population trend in the Dunlin Calidris alpina schinzii on the Swedish west coast. Ornis Svec. 1, 39–46 (1991).
Google Scholar 
61.Jönsson, P. E. Reproduction and survival in a declining population of the southern Dunlin Calidris alpina schinzii. Wader Study Gr. Bull. 61, 56–68 (1991).
Google Scholar 
62.Pienkowski, M. W. Behaviour of young Ringed Plovers Charadrius hiaticula and its relationship to growth and survival to reproductive age. Ibis 126, 133–155 (1984).Article 

Google Scholar 
63.Liebezeit, J. R. & Zack, S. Point counts underestimate the importance of arctic foxes as avian nest predators: Evidence from remote video cameras in Arctic Alaskan oil fields. Arctic 61, 153–161 (2008).
Google Scholar 
64.Bentzen, R. et al. Assessing development impacts on Arctic nesting birds using real and artificial nests. Polar Biol. 40, 1527–1536 (2017).Article 

Google Scholar 
65.McKinnon, L. & Bêty, J. Effect of camera monitoring on survival rates of High-Arctic shorebird nests. J. Field Ornithol. 80, 280–288 (2009).Article 

Google Scholar 
66.Bolton, M., Tyler, G., Smith, K. & Bamford, R. The impact of predator control on Lapwing Vanellus vanellus breeding success on wet grassland nature reserves. J. Appl. Ecol. 44, 534–544 (2007).Article 

Google Scholar 
67.Fletcher, K., Aebischer, N. J., Baines, D., Foster, R. & Hoodless, A. N. Changes in breeding success and abundance of ground-nesting moorland birds in relation to the experimental deployment of legal predator control. J. Appl. Ecol. 47, 263–272 (2010).Article 

Google Scholar 
68.McGuire, R. L., Lanctot, R. B., Saalfeld, S. T., Ruthrauff, D. R. & Liebezeit, J. R. Shorebird reproductive response to exceptionally early and late springs varies across sites in Arctic Alaska. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2020.577652 (2020).Article 

Google Scholar 
69.Lackmann, A. R. Chironomids Then and Now: Climate Change Effects on a Tundra Food Web in the Alaskan Arctic (North Dakota State University, 2019).
Google Scholar 
70.McEwen, D. C. & Butler, M. G. Growing-season temperature change across four decades in an Arctic tundra pond. Arctic 71, 281–291 (2018).Article 

Google Scholar 
71.Shaftel, R. et al. Predictors of invertebrate biomass and rate of advancement of invertebrate phenology across eight sites in the North American Arctic. Polar Biol. 44, 237–257 (2021).Article 

Google Scholar 
72.Butler, M., Miller, M. C. & Mozley, S. Macrobenthos. In Limnology of Tundra Ponds, Barrow, Alaska (ed. Hobbie, J. E.) 297–339 (Dowden, Hutchinson, and Ross, Inc., 1980).
Google Scholar 
73.Kingsolver, J. G. & Huey, R. B. Size, temperature, and fitness: Three rules. Evol. Ecol. Res. 10, 251–268 (2008).
Google Scholar 
74.Schekkerman, H. & Boele, A. Foraging in precocial chicks of the Black-tailed Godwit Limosa limosa: Vulnerability to weather and prey size. J. Avian Biol. 40, 369–379 (2009).Article 

Google Scholar 
75.Krijgsveld, K. L., Reneerkens, J. W. H., McNett, G. D. & Ricklefs, R. E. Time budgets and body temperatures of American Golden-Plover chicks in relation to ambient temperature. Condor 105, 268–278 (2003).Article 

Google Scholar 
76.Cosgrove, J., Dugger, B. & Lanctot, R. B. No renesting observed after experimental clutch removal in Red Phalaropes breeding near Utqiaģvik, Alaska. Wader Study 127, 236–243 (2020).Article 

Google Scholar 
77.Fernández, G., Buchanan, J. B., Gill, R. E. Jr., Lanctot, R. & Warnock, N. Conservation Plan for Dunlin with Breeding Populations in North America (Calidris alpina arcticola, C. a. pacifica, and C. a. hudsonia), Version 1.1 (Manomet Center for Conservation Sciences, 2010).
Google Scholar 
78.Lagassé, B. J. et al. Dunlin subspecies exhibit regional segregation and high site fidelity along the East Asian-Australasian flyway. Condor https://doi.org/10.1093/condor/duaa054 (2020).Article 

Google Scholar 
79.Andres, B. A. et al. Population estimates of North American shorebirds, 2012. Wader Study Gr. Bull. 119, 178–194 (2012).
Google Scholar 
80.Warnock, N. The Alaska WatchList 2017 (Audubon Alaska, 2017).
Google Scholar 
81.Alaska Shorebird Group. Alaska Shorebird Conservation Plan. Version III (Alaska Shorebird Group, 2019).
Google Scholar 
82.CAFF. Arctic Migratory Birds Initiative (AMBI): Workplan 2019–2023. CAFF Strategies Series No. 30. (Conservation of Arctic Flora and Fauna, ISBN: 978-9935-431-79-0, 2019).83.Warnock, N. D. & Gill, R. E. Dunlin (Calidris alpina), version 1.0. In Birds of the World (ed. Billerman, S. M.) (Cornell Lab of Ornithology, 2020).
Google Scholar 
84.Saalfeld, S. T. & Lanctot, R. B. Conservative and opportunistic settlement strategies in Arctic-breeding shorebirds. Auk 132, 212–234 (2015).Article 

Google Scholar 
85.Weiser, E. L. et al. Life-history tradeoffs revealed by seasonal declines in reproductive traits of Arctic-breeding shorebirds. J. Avian Biol. https://doi.org/10.1111/jav.01531 (2017).Article 

Google Scholar 
86.Villarreal, S. et al. Tundra vegetation change near Barrow, Alaska (1972–2010). Environ. Res. Lett. https://doi.org/10.1088/1748-9326/7/1/015508 (2012).Article 

Google Scholar 
87.Liebezeit, J. R. et al. Assessing the development of shorebird eggs using the flotation method: Species-specific and generalized regression models. Condor 109, 32–47 (2007).Article 

Google Scholar 
88.Priklonsky, S. G. Application of small automatic bows for catching birds. Zool. Zh. 39, 623–624 (1960).
Google Scholar 
89.Gates, H. R. et al. Differentiation of subspecies and sexes of Beringian Dunlin using morphometric measures. J. Field Ornithol. 84, 389–402 (2013).Article 

Google Scholar 
90.Warnock, N. & Warnock, S. Attachment of radio-transmitters to sandpipers: Review and methods. Wader Study Gr. Bull. 70, 28–30 (1993).
Google Scholar 
91.Bart, J., Battaglia, D. & Senner, N. Effects of color bands on Semipalmated Sandpipers banded at hatch. J. Field Ornithol. 72, 521–526 (2001).Article 

Google Scholar 
92.Whittier, J. B. & Leslie, D. M. Jr. Efficacy of using radio transmitters to monitor Least Tern chicks. Wilson Bull. 117, 85–91 (2005).Article 

Google Scholar 
93.Lees, D. et al. An assessment of radio telemetry for monitoring shorebird chick survival and causes of mortality. Wildl. Res. 46, 622–627 (2019).Article 

Google Scholar 
94.Schekkerman, H., Teunissen, W. & Oosterveld, E. Mortality of Black-tailed Godwit Limosa limosa and Northern Lapwing Vanellus vanellus chicks in wet grasslands: Influence of predation and agriculture. J. Ornithol. 150, 133–145 (2009).Article 

Google Scholar 
95.Johnson, M., Aref, S. & Walters, J. R. Parent-offspring communication in the Western Sandpiper. Behav. Ecol. 19, 489–501 (2008).Article 

Google Scholar 
96.Brown, R. G. B. The aggressive and distraction behavior of the Western Sandpiper Ereunetes mauri. Ibis 104, 1–12 (1962).Article 

Google Scholar 
97.Rogers, L. E., Buschbom, R. L. & Watson, C. R. Length-weight relationships of shrub-steppe invertebrates. Ann. Entomol. Soc. Am. 70, 51–53 (1977).Article 

Google Scholar 
98.Cooch, E. G. & White, G. C. (eds.) Program MARK: A Gentle Introduction, 19th ed. http://www.phidot.org/software/mark/docs/book/ (2019).99.Rotella, J. J., Dinsmore, S. J. & Shaffer, T. L. Modeling nest-survival data: A comparison of recently developed methods that can be implemented in MARK and SAS. Anim. Biodivers. Conserv. 27, 187–205 (2004).
Google Scholar 
100.Dinsmore, S. J., White, G. C. & Knopf, F. L. Advanced techniques for modeling avian nest survival. Ecology 83, 3476–3488 (2002).Article 

Google Scholar 
101.Hill, B. L. Factors Affecting Survival of Arctic-Breeding Dunlin (Calidris alpina arcticola) Adults and Chicks (University of Alaska Fairbanks, 2012).
Google Scholar 
102.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
103.Arnold, T. W. Uninformative parameters and model selection using Akaike’s Information Criterion. J. Wildl. Manag. 74, 1175–1178 (2010).Article 

Google Scholar  More

Biostimulation of ureolytic communitiesEnrichmentThe native communities of the soils were successfully grown in the enrichment media (NB5U). The cultivated communities after two subcultures were serially diluted (10–2 to 10–6) and were spread with a sterile loop over Nutrient agar plates supplemented with 2% urea. Later, 36 morphologically distinct single colonies were obtained on the urea agar base plates based on visual observation.Isolation, identification, and characterization of the ureolytic isolatesOut of 36 isolated bacteria, six isolates (BS1, BS2, BS3, BS4, LS1, and LS2) were selected after checking for the urease activity test on the urea agar base (UAB) plate. These selected isolates turned the color of the UAB plate from orange to pink within 12 h. The 16 s rRNA sequence revealed the isolates as relatives of Sporosarcina pasteurii (SP). The details of the identified isolates are provided by NCMR (details in Supplementary Table 1). The biochemical characterization (details in Supplementary Table 2) of the isolates revealed that all the isolates are Gram-positive. All the isolates were rod-shaped, endospore-forming, urease, and oxidase positive. All the isolates were not able to utilize the Lysine and ONPG, contrary to SP.Further investigation of the isolated sequence was done via the NCBI database. The sequences were submitted to the GenBank database of the NCBI (National Center for Biotechnology Information) under the accession number MW024144 to MW024149. The BLAST analysis suggested that these strains are close relatives and indicate the possibility of being novel strains of the Sporosarcina family. We found that the isolate BS1 and BS2 had 96.62% (coverage 100%) and 96.22% (coverage 99%) identity with Sporosarcina siberinisis (NCBI accession number NR 134188). BS3 had 98.8% identity (coverage 97%) with Sporosarcina pasteurii (NCBI accession number NR 104923). BS4 and LS1 had 97.4% (coverage 99%) and 97.37% identity (coverage 100%) with Sporosarcina soli (NCBI accession number NR 043527). Contrarily, LS2 was found to be related to the Pseudogracilibacillus family. LS2 was observed to be closely related to Pseudogracilibacillus auburnensis P-207 with 97.06% identity (96% coverage). Based on these findings, the Phylogenetic tree was constructed with bootstrap (1000 replicates) considering the reference sequences obtained from the BLAST analysis, as shown in Fig. 3. The threshold criteria to differentiate two species is defined as 98.65% similarity score with the reference culture from databank40, while another study has suggested that in case of similarity index is  > 99%, the unknown isolate should be assigned to a species, and if the unknown isolates have similarity score between 95 to 99% to a reference sequence, the isolate should be assigned to the genus41. However, further investigation is suggested to conclude if the reported strains are novel or merely mutants of the reference strains of the databank. Similar observations were made at Graddy et al.22, where the majority of the isolated strains (47 out of 57) from bio-stimulation soil tanks were found to be strains of the Sporosarcina genus. It is worth noting that the soil enrichment media for stimulation was rich in urea, similar to Gomez et al.42 and Graddy et al.22, which is conditional stress for selective stimulation of ureolytic microorganisms. Moreover, the isolated strains were screened based on morphology and qualitative urease activity.Figure 3Neighbor-joining phylogenetic tree based on the 16S rRNA sequence of the isolates and reference sequence from the GenBank database (NCBI).Full size imageEvaluation of biocementation potential of the isolated strainsGrowth and pHThe various parameters of the biocementation potential of the isolated strains in comparison with Sporosarcina pasteurii (SP) have been plotted in Fig. 4. The growth characteristics of the isolates in NBU media and pH during growth have been represented in Fig. 4a and b. The initial pH of the growth media is kept at 7.5. It was observed that the pH of the growth media rises to 9.5 within 24 h of growth, indicating that these strains favor an alkaline environment to grow similar to SP43. All the isolates start growing when the pH of the media rises to 8.5 or above. Isolate LS2 was observed to have slower growth when compared with other isolates. This can be explained as LS2 belongs to different genera (Pseudogracilibacillus).Figure 4(a) Growth characteristics, (b) pH, (c) specific urease activity, (d) calcium utilization rate, and (e) carbonate precipitation rate of the isolates and consortia.Full size imageSpecific urease activityThe specific urease activities of the isolates were found to be comparable with SP (shown in Fig. 4c). Based on the provided NBU media and growth condition, the specific urease activity of SP is found to be 173.44 mM urea hydrolyzed h−1 (OD600)−1, which is around 2.9 mM urea hydrolyzed min−1 (OD600)−1. The specific urease activity of the isolate BS3 was observed to be maximum as 186.6 mM urea hydrolyzed h−1 (OD600)−1 during a growth period of 24 h and pH  > 9. Consortia also demonstrated significant urease activity as 160 mM urea hydrolyzed h−1 (OD600)−1 at a growth period of 48 h. The maximum ureolytic activity in BS1 was observed after 72 h of growth with a value of 106.67 mM urea hydrolyzed h−1 (OD600)−1. Maximum specific urease activity of the isolate BS2, BS4, and LS1 was observed to be 160.2, 120, and 173.4 mM urea hydrolyzed h−1 (OD600)−1 respectively after a growth duration of 48 h. LS2 demonstrated the maximum specific urease activity of 146.4 mM urea hydrolyzed h−1 (OD600)−1. The observed order of specific urease activity at 24 h of growth period is BS3  > SP  > Consortia  > LS1  > BS2  > BS4  > LS2  > BS1. As the urease activity of the strains depends on the growth media, urea content, and environmental conditions such as pH and Temperature44, we considered the conditions at the riverbank at the time of isolation, and the pH and temperature of the growth media were set at 7.5 and 37 degrees Celsius. The specific urease measured by the electrical conductivity method is reported to be between 3 to 9.7 mM urea hydrolyzed min−1 (OD600)−1 in yeast-extract urea media at pH 7 and temperature 30 degrees Celsius43. It is reported around 5 mM urea hydrolyzed min−1 (OD600)−1 in the nutrient broth urea (2%) media at a temperature of 25 degrees Celsius44. The comparative analysis of the urease activity (measured by electrical conductivity method) was done considering SP as positive control in this study. The maximum specific urease activities of all isolates were found to be in a range of 106.67 to 186.67 mM urea hydrolyzed h−1 (OD600)−1 (1.78 to 3.11 mM urea hydrolyzed min−1 OD600–1), which indicates that all of the isolated strains are capable of biocementation43,45.Calcium utilization and carbonate precipitation potentialIt was experimentally observed that the depletion of the supplemented soluble calcium in the precipitation media (PM) was corresponding to the ureolytic activities of the isolated strains. Within 48 h of introducing 1% bacteria (OD600 = 1) in the precipitation media, the soluble calcium chloride (50 mM) was utilized to precipitate carbonate crystals, as illustrated in Fig. 4d. Within 12 h of the inoculation period, BS3 was able to utilize 75% of the supplied calcium, while SP was able to utilize only 62.5% of the soluble calcium. The order of the calcium utilization potential in the isolates was observed as BS3 ≥ LS2  > L.S.1  > Consortia  > SP  > BS4  > BS2  > BS1 during the inoculation period. Contrarily, LS2, despite being a comparatively slow urease-producing bacteria, was able to utilize calcium ions at par with other isolates. Negligible changes were observed in the soluble calcium concentration of the control group eliminating the possibility of abiotic precipitation.The carbonate precipitation rate for each isolate (1% at OD600 = 1) for the 50 mM cementation media is plotted in Fig. 4e. The isolate BS3 with maximum ureolytic activity (specific urease activity 186.6 mM urea hydrolyzed min−1 OD600–1) precipitated the highest carbonate crystals after 96 h of the incubation period. BS3 precipitated 438 mg/100 ml of carbonate crystals, which is around 87.66% precipitation from the total supplied CaCl2, while precipitation with SP was quantified as 389 mg/100 ml (78%). The precipitation in consortia was observed to be 407 mg/100 ml (81%), which is slightly higher than SP. Precipitation in other isolates was found to be significantly lower than isolate BS3. Isolate BS1and BS2 precipitated 334 mg/100 ml (67%) and 343 mg/100 ml (69%) of carbonate crystals respectively, whereas isolate LS1 and LS2 precipitated around 357 mg/100 (71%) ml of carbonate crystals each. Isolate BS4 precipitated minimum carbonate crystals 292 mg/100 ml (58%). No precipitation was observed in the negative control set. Low concentrations of bacterial cells (1%) were considered in this experiment to slow down the urea hydrolysis in order to differentiate the calcium utilization potential of the isolated strains. This approach was modified from Dhami et al.46, and our results show agreement with their finding where 1% of SP cells depletes the 25 mM of CaCl2 within 24 h. It was observed that all the isolates took approximately 48 h to deplete the 50 mM CaCl2. The depletion of soluble calcium concentration was rapid in the initial 24 h in all the isolates. After 48 h, the residual soluble calcium was observed to be in the range of 2.5–5 mM in all the isolates (except BS1 and BS2), which might be due to loss of super-saturation caused by the unavailability of nutrient for bacterial cells to continue urea hydrolysis in the precipitation media13,43,47. The mineralogy of precipitated carbonate polymorphs (calcite, aragonite, vaterite) and the residual calcium are also influenced by pH, temperature, saturation index, dissolved organic carbon concentration, and the Ca2+ /CO32−ratio along with the presence of metabolites in the precipitation media13,47,48. As maximum precipitation was recovered with the isolate BS3, the isolate BS3 was selected for further investigation on soil improvement.Microstructure analysis of the precipitatesThe FESEM images of the carbonate crystal precipitated from BS3 were investigated further. The shape of the precipitated crystals was observed to be rhombohedral and trigonal (Fig. 5a). The average size of the crystals was observed in a range of 25 to 50 microns. The entrapped bacteria and rod-shaped bacterial imprints were identified (Fig. 5b), indicating that the bacteria acted as a nucleation site14. The smaller crystals were observed to coagulate in layers to develop larger calcite crystals. The entrapped bacteria were noticed on the grown and coagulated calcite crystal in Fig. 5c. After taking the FESEM image (Fig. 5a) of the precipitate, EDX analysis was conducted, and the elemental composition suggested an abundance of calcium, carbon, and oxygen, which indicates the presence of calcium carbonate crystals (Supplementary Fig. 1). XRD analysis was conducted to confirm the mineralogy of the precipitates, and the majority of the observed peaks of the XRD plot belonged to calcite, which is consistent with the observation of rhombohedral crystal shapes in the FESEM image. The XRD analysis also suggested an insignificant presence of aragonite in the precipitates.Figure 5FESEM images of the calcite precipitated from BS3 (a) Coagulated crystals (b) Bacterial imprints, (c) Entrapped bacteria on the precipitates.Full size imageApplication of native communities on riverbank soil and its influence on soil strengthNeedle penetration resistance of treated soilThe average NPI (N/mm) for different cases has been shown in Fig. 6a. No notable resistance was observed in the loose untreated sand (control) against the needle penetration. With one bio cementation cycle treatment, the consortia-treated soil sample (Consortia-BC1) demonstrated a higher value of NPI (5.15 N/mm) than SP-BC1 (4.19 N/mm) and BS3-BC1 (4.64 N/mm). The increase in the biocementation cycle treatment significantly improved the needle penetration resistance. Sample BS3-BC2 showed 116% improvement with the NPI value of 10.03 N/mm when compared to one cycle treated sample BS3-BC1. A similar trend was observed in the sample BS3-BC3 (NPI = 16.12 N/mm), which showed around 347% improvement in NPI when compared to sample BS3-BC1. From the needle penetration test, it was evident that the penetration resistance of treated soil improves significantly with the increased level of biocementation cycles, indirectly indicating an improvement in the soil erodibility resilience. Since non-uniformity is one of the undesired traits of MICP, a contour was plotted corresponding to the 25 points NPI, as shown in Fig. 6b. The contrasting color difference in the contours of the samples BS3-BC1, BS3-BC2, and BS-BC3 clearly demonstrates the stark difference in the strength of treated samples. The non-uniformity in the strength of treated soil crust of sample BS3-BC2 and BS3-BC3 can also be realized with the contrasting color gradient of the NPI contours.Figure 6Comparison of the Needle penetration resistance (N/mm) of the treated soil specimen (a) average values and (b) the contours.Full size imageSince the rate of penetration has an insignificant influence on the test results, the needle penetration test is recommended by the International Society of Rock Mechanics (ISRM) for quick, non-destructive testing of the strength of the stabilized soils and soft rocks49. As a large number of tests can be conducted due to the small diameter of the needle without destroying the sample, the needle penetration test is a better alternative to evaluate the local grain bonding in the biocemented soil than bulk strength properties like unconfined compressive strength and calcite content. Another rationale for choosing needle penetration test over conventional soil strength evaluation tests was that a pocket type penetrometer could be developed with the configuration in the present study for non-destructive monitoring of the soil strength improvement with biocementation application in the field. The response of the needle penetration resistance in terms of nominal strain (ratio of penetration to rod diameter) also indicated that the measured responses are independent of needle diameter for a small range, i.e., 1 to 3 mm49,50. A portable penetrometer of Maruto. Co. ltd. (needle maximum diameter 0.84 mm at 12 mm from the tip) have been correlated with high confidence value to conventional physicochemical parameters such as unconfined compressive strength (UCS), elasticity modulus, and elastic wave velocity in several studies50. In our setup, we have utilized a similar configuration chenille 22 needle with (maximum diameter 0.86 mm at 9 mm from the tip) and a penetration rate of 15 mm/minute for measuring the strength properties of cemented soil. Adopting the UCS and NPI correlation suggested by Ulusay et al.51, the UCS of samples BS3-BC1, BS3-BC2, and BS3-BC3 are around 1.67 MPa, 3.4 Mpa, and 5.3 Mpa.It is worth noting that in the needle penetration resistance tests, the boundary of the Petri dish can influence the test results. Therefore, trials were conducted and based on the findings, all penetrations were conducted at points at least five times the diameter of the needle away from the boundary to negate the influence of boundary conditions. The maximum penetration was conducted only up to 50% of the depth of prepared biocemented soil samples in the Petri dishes to avoid inference from the bottom of the Petri dish.Erodibility test in the hydraulic flumeTo investigate the influence of hydraulic current on different levels of biocementation, all the treated samples were exposed to hydraulic current gradually varying from gentle flow (0.06 m/s) to five times the critical velocity (0.75 m/s) in a 45-min duration test and soil mass loss percentage by the initial dry mass of the treated sample is presented as a measure of soil erodibility in Fig. 7. As expected, with an increase of biocementation cycles, i.e., calcite content, the soil erodibility reduced substantially. The initial dry weight of the samples control, BC1, BC2, and BC3, were measured as 398, 403, 406, and 410 g, respectively. Approximately 7.3% of calcite content resulted in a drastic reduction in erodibility (12% mass loss), while 56% soil mass loss was recorded for control (untreated sand). One biocementation cycle treatment (sample BS3-BC1) produced an average of 2.5% of calcite, reducing the soil loss to 31%. Sample BS3-BC2 with 4.93% calcite content resulted in 22% soil mass loss during the hydraulic flume test. It is worth noting that higher precipitation in the soil pores may hinder the flow of water in the soil matrix and increase the pore water pressure resulting in catastrophic failures. However, the MICP technique is reported to be a great tool to improve soil strength, maintaining an adequate hydraulic conductivity to prevent the build-up of the excess pore water pressure11. Theoretically, the percentage pore volume filled with precipitates for samples BS3-BC1, BS3-BC2, and BS3-BC3 considering the observed calcite contents and pore volume (100 ml) is around 3.7, 7.14, and 11.08%. The influence of pore water pressure on erodibility has not been established in the present study, and it certainly is one of the exciting parameters to consider for future studies.Figure 7Weight of the eroded soil (%) after the hydraulic flume test.Full size imageFrom the visual observation of the soil specimen after the flume test, it was evident that the soil particles start bonding with an increased level of bio cementation. A tough crust was formed on the top of BS3-BC2 and BS3-BC3 the samples, which got eroded with the fluvial current. Insignificant aggregation was observed in the sample BS3-BC1. However, with two and three cycles of biocementation treatment (BS3-BC2 and BS3-BC3), the biocemented soil particles (BCS) were evidently noticed (photos are shown in Supplementary Fig. 4).Clarà Saracho et al.27 addressed the erosion due to tangential flow (similar to river current) by treating the soil specimen with ten pore volume of low concentration of cementation media (0.02 M to 0.1 M) by injection strategy and tested the specimen in the flow velocity ranging from 0.035 to 0.185 m/sec for 120 min in a modified erosion function apparatus (EFA). The study concluded that the treatment with 0.08 M cementation media (calcite content varying from 1.2 to 4%) resulted in negligible erosion in the stated test conditions, and with the increase in MICP treatment, a shift in the erosion mode from particulate mode to block failure was observed indicating that with the increase in calcite content and needle penetration resistance, there might be a threshold for biocemented soil, where the soil erosion might be catastrophic due to block failure. However, in this study, a consistent decrease in soil erodibility is observed with the increase in needle penetration resistance. We found that 7.3% of calcite content was required to control the soil erodibility substantially in the test flow range (0.06 m/s to 0.75 m/s). A similar trend was observed by Kou et al.52 and Chung et al.53, where consistent reduction in wave-induced erosion and rainfall-induced erosion was observed with an increase in needle penetration resistance for biocemented fine sand treated with the exogenous bacteria.Another aspect to note in the context of the applied treatment is the produced ammonia which can be toxic for riparian flora and fauna15. From stochiometric calculations, for each biocementation cycle, the produced ammonia is evaluated as 8.5 kg per metric ton of soil treated, and for the best performing treatment approach, i.e., BS3-BC3, the ammonia generated is evaluated as 2.63% by weight of the retained soil. The acceptable limit of ammonia in the surface water was recommended as 17 mg per liter for acute exposure and 1.9 mg/l for chronic exposure for protecting the aquatic life in the freshwater as per the environmental protection agency54. With the MICP technique, the threat of produced ammonia crossing the maximum acceptable quantity is highly plausible; however, for the field application, the ammonia generated can be reduced by reducing the quantity of reagents and increasing the period of applications. It is to be noted that the produced ammonia will also be subjected to dilution in the river stream. The average discharge of the Brahmaputra river is around 19.8 megaliters per second in the Assam valley33. Therefore, the area of the riverbank to be biocemented must be decided judiciously with the context of the produced ammonia quantity and its possible dilution to non-toxic levels.Microstructure and mineralogical analysis of the biocemented samplesTo investigate the influence of different biocementation levels on the erodibility of the treated sand grains, FESEM imaging was conducted for light biocemented samples (BS3-BC1) and heavy biocemented samples (BS3-BC3). While precipitated crystals were observed to be growing on the grooves of sand grains in the light biocemented sample (BS3-BC1), bridging of sand grains with rhombohedral crystals was observed in the heavy biocemented sample (BS3-BC3), as shown in Fig. 8. The effective calcium carbonate bridging between sand grains increases the frictional and cohesive property of sand grains55,56, leading to a substantial reduction in the erodibility of the soil. Bacterial imprints were observed in both cases, suggesting the precipitation to be biodgenic14. Further EDX analysis on a bridged sand grain (Supplementary Fig. 5) suggested an abundance of silicon and oxygen on the sand grains with a trace amount of chlorine and calcium. This indicates the presence of residual calcium chloride on silica grains. The EDX analysis on the grain bridge indicated the presence of calcium, carbon, and oxygen, suggesting CaCO3 precipitation. XRD analysis on treated and untreated sand confirmed the precipitation of calcite. Most of the peaks correspond to quartz (silica). In the biocemented sand sample, a visible peak of calcite was observed at around 29 degrees of 2Ɵ (Details in Supplementary Fig. 6). Therefore, the incorporation of microbial calcite as a binding agent for loose grain silica soil was found to reduce the soil erodibility.Figure 8FESEM images of the treated sand grains (a). Calcite crystal growing on the sand grains on BS3- BC1 sample (b). Calcite bridging in BS3-BC3 samples.Full size image More

1.Pagano, M. C. & Miransari, M. The importance of soybean production worldwide. In Abiotic and Biotic Stresses in Soybean Production Vol. 1 (ed. Miransari, M.) 1–26 (Academic Press, 2016).
Google Scholar 
2.FAOSTAT. Food and Agriculture Organisation Statistical Database http://www.apps.fao.org/faostat. Accessed 23 May 2021.3.MacDonald, R. S. et al. Environmental influences on isoflavones and saponins in soybeans and their role in colon cancer. J. Nutr. 135, 1239–1242 (2005).CAS 
PubMed 
Article 

Google Scholar 
4.Tidke, S. A. et al. Assessment of anticancer, anti-inflammatory and antioxidant properties of isoflavones present in soybean. Res. J. Phytochem. 12, 35–42 (2018).ADS 
CAS 
Article 

Google Scholar 
5.Hill, J. H. & Whitham, S. A. Control of virus diseases in soybeans. Adv. Virus Res. 90, 355–390 (2014).PubMed 
Article 

Google Scholar 
6.Tian, B. et al. Host adaptation of soybean dwarf virus following serial passages on pea (Pisum sativum) and soybean (Glycine max). Viruses 9, 155 (2017).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
7.Wang, R. Y., Kritzman, A., Hershman, D. E. & Ghabrial, S. A. Aphis glycines as a vector of persistently and nonpersistently transmitted viruses and potential risks for soybean and other crops. Plant Dis. 90, 920–926 (2006).CAS 
PubMed 
Article 

Google Scholar 
8.Hesler, L. S., Dashiell, E., Jonathan, A. E. & Lundgren, G. Characterization of resistance to Aphis glycines in soybean accessions. Euphytica 154, 91–99 (2007).Article 

Google Scholar 
9.Baldin, E. L. L. et al. Feeding behavior of Aphis glycines (Hemiptera: Aphididae) on soybeans exhibiting antibiosis, antixenosis, and tolerance resistance. Fla. Entomol. 101, 223–228 (2018).Article 

Google Scholar 
10.Chang, H.-X. & Hartman, G. L. Characterization of insect resistance loci in the USDA soybean germplasm collection using genome-wide association studies. Front. Plant Sci. 8, 670. https://doi.org/10.3389/fpls.2017.00670 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Bansal, R., Mian, M. A. R. & Michel, A. Characterizing resistance to soybean aphid (Hemiptera: Aphididae): Antibiosis and antixenosis assessment. J. Econ. Entomol. https://doi.org/10.1093/jee/toab038 (2021).Article 
PubMed 

Google Scholar 
12.Klein, A. T. et al. Investigation of the chemical interface in the soybean−aphid and rice−bacteria interactions using MALDI-Mass Spectrometry Imaging. Anal. Chem. 87, 5294–5301 (2015).CAS 
PubMed 
Article 

Google Scholar 
13.Hohenstein, J. D. et al. Transcriptional and chemical changes in soybean leaves in response to long-term aphid colonization. Front. Plant Sci. 10, 310 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
14.Blackman, R. L. & Eastop, V. F. Taxonomic issues. In Aphids as Crop Pests (eds van Emden, H. F. & Harrington, R.) 1–36 (CABI, 2017).
Google Scholar 
15.Wale, M., Jembere, B. & Seyoum, E. Occurrence of the pea aphid, Acyrthosiphon pisum (Harris) (Homoptera: Aphididae) on wild leguminous plants in West Gojam, Ethiopia, Sinet. Ethiopian J. Sci. 26, 83–87 (2003).
Google Scholar 
16.Chan, C. K., Forbes, A. R. & Raworth, D. A. Aphid-transmitted viruses and their vectors of the world. Agric. Can. Tech. Bull. 3E, 1–216 (1991).
Google Scholar 
17.Rashed, A. et al. Vector-borne viruses of pulse crops, with a particular emphasis on North American cropping system. Ann. Entomol. Soc. Am. 111, 205–227 (2018).CAS 
Article 

Google Scholar 
18.Stavrinides, J., McCloskey, J. K. & Ochman, H. Pea Aphid as both host and vector for the phytopathogenic bacterium Pseudomonas syringae. Appl. Environ. Microbiol. 75, 2230–2235 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Peccoud, J., Ollivier, A., Plantegenest, M. & Simon, J.-C. A continuum of genetic divergence from sympatric host races to species in the pea aphid complex. PNAS 106, 7495–7500 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Caillaud, M. C. & Via, S. Specialized feeding behavior influences both ecological specialization and assortative mating in sympatric host races of pea aphids. Am. Nat. 156, 606–621 (2000).PubMed 
Article 

Google Scholar 
21.Ferrari, J., Godfray, H. C., Faulconbridge, A. S., Prior, K. & Via, S. Population differentiation and genetic variation in host choice among pea aphids from eight host plant genera. Evolution 60, 1574–1584 (2006).PubMed 
Article 

Google Scholar 
22.Mitku, G. & Damte, T. Development, reproduction, and host preference of Acyrthosiphon pisum (Harris) (Homoptera: Aphididae) on selected lentil genotypes and resistance index of these selected lentil genotypes to pea aphid. Int. J. Entomol. Res. 4, 16–22 (2019).
Google Scholar 
23.Powell, G., Tosh, C. R. & Hardie, J. Host plant selection by aphids: behavioral, evolutionary, and applied perspectives. Annu. Rev. Entomol. 51, 309–330 (2006).CAS 
PubMed 
Article 

Google Scholar 
24.Kordan, B. et al. European yellow lupine Lupinus luteus and narrow-leaf lupine Lupinus angustifolius as hosts for the pea aphid Acyrthosiphon pisum. Entomol. Exp. Appl. 128, 139–146 (2008).Article 

Google Scholar 
25.Kordan, B. et al. Susceptibility of forage legumes to infestation by the pea aphid Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae). Crop Pasture Sci. 69, 775–784 (2018).Article 

Google Scholar 
26.Kordan, B. et al. Antixenosis potential in pulses against the pea aphid (Hemiptera: Aphididae). J. Econ. Entomol. 112, 465–474 (2019).PubMed 
Article 

Google Scholar 
27.Pettersson, J., Tjallingii, W. F. & Hardie, J. Host-plant selection and feeding. In Aphids as Crop Pests (eds van Emden, H. F. & Harrington, R.) 173–195 (CABI, 2017).Chapter 

Google Scholar 
28.Martin, B., Collar, J. L., Tjallingi, W. F. & Fereres, A. Intracellular ingestion and salivation by aphids may cause the acquisition and inoculation of non-persistently transmitted plant viruses. J. Gen. Virol. 78, 2701–2705 (1997).CAS 
PubMed 
Article 

Google Scholar 
29.Garzo, E., Moreno, A., Plaza, M. & Fereres, A. Feeding behavior and virus-transmission ability of insect vectors exposed to systemic insecticides. Plants 9, 895. https://doi.org/10.3390/plants9070895 (2020).CAS 
Article 
PubMed Central 
PubMed 

Google Scholar 
30.Onstad, D. W. & Knolhoff, L. Arthropod resistance to crops. In Insect Resistance Management (ed. Onstad, D. W.) 293–326 (Academic Press, 2014).Chapter 

Google Scholar 
31.Smith, C. M. & Clement, S. L. Molecular bases of plant resistance to arthropods. Annu. Rev. Entomol. 57, 309–328 (2012).CAS 
PubMed 
Article 

Google Scholar 
32.Stout, M. J. Reevaluating the conceptual framework for applied research on host-plant resistance. Insect Sci. 20, 263–272 (2013).PubMed 
Article 

Google Scholar 
33.Smith, C. M. & Chuang, W. P. Plant resistance to aphid feeding: behavioral, physiological, genetic and molecular cues regulate aphid host selection and feeding. Pest Manag. Sci. 70, 528–540 (2014).PubMed 
Article 
CAS 

Google Scholar 
34.Dogimont, C., Bendahmane, A., Chovelon, V. & Boissot, N. Host plant resistance to aphids in cultivated crops: Genetic and molecular bases, and interactions with aphid populations. C. R. Biol 333, 566–573 (2010).CAS 
PubMed 
Article 

Google Scholar 
35.Chandran, P. et al. Feeding behavior comparison of soybean aphid (Hemiptera: Aphididae) biotypes on different soybean genotypes. J. Econ. Entomol. 106, 2234–2240 (2013).PubMed 
Article 

Google Scholar 
36.Simmonds, M. S. J. Flavonoid-insect interactions: Recent advances in our knowledge. Phytochemistry 64, 21–30 (2003).CAS 
Article 

Google Scholar 
37.Mai, V. C. et al. Differential induction of Pisum sativum defense signaling molecules in response to pea aphid infestation. Plant Sci. 221–222, 1–12 (2014).PubMed 
Article 
CAS 

Google Scholar 
38.Morkunas, I. et al. Pea aphid infestation induces changes in flavonoids, antioxidative defence, soluble sugars and sugar transporter expression in leaves of pea seedlings. Protoplasma 253, 1063–1079 (2016).CAS 
PubMed 
Article 

Google Scholar 
39.Woźniak, A. et al. The dynamics of the defense strategy of pea induced by exogenous nitric oxide in response to aphid infestation. Int. J. Mol. Sci. 18, 329. https://doi.org/10.3390/ijms18020329 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
40.Buer, C. S., Muday, G. K. & Djordjevic, M. A. Implications of long-distance flavonoid movement in Arabidopsis thaliana. Plant Signal. Behav. 3, 415–417 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Petrussa, E. et al. Plant flavonoids: Biosynthesis, transport and involvement in stress responses. Int. J. Mol. Sci. 14, 14950–14973 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Zhao, J. Flavonoid transport mechanisms: How to go, and with whom. Trends Plant Sci. 20, 576–585 (2015).CAS 
PubMed 
Article 

Google Scholar 
43.Alseekh, S., de Souza, L. P., Benina, M. & Fernie, A. L. The style and substance of plant flavonoid decoration; Towards defining both structure and function. Phytochemistry 174, 112347. https://doi.org/10.1016/j.phytochem.2020.112347 (2020).CAS 
Article 
PubMed 

Google Scholar 
44.Klingauf, F. A. Host plant finding and acceptance. In Aphids, Their Biology, Natural Enemies and Control Vol. 2 (eds Minks, A. K. & Harrewijn, P.) 209–223 (Elsevier, 1987).
Google Scholar 
45.Tjallingii, W. F. & Mayoral, A. M. Criteria for host plant acceptance by aphids. In Proceeding 8th International Symposium Insect–Plant Relationships (eds Menken, S. B. J. et al.) 280–282 (Kluwer Academic Publishers, 1992).Chapter 

Google Scholar 
46.Wensler, R. J. & Filshie, B. K. Gustatory sense organs in the food canal of aphids. J. Morph. 129, 473–492 (1969).Article 

Google Scholar 
47.Gabryś, B. & Tjallingii, W. F. The role of sinigrin in host plant recognition by aphids during initial plant penetration. Entomol. Exp. Appl. 104, 89–93 (2002).Article 

Google Scholar 
48.Philippi, J. et al. Correlation of the alkaloid content and composition of narrow-leafed lupins (Lupinus angustifolius L.) to aphid susceptibility. J. Pest Sci. 89, 359–373 (2016).Article 

Google Scholar 
49.Van Hoof, H. A. An investigation of the biological transmission of a non-persistent virus. Doctoral thesis (Van Putten and Oortmijer, 1958).50.Dancewicz, K., Szumny, A., Wawrzeńczyk, C. & Gabryś, B. Repellent and antifeedant activities of citral-derived lactones against the peach potato aphid. Int. J. Mol. Sci. 21, 8029. https://doi.org/10.3390/ijms21218029 (2020).CAS 
Article 
PubMed Central 
PubMed 

Google Scholar 
51.Kordan, B. et al. Variation in susceptibility of rapeseed cultivars to the peach potato aphid. J. Pest. Sci. 94, 435–449 (2021).Article 

Google Scholar 
52.Pritchard, J. & Vickers, L. H. Aphids and stress. In Aphids as Crop Pests (eds Van Emden, H. F. & Harrington, R.) 132–147 (CABI, 2017).Chapter 

Google Scholar 
53.Pompon, J. & Pelletier, Y. Changes in aphid probing behaviour as a function of insect age and plant resistance level. Bull. Entomol. Res. 102, 550–557 (2012).CAS 
PubMed 
Article 

Google Scholar 
54.van Emden, H. F. Host-plant resistance. In Aphids as Crop Pests (eds van Emden, H. F. & Harrington, R.) 515–532 (CABI, 2017).Chapter 

Google Scholar 
55.Gould, K. S. & Lister, C. Flavonoid functions in plants. In Flavonoids, Chemistry, Biochemistry and Applications (eds Andersen, Ø. M. & Markham, K. R.) 397–442 (CRC Press, 2006).
Google Scholar 
56.Goławska, S., Kapusta, I., Łukasik, I. & Wójcicka, A. Effect of phenolics on the pea aphid, Acyrthosiphon pisum (Harris) population on Pisum sativum L. (Fabaceae). Pestycydy. 3–4, 71–77 (2008).
Google Scholar 
57.Goławska, S. & Łukasik, I. Antifeedant activity of luteolin and genistein against the pea aphid, Acyrthosiphon pisum. J. Pest Sci. 85, 443–450 (2012).Article 

Google Scholar 
58.Goławska, S. et al. Alfalfa (Medicago sativa L.) apigenin glycosides and their effect on the pea aphid (Acyrthosiphon pisum). Polish J. Environ. Stud. 19, 913–919 (2010).
Google Scholar 
59.Johnson, A. D. & Singh, A. Larvicidal activity and biochemical effects of apigenin against filarial vector Culex quinquefasciatus. Int. J. Life. Sci. Sci. Res. 3, 1315–1321 (2017).
Google Scholar 
60.Boué, S. M. & Raina, A. K. Effects of plant flavonoids on fecundity, survival, and feeding of the formosan subterranean termite. J. Chem. Ecol. 29, 2575–2584 (2003).PubMed 
Article 

Google Scholar 
61.Xu, D. et al. Antifeedant activities of secondary metabolites from Ajuga nipponensis against adult of striped flea beetles, Phyllotreta striolata. J. Pest Sci. 82, 195–202 (2009).Article 

Google Scholar 
62.Goławska, S., Sprawka, I. & Łukasik, I. Effect of saponins and apigenin mixtures on feeding behavior of the pea aphid, Acyrthosiphon pisum Harris. Biochem. Syst. Ecol. 55, 137–144 (2014).Article 
CAS 

Google Scholar 
63.Zavala, J. A., Scopel, A. L. & Ballaré, C. L. Effects of ambient UV-B radiation on soybean crops: Impact on leaf herbivory by Anticarsia gemmatalis. Plant Ecol. 156, 121–130 (2001).Article 

Google Scholar 
64.Bentivenha, J. P. F. et al. Role of the rutin and genistein flavonoids in soybean resistance to Piezodorus guildinii (Hemiptera: Pentatomidae). Arthropod Plant Interact. 12, 311–320 (2018).Article 

Google Scholar 
65.Hoffmann-Campo, C. B., Harborne, J. B. & McCaffery, A. R. Pre-ingestive and post-ingestive effects of soya bean extracts and rutin on Trichoplusia ni growth. Entomol. Exp. Appl. 98, 181–194 (2001).Article 

Google Scholar 
66.Yuan, E. et al. Increases in genistein in Medicago sativa confer resistance against the Pisum host race of Acyrthosiphon pisum. Insects. 10, 97. https://doi.org/10.3390/insects10040097 (2019).Article 
PubMed Central 
PubMed 

Google Scholar 
67.Meng, F. et al. QTL underlying the resistance to soybean aphid (Aphis glycines Matsumura) through isoflavone-mediated antibiosis in soybean cultivar ‘Zhongdou 27’. Theor Appl. Genet. 123, 1459–1465 (2011).CAS 
PubMed 
Article 

Google Scholar 
68.Murakami, S. et al. Insect-induced daidzein, formononetin and their conjugates in soybean leaves. Metabolites 4, 532–546 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Lattanzio, V. et al. Role of endogenous flavonoids in resistance mechanism of Vigna to aphids. J. Agric. Food Chem. 48, 5316–5320 (2000).CAS 
PubMed 
Article 

Google Scholar 
70.Delgado-Núñez, E. J. et al. Isorhamnetin: A nematocidal flavonoid from Prosopis laevigata leaves against Haemonchus contortus eggs and larvae. Biomolecules 10, 773 (2020).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
71.Gómez, J. D., Vital, C. E., Oliveira, M. G. A. & Ramos, H. J. O. Broad range flavonoid profiling by LC/MS of soybean genotypes contrasting for resistance to Anticarsia gemmatalis (Lepidoptera: Noctuidae). PLoS ONE https://doi.org/10.1371/journal.pone.0205010 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Khan, M. A. M., Ulrichs, C. & Mewis, I. Effect of water stress and aphid herbivory on flavonoids in broccoli (Brassica oleracea var. italica Plenck). J. Appl. Bot. Food Qual. 84, 178–182 (2011).CAS 

Google Scholar 
73.Bale, J. S., Ponder, K. L. & Pritchard, J. Coping with stress. In Aphids as Crop Pests (eds van Emden, H. F. & Harrington, R.) 287–309 (CABI, 2007).Chapter 

Google Scholar 
74.Atteyat, M., Abu-Romman, S., Abu-Darwish, M. & Ghabeish, I. Impact of flavonoids against woolly apple aphid, Eriosoma lanigerum (Hausmann) and its sole parasitoid, Aphelinus mali (Hald). J. Agric. Sci. 4, 227–236 (2012).
Google Scholar 
75.Tjallingii, W. F. & Hogen Esch, T. Fine structure of aphid stylet routes in plant tissues in correlation with EPG signals. Physiol. Entomol. 18, 317–328 (1993).Article 

Google Scholar 
76.Cherqui, A. & Tjallingii, W. F. Salivary proteins of aphids, a pilot study on identification, separation and immunolocalisation. J. Insect Physiol. 46, 1177–1186 (2000).CAS 
PubMed 
Article 

Google Scholar 
77.Silva-Sanzana, C., Estevez, J. M. & Blanco-Herrera, F. Influence of cell wall polymers and their modifying enzymes during plant–aphid interactions. J. Exp. Bot. 71, 3854–3864 (2020).CAS 
PubMed 
Article 

Google Scholar 
78.Alvarez, A. E. et al. Location of resistance factors in the leaves of potato and wild tuber-bearing Solanum species to aphid Myzus persicae. Entomol. Exp. Appl. 121, 145–157 (2006).Article 

Google Scholar 
79.Alvarez, A. E. et al. Infection of potato plants with potato leafroll virus changes attraction and feeding behaviour of Myzus persicae. Entomol. Exp. Appl. 125, 135–144 (2007).Article 

Google Scholar 
80.Machado-Assefh, C. R. & Alvarez, A. E. Probing behavior of aposymbiotic green peach aphid (Myzus persicae) on susceptible Solanum tuberosum and resistant Solanum stoloniferum plants. Insect Sci. 25, 127–136 (2018).CAS 
PubMed 
Article 

Google Scholar 
81.Common Catalogue of Varieties of Agricultural Plant Species [CCA]. 37th complete edition. Official Journal of the European Union C 13/1 (2019). Accessed 23 May 2021.82.Porejestrowe doświadczalnictwo odmianowe. Charakterystyka odmian. http://www.coboru.gov.pl/Polska/Rejestr/odm_w_rej.aspx?kodgatunku=SOS. Accessed 23 May 2021.83.Meier, U. Growth stages of mono- and dicotyledonous plants: BBCH. Monograph (Julius Kühn-Institut, 2018).84.Beer, K., Joschinski, J., Sastre, A. A., Kraus, J. & Helfrich-Forster, C. A damping circadian clock drives weak oscillations in metabolism and locomotor activity of aphids (Acyrthosiphon pisum). Sci. Rep. 7, 1–5. https://doi.org/10.1038/s41598-017-15014-3 (2017).CAS 
Article 

Google Scholar 
85.Joschinski, J., Beer, K., Helfrich-Forster, C. & Krauss, J. Pea aphids (Hemiptera: Aphididae) have diurnal rhythms when raised independently of a host plant. J. Insect. Sci. 16, 1–5 (2016).Article 

Google Scholar 
86.Graham, T. L. Flavonoid and isoflavonoid distribution in developing soybean seedling tissues and in seed and root exudates. Plant Physiol. 95, 594–603 (1991).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Lee, J. H. et al. Characterization of isoflavones accumulation in developing leaves of soybean (Glycine max) cultivars. J. Korean Soc. Appl. Biol. Chem. 52(2), 139–143 (2009).CAS 
Article 

Google Scholar 
88.Magarelli, G. et al. Rutin and total isoflavone determination in soybean at different growth stages by using voltammetric methods. Microchem. J. 117, 149–155 (2014).CAS 
Article 

Google Scholar 
89.Perlatti, B. et al. Application of a quantitative HPLC-ESI-MS/MS method for flavonoids in different vegetables matrices. J. Braz. Chem. Soc. 27(3), 475–483 (2016).CAS 

Google Scholar 
90.Biesaga, M. & Pyrzyńska, K. Stability of bioactive polyphenols from honey during different extraction methods. Food Chem. 136, 46–54 (2013).CAS 
PubMed 
Article 

Google Scholar 
91.Sergiel, I., Pohl, P. & Biesaga, M. Characterisation of honeys according to their content of phenolic compounds using high performance liquid chromatography/tandem mass spectrometry. Food Chem. 145, 404–408 (2014).CAS 
PubMed 
Article 

Google Scholar  More

1.Arbour, J. H. & Santana, S. E. A major shift in diversification rate helps explain macroevolutionary patterns in primate species diversity. Evolution 71, 1600–1613 (2017).PubMed 
Article 

Google Scholar 
2.Groves, C. Primates (Taxonomy) in The International Encyclopedia of Primatology (ed Augustin Fuentes) (John Wiley & Sons, Inc., 2016).3.Cotton, A., Clark, F., Boubli, J. & Schwitzer, C. IUCN red list of threatened primate species in An Introduction to Primate Conservation 31–18 (Oxford University Press, 2016).
Google Scholar 
4.Stumpf, R. M. et al. Microbiomes, metagenomics, and primate conservation: New strategies, tools, and applications. Biol. Conserv. 199, 56–66 (2016).Article 

Google Scholar 
5.West, A. G. et al. The microbiome in threatened species conservation. Biol. Conserv. 229, 85–98 (2019).Article 

Google Scholar 
6.Cunningham, A. A., Daszak, P. & Wood, J. L. N. One Health, emerging infectious diseases and wildlife: two decades of progress?. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160167 (2017).Article 

Google Scholar 
7.Ramey, A. M. & Ahlstrom, C. A. Antibiotic resistant bacteria in wildlife: Perspectives on trends, acquisition and dissemination, data gaps, and future directions. J. Wildl. Dis. 56, 1–15 (2020).PubMed 
Article 

Google Scholar 
8.Clayton, J. B. et al. Captivity humanizes the primate microbiome. Proc. Natl. Acad. Sci. 113, 10376–10381 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Hale, V. L. et al. Gut microbiota in wild and captive Guizhou snub-nosed monkeys. Rhinopithecus brelichi. Am. J. Primatol. 81, e22989 (2019).CAS 
PubMed 

Google Scholar 
10.Kriss, M., Hazleton, K. Z., Nusbacher, N. M., Martin, C. G. & Lozupone, C. A. Low diversity gut microbiota dysbiosis: drivers, functional implications and recovery. Curr. Opin. Microbiol. 44, 34–40 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Mahnert, A. et al. Man-made microbial resistances in built environments. Nat. Commun. 10, 1–12 (2019).CAS 
Article 

Google Scholar 
12.Amato, K. R. et al. Using the gut microbiota as a novel tool for examining colobine primate GI health. Glob. Ecol. Conserv. 7, 225–237 (2016).Article 

Google Scholar 
13.Zhu, H. et al. Diarrhea-associated intestinal microbiota in captive Sichuan golden snub-nosed monkeys (Rhinopithecus roxellana). Microbes Environ. ME17163 (2018).14.Campbell, T. P. et al. The microbiome and resistome of chimpanzees, gorillas, and humans across host lifestyle and geography. ISME J. 14, 1584–1599 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Buzzard, P. J. Ecological partitioning of Cercopithecus campbelli, C. petaurista, and C. diana in the Taï Forest. Int. J. Primatol. 27, 529–558 (2006).Article 

Google Scholar 
16.Chapman, C. A. et al. The guenons: diversity and adaptation in African monkeys. 325–350 (Springer, 2004).17.Krishnadas, M., Chandrasekhara, K. & Kumar, A. The response of the frugivorous lion-tailed macaque (Macaca silenus) to a period of fruit scarcity. Am. J. Primatol. 73, 1250–1260 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Swedell, L., Hailemeskel, G. & Schreier, A. Composition and seasonality of diet in wild hamadryas baboons: preliminary findings from Filoha. Folia Primatol. 79, 476–490 (2008).Article 

Google Scholar 
19.Basabose, A. K. Diet composition of chimpanzees inhabiting the montane forest of Kahuzi, Democratic Republic of Congo. Am. J. Primatol. 58, 1–21 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
20.McLennan, M. R. & Ganzhorn, J. U. Nutritional characteristics of wild and cultivated foods for chimpanzees (Pan troglodytes) in agricultural landscapes. Int. J. Primatol. 38, 122–150 (2017).Article 

Google Scholar 
21.Newton-Fisher, N. E. The diet of chimpanzees in the Budongo Forest Reserve Uganda. Afr. J. Ecol. 37, 344–354 (1999).Article 

Google Scholar 
22.Bach, T. H., Chen, J., Hoang, M. D., Beng, K. C. & Nguyen, V. T. Feeding behavior and activity budget of the southern yellow-cheeked crested gibbons (Nomascus gabriellae) in a lowland tropical forest. Am. J. Primatol. 79, e22667 (2017).Article 

Google Scholar 
23.Fan, P.-F., Fei, H.-L., Scott, M. B., Zhang, W. & Ma, C.-Y. Habitat and food choice of the critically endangered cao vit gibbon (Nomascus nasutus) in China: implications for conservation. Biol. Conserv. 144, 2247–2254 (2011).Article 

Google Scholar 
24.Fan, P. F., Fei, H. L. & Ma, C. Y. Behavioral responses of cao vit gibbon (Nomascus nasutus) to variations in food abundance and temperature in Bangliang, Jingxi China. Am. J. Primatol. 74, 632–641 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
25.McConkey, K. R., Ario, A., Aldy, F. & Chivers, D. J. Influence of forest seasonality on gibbon food choice in the rain forests of Barito Ulu Central Kalimantan. Int. J. Primatol. 24, 19–32 (2003).Article 

Google Scholar 
26.Amora, T. D., BeltrÃO-Mendes, R. & Ferrari, S. F. Use of alternative plant resources by common marmosets (Callithrix jacchus) in the semi-arid Caatinga scrub forests of northeastern Brazil. Am. J. Primatol. 75, 333–341 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Dietz, J. M., Peres, C. A. & Pinder, L. Foraging ecology and use of space in wild golden lion tamarins (Leontopithecus rosalia). Am. J. Primatol. 41, 289–305 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Garber, P. A. Feeding ecology and behaviour of the genus Saguinus. Marmosets and tamarins: systematics behaviour and ecology (1993).29.Heymann, E. W., Knogge, C. & Tirado Herrera, E. R. Vertebrate predation by sympatric tamarins, Saguinus mystax and Saguinus fuscicollis. Am. J. Primatol. 51, 153–158 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Porter, L. M. Dietary differences among sympatric Callitrichinae in northern Bolivia: Callimico goeldii, Saguinus fuscicollis and S. labiatus. Int. J. Primatol. 22, 961–992 (2001).Article 

Google Scholar 
31.Anapol, F. & Lee, S. Morphological adaptation to diet in platyrrhine primates. Am. J. Phys. Anthropol. 94, 239–261 (1994).CAS 
PubMed 
Article 

Google Scholar 
32.Nash, L. T. Dietary, behavioral, and morphological aspects of gummivory in primates. Am. J. Phys. Anthropol. 29, 113–137 (1986).Article 

Google Scholar 
33.Abreu, F., De la Fuente, M. F. C., Schiel, N. & Souto, A. Feeding ecology and behavioral adjustments: flexibility of a small neotropical primate (Callithrix jacchus) to survive in a semiarid environment. Mammal Res. 61, 221–229 (2016).Article 

Google Scholar 
34.Cunha, A. A., Vieira, M. V. & Grelle, C. E. V. Preliminary observations on habitat, support use and diet in two non-native primates in an urban Atlantic forest fragment: the capuchin monkey (Cebus sp.) and the common marmoset (Callithrix jacchus) in the Tijuca forest Rio de Janeiro. Urban Ecosyst. 9, 351–359 (2006).Article 

Google Scholar 
35.Passamani, M. & Rylands, A. B. Feeding behavior of Geoffroy’s marmoset (Callithrix geoffroyi) in an Atlantic forest fragment of south-eastern Brazil. Primates 41, 27–38 (2000).PubMed 
Article 

Google Scholar 
36.Veracini, C. Habitat use and ranging behavior of the silvery marmoset (Mico argentatus) at Caxiuanã National Forest (eastern Brazilian Amazonia) in The smallest anthropoids 221–240 (Springer, 2009).37.Yépez, P., De La Torre, S. & Snowdon, C. T. Interpopulation differences in exudate feeding of pygmy marmosets in Ecuadorian Amazonia. Am. J. Primatol. 66, 145–158 (2005).PubMed 
Article 

Google Scholar 
38.Hale, V. L. et al. Diet versus phylogeny: a comparison of gut microbiota in captive colobine monkey species. Microb. Ecol. 75, 515–527 (2018).PubMed 
Article 

Google Scholar 
39.Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69, 434–443 (2015).CAS 
PubMed 
Article 

Google Scholar 
40.Frankel, J. S., Mallott, E. K., Hopper, L. M., Ross, S. R. & Amato, K. R. The effect of captivity on the primate gut microbiome varies with host dietary niche. Am. J. Primatol. 81, e23061 (2019).PubMed 
Article 

Google Scholar 
41.McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Lugli, G. A. et al. Evolutionary development and co‐phylogeny of primate‐associated bifidobacteria. Environ. Microbiol. (2020).43.Milani, C. et al. Unveiling bifidobacterial biogeography across the mammalian branch of the tree of life. ISME J. 11, 2834–2847 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Lugli, G. A. et al. Comparative genomic and phylogenomic analyses of the Bifidobacteriaceae family. BMC Genom. 18, 568 (2017).Article 
CAS 

Google Scholar 
45.Pokusaeva, K., Fitzgerald, G. F. & van Sinderen, D. Carbohydrate metabolism in Bifidobacteria. Genes Nutr. 6, 285–306 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Orkin, J. D. et al. Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest. ISME J. 13, 183–196 (2019).CAS 
PubMed 
Article 

Google Scholar 
48.Neuzil-Bunesova, V. et al. Five novel bifidobacterial species isolated from faeces of primates in two Czech zoos: Bifidobacterium erythrocebi sp. nov., Bifidobacterium moraviense sp. nov., Bifidobacterium oedipodis sp. nov., Bifidobacterium olomucense sp. nov. and Bifidobacterium panos sp. nov. Int. J. Syst. Evol. Microbiol. (2020).49.Duranti, S. et al. Characterization of the phylogenetic diversity of two novel species belonging to the genus Bifidobacterium: Bifidobacterium cebidarum sp. Nov. and Bifidobacterium leontopitheci sp. nov.. Int. J. Syst. Evol. Microbiol. 70, 2288–2297 (2020).CAS 
PubMed 
Article 

Google Scholar 
50.Modesto, M. et al. Bifidobacterium primatium sp. nov., Bifidobacterium scaligerum sp. nov., Bifidobacterium felsineum sp. nov. and Bifidobacterium simiarum sp. nov.: Four novel taxa isolated from the faeces of the cotton top tamarin (Saguinus oedipus) and the emperor tamarin (Saguinus imperator). Syst. Appl. Microbiol. (2018).51.Neuzil-Bunesova, V. et al. Bifidobacterium canis sp nov a novel member of the Bifidobacterium pseudolongum phylogenetic group isolated from faeces of a dog (Canis lupus f. familiaris). Int. J. Syst. Evol. Microbiol. 70, 5040–5047 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Vlková, E. et al. A new medium containing mupirocin, acetic acid, and norfloxacin for the selective cultivation of bifidobacteria. Anaerobe 34, 27–33 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Carding, S., Verbeke, K., Vipond, D. T., Corfe, B. M. & Owen, L. J. Dysbiosis of the gut microbiota in disease. Microb. Ecol. Health Dis. 26, 26191 (2015).
Google Scholar 
54.WagnerMackenzie, B. et al. Bacterial community collapse: a meta-analysis of the sinonasal microbiota in chronic rhinosinusitis. Environ. Microbiol. 19, 381–392 (2017).CAS 
Article 

Google Scholar 
55.Arboleya, S., Watkins, C., Stanton, C. & Ross, R. P. Gut bifidobacteria populations in human health and aging. Front. Microbiol. 7 (2016).56.Binda, C. et al. Actinobacteria: a relevant minority for the maintenance of gut homeostasis. Dig. Liver Dis. 50, 421–428 (2018).MathSciNet 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Tojo, R. et al. Intestinal microbiota in health and disease: role of bifidobacteria in gut homeostasis. World J. Gastroenterol. 20, 15163 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Rodriguez, C. I. & Martiny, J. B. H. Evolutionary relationships among bifidobacteria and their hosts and environments. BMC Genom. 21, 1–12 (2020).Article 

Google Scholar 
59.Sharma, V., Mobeen, F. & Prakash, T. Exploration of survival traits, probiotic determinants, host interactions, and functional evolution of bifidobacterial genomes using comparative genomics. Genes 9, 477 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
60.Sun, Z. et al. Comparative genomic analysis of 45 type strains of the genus Bifidobacterium. a snapshot of its genetic diversity and evolution. PLoS One 10, 0117912 (2015).
Google Scholar 
61.Frey, J. C. et al. Fecal bacterial diversity in a wild gorilla. Appl. Environ. Microbiol. 72, 3788–3792 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Makovska, M., Modrackova, N., Bolechova, P., Drnkova, B. & Neuzil-Bunesova, V. Antibiotic susceptibility screening of primate-associated Clostridium ventriculi. Anaerobe, 102347 (2021).63.Ushida, K. et al. Draft genome sequences of Sarcina ventriculi strains isolated from wild Japanese macaques in Yakushima Island. Genome announcements 4 (2016).64.Owens, L. A. et al. A Sarcina bacterium linked to lethal disease in sanctuary chimpanzees in Sierra Leone. Nat. Commun. 12, 1–16 (2021).ADS 
Article 
CAS 

Google Scholar 
65.Vlková, E., Rada, V., Šmehilová, M. & Killer, J. Auto-aggregation and co-aggregation ability in bifidobacteria and clostridia. Folia Microbiol. 53, 263–269 (2008).Article 
CAS 

Google Scholar 
66.Wang, L. et al. Adhesive Bifidobacterium induced changes in cecal microbiome alleviated constipation in mice. Front. Microbiol. 10, 1721 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
67.Wei, Y. et al. Protective effects of bifidobacterial strains against toxigenic Clostridium difficile. Front. Microbiol. 9, 888 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Guittar, J., Shade, A. & Litchman, E. Trait-based community assembly and succession of the infant gut microbiome. Nature Commun. 10, 1–11 (2019).Article 
CAS 

Google Scholar 
69.Moore, R. E. & Townsend, S. D. Temporal development of the infant gut microbiome. Open Biol. 9, 190128 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Korpela, K. et al. Probiotic supplementation restores normal microbiota composition and function in antibiotic-treated and in caesarean-born infants. Microbiome 6, 1–11 (2018).Article 

Google Scholar 
71.Timperio, A. M., Gorrasi, S., Zolla, L. & Fenice, M. Evaluation of MALDI-TOF mass spectrometry and MALDI BioTyper in comparison to 16S rDNA sequencing for the identification of bacteria isolated from Arctic sea water. PloS One 12, 0181860 (2017).Article 
CAS 

Google Scholar 
72.Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).PubMed 
Article 
CAS 

Google Scholar 
73.Brown, C. J. et al. Comparative genomics of Bifidobacterium species isolated from marmosets and humans. Am. J. Primatol. 81, e983 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Killer, J. et al. Gene encoding the CTP synthetase as an appropriate molecular tool for identification and phylogenetic study of the family Bifidobacteriaceae. MicrobiologyOpen 7, e00579 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
75.Milani, C. et al. Evaluation of bifidobacterial community composition in the human gut by means of a targeted amplicon sequencing (ITS) protocol. FEMS Microbiol. Ecol. 90, 493–503 (2014).CAS 
PubMed 

Google Scholar 
76.Srinivasan, R. et al. Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens. PloS One 10, e0117617 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Maiden, M. C. J. et al. MLST revisited: the gene-by-gene approach to bacterial genomics. Nature Rev. Microbiol. 11, 728–736 (2013).CAS 
Article 

Google Scholar 
78.Lugli, G. A. et al. Phylogenetic classification of six novel species belonging to the genus Bifidobacterium comprising Bifidobacterium anseris sp. nov., Bifidobacterium criceti sp. nov., Bifidobacterium imperatoris sp. nov., Bifidobacterium italicum sp. nov., Bifidobacterium margollesii sp. nov. and Bifidobacterium parmae sp. nov. Syst. Appl. Microbiol. 41, 173–183 (2018).PubMed 
Article 

Google Scholar 
79.Malukiewicz, J. et al. The effects of host taxon, hybridization, and environment on the gut microbiome of Callithrix marmosets. BioRxiv, 708255 (2019).80.Amato, K. R. et al. Phylogenetic and ecological factors impact the gut microbiota of two Neotropical primate species. Oecologia 180, 717–733 (2016).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Hernández‐Rodríguez, D., Vásquez‐Aguilar, A. A., Serio‐Silva, J. C., Rebollar, E. A. & Azaola‐Espinosa, A. Molecular detection of Bifidobacterium spp. in faeces of black howler monkeys (Alouatta pigra). J. Med. Primatol. 48, 99–105 (2019).82.Zhu, L. et al. Sex bias in gut microbiome transmission in newly paired marmosets (Callithrix jacchus). Msystems 5, e00910-00919 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Kap, Y. S. et al. Targeted diet modification reduces multiple sclerosis–like disease in adult marmoset monkeys from an outbred colony. J. Immunol. 201, 3229–3243 (2018).CAS 
PubMed 
Article 

Google Scholar 
84.Ren, T., Grieneisen, L. E., Alberts, S. C., Archie, E. A. & Wu, M. Development, diet and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ. Microbiol. 18, 1312–1325 (2016).PubMed 
Article 

Google Scholar 
85.Xu, B. et al. Metagenomic analysis of the Rhinopithecus bieti fecal microbiome reveals a broad diversity of bacterial and glycoside hydrolase profiles related to lignocellulose degradation. BMC Genom. 16, 1–11 (2015).Article 
CAS 

Google Scholar 
86.Baumann, P. Biology of bacteriocyte-associated endosymbionts of plant sap-sucking insects. Annu. Rev. Microbiol. 59, 155–189 (2005).CAS 
PubMed 
Article 

Google Scholar 
87.Killer, J. et al. Bifidobacterium actinocoloniiforme sp. nov. and Bifidobacterium bohemicum sp. nov., from the bumblebee digestive tract. Int. J. Syst. Evol. Microbiol. 61, 1315–1321 (2011).88.Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).CAS 
PubMed 
Article 

Google Scholar 
89.Garber, P. A., Mallott, E. K., Porter, L. M. & Gomez, A. The gut microbiome and metabolome of saddleback tamarins (Leontocebus weddelli): Insights into the foraging ecology of a small‐bodied primate. Am. J. Primatol. 81, e23003 (2019).90.Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Clayton, J. B. et al. Associations between nutrition, gut microbiome, and health in a novel nonhuman primate model. Sci. Rep. 8, 1–16 (2018).CAS 
Article 

Google Scholar 
92.Koo, B. S. et al. Idiopathic chronic diarrhea associated with dysbiosis in a captive cynomolgus macaque (Macaca fascicularis). J. Med. Primatol. 49, 56–59 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Krynak, K. L., Burke, D. J., Martin, R. A. & Dennis, P. M. Gut microbiome composition is associated with cardiac disease in zoo-housed western lowland gorillas (Gorilla gorilla gorilla). FEMS Microbiol. Lett. 364 (2017).94.Modrackova, N. et al. Prebiotic potential of natural gums and starch for bifidobacteria of variable origins. Bioact. Carbohydr. Diet. Fibre 20, 100199 (2019).95.McKenzie, V. J., Kueneman, J. G. & Harris, R. N. Probiotics as a tool for disease mitigation in wildlife: insights from food production and medicine. Ann. N. Y. Acad. Sci. 1429, 18–30 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Hicks, A. L. et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9, 1–18 (2018).CAS 
Article 

Google Scholar 
97.Hungate, R. E. & Macy, J. The roll-tube method for cultivation of strict anaerobes. Bulletins from the ecological research committee, 123–126 (1973).98.Rada, V. & Petr, J. A new selective medium for the isolation of glucose non-fermenting bifidobacteria from hen caeca. J. Microbiol. Methods 43, 127–132 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Orban, J. I. & Patterson, J. A. Modification of the phosphoketolase assay for rapid identification of bifidobacteria. J. Microbiol. Methods 40, 221–224 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Kim, B. J., Kim, H.-Y., Yun, Y.-J., Kim, B.-J. & Kook, Y.-H. Differentiation of Bifidobacterium species using partial RNA polymerase β-subunit (rpoB) gene sequences. Int. J. Syst. Evol. Microbiol. 60, 2697–2704 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. 41 edn 95–98 ([London]: Information Retrieval Ltd., c1979-c2000.).102.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
103.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Ress 41, D590–D596 (2012).Article 
CAS 

Google Scholar 
105.Shannon, C. E. & Weaver, W. The mathematical theory of information. Urbana: University of Illinois Press 97 (1949).106.Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 
Article 

Google Scholar 
107.Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).PubMed 
PubMed Central 

Google Scholar 
108.fundamental algorithms for scientific computing in Python. Virtanen, P. et al. SciPy 1.0. Nat. Methods 17, 261–272 (2020).Article 
CAS 

Google Scholar 
109.Seabold, S. & Perktold, J. Statsmodels: Econometric and statistical modeling with python in Proceedings of the 9th Python in Science Conference 57 (Austin, TX, 2010).110.MacKinnon, J. G. & White, H. Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. J. Econom. 29, 305–325 (1985).Article 

Google Scholar  More